SMEs’ Adaptive Capabilities and Growth Intentions: Evidence across Different Disruption Types

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Our findings show significant heterogeneity in how different disruption strands are perceived by SME entrepreneurs and how these, in turn, influence growth intentions. Specifically, disruptions to capital investment, leadership training, export, and working practice are perceived as exogenous threats beyond entrepreneurs’ control, leading to lower growth intentions. In contrast, disruptions to innovation and workforce do not appear to reduce growth intentions, meaning that SMEs may leverage these disruptions to mitigate the negative effects or enhance internal efficiency. The role of adaptive capabilities is also differentiated. Innovation capability emerges as a general-purpose adaptive mechanism, underpinning consistently high growth intentions across all disruption strands. Export capability is generally associated with stronger growth intention, although its positive effect weakens under export-related disruptions. Training capability shows a limited effect on growth intentions, except disruptions to investment and export. Overall, the findings suggest that growth intentions are shaped by adaptive capabilities through different forms of resource orchestration, the effectiveness of which depends on their alignment with specific disruption types. These insights contribute to the literature on SMEs and crisis in entrepreneurship research and offer important implications for both research and practice. SMEs growth intentions crisis disruption strands dynamic capabilities and entrepreneurship 1. Introduction Small and medium-sized enterprises (SMEs) are responsible for innovation, job creation, and economic development across countries (Cowling et al., 2024 ; Smallbone et al., 2020 ). Growth intentions are critical to such a contribution, particularly in dynamic environments, as they reflected in sustained efforts to entrepreneurship. Growth rarely occurs unless firms actively seek it (Autio & Acs, 2010 ; Autio & Rannikko, 2016 ), and growth intentions are influential in this regard. Wiklund and Shepherd (2013) suggest that actual business growth is strongly correlated with growth intentions in a relatively stable environment. In reality, however, SMEs often struggle to sustain growth because the entrepreneurial process is embedded in challenging conditions, which create difficulties for firms attempting to position themselves for sustainable growth (Doern et al., 2019 ). Understanding how SMEs adapt to environmental dynamism is increasingly important. This paper provides rigorous empirical evidence on SME intentions to pursue opportunities to for business expansions in the aftermath of crisis. Growth intentions are dynamic and influenced by both internal factors and external environments (Freel at al., 2024). Yet studies on the growth intention of firms have a focus on the context of relatively stable environments, with comparatively limited attentions paid to firms operating in disruptive conditions. Disruptive environments increase uncertainty and risk. When crises occur, SMEs experience a variety of disruption strands, including disruptions to capital investment , workforce , working practice , innovation , and export (Brown et al., 2019 ). Hernandez-Linares et al. ( 2021 ) argue that disruptions can moderate the influence of capabilities on realised firm performance, however not all dimensions of these capabilities contribute equally to such outcomes. Such experiences require stronger entrepreneurial orientation and adaptive capabilities for SMEs to pursue growth opportunity (Wiklund and Shepherd, 2005). So far, knowledge of how entrepreneurs perceive individual disruption strands, and how their assessment of the effects and duration of such disruptions shape the intentions beyond crisis, remains limited (Shirokova et al., 2020 ; Kukkamala & Koporcic, 2024; Miklian & Hoelscher, 2022 ). Moreover, we know little about how adaptive capabilities enables SMEs to adjust their growth intentions in response to individual disruptive strands. Understanding how growth intentions and adaptive capabilities interact across individual disruption strands is of great relevance to research on crisis and entrepreneurship, and it is critical for policy-makers tasked with alleviating the effects of environmental disruptions on the SME sector. Recent studies highlight the importance of adaptive capabilities for SME survival and growth in disruptive environments (Martin-Rojas, 2026). Fundamental to adaptive capabilities is a firm’s ability to sense, respond to, and capitalise environmental changes (Dewi et al., 2020). Most studies that adopt adaptive capabilities as a theoretical lens focus on the realised growth, and tend to treat environmental disruption as a single or aggregated construct (e.g. uncertainty, turbulence, or crisis). While this approach overlooks the heterogeneity of disruption, it nevertheless provides valuable insights into the roles of adaptive capabilities in shaping actual growth in the post-crisis period, thus implying their relevance for growth intentions. Firm-level adaptive capability can be translated into entrepreneurs’ intentions through both structural and psychological mechanisms. Adaptive firms typically promote flexible routines, continuous learning new knowledge, and reconfigure organisational resources to engage proactively with changes (Xiao and Hughes, 2026 ). In such contexts, entrepreneurs of SMEs with stronger adaptive capabilities are likely to consider themselves as capable of mitigating the effects of environmental disruption and, which in turn strengthens their confidence and fosters higher growth intentions (Panjaitan et al., 2025). In other words, adaptive capabilities may enable entrepreneurs to interpret and enact responses to significant changes to the business and the entrepreneur’s work practices, thus enhancing their motivation and shaping growth intentions. However, both theoretical and empirical research remains limited regarding the conditions under which post-crisis growth intentions, particularly when accounting for different types of disruptions (Jantunen et al, 2018 ). This leads us to the following research questions: 1) how SME intentions to growth are affected by individual disruption strands and 2) how adaptive capacities shaped the growth intentions across different disruption strands. We use a large panel dataset of UK SMEs from the Longitudinal Small Business Survey (LSBS), covering a period 2015–2020. This longitudinal dataset follows UK SMEs beyond Brexit for five years. Brexit is a major form of unprecedent political event facing SMEs (Brown et al., 2019 ; Calabrese et al., 2022 ), and the UK SMEs are disproportionately affected by Brexit-induced uncertainties (Eggers, 2020 ). These surveys allow us to capture relatively long-term repercussions on the SMEs and the information on the intentions, individual disruption strands, adaptive capabilities, and other variables. These, taken together, enable us to fully address our research questions. We offer contributions. First, we contribute to the entrepreneurship crisis literature by conceptualising a crisis as a bundle of discrete disruption strands and revealing how each strand uniquely influence entrepreneurs’ confidence for future growth (Doern et al, 2019 ; Shepherd & Williams, 2020 ; Shirokova et al., 2020 ). Our findings show significant heterogeneity in how different disruption strands affect growth intentions. SMEs perceive disruptions to capital investment, leadership training, export, and working practice as exogenous threats beyond entrepreneurs’ control, and thus lower the intentions. While they treat disruptions to innovation and workforce as a tool to exert business expansion, and thus the intentions remain uninfluenced (Freel et al., 2024 ; Lien & Timmermans, 2024 ). Prior studies considering crisis as integrated primarily indicate a temporal setback in growth (Cowling et al., 2015 ; Lim et al., 2020 ), however, our findings show this is not necessarily the case. We thus provide advanced understanding of the heterogeneous effects of disruptive strands on SMEs’ growth intentions. Second, our findings reveal that the extent to which the adaptive capacities (e.g. observable strategic activities) shape the growth intentions varies across different disruption strands. SMEs may adapt and adjust their intentions during and after a crisis, with adaptive capabilities playing varying roles in shaping these intentions across different disruption strands. This suggests that the effectiveness of adaptive capacities is contingent upon the perceived threats, highlighting the heterogeneous conditions that SMEs faced during a crisis. Adaptive capacities, as resources possessed by SMEs, enable entrepreneurs to interpret specific disruption strands as manageable which in turn supports the enhancement of their growth intentions. We thus contribute to the literature by highlighting the heterogeneous nature of environmental shocks and providing a more nuanced understanding of how SMEs strategically respond to different types of disruption. Third, we contribute to the stream of entrepreneurship research that adopts a view of adaptive capabilities by offering valuable insights into the ways that SMEs specify dimensions of adaptive capabilities in response to different disruption strands and adjust the intentions (Teece, 2018 ; Soluk et al., 2021 ). 2. Theoretical Background 2.1 Growth intentions and disruptive environments Growth intentions reflect an entrepreneur’s motivation and willingness to exert business expansion (Delmar & Wiklund, 2008 ). A crisis entails significant changes to the business and entrepreneur’s work practices. Consequently, many SMEs experience a temporary growth setback (Doern et al., 2019 ; Birhanu et al., 2022 ; Kukkamala & Koporcic, 2024), although recovery can occur relatively quickly once conditions stabilise (Cowling et al., 2015 ; Lim et al., 2020 ). Prior studies have predominantly examined the effects of crisis as integrated, single events on actual growth, often focus on the onset or immediate aftermath (Doern et al., 2019 ; Cowling et al., 2020 ; Birhanu et al., 2022 ; Kukkamala & Koporcic, 2024). Treating a crisis as a single, homogeneous event is problematic. This approach overlooks the fact that disruption often occur through a set of distinct strands, and the effects of these strands on SME growth may vary depending on their nature and duration. These disruption effects typically include delays or a reduction in capital investment for growth-orientated business activities and R&D (Brown et al., 2019 ; Calabrese et al., 2022 ; Qamar et al., 2023 ), interruption to new product development and launch (Brown et al., 2019 ), uncertain trading conditions, and declines in income streams (Cowling et al., 2020 ). In realities, entrepreneurs of SMEs often assess the effects of these disruption strands separately and form expectation about their duration and controllability (Lien & Timmermans, 2024 ). Their growth intentions are likely influenced by the perceived locus of control over each disruption strand (Freel et al., 2024 ). When disruption strands are perceived as external and uncontrollable, entrepreneurs tend to exhibit greater hesitation in pursuing growth. Conversely, when disruptions are seen as within their ambit, they feel more confident in their ability to achieve growth. Recognising this distinction is important, as it provides a conceptual framework for understanding how SMEs develop more efficient and targeted contingency plans for future crises. To capture the multifaceted nature of disruption, we categorise it into six specific strands: disruptions to capital investment, working practices, leadership training, export, innovation , and workforce . Empirically, these strands reflect recurrent themes identified in prior crisis-entrepreneurship studies, which documents how resource constraints, operational interruptions, and market shocks differentially affect firms’ strategic responses (Doern et al., 2019 ; Shepherd and Williams, 202; Branzei and Fathallah, 2023 ). Theoretically, this categorisation aligns with the resource-based and adaptive capabilities perspectives, both of which emphasise that disruptions occur across distinct yet interrelated domains of firm resources and capabilities—financial (capital investment), operational (working practices, workforce), strategic (leadership training), and market-oriented (exports, innovation). Each strand, thus, represents a unique mechanism through which adaptive capabilities may differentially influence entrepreneurial motivation to achieve sustained competitiveness. 2.2 Growth intentions and adaptive capabilities Adaptive capabilities are commonly conceptualised within the dynamic capabilities perspective, referring to a firm’s ability to adjust to changing environments by reconfiguring resources, processes, and strategies (Teece, 2007 ; Teece, 2014 ). Adaptive capabilities represent a key dimension of firm heterogeneity, both influencing and reflecting patterns of resource deployment (Helfat & Winter, 2011 ). An early view proposed in conceptual studies posits a positive and direct relation between dynamic capabilities—i.e., a firm’s ability to adjust to environmental changes—and firm performance outcomes (Teece et al., 1997 ). However, empirical findings have been inconsistent. For instance, recent studies find insignificant and negative relations (Karna et al., 2016 ; Girod & Whittington, 2017 ). Jantunen et al. ( 2018 ) state that higher performance outcomes are more likely when adaptive capabilities interact with operational-level changes (i.e. changes in management and practices or changes in used technologies and target market). Furthermore, market orientation significantly moderates the relationship between adaptive capabilities—specifically, sensing and learning and firm performance (Hernandez-Linares et al., 2021 ). The inconsistency of these studies can be attributed to contextual conditions and specific dimensions of adaptive capabilities studied (Fainshmidt et al., 2016 ). So far, most studies have focused mainly on relatively stable environments and realised growth, suggesting that the effects of adaptive capabilities on performance outcomes may vary depending on the nature of challenging conditions (Girod & Whittington, 2017 ), implying their relevance for growth intentions. As an internal firm-level factor, adaptive capabilities can be translated into entrepreneur intentions through both structural and psychological mechanisms, thus shaping entrepreneurial motivations and intentions to pursue growth. At the structural level, adaptive firms tend to promote flexible routines and continuous developing expertise, which empower employees to experiment and engage proactively with change (Xiao and Hughes, 2026 ). At the psychological level, such environments enhance individuals’ self-efficacy, perceived behavioural control, and opportunity recognition—key antecedents of intention formation. Employees internalize the firm’s adaptive orientation, becoming more willing to pursue entrepreneurial, innovative, or change-oriented actions. Consequently, firm-level adaptive capability becomes embedded in individuals’ cognitive frameworks, motivating intentions that align with adaptability, resilience, and proactive engagement. This highlights the need to investigate how growth intentions are shaped by adaptive capabilities across different disruption strands (Fainshmidt et al., 2016 ; Jantunen et al., 2018 ). As a key dimension of firm heterogeneity, adaptive capabilities are often conceptualised as a multidimensional construct reflecting a firm’s ability to respond effectively to environmental change. Entrepreneurs’ adjustments to growth intentions are likely shaped by particular dimensions of adaptive capabilities, representing patterns of resource deployment and their subjective assessment of the effects of individual disruption strands. Differentiating between dimensions of adaptive capabilities is essential for understanding how entrepreneurs adjust their growth intentions in response to different types of disruption (Helfat & Winter, 2011 ; Hernandez-Linares et al., 2021 ; Soluk et al., 2021 ). In the context of SMEs, three specific components — innovation , export , and training capability —are particularly salient because these components directly influence how entrepreneurs are motivated to take actions to mitigate disruptions’ effects and seize growth opportunities. Each captures a distinct yet interrelated mechanism through which entrepreneurs of firms reconfigure their resources, processes and strategies to respond to changing conditions. Innovation capability enables firms to develop new products and processes in response to changing market conditions, while export capability reflects a firm’s ability to expand its market reach beyond existing market and allow the firm to pursue growth opportunities when the environment is uncertain. Learning and training capability enhance the human and strategic competence necessary to recognise emerging opportunities and reconfigure resources in response to environmental challenges (Zahra & George, 2002; Teece, 2007 ). Thus, these dimensions of adaptive capabilities serve as key mechanisms through which entrepreneurs form perceptions of disruption and strategically adjust their growth intentions. Innovation capability reflects a firm’s ability to gain and recombine knowledge to develop new or improve products, services, and processes. In dynamic environments, it represents a critical mechanism that allow entrepreneurs to adapt proactively and renew their strategic direction (Xiao & Ramsden, 2016 ; Crescenzi & Gagliardi, 2018 ). Export capability , by contrast, captures a firm’s outward-oriented capability to identify, access, and exploit opportunities in international markets. This capability can be leveraged to diversify risk exposure, expand their knowledge base, and enhance resilience through engagement in global markets (Gkypali at al., 2021). Finally, training capability underpins the transforming function of adaptive capabilities by aligning employees’ skills and competences with the firm’s growth objectives. By effectively transferring of training into practice, firms can reconfigure internal resources, enhance adaptability, and improve operational efficiency under changing conditions (Burford et al., 2022 ). Together, these three dimensions provide a theoretically grounded framework for explaining how SMEs renew, reconfigure, and deploy their capabilities that may shape growth intentions across different disruption strands. Building on this framework, our study examines the conditions under which these adaptive capabilities shape growth intentions, providing insights into factors that influence how SMEs translate adaptive capabilities into strategic outcomes. 3. Hypotheses development 3.1 The relationship between individual disruption strands and growth intentions Our concern is with how entrepreneurs individually perceive disruption strands to form their growth intentions. Although disruption strands originate from external crises, their influence in firm responses is mediated through entrepreneurs’ subjective interpretations, making an individual-level focus essential for explaining variation in growth intentions. Accordingly, we develop hypotheses as follow: Disruption to capital investment . Making investments in growth-orientated entrepreneurial activities is a strong indicator of growth intentions (Brown et al., 2020 ). When experiencing crisis-induced uncertainties, SMEs tend to scale back or delay future investment plans and strategic actions to avoid costly missteps (Calabrese et al., 2022 ). By adopting a “wait-and-see” approach, they allow time to anticipate how environmental changes may influence their businesses (Lim et al., 2020 ). However, this caution may also slow the pace of recovery. Meanwhile, constraints on the finance supply side also contributes to a capital investment holdback. In challenging conditions, banks— the main external funding sources for SMEs (OECD, 2018 )—tend to cut their lending towards small firms (Cowling et al., 2012 ), while SMEs become more reliant on external finance (Block et al., 2018 ; Cowling et al., 2020 ). This mismatch discourages SMEs from applying for external funding due to expectation of higher rejection rates, including those identifying viable investment opportunities (Cowling et al., 2022 ), though relationship lending can significantly reduce firms’ concerns about access to debt finance (Calabrese et al., 2022 ). Nevertheless, by scaling back or postponing capital investments, entrepreneurs adopt a more cautious approach to pursuing growth opportunities, which can suppress their growth intentions. we thus expect that capital investment disruption is negatively associated with growth intentions. Disruption to working practices . When a crisis hits, SMEs often change their operation routines and processes in response to its effects, which can lead to either improved resilience or performance decline (Xiao & Hughes, 2026 ). For instance, the cancellation of signed purchase orders may compel SMEs to redeploy employees to new tasks and projects that could benefit to the business when the recovery comes. The ability of SMEs to make such effective adjustments rests on their capacity to mobilise and reconfigure necessary resources (Mittermaier et al., 2021 ). However, small firms generally operate under significant constraints, including shortages of skilled labour and limited access to external resources and facilities, which are often exacerbated during crisis, further reducing their capacity for adjustment (Shirokova et al., 2020 ; Doern et al., 2019 ). Consequently, entrepreneurs of SMEs experiencing disruptions to working practices are less likely to anticipate a subsequent increase in revenue. As Gulati et al. ( 2010 ) argue, SMEs that fail to adapt adequately to new environment realities tend to take significantly longer to develop positive expectation about future growth. We thus expect that working practices disruption is negatively associated with entrepreneurs’ growth intentions. Disruption to leadership training . In the absence of leadership training, entrepreneurs and senior managers are deprived of vital skills updates, which can undermine their strategic and operational competencies and, in turn, their growth intentions. Leadership training provides entrepreneurs/senior managers with foundational capabilities such as effective communication, strategic decision-making, and complex problem-solving (Franco & Matos, 2015 ). We argue that these competencies enable managers to anticipate negative effects, reconfigure resources, and transform operations in response to disruptions, and thus increase entrepreneurs’ confidence in their ability to cope with crisis-induced changes to their business and work environment. For instance, effective communication with key employees and business partners helps entrepreneurs to evaluate the potential of emerging opportunities and to assess their firm’s capability to realise them. Similarly, strong problems-solving skills enable managers to make difficult and timely decisions required to navigate turbulent conditions with greater confidence and effectiveness. Despite its importance and significance, few work have examined the role of leadership training in shaping SME growth intentions under challenging conditions (Miklian & Heolscher, 2022). Prior studies suggest the direct contribution of leadership to innovation and export performance (Dunne et al., 2016 ; Freel, 2005 ) and that the leadership training can have sustained positive effects on firms in overcoming exporting hurdle (Love & Roper, 2015 ). When leadership training is disrupted, entrepreneurs and senior managers may overestimate the likelihood of failure, and become hesitate to pursue growth opportunities and reluctant to committing resources to projects that they feel ill-equipped to manage. We thus expect the leadership training disruption is negatively associated with growth intentions. Disruption to export . Export disruptions generate formidable barriers for SMEs seeking to access and compete in international markets, affecting their attitude towards future growth (Freel et al., 2024 ). For instance, the UK’s new relationship with the EU, which leads to increased costs and paperwork complication, creates new barriers for the UK SMEs to export products or services to the EU market. The effects of such disruption vary considerably based on a firm’s level of internationalization and openness to trade, and broader uncertain trading conditions. The negative effects of Brexit are disproportionately felt by exporters and importers located in peripheral regions of the UK (Brown et al. 2019 ). This is partly because exporting itself serves as a critical mechanism for developing export capability (Gkypali at al. 2021); disruptions to export activities thus hinder capability development, particularly for firms in peripheral regions, amplifying the impact on their international performance. Through export practice, firms gain tacit knowledge and critical information about overseas markets and the value of their offering, which support more informed decisions on subsequent entrepreneurial activities. Furthermore, translating the knowledge into successful market entry requires more than just information; it also demands strategic vision and managerial expertise to re-configure a firm’s resource base, including managerial, marketing, and financial resources. Thus, entrepreneurs of SMEs experiencing a drop in goods sales are likely to anticipate reduced future business growth. This leads us to assume that export disruption is negatively associated with growth intentions. The above discussion suggests that entrepreneurs’ ability to adapt to change conditions caused by disruptions to capital investment, working practices , export activities, and leadership training is constrained. Therefore, we propose: H1a: Disruptions to capital investment, working practices , export activities, and leadership training have negative and significant effects on SME growth intentions. Disruption to workforce . Access to a skilled workforce is critical for growth-orientated and innovative SMEs to operate at full capacity. Shortages of skilled workforce, coupled with raising labour costs, can undermine SMEs’ ability to retain employees and maintain cost-efficient task delivery (Qamar et al., 2023 ). Consequently, SMEs are particularly vulnerable to workforce disruption in the immediate aftermath of a crisis. For instance, during the early debates on Brexit, public policy changes affecting EU immigration were anticipated to worsen labour market conditions for SMEs, particularly in the manufacturing and construction sector, where skilled labour shortages are more pronounced than in the service sector (Hopley, 2021 ). However, recent research shows that such workforce disruptions are often temporary. Across the UK, SMEs have mitigated labour shortage by employing EU nationals, who fill about 10% of manufacturing jobs (Cowling et al., 2020 ). It indicates that deficiencies in skilled labour at the onset of a crisis can recover relatively quickly. Furthermore, SMEs may proactively respond by developing internal capacities, such as building a skilled workforce through targeted training and upskilling initiatives (Burford et al., 2022 ), or increase their automation to reduce reliance on labour. Given these adaptive strategies, temporary workforce disruption does not necessarily undermine entrepreneurs’ confidence in pursuing business expansion. Entrepreneurs may view such disruptions as manageable challenges rather than insurmountable obstacles. We therefore argue that workforce disruption, while significant, do not inherently reduce SMEs’ growth intention, particularly when firms possess the capabilities to respond strategically. Disruption to innovation . Innovation disruption refers to a pause in SMEs’ ongoing innovation activities, during which they may be compelled to redirect their efforts toward crisis-driven innovation. In this sense, innovation is used as a resilience-building tool to mitigate the disruption effects of crises (Lien & Timmermans, 2024 ). For SMEs, innovation activities are often undertaken by skilled employers and senior tech team members (Xiao & Ramsden, 2016 ) and typically draw on publicly available external scientific knowledge (Freel, 2005 ) or created by partners undertaking R&D (Love & Roper, 2015 ). This highlights the importance of knowledge-based resources and internal capabilities in sustaining entrepreneurial resilience and supporting recovery. Under particularly challenging conditions, SMEs shift from improvising products/services and launching the revised products to meet the future market demands, towards actively realising emerge business opportunities (Xiao & Hughes, 2026 ). In this regard, SMEs not only engage in intensive innovation but focus on the long-term value creation, with the expectation of achieving returns over the long-run (Lien & Timmermans, 2024 ). Moreover, it is not evident that UK SMEs’ access to external knowledge from the EU has been reduced by new relationship with the EU. Following this line of argument, innovation disruption can act as an enabler for organizational adaptation, influencing SMEs motivations and efforts to pursue long-term value creation. The discussion above indicates that SMEs exhibit a degree of resilience in the face of workforce and innovation disruptions. We thus propose: H1b: workforce and innovation disruptions have no significant effects on SMEs’ growth intention. 3.2 The relationship between adaptive capabilities and growth intentions We now discuss the roles of adaptive capabilities in shaping SME growth intentions when experiencing different disruptions, with a focus on the three specific dimensions introduced earlier. Innovation capability , as a key dimension of adaptive capability, refers to a firm’s ability to continuously identify new knowledge and ideas as well as transform them into new/improved products, processes, and system (Autio et al., 2014 ). Thus, innovation capability supports firm’s adaptation to new and changing conditions. It can come at a variety of forms, and encompasses both the technological and non-technological dimensions of innovative activities and the potential for both radical and incremental changes (Love & Roper, 2015 ). Literature posits that product and process innovation play a central role in the firm performance, and a positive association between innovation and productivity (Hall et al., 2009 ) and between innovation and realised growth (indicated by employment and sales) holds in more stable environments (Freel, 2005 ). Across different types of disruption, innovation reflects firms’ ability to respond effectively to diverse challenges while seizing new opportunities arising from a crisis (Taalbi, 2017 ). A firm’s ability to innovate shapes creative responses, enabling it to address immediate problems while also pursuing opportunities that extend beyond the crisis (Greenstein et al., 2013 ; Teece, 2018 ). Such innovation capability, which support the development of solutions to distinct challenges, is likely to enhance entrepreneurs’ confidence in pursuing future business growth. The greater a firm’s innovation capacity, the higher its potential to increase the desirability of future growth (Lim et al., 2020 ). We therefore expect that innovation capability is positively associated with the growth intentions across different disruption strands. Export capability refers to a firm’ ability to achieve competitive advantages and enhance export performance through the effective mobilisation of skills and resources (Efrat et al., 2018 ). When facing a range of disruptions (Brown et al., 2019 ), export capability, at its core, facilitates adaptation strategies by enabling firms to restructure its skills and resources to respond effectively to changing conditions. Knowledge and expertise gained from export and import practices strengthen SMEs’ ability to assess the effects of disruptions and address the evolving needs of new customers. As a result, they are better positioned to make timely and informed strategic decisions. SMEs with the higher level of export capability are consequently better equipped to respond effectively to affected activities, particularly in their international operation. Strong export capability provides firms with knowledge, skills, and resource necessary to assess the impact of different disruptions on international activities, make contingencies to reduce the such effects, shorten the duration of declines in export growth, and adapt their offerings or processes to evolving market demands (Fang & Zou, 2009 ; Helfat & Winter, 2011 ). In this sense, export capability functions not only as a driver of international performance but as a critical component of organisational resilience in the post crisis period, supporting firms in restructuring their resources to mitigate the effects of different types of disruption. It is logical to expect that the strength of this relationship may vary across different disruption strands, as the way export capability is applied—and the extent to which it mitigates or leverages the effects of disruption—differs depending on the nature of each disruption. Basing on these analyses, we thus assume that export capability is positively associated growth intentions and the strength of this association may vary across different types of disruption. Training capability refers to a firm’s ability to update and enhance employees’ skills and knowledge, ensuring that they remain effective in areas aligned with the firm’s core strengths and strategic mission (Lim et al., 2020 ). Through training, knowledge and expertise flow within a firm and among the members, while also incorporating specialised insights from external experts (Brown et al., 2019 ). Outcomes of training include improved cognitive ability, increased self-efficacy, and updated role-specific skills and knowledge. Training thus plays a central role in the development of a firm’s broad capacity for growth, innovation, and resilience. Training helps SMEs to develop an internal pool of skilful workforce to compensate for shortages in the entrepreneurship ecosystem. For tech firms, training facilitates the advancements of collective technological expertise to achieve competitive advantages in the market, such collective expertise is critical to form new teams quickly to pursue emerging opportunities (Xiao & Hughes, 2026 ). In times of crisis, training is, at its core, about equipping employees to address both challenges and new tasks with flexible, adaptive solutions. Training capability provides feedback that improves entrepreneurs’ confidence in their employees’ ability to perform new tasks effectively, thereby motivating them to pursue growth beyond the crisis. Similar to export capability, the way training capability is applied—and the extent to which it mitigates or leverages the effects of disruption—differs depending on the nature of each disruption. We thus expect that training capability is positively associated growth intentions, with the magnitude of these effects potentially differing across various types of disruption. Taken together, we argue that growth intentions are cultivated by three dimensions of their adaptive capabilities that emphasize SMEs’ flexibility and responsiveness, and the extent to which adaptive capabilities that leverage the effect of disruption may differ depending on the nature of each disruption. Specifically, innovation capability enables SMEs to develop solutions to crisis-induced problems through adaptive and innovative responses to changing conditions; export capability facilitates contingencies for affected export activities and effective exploration of new markets; and training capability prepares employees to perform new and emerging tasks and to manage unprecedent challenges effectively. Thus, we posit: H2: Adaptive capabilities (i.e. export, innovate, and training capability ) have positive effects on SME growth intentions, with the magnitude of these effects potentially differing across disruption strands. 4. Data and methods 4.1 Data source and sample The dataset for this study is drawn from waves 1 to 6 of LSBS, covering time period from 2015 to 2020 where firms experiencing Brexit-induced uncertainties. The sample was stratified by firm size, region, and industrial sector. The UK administrative Inter-Departmental Business Register was the source for sampling of registered businesses, while Dun and Bradstreet’s database was the source for sampling of unregistered businesses. The dataset provides information on a variety of disruptive strands, growth intentions, specific dynamic capabilities, firm characteristics. 4.2 Variables 4.2.1 Dependent variable Our interest is in growth intentions ( GROWTH ) of SMEs beyond crisis. The dependent variable is a categorical variable that measures owners’ expectation of turnover growth in the next 12 months from 2015. The variable is coded as 1 if a firm responded “increased”, and 0 if “decreased” or “stay the same”. 4.2.2 Independent variable Disruptive strands . The variable includes six Brexit-induced disruptive strands based on the following question in the LSBS dataset: “Whether plans of each of six strands over the next three years have been affected by Brexit”. Capital investment disruption (CAPITAL) is operationalised as 1 if a firm answered “Yes” to “Capital investment”, 0 if otherwise; Workforce disruption (WORKFORCE) is operationalised as 1 if a firm answered “Yes” to “Increase the skills of the workforce”, 0 if otherwise; Leadership disruption (LEADERSHIP) is operationalised as 1 if a firm answered “Yes” to “Increase the leadership capability of managers”, 0 if otherwise; Innovation disruption (INNOVATION) is operationalised as 1 if a firm answered “Yes” to “Develop and launch new products/services”; Working practices disruption (PRACTICE) is operationalised as 1 if a firm answered “Yes” to “Introduce new working practices”; lastly, Export disruption (EXPORT) is operationalised as 1 if a firm answered “Yes” to “Increase export sales or begin selling to new overseas markets”. Adaptive capabilities . The adaptive capabilities variable is constructed to measure observable strategic activities, specifically export, innovation and training capabilities . A measure of export capability (EXPCAP) is a dichotomous variable that takes the value 1 if a firm is an exporter, and 0 otherwise. An innovation capability measure (INNOCAP) is a dichotomous variable that equals 1 if a firm has new to market innovation for the any three consecutive years during the period of 2015–2020, and 0 otherwise. A measure of training capability (TRAINCAP) is operationalised as a dummy variable that takes the value 1 if the firm provided both formal and informal training in the past year, and 0 if otherwise. 4.2.3 Control variable We included control variables - a firm’s size and age, region, sector, and year in our analyses. Specifically, a firm’s size (SIZE) is indicated by the log form of employees. A firm’s age (AGE) is a categorical variable that takes the value 1 if firm is 0 to 5 years old (reference group), 2 if firm is 6 to 10 years old, 3 if firm is 11–20 years old, or 4 if more than 20 years old. REGION is a categorical variable indicating the location where a business is operating in (in total, 12 regions included in the dataset). SECTOR is a categorical variable showing the sector in which a firm is operating (a total of 14 sectors included). YEAR is a categorical variable indicating the time period from 2015 to 2020. 4.3 Empirical strategy We adapt a random-effects probit model for testing hypotheses proposed earlier, as the dependent variable is a binary. This approach is preferred because it accounts for time invariant unobserved firm-specific effect and a random unobserved shock, unlike fixed-effects probit model (Bernard & Jensen, 2004 ). the Wu-Hausman test failure to reject the null hypothesis, further supporting the use of the random-effects specification. We follow a lagged approach that enables us to overcome the problem of endogeneity partially (Almeida & Phene, 2004 ). Endogeneity is a common problem in the research. Endogeneity occurs when the independent variable and the error term (which is omitted from the equation) is correlated. In this study, adaptive capability variables (i.e., innovation, export, and training) are considered endogenous to SMEs’ growth intentions. We recognise that effects of disruptions and adaptive capabilities may require some time to manifest on the intentions. We specify the equation as: GROWTH it = ꞵ 0 + ꞵ 1 Disruptive strands it−1 + ꞵ 2 Adaptive capabilities it−1 + ꞵ 3 Controls it−1 + \(\:{\text{ϵ}}_{\text{i}}\) where Disruptive strands it−1 are a set of dummy variables indicating whether a firm indicates whether plans over the next three years have been affected by Brexit in the past year; Adaptive capabilities it−1 is a set of three variables indicating whether a firm has exported services or goods overseas, introduced consistent new-to-market innovations, provided both informal and formal training to its managers and employees in the past year; Controls it is a vector for control variables, \(\:{\beta\:}\) 0 is a constant, and \(\:{\text{ϵ}}_{\text{i}}\) is the error term. 4.4 Addressing endogeneity To further alleviate potential endogeneity between dynamic capabilities and SMEs’ growth intentions, we apply propensity score matching (PSM) with a kernel estimator. ThePSM technique samples data that are not involved in the intervention to determine the likely outcomes for those that did participate, had they not been part of the intervention. The technique can address selection bias by eliminating systematic differences between the treated and control group. The treated group includes units received the treatment, whereas the control group include comparable firms that do not. A kernal-based matching method is used because it uses information on all available controls and downweighs distant observations, providing superior matching quality and balance between the treated and control groups . PSM estimates the average treatment effect for the treated (ATT), caputring the effect of intervention on firms that receive it. We construct a set of dummy variables equal to 1 if a firm exports, is a serial innovator, or undertakes training, and 0 otherwise. This allows us to compare firms engaging in these activities with similar firms across the control variables, ensuring that observed differences in expected performance can be attributed to exporting, innovation, or training. 5. Findings We present the sample descriptive statistics, the correlation matrix for the variables; followed by the empirical results from random-effects probit regressions and propensity score matching. Due to the non-linear nature of our models, we report average marginal effects, indicating the average change in the probability of growth intentions when a firm possesses a specific characteristic. 5.1 Descriptive statistics Table 1 presents the summary statistics for all variables included. The analysis includes 64,876 firm-level observations of SMEs’ expected growth from 2015 to 2020. Within the sample, micro firms (fewer than 10 employees) make up 61.6% of all firms, small firms (11–49 employees) constitute 23.3%, and medium-sized firms (50–249 employees) represent 15.1%. Table 1 Summary statistics z,2 Variable Label Definition Obs Mean Std. Dev. Min Max Intention to growth GROWTH Expectation of turnover growth in next 12 months: =1 if firm expected turnover growth in the next 12 months; =0 if decrease or stay the firm. 64,876 0.41083 0.491988 0 1 Export disruptions EXPORT Dummy variable: =1 Plan about export over the next three years have been affected by Brexit; 0 if otherwise. 4,312 0.1804267 0.3845871 0 1 Capital investment disruptions CAPITAL Dummy variable: =1 Plan about capital investment over the next three years have been affected by Brexit; 0 if otherwise. 7,087 0.113871 0.317676 0 1 Workforce disruptions WORKFORCE Dummy variable: =1 Plan about workforce over the next three years have been affected by Brexit; 0 if otherwise. 10,671 0.076844 0.266356 0 1 Leadership disruptions LEADERSHIP Dummy variable: =1 Plan about leadership training over the next three years have been affected by Brexit; 0 if otherwise. 7,908 0.063733 0.244292 0 1 Working practice disruptions PRACTICE Dummy variable: =1 Plan about introducing new working practices over the next three years have been affected by Brexit; 0 if otherwise. 7,688 0.082726 0.275486 0 1 Innovation disruptions INNOVATION Dummy variable: =1 Plan about introducing innovation over the next three years have been affected by Brexit; 0 if otherwise. 7,154 0.119933 0.324906 0 1 Export capability EXPCAP Dummy variable: =1 if firm exported goods or services in the past year; 0 if otherwise. 64,582 0.220588 0.414646 0 1 Training capability TRAINCAP Dummy variable: =1 if firm provided both formal and informal training in the past year; 0 if otherwise. 47,969 .233359 .4229732 0 1 Innovation capability INNOCAP Dummy variable: = 1 if firm had introduced new to market innovation for three consecutive years; 0 if otherwise. 64,876 0.008401 0.09127 0 1 Firm age AGE Categorical variable: = 1 if 0–5 years, = 2 if 6–10 years, = 3 if 11–20 years, = 4 if more than 20 years. 57,431 3.11 1.06 1 4 Firm size SIZE Number of employees (ln) 64,876 1.942692 1.574279 0 5.521461 Sector of firm SECTOR Categorical variable that indicates the sector of the business. 64,874 6.061935 3.128514 1 13 Location of firm REGION Categorical variable that indicates the location of the business. 64,876 6.815109 3.709309 1 14 Time YEAR Categorical variable that indicates the time period (2015–2020). 64,876 2017.301 1.723682 2015 2020 Table 1 about here Since the key variables of the sample are dummy variables, the mean of each dummy represents the percentage of observations where the variable equals 1. Table 1 shows that approximately 41.1% of firms anticipated turnover growth within the next 12 months from 2015 to 2020. Around 18% of firms indicated that their export plans for the next three years were affected by Brexit. Additionally, 12% of firms reported that their plans to introduce innovation were impacted, 11.4% noted that their capital investment plans were affected, 8.3% mentioned disruptions to working practices, and 7.7% experienced workforce disruptions. Lastly, 6.4% of firms reported disruptions to leadership training. Regarding adaptive capabilities, 22.1% of firms exported goods or services in the past year; 23.3% of firms provided both formal and informal training; and only 0.8% introduced new-to-market innovations for three consecutive years. Table 2 about here Table 2 Correlation matrix 1. GROWTH 1 2 3 4 5 6 7 8 9 10 11 1 2. INNOCAP 0.0409* 1 3. EXPCAP 0.1100* 0.0931* 1 4. TRAINCAP 0.1255* 0.0440* 0.0705* 1 5. CAPITAL -0.0454* 0.0178 0.0826* 0.0264 1 6. EXPORT -0.0096 0.0399* 0.2519* 0.0478* 0.5209* 1 7. INNOVATION -0.0531* 0.0224 0.0918* 0.0032 0.6374* 0.5664* 1 8. PRACTICE -0.0511* 0.0184 0.0796* -0.0029 0.5467* 0.4307* 0.6170* 1 9. WORKFORCE -0.0415* 0.0282* 0.0614* -0.0052 0.5319* 0.4323* 0.5989* 0.6501* 1 10. LEADERSHIP -0.0416* 0.0044 0.0564* -0.007 0.5487* 0.4311* 0.6071* 0.6809* 0.7315* 1 11. SIZE 0.1538* 0.0083* 0.1344* 0.4722* -0.0021 -0.0181 -0.0374* -0.0438* -0.0076 -0.0342* 1 Table 2 presents the Pearson’s correlation matrix for both dichotomous and continuous variables. The table indicates that the correlation between disruptions to capital investment and growth intentions is positive and significant at 1% level. In contrast, disruptions to innovation, workplace practice, workforce, and leadership training are negatively and significantly correlated with growth intentions at the 1% level. The correlation between disruptions to export activities and growth intentions is negative but not significant. There is a strong correlation between adaptive capability variables (i.e. INNOCAP, EXPCAP, and TRAINCAP) and growth intentions. Table 3 about here Table 3 The effect of disruptive strands on SMEs’ growth intentions. Model 1 2 3 4 5 6 CAPITAL t−1 -0.0819 ** (-3.13) EXPORT t−1 -0.0589 (-1.93) INNOVATION t−1 -0.0227 (-0.83) PRACTICE t−1 -0.0822 ** (-2.60) WORKFORCE t−1 -0.0239 (-0.92) LEADERSHIP t−1 -0.114 *** (-3.46) Age t−1 6–10 years -0.0896 * 0.0203 -0.103 ** -0.0423 -0.0765 * -0.115 ** (-2.06) (0.35) (-2.58) (-1.03) (-2.26) (-2.91) 11–20 years -0.149 *** -0.0274 -0.119 ** -0.102 ** -0.125 *** -0.142 *** (-3.64) (-0.49) (-3.14) (-2.62) (-3.90) (-3.79) More than 20 years -0.201 *** -0.0394 -0.176 *** -0.169 *** -0.185 *** -0.174 *** (-5.18) (-0.72) (-4.85) (-4.55) (-6.05) (-4.89) Size t−1 0.0359 *** 0.0297 ** 0.0509 *** 0.0410 *** 0.0431 *** 0.0298 *** (5.51) (3.00) (7.76) (6.29) (8.01) (4.43) Manufacturing 0.150 *** 0.217 ** 0.0556 0.109 0.151 *** 0.166 ** (3.34) (2.71) (0.87) (1.93) (3.39) (2.88) Construction 0.0738 0.272 * 0.115 0.0161 0.0530 0.0956 (1.49) (2.31) (1.61) (0.27) (1.15) (1.58) Wholesale/ Retail 0.0730 0.225 ** -0.00683 0.0161 0.0998 * 0.132 * (1.69) (2.79) (-0.11) (0.27) (2.33) (2.37) Transport/ Storage 0.00945 0.317 ** 0.0127 0.0521 0.0331 0.0525 (0.16) (2.93) (0.15) (0.97) (0.59) (0.74) Accommodation/ Food 0.0716 0.0113 -0.0633 -0.0253 0.0969 * 0.117 (1.42) (0.08) (-0.90) (-0.36) (1.98) (1.89) Information/ Communication 0.215 *** 0.338 *** 0.114 0.0280 0.187 *** 0.308 *** (4.01) (4.05) (1.72) (0.47) (3.85) (4.91) Financial/ Real Estate 0.162 ** 0.182 -0.00453 0.162 ** 0.0655 0.0770 (2.69) (1.52) (-0.06) (2.68) (1.24) (1.14) Professional/ Scientific 0.141 ** 0.211 ** 0.0457 -0.0215 0.104 * 0.163 ** (3.11) (2.63) (0.73) (-0.32) (2.46) (2.93) Administrative/ Support 0.124 * 0.250 ** 0.0803 0.0734 0.107 * 0.160 ** (2.36) (2.62) (1.17) (1.35) (2.26) (2.68) Education 0.0554 0.314 ** -0.0406 0.0626 0.00139 0.0480 (0.83) (2.84) (-0.53) (1.06) (0.03) (0.72) Health/ Social Work 0.0480 0.369 * -0.117 -0.0487 -0.0306 -0.00925 (0.91) (2.33) (-1.74) (-0.72) (-0.68) (-0.16) Arts/ Entertainment 0.00494 0.0368 -0.0730 -0.1000 -0.00565 -0.0283 (0.08) (0.32) (-0.92) (-1.78) (-0.10) (-0.39) Other service -0.0214 0.131 -0.119 -0.0551 -0.0435 -0.00743 (-0.32) (0.97) (-1.56) (-0.76) (-0.79) (-0.11) Region dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Observations 2,913 1,323 2,816 3,050 4,739 3,185 Number of firms 2,534 1,126 2,444 2,668 3,966 2,736 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 5.2 Regression results The starting point was to estimate the probability that entrepreneurs exhibit growth intentions when experiencing individual disruption strands. Table 3 reports the outcomes of models 1–6, indicating that the extent to which individual disruption strands affect the intentions varies according to a strand. H1a and H1b are fully supported. It is found that disruption to capital investment plans (Model 1) decreases the likelihood of the intentions by 8.2% (β=-0.0819; p < 0.05). Similarly, when disruption to the introduction of new working practices occurs, the probability of the intentions is lowered by 8.2% (β=-0.0822; p < 0.05) (Model 4). Disruption to leadership training plans (Model 6) reduces the likelihood of SMEs’ growth intentions by 11.4% (β=-0.114; p < 0.01). These significant reductions in growth intentions associated with disruptions in leadership training , working practices , export activities and capital investment suggest that SMEs perceive these disruption strands as beyond their controls affecting their confidence for future growth. Such a setback of the intentions is not temporal and takes years to recover if it can be reversed. In contrast, the results reveals that disruptions to (Model 2), innovation (Model 3) and workforce (Model 5) have a negative but statistically insignificant effect on the intentions, meaning these disruptions are used by SMEs as a tool to either mitigate the negative effects or promote the internal efficiencies. The analysis also includes firm characteristics as control variables for SME growth intentions. Firm age (AGE) generally shows a significant and negative association with the intentions, as demonstrated in Models 1, 3, 4, 5 and 6. Model 1 shows that disruptions to capital investment negatively affect intentions across all firm ages, with established firms (β=-0.201; p < 0.01), middle-aged firms (β=-0.149; p < 0.01), and young firms (β=-0.0896; p < 0.1) being significantly impacted. Models 3–6 (disruptions to innovation, working practices, workforce, and leadership training) yield similar results, indicating older firms are more conservative in their intentions. However, Model 2 (disruption to export activities) shows no significant relationship between firm age and growth intentions, suggesting external market factors are more influential. Additionally, Model 4 indicates young firms (6–10 years) are less affected by working practices disruptions, showing better adaptability compared to older firms. Considering the size of a firm (SIZE), we find a consistent and positive relationship withgrowth intentions across all models (Models 1 to 6). This suggests that larger firms are more likely to anticipate growth even when facing various disruptions. the consistency of this effect indicates that larger firms are better able to handle disruption strands encountered and thus more confidence for future growth. The findings reveal significant sectoral differences in how disruptions affect growth intentions. Disruption to capital investment shows strong positive effects in manufacturing and information/communication, with moderate or slight effects in other sectors (i.e. financial/real estate, professional/scientific, and administrative/support sectors). Export disruption are positively correlated across multiple sectors, strongest in information/communication and weakest in health/social work and construction. Disruption to innovation shows no significant correlations, while. workplace disruption to positively affects only the financial/real estate sector. Workforce disruption are significant only in manufacturing and information/communication. leadership disruptions have positive influence in manufacturing, professional/scientific, and administrative/support, with the strongest impact in information/communication and the weakest in wholesale/retail. Table 4 about here Table 4 The effect of dynamic capabilities on SMEs’ growth intentions. INNOCAP t−1 7 8 9 0.136 *** (4.13) EXPCAP t−1 0.0736 *** (7.80) TRAINCAP t−1 0.0523 *** (4.54) Age t−1 6–10 years -0.101 *** -0.102 *** -0.0745 *** (-6.32) (-6.37) (-3.72) 11–20 years -0.159 *** -0.162 *** -0.139 *** (-10.41) (-10.60) (-7.28) More than 20 years -0.215 *** -0.217 *** -0.194 *** (-14.94) (-15.09) (-10.78) Size t−1 0.0513 *** 0.0489 *** 0.0482 *** (19.68) (18.60) (12.21) Manufacturing 0.0595 ** 0.0347 0.0678 * (2.66) (1.54) (2.53) Construction 0.00514 0.0111 0.0103 (0.23) (0.49) (0.37) Wholesale/ Retail 0.0345 0.0202 0.0337 (1.65) (0.96) (1.32) Transport/ Storage -0.0258 -0.0323 -0.0121 (-0.96) (-1.20) (-0.37) Accommodation/ Food 0.0227 0.0306 0.0283 (0.96) (1.29) (1.01) Information/ Communication 0.0923 *** 0.0752 ** 0.104 *** (3.71) (3.01) (3.37) Financial/ Real Estate 0.000541 0.00362 -0.00930 (0.02) (0.14) (-0.30) Professional/ Scientific 0.0479 * 0.0363 0.0592 * (2.26) (1.71) (2.25) Administrative/ Support 0.0659 ** 0.0631 ** 0.0645 * (2.80) (2.68) (2.28) Education -0.0104 -0.0107 -0.0668 * (-0.37) (-0.38) (-1.99) Health/ Social Work -0.0446 -0.0343 -0.0678 * (-1.93) (-1.47) (-2.47) Arts/ Entertainment 0.0289 0.0275 0.00180 (0.98) (0.93) (0.05) Other service -0.0247 -0.0223 -0.0446 (-0.93) (-0.83) (-1.39) Region dummies Yes Yes Yes Year dummies Yes Yes Yes Observations 20,505 20,456 15,462 Number of firms 11,208 11,185 8,839 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 We then move to model econometrically the correlation between specific adaptive capabilities and growth intentions in disruptive environment. Table 4 indicates that the three adaptive capabilities are significantly and positively correlated with SMEs’ growth intentions. Model 7 shows that possessed innovation capability significantly enhances the likelihood of SMEs’ growth intentions by 13.6% (β = 0.136; p < 0.01). Similarly, Model 8 indicates that export capability held boosts the probability of growth intentions by 7.4% (β = 0.0736; p < 0.01). Additionally, Model 9 demonstrates that training capability held increases the likelihood of growth intentions by 5.2% (β = 0.0523; p < 0.01). These results underscore the importance of adaptive capabilities in fostering growth intentions among SMEs in times of crisis. Similar to the results in Table 3 , firm age (AGE) is significantly and negatively associated with SMEs' growth intentions, as shown in Models 7, 8, and 9. Conversely, firm size (SIZE) positively correlates with the intentions, as indicated in Models 7, 8, and 9. The analysis reveals that innovation capability positively affects growth intentions in manufacturing, administrative/support, and information/communication sectors. Export capability positively impacts growth intentions in information/communication and administrative/support sectors. Training capability strongly links to growth intentions in the information/communication sector, with slight positive correlations in manufacturing, professional/scientific, and administrative/support sectors. Conversely, education and health/social work sectors are negatively associated with growth intentions. Table 5 about here Table 5 The effect of disruptive strands and dynamic capabilities on SMEs’ growth intentions. Model 10 11 12 13 14 15 CAPITAL t−1 -0.0930 ** (-3.21) EXPORT t−1 -0.0759 * (-2.19) INNOVATION t−1 -0.0201 (-0.65) PRACTICE t−1 -0.122 *** (-3.55) WORKFORCE t−1 -0.0402 (-1.42) LEADERSHIP t−1 -0.133 *** (-3.74) INNOCAP t−1 0.222 *** 0.179 *** 0.146 ** 0.145 * 0.178 *** 0.167 ** (3.55) (3.32) (2.89) (2.36) (3.30) (2.91) EXPCAP t−1 0.0944 *** 0.0173 0.0918 *** 0.117 *** 0.0859 *** 0.113 *** (3.73) (0.41) (3.76) (4.76) (4.30) (4.75) TRAINCAP t−1 0.0592 * 0.0914 * 0.0505 0.0251 0.0317 0.00187 (2.20) (2.29) (1.84) (0.97) (1.53) (0.07) Age t−1 6–10 years -0.0885 0.0166 -0.114 * -0.0575 -0.0802 * -0.129 ** (-1.70) (0.23) (-2.33) (-1.19) (-2.01) (-2.84) 11–20 years -0.132 ** -0.0304 -0.112 * -0.112 * -0.128 *** -0.160 *** (-2.67) (-0.44) (-2.42) (-2.46) (-3.38) (-3.70) More than 20 years -0.180 *** -0.0587 -0.169 *** -0.158 *** -0.179 *** -0.178 *** (-3.87) (-0.88) (-3.82) (-3.65) (-4.95) (-4.34) Size t−1 0.0207 * 0.0226 0.0504 *** 0.0396 *** 0.0406 *** 0.0347 *** (2.21) (1.54) (5.19) (4.43) (5.52) (4.00) Manufacturing 0.127 * 0.224 ** -0.00404 0.0501 0.0944 0.0834 (2.39) (2.61) (-0.06) (0.79) (1.89) (1.31) Construction 0.0717 0.293 * 0.0887 0.0341 0.0372 0.0993 (1.23) (2.27) (1.12) (0.51) (0.72) (1.50) Wholesale/ Retail 0.0641 0.282 ** -0.0363 0.0229 0.0726 0.0943 (1.27) (3.26) (-0.53) (0.38) (1.51) (1.55) Transport/ Storage 0.00579 0.343 ** -0.00915 -0.0418 0.0395 0.0269 (0.09) (3.05) (-0.10) (-0.55) (0.64) (0.35) Accommodation/ Food 0.119 * 0.0222 -0.0544 0.0574 0.103 0.123 (2.07) (0.15) (-0.71) (0.87) (1.92) (1.85) Information/ Communication 0.221 *** 0.324 *** 0.0570 0.130 0.170 ** 0.263 *** (3.39) (3.50) (0.76) (1.90) (3.01) (3.74) Financial/ Real Estate 0.124 0.134 -0.0353 -0.0545 0.0276 0.0427 (1.76) (0.97) (-0.41) (-0.75) (0.47) (0.58) Professional/ Scientific 0.124 * 0.227 ** -0.00572 0.0466 0.0852 0.131 * (2.30) (2.60) (-0.08) (0.76) (1.76) (2.12) Administrative/ Support 0.153 * 0.231 * 0.0531 0.0753 0.103 * 0.146 * (2.56) (2.23) (0.70) (1.15) (1.96) (2.25) Education 0.0528 0.272 -0.145 -0.0880 -0.0550 -0.00224 (0.70) (1.94) (-1.67) (-1.18) (-0.93) (-0.03) Health/ Social Work 0.0642 0.237 -0.145 -0.0896 -0.0304 -0.00829 (1.08) (1.22) (-1.93) (-1.43) (-0.61) (-0.13) Arts/ Entertainment 0.0344 0.103 -0.152 -0.0622 -0.0184 -0.0227 (0.45) (0.75) (-1.64) (-0.75) (-0.27) (-0.28) Other service -0.0316 0.0564 -0.190 * -0.0633 -0.0588 -0.0156 (-0.42) (0.37) (-2.23) (-0.80) (-0.96) (-0.20) Region dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Observations 2,411 1,048 2,232 2,593 4,024 2,838 Number of firms 2,104 901 1,957 2,281 3,380 2,433 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Models 10–15 combine disruptive strands and adaptive capability variables to analyse their impact on growth intentions (see Table 5 ). The aim is to examine how adaptive capabilities affect SMEs’ growth intentions across different disruption strands. The results support H2. Across these models, the effect of innovation capability on growth intention is more positive and significant with disruptions to capital investment (β = 0.222; p < 0.01), export activities (β = 0.179; p < 0.01), and workforce (β = 0.178; p < 0.01) than with disruptions to innovation activities (β = 0.146; p < 0.05), leadership training (β = 0.167; p < 0.05), and workplace practices (β = 0.145; p < 0.1). The effect of export capability is significant and positive in the context of capital investment disruptions (β = 0.0944; p < 0.01), innovation disruptions (β = 0.0918; p < 0.01), workplace practice disruptions (β = 0.117; p < 0.01), workforce disruptions (β = 0.0859; p < 0.01), and leadership training disruptions (β = 0.113; p < 0.01), but not export disruptions. The effect of training capability is slightly significant in the presence of disruptions to capital investment plans (β = 0.0592; p < 0.1) and export activities (β = 0.0914; p < 0.1), but not in the context of disruptions to innovation, workplace practice, workforce, and leadership training. Model 10 shows that disruptions to capital investment reduce SMEs' growth intentions by 9.3% (β=-0.0093; p < 0.05). Similarly, Model 11 indicates that disruptions to export activities decrease SMEs' growth intentions by 7.6% (β=-0.0759; p < 0.1). Model 13 reveals that disruptions to plans for new workplace practices lower the likelihood of SMEs' growth intentions by 12.2% (β=-0.122; p < 0.01). Furthermore, Model 15 demonstrates that disruption to leadership training decrease the likelihood of SMEs' growth intentions by 13.3% (β=-0.133; p < 0.01). It is noteworthy that the negative effects observed in these models are more pronounced than those reported in Table 3 . Models 12 and 14, which examine the impacts of disruptions to innovation and workforce, respectively, do not show significant effects. This finding aligns with the results presented in Table 3 . Similarly, the results for the control variables are largely consistent with those in Tables 3 and 4 . Specifically, firm age (AGE) is generally found to be significantly and negatively associated with growth intentions. Firm size (SIZE) demonstrates a significant and positive effect on growth intentions under various disruptions, except for disruptions to export activities. The findings reveal sector-specific effects of disruptions on growth intentions. Capital investment disruptions show slightly significant positive associations in manufacturing, accommodation/food, professional/scientific, and administrative/support sectors, with a strong positive effect in the information/communication sector. Disruptions to export activities show moderately significant positive in manufacturing, wholesale, transport/storage, and professional/scientific sectors, with slightly significant positive effects in construction and administrative, and strongly positive in information/communication. Disruptions to innovation activities show a slightly significant negative relationship only in the 'other service' sector. Workplace disruptions show no effects. Workforce disruptions are significant in the information/communication and administrative/support. Leadership training disruptions are strong in the information/communication sector, with slightly positive in professional/scientific and administrative/support. Table 6 about here Table 6 Results of Propensity Score Matching. Treatment 1 2 3 ATT z,2 Summary statistics based on the full sample. S.E. T-stat EXPCAP 0.081 0.011 7.52 INNOCAP 0.245 0.034 7.12 TRAINCAP 0.025 0.016 1.58 We also apply PSM to further alleviate potential endogeneity for the role of dynamic capabilities in shaping the intentions. Table 6 presents the average treatment effect statistics for the treated firms. The analysis reveals that all the treatments namely, being an exporter, being a serial innovator, and providing both informal and formal training – significantly enhance the probability of SME growth intentions. In column 1, the data shows that firms with export capability are 8.1% more likely to anticipate growth compared to those without such capabilities. Column 2 highlights that innovation capability increases the likelihood of growth intentions by 24.5%, indicating a substantial positive impact. Column 3 demonstrates that training capability, which includes both informal and formal training, boosts the probability of growth intentions by 2.5%. These findings further underscore the importance of export, innovation, and training capability held by SMEs in improving the entrepreneurs’ confidence for future growth beyond crisis. 6. Discussion and Conclusion This study set out to examine how SME growth intentions are shaped by adaptative capabilities across individual disruption strands, using longitudinal data on the UK SMEs. The examined period 2015–2020 was when the UK SMEs were challenged to cope with the Brexit. The findings from our analysis suggest that growth intentions in the post-crisis period are neither static nor uniformly constrained by external shocks. Instead, SME entrepreneurs’ perceptions of crisis conditions as both constraining and enabling entrepreneurial growth depend on how disruptions are interpreted and enacted, leading to rational adjustments in growth intentions. In this regard, disruption does not operate solely as a limiting force; rather, certain forms of disruption can activate adaptive capabilities and stimulate opportunity-seeking behaviour. Specifically, disruptions to capital investment, work practices, export, and leadership training —each of which directly constrain resource access, operational stability, and strategic direction—tend to negatively affect entrepreneurs’ growth attitudes in SMEs. In contrast, this pattern does not hold for disruptions to innovation and workforce . This highlights the dual nature of crisis contexts, in which constraints and opportunities coexist, and where entrepreneurial responses are shaped by sensemaking processes and strategic enactment. From a crisis perspective in entrepreneurship research, this distinction can be understood by differentiating between disruption types that constrain core resource orchestration and those that allow for adaptive recombination. Disruptions to capital investment, work practices, export, and leadership training are more likely to be perceived as externally imposed constrains that fall beyond the immediate control of entrepreneurs, limiting their capacity to enable adaptive responses, thereby weakening expectations of future growth. By contrast, disruptions to innovation and workforce do not exhibit the same negative pattern, as these domains are inherently more flexible and amendable to recombination. Such disruption may instead prompt entrepreneurs to reconfigure knowledge, skills, and routines in response to changing conditions. These findings complement the extant literature, which has predominantly focused on integrated crisis (Cowling et al., 2015 ; Doern et al, 2019 ; Lim et al., 2020 ; Shepherd & Williams, 2020 ; Branzei & Fathallah, 2023 ) and on how growth intentions are shaped by entrepreneurial experience in relatively stable environments (Freel et al., 2024 ). By unpacking the distinct effects of individual disruption strands, we advance our understanding of growth intentions beyond the traditional treatment of crisis as a monolithic condition. In so doing, we contribute to the growing body of literature on crisis in entrepreneurship (Doern et al, 2019 ; Shepherd & Williams, 2020 ; Branzei & Fathallah, 2023 ), which has primarily examined the relationship between integrated crises and actual growth. Our findings instead highlight the importance of growth intentions, showing that SME entrepreneurs’ responses to crises—and their adjustments in growth intentions—are shaped how disruptions are interpreted and enacted. We also contribute to the stream of entrepreneurship research adopting adaptive capabilities perspective (Teece, 2007 ; Soluk et al., 2021 ) by demonstrating that three dimensions of adaptive capacities shaping growth intentions, and that the strength of their effects varies depending on both the type of capability and the type of disruption. These findings conceptualise adaptive capabilities as multidimensional mechanisms whose influence on growth intentions is contingent upon their alignment with specific disruption contexts. Specifically, the three dimensions of adaptive capabilities shape growth intentions by enabling different forms of resource orchestration—such as reconfiguration, knowledge recombination, and opportunity recognition. However, their effectiveness depends on how well they match the demands imposed by particular types of disruption. Innovation capability emerges as a general-purpose adaptive mechanism, underpinning consistently high growth intentions across all disruption strands, albeit with varying effect sizes. Innovation activities—such as developing new products, and improving processes—enhance entrepreneurs’ ability to sense and respond to environment changes. Through these activities, entrepreneurs build experiential knowledge, and develop adaptive routines, and strengthen opportunity recognition, enabling them to perceive disruptions more controllable. This reinforces self-efficacy and confidence in pursuing growth, even under adverse conditions. In line with dynamic capabilities theory, innovation capability not only supports effective responses to immediate challenges but also facilitates proactive adaptation, thereby sustaining longer-term strategic orientation and growth prospects (Teece, 2014 ). By contrast, export capability operates as a context-dependent adaptive mechanism. While it is generally associated with stronger growth intention, its positive effect weakens under export-related disruptions. This suggests that when disruptions directly target the domain in which the capability is embedded, its effectiveness is temporarily constrained. Entrepreneurs may recognise that compensatory strategies—such as identifying and entering alternative markets—require time and resources, thereby dampening immediate growth expectations. Nonetheless, under other disruption strands, export capability remains valuable by enabling market diversification, knowledge acquisition, and strategic flexibility. Training capability, in turn, reflects a more indirect and temporally lagged adaptive mechanism, with limited influence on growth intentions across most disruption strands. Its effect becomes salient primarily in the context of capital investment and export disruptions, where skill development and knowledge upgrading are more directly linked to addressing specific constraints. The generally weak impact of training capability may stem from its less immediate and more ambiguous feedback loop; investments in training often yield delayed and uncertain returns, making it less effective in shaping short-term growth intentions during crisis conditions (Miocevic, 2021 ). Taken together, these patterns highlight the heterogeneous nature of environmental shocks and underscores the importance of capability–disruption alignment in explaining how SMEs strategically adjust their growth intentions in crisis contexts. Adaptive capabilities are not uniformly beneficial; rather, their influence varies because each capability dimension addresses distinct constraints and opportunities. The strength of their effects on growth intentions reflects a contingency logic, where entrepreneurs selectively activate and interpret different capabilities in response to specific disruption strands. This perspective highlights that growth intentions emerge not only from the presence of adaptive capacities but from their context-sensitive deployment, reinforcing the idea that crisis conditions amplify the differentiated value of distinct capability dimensions. Prior studies in more stable environments have identified the mechanisms and conditions under which dynamic capabilities positively influence realised growth outcomes (Jantunen et al., 2018 ; Hernandez-Linares et al., 2021 ). We extend this research by demonstrating how SMEs map specific disruption experiences onto particular dimensions of adaptive capabilities to inform and adjust their growth intentions. In doing so, we show that adaptive capability is not uniformly enacted but selectively mobilised depending on disruption type. Capabilities that are more readily deployable and reconfigurable—such as innovation and export capabilities—play a more prominent role in shaping growth intentions under disruption, as they enable firms to both “do different things” and “do things differently” in response to changing environments (Branzei & Fathallah, 2023 ; Freel et al., 2024 ). In contrast, capabilities with more indirect or delayed effects, such as training, contribute less to intention formation. Our work has important practical implications. Entrepreneurs, managers, and advisors of SMEs are increasingly required to address managerial challenges arising from recurrent disruptions embodied in the entrepreneurial process (Branzei & Fathallah, 2023 ; Miklian & Hoelscher; 2022 ). First, our longitudinal evidence shows the conditions under which SMEs can still expect to grow their business beyond crisis. SMEs holding innovation capability exhibit a 24.5% higher likelihood of growth intentions, while those with export capability show an 8.1% higher likelihood compared to firms lacking these capabilities. These findings demonstrate the importance of developing innovation capability to improve entrepreneurs’ confidence in their ability to pursue growth under crisis conditions. They also have practical implications for shaping strategic actions and investment focus, encouraging SME entrepreneurs and managers to continuously develop specific dimensions of adaptive capabilities that support the identification of opportunities and manage threats beyond crisis (Kukkamala & Koporcic, 2024). Second, our findings indicate that SMEs experiencing innovation and workforce disruption do not lower their growth aspiration unlike disruption in other areas such as capital investment, leadership training , export , and working practice . This suggests that entrepreneurs and managers of SMEs could benefit from bespoke support that target most impactful disruption strands. By focusing on individual disruptions, SMEs can develop greater confident in their ability to respond effectively to changing environments, thus facilitating the timely and efficient pursuit of growth opportunities. The findings of this study have strong implications for policymakers, serving as a basis for policy actions aimed at helping SMEs emerge from crises with minimal damage, which is critical for long-term success. The results highlight that disruptions to capital investment, leadership training, export, and working practices have negative effects on growth intentions. This suggests that targeted support measures—beyond access to external finance (Cowling et al., 2004 )—such as programs focused on leadership training, export facilitation, and working practice , could effectively motivate entrepreneurs of SMEs to pursue growth beyond crisis. Such support is likely to enhance SMEs confidence in their post-crisis growth intentions. Furthermore, the findings reveal that entrepreneurial intentions are negatively associated with firm age but positively correlated with firm size under most disruptions, with exception of export disruption. This -has significant policy implications for governments seeking to encourage SMEs growth in disruptive environments. Policymakers could consider promoting new forms of collaborations among SMEs, such as online platforms that include SMEs, bespoke government schemes, and organisations providing targeted business support. By facilitating access to resources, knowledge, and networks tailored to specific disruption contexts, these measures can strengthen SMEs’ capacity to adapt and maintain growth intentions in times of crisis. Our study documents that innovation and export capability are positively associated with growth intentions across almost all individual disruptions, highlighting the enduring relevance of dynamic capabilities in shaping growth intentions under rapidly changing conditions. Future research could build on this insight by examining the conditions under which different dimensions of adaptive capabilities (for instance, sensing, learning, and integrating capability) affect growth intentions of SMEs operating in disruptive environments. Such work would further clarify how capability heterogeneity and context interact to influence entrepreneurial decision-making during crises. We also acknowledge potential overlap between certain disruption strands and adaptive capabilities under examination. For example, export disruption is likely to be more relevant to firms possessing established export capability, while innovation disruption may disproportionately affect firms with higher innovation capability. This overlap stems from the interdependence between exposure and capability—firms that operate more actively in a particular domain are simultaneously more capable and more vulnerable to disruption in that area. Consequently, the interpretation of results requires cautions, as stronger observed relationships may partly reflect differential exposure rather than purely capability effects. Nonetheless, distinguishing between these domains remains meaningful, as the experience of disruption and the capability to respond represent distinct mechanisms within the dynamic capabilities framework (Teece, 2007 ; Helfat and Winter, 2011 ). Having broadly established the association between adaptive capabilities and growth intention across different types of disruption, an important and interesting question is how SMEs ensure that adaptive capabilities remain useful and relevant for future conditions. The observed negative effects of entrepreneur’s income on growth intentions (Freel et al., 2024 ) highlight a limitation of our study, as we did not observe entrepreneur attributes. Future research could deepen our understanding of crisis in entrepreneurship by examining how women-owned SMEs build and deploy specific dimensions of dynamic capabilities to meet the demands of the disruptive environments. A further concern lies in the operationalization of adaptive capabilities. training capability is measured based on the provision of training, yet the frequency or mere presence of training provides limited insight into whether firms can effectively reconfigure skills, redeploy knowledge, or adapt routines in response to environmental shocks. Similarly, innovation capability is operationalized as serial new-to-market innovations, which reflects observable outcomes rather than the underlying processes that enable adaptation. In this study, we employ these measures to capture patterns of resource deployment rather than dynamic capabilities in the sense articulated by Teece ( 2007 ) and other scholars. Our findings thus provide valuable evidence on the alignment between certain capabilities and growth intentions, and the strength of the relations varies depending on capability dimension and disruption type. Future research could take longitudinal or process-based approaches that track capability enactment in real time could provide richer insight into how SMEs dynamically adapt to crises and which mechanisms most effectively translate capabilities into intentional and strategic growth outcomes. Such studies would help unpack the micro-foundations of adaptive capabilities and clarify how specific processes, rather than outcomes alone, shape entrepreneurial decision-making under disruptive conditions Disclosure statement No potential conflict of interest was reported by the author(s). Declarations Author Contribution LX contributes wrote and reviewed the main manuscript text RZ prepared tables and drafted findings References Almeida, P., & Phene, A. (2004). Subsidiaries and knowledge creation: The influence of the MNC and host country on innovation. Strategic management journal , 25 (8-9), 847–864. https://doi.org/10.1002/smj.388 Atkin, D., Khandelwal, A. K., & Osman, A. (2017). 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Journal of Small Business Management , 54 (3), 892–911. https://doi.org/10.1111/jsbm.12230 Footnotes A control group is constructed by matching each treated unit with a non-treated unit of similar characteristics. For dichotomous and continuous variables. Reference group: firms aged 0–5 years. Reference group: firms aged 0–5 years. Reference group: firms aged 0–5 years. Standard error. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9293921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617574495,"identity":"de3423e2-cd81-45e0-9dd4-9f62a882f3b3","order_by":0,"name":"Li Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie2RsWrDQAyGFQ7Oy5/c6pDQZ7jgqWCcVzkweCpkzRgoeAr0BfowZwSdTLsGssRLJg/xZg+ldTAUGsg1kKXDfSAEEh+SEJHH8w+Z9GGJ4jmJX3V7XZFDO8OgmBuVHsaQb1OwsO36AyoQVdW18YoCPgiULkWaYlvuMX2WUQSTPW6QaYGdSxHWjvM9NEPOyLAmeiKBk0sZbYrP/B1LRtC15kuTqv9ShOVxbtEv099lrKbwPMW1mJCG52WKkGU0Q5ZqGR518eo4XwXbqKnXyYN64app40QrlVaH+u26cvF0+nmux+PxeO7hGzSmREp7gnXvAAAAAElFTkSuQmCC","orcid":"","institution":"University of Leicester","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Xiao","suffix":""},{"id":617574496,"identity":"a329934d-c835-40af-845b-eefc511c5771","order_by":1,"name":"Ruoying Zhou","email":"","orcid":"","institution":"University of Leicester","correspondingAuthor":false,"prefix":"","firstName":"Ruoying","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-04-01 15:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9293921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9293921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107704780,"identity":"fec3da41-7b70-4245-b1e9-763d1b7a7431","added_by":"auto","created_at":"2026-04-24 08:58:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1245075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9293921/v1/cf2b266c-c16c-4596-aab9-846a52c4b1aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SMEs’ Adaptive Capabilities and Growth Intentions: Evidence across Different Disruption Types","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmall and medium-sized enterprises (SMEs) are responsible for innovation, job creation, and economic development across countries (Cowling et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Smallbone et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Growth intentions are critical to such a contribution, particularly in dynamic environments, as they reflected in sustained efforts to entrepreneurship. Growth rarely occurs unless firms actively seek it (Autio \u0026amp; Acs, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Autio \u0026amp; Rannikko, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and growth intentions are influential in this regard. Wiklund and Shepherd (2013) suggest that actual business growth is strongly correlated with growth intentions in a relatively stable environment. In reality, however, SMEs often struggle to sustain growth because the entrepreneurial process is embedded in challenging conditions, which create difficulties for firms attempting to position themselves for sustainable growth (Doern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Understanding how SMEs adapt to environmental dynamism is increasingly important. This paper provides rigorous empirical evidence on SME intentions to pursue opportunities to for business expansions in the aftermath of crisis.\u003c/p\u003e \u003cp\u003eGrowth intentions are dynamic and influenced by both internal factors and external environments (Freel at al., 2024). Yet studies on the growth intention of firms have a focus on the context of relatively stable environments, with comparatively limited attentions paid to firms operating in disruptive conditions. Disruptive environments increase uncertainty and risk. When crises occur, SMEs experience a variety of disruption strands, including disruptions to \u003cem\u003ecapital investment\u003c/em\u003e, \u003cem\u003eworkforce\u003c/em\u003e, \u003cem\u003eworking practice\u003c/em\u003e, \u003cem\u003einnovation\u003c/em\u003e, and \u003cem\u003eexport\u003c/em\u003e (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hernandez-Linares et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argue that disruptions can moderate the influence of capabilities on realised firm performance, however not all dimensions of these capabilities contribute equally to such outcomes. Such experiences require stronger entrepreneurial orientation and adaptive capabilities for SMEs to pursue growth opportunity (Wiklund and Shepherd, 2005). So far, knowledge of how entrepreneurs perceive individual disruption strands, and how their assessment of the effects and duration of such disruptions shape the intentions beyond crisis, remains limited (Shirokova et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kukkamala \u0026amp; Koporcic, 2024; Miklian \u0026amp; Hoelscher, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, we know little about how adaptive capabilities enables SMEs to adjust their growth intentions in response to individual disruptive strands. Understanding how growth intentions and adaptive capabilities interact across individual disruption strands is of great relevance to research on crisis and entrepreneurship, and it is critical for policy-makers tasked with alleviating the effects of environmental disruptions on the SME sector.\u003c/p\u003e \u003cp\u003eRecent studies highlight the importance of adaptive capabilities for SME survival and growth in disruptive environments (Martin-Rojas, 2026). Fundamental to adaptive capabilities is a firm\u0026rsquo;s ability to sense, respond to, and capitalise environmental changes (Dewi et al., 2020). Most studies that adopt adaptive capabilities as a theoretical lens focus on the realised growth, and tend to treat environmental disruption as a single or aggregated construct (e.g. uncertainty, turbulence, or crisis). While this approach overlooks the heterogeneity of disruption, it nevertheless provides valuable insights into the roles of adaptive capabilities in shaping actual growth in the post-crisis period, thus implying their relevance for growth intentions. Firm-level adaptive capability can be translated into entrepreneurs\u0026rsquo; intentions through both structural and psychological mechanisms. Adaptive firms typically promote flexible routines, continuous learning new knowledge, and reconfigure organisational resources to engage proactively with changes (Xiao and Hughes, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In such contexts, entrepreneurs of SMEs with stronger adaptive capabilities are likely to consider themselves as capable of mitigating the effects of environmental disruption and, which in turn strengthens their confidence and fosters higher growth intentions (Panjaitan et al., 2025). In other words, adaptive capabilities may enable entrepreneurs to interpret and enact responses to significant changes to the business and the entrepreneur\u0026rsquo;s work practices, thus enhancing their motivation and shaping growth intentions. However, both theoretical and empirical research remains limited regarding the conditions under which post-crisis growth intentions, particularly when accounting for different types of disruptions (Jantunen et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This leads us to the following research questions: \u003cem\u003e1) how SME intentions to growth are affected by individual disruption strands and 2) how adaptive capacities shaped the growth intentions across different disruption strands.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWe use a large panel dataset of UK SMEs from the Longitudinal Small Business Survey (LSBS), covering a period 2015\u0026ndash;2020. This longitudinal dataset follows UK SMEs beyond Brexit for five years. Brexit is a major form of unprecedent political event facing SMEs (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Calabrese et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the UK SMEs are disproportionately affected by Brexit-induced uncertainties (Eggers, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These surveys allow us to capture relatively long-term repercussions on the SMEs \u003cem\u003eand\u003c/em\u003e the information on the intentions, individual disruption strands, adaptive capabilities, and other variables. These, taken together, enable us to fully address our research questions.\u003c/p\u003e \u003cp\u003eWe offer contributions. First, we contribute to the entrepreneurship crisis literature by conceptualising a crisis as a bundle of discrete disruption strands and revealing how each strand uniquely influence entrepreneurs\u0026rsquo; confidence for future growth (Doern et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shepherd \u0026amp; Williams, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shirokova et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our findings show significant heterogeneity in how different disruption strands affect growth intentions. SMEs perceive disruptions to \u003cem\u003ecapital investment, leadership training, export, and working practice\u003c/em\u003e as exogenous threats beyond entrepreneurs\u0026rsquo; control, and thus lower the intentions. While they treat disruptions to \u003cem\u003einnovation and workforce\u003c/em\u003e as a tool to exert business expansion, and thus the intentions remain uninfluenced (Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lien \u0026amp; Timmermans, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Prior studies considering crisis as integrated primarily indicate a temporal setback in growth (Cowling et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however, our findings show this is not necessarily the case. We thus provide advanced understanding of the heterogeneous effects of disruptive strands on SMEs\u0026rsquo; growth intentions. Second, our findings reveal that the extent to which the adaptive capacities (e.g. observable strategic activities) shape the growth intentions varies across different disruption strands. SMEs may adapt and adjust their intentions during and after a crisis, with adaptive capabilities playing varying roles in shaping these intentions across different disruption strands. This suggests that the effectiveness of adaptive capacities is contingent upon the perceived threats, highlighting the heterogeneous conditions that SMEs faced during a crisis. Adaptive capacities, as resources possessed by SMEs, enable entrepreneurs to interpret specific disruption strands as manageable which in turn supports the enhancement of their growth intentions. We thus contribute to the literature by highlighting the heterogeneous nature of environmental shocks and providing a more nuanced understanding of how SMEs strategically respond to different types of disruption. Third, we contribute to the stream of entrepreneurship research that adopts a view of adaptive capabilities by offering valuable insights into the ways that SMEs specify dimensions of adaptive \u003cem\u003ecapabilities\u003c/em\u003e in response to different disruption strands and adjust the intentions (Teece, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Soluk et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Theoretical Background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Growth intentions and disruptive environments\u003c/h2\u003e \u003cp\u003eGrowth intentions reflect an entrepreneur\u0026rsquo;s motivation and willingness to exert business expansion (Delmar \u0026amp; Wiklund, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A crisis entails significant changes to the business and entrepreneur\u0026rsquo;s work practices. Consequently, many SMEs experience a temporary growth setback (Doern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Birhanu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kukkamala \u0026amp; Koporcic, 2024), although recovery can occur relatively quickly once conditions stabilise (Cowling et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Prior studies have predominantly examined the effects of crisis as integrated, single events on actual growth, often focus on the onset or immediate aftermath (Doern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cowling et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Birhanu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kukkamala \u0026amp; Koporcic, 2024).\u003c/p\u003e \u003cp\u003eTreating a crisis as a single, homogeneous event is problematic. This approach overlooks the fact that disruption often occur through a set of distinct strands, and the effects of these strands on SME growth may vary depending on their nature and duration. These disruption effects typically include delays or a reduction in capital investment for growth-orientated business activities and R\u0026amp;D (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Calabrese et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qamar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), interruption to new product development and launch (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), uncertain trading conditions, and declines in income streams (Cowling et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In realities, entrepreneurs of SMEs often assess the effects of these disruption strands separately and form expectation about their duration and controllability (Lien \u0026amp; Timmermans, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Their growth intentions are likely influenced by the perceived locus of control over each disruption strand (Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When disruption strands are perceived as external and uncontrollable, entrepreneurs tend to exhibit greater hesitation in pursuing growth. Conversely, when disruptions are seen as within their ambit, they feel more confident in their ability to achieve growth. Recognising this distinction is important, as it provides a conceptual framework for understanding how SMEs develop more efficient and targeted contingency plans for future crises.\u003c/p\u003e \u003cp\u003eTo capture the multifaceted nature of disruption, we categorise it into six specific strands: disruptions to \u003cem\u003ecapital investment, working practices, leadership training, export, innovation\u003c/em\u003e, and \u003cem\u003eworkforce\u003c/em\u003e. Empirically, these strands reflect recurrent themes identified in prior crisis-entrepreneurship studies, which documents how resource constraints, operational interruptions, and market shocks differentially affect firms\u0026rsquo; strategic responses (Doern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shepherd and Williams, 202; Branzei and Fathallah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Theoretically, this categorisation aligns with the resource-based and adaptive capabilities perspectives, both of which emphasise that disruptions occur across distinct yet interrelated domains of firm resources and capabilities\u0026mdash;financial (capital investment), operational (working practices, workforce), strategic (leadership training), and market-oriented (exports, innovation). Each strand, thus, represents a unique mechanism through which adaptive capabilities may differentially influence entrepreneurial motivation to achieve sustained competitiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Growth intentions and adaptive capabilities\u003c/h2\u003e \u003cp\u003eAdaptive capabilities are commonly conceptualised within the dynamic capabilities perspective, referring to a firm\u0026rsquo;s ability to adjust to changing environments by reconfiguring resources, processes, and strategies (Teece, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Teece, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Adaptive capabilities represent a key dimension of firm heterogeneity, both influencing and reflecting patterns of resource deployment (Helfat \u0026amp; Winter, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). An early view proposed in conceptual studies posits a positive and direct relation between dynamic capabilities\u0026mdash;i.e., a firm\u0026rsquo;s ability to adjust to environmental changes\u0026mdash;and firm performance outcomes (Teece et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). However, empirical findings have been inconsistent. For instance, recent studies find insignificant and negative relations (Karna et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Girod \u0026amp; Whittington, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Jantunen et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) state that higher performance outcomes are more likely when adaptive capabilities interact with operational-level changes (i.e. changes in management and practices or changes in used technologies and target market). Furthermore, market orientation significantly moderates the relationship between adaptive capabilities\u0026mdash;specifically, sensing and learning and firm performance (Hernandez-Linares et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The inconsistency of these studies can be attributed to contextual conditions and specific dimensions of adaptive capabilities studied (Fainshmidt et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSo far, most studies have focused mainly on relatively stable environments and realised growth, suggesting that the effects of adaptive capabilities on performance outcomes may vary depending on the nature of challenging conditions (Girod \u0026amp; Whittington, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), implying their relevance for growth intentions. As an internal firm-level factor, adaptive capabilities can be translated into entrepreneur intentions through both structural and psychological mechanisms, thus shaping entrepreneurial motivations and intentions to pursue growth. At the structural level, adaptive firms tend to promote flexible routines and continuous developing expertise, which empower employees to experiment and engage proactively with change (Xiao and Hughes, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). At the psychological level, such environments enhance individuals\u0026rsquo; self-efficacy, perceived behavioural control, and opportunity recognition\u0026mdash;key antecedents of intention formation. Employees internalize the firm\u0026rsquo;s adaptive orientation, becoming more willing to pursue entrepreneurial, innovative, or change-oriented actions. Consequently, firm-level adaptive capability becomes embedded in individuals\u0026rsquo; cognitive frameworks, motivating intentions that align with adaptability, resilience, and proactive engagement. This highlights the need to investigate how growth intentions are shaped by adaptive capabilities across different disruption strands (Fainshmidt et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jantunen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a key dimension of firm heterogeneity, adaptive capabilities are often conceptualised as a multidimensional construct reflecting a firm\u0026rsquo;s ability to respond effectively to environmental change. Entrepreneurs\u0026rsquo; adjustments to growth intentions are likely shaped by particular dimensions of adaptive capabilities, representing patterns of resource deployment and their subjective assessment of the effects of individual disruption strands. Differentiating between dimensions of adaptive capabilities is essential for understanding how entrepreneurs adjust their growth intentions in response to different types of disruption (Helfat \u0026amp; Winter, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hernandez-Linares et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soluk et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of SMEs, three specific components \u0026mdash;\u003cb\u003einnovation\u003c/b\u003e, \u003cb\u003eexport\u003c/b\u003e, and \u003cb\u003etraining capability\u003c/b\u003e\u0026mdash;are particularly salient because these components directly influence how entrepreneurs are motivated to take actions to mitigate disruptions\u0026rsquo; effects and seize growth opportunities. Each captures a distinct yet interrelated mechanism through which entrepreneurs of firms reconfigure their resources, processes and strategies to respond to changing conditions. Innovation capability enables firms to develop new products and processes in response to changing market conditions, while export capability reflects a firm\u0026rsquo;s ability to expand its market reach beyond existing market and allow the firm to pursue growth opportunities when the environment is uncertain. Learning and training capability enhance the human and strategic competence necessary to recognise emerging opportunities and reconfigure resources in response to environmental challenges (Zahra \u0026amp; George, 2002; Teece, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, these dimensions of adaptive capabilities serve as key mechanisms through which entrepreneurs form perceptions of disruption and strategically adjust their growth intentions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInnovation capability\u003c/em\u003e reflects a firm\u0026rsquo;s ability to gain and recombine knowledge to develop new or improve products, services, and processes. In dynamic environments, it represents a critical mechanism that allow entrepreneurs to adapt proactively and renew their strategic direction (Xiao \u0026amp; Ramsden, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Crescenzi \u0026amp; Gagliardi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eExport capability\u003c/em\u003e, by contrast, captures a firm\u0026rsquo;s outward-oriented capability to identify, access, and exploit opportunities in international markets. This capability can be leveraged to diversify risk exposure, expand their knowledge base, and enhance resilience through engagement in global markets (Gkypali at al., 2021). Finally, \u003cem\u003etraining capability\u003c/em\u003e underpins the transforming function of adaptive capabilities by aligning employees\u0026rsquo; skills and competences with the firm\u0026rsquo;s growth objectives. By effectively transferring of training into practice, firms can reconfigure internal resources, enhance adaptability, and improve operational efficiency under changing conditions (Burford et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, these three dimensions provide a theoretically grounded framework for explaining how SMEs renew, reconfigure, and deploy their capabilities that may shape growth intentions across different disruption strands. Building on this framework, our study examines the conditions under which these adaptive capabilities shape growth intentions, providing insights into factors that influence how SMEs translate adaptive capabilities into strategic outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Hypotheses development","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The relationship between individual disruption strands and growth intentions\u003c/h2\u003e \u003cp\u003eOur concern is with how entrepreneurs individually perceive disruption strands to form their growth intentions. Although disruption strands originate from external crises, their influence in firm responses is mediated through entrepreneurs\u0026rsquo; subjective interpretations, making an individual-level focus essential for explaining variation in growth intentions. Accordingly, we develop hypotheses as follow:\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to capital investment\u003c/em\u003e. Making investments in growth-orientated entrepreneurial activities is a strong indicator of growth intentions (Brown et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When experiencing crisis-induced uncertainties, SMEs tend to scale back or delay future investment plans and strategic actions to avoid costly missteps (Calabrese et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By adopting a \u0026ldquo;wait-and-see\u0026rdquo; approach, they allow time to anticipate how environmental changes may influence their businesses (Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, this caution may also slow the pace of recovery. Meanwhile, constraints on the finance supply side also contributes to a capital investment holdback. In challenging conditions, banks\u0026mdash; the main external funding sources for SMEs (OECD, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u0026mdash;tend to cut their lending towards small firms (Cowling et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while SMEs become more reliant on external finance (Block et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cowling et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This mismatch discourages SMEs from applying for external funding due to expectation of higher rejection rates, including those identifying viable investment opportunities (Cowling et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), though relationship lending can significantly reduce firms\u0026rsquo; concerns about access to debt finance (Calabrese et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, by scaling back or postponing capital investments, entrepreneurs adopt a more cautious approach to pursuing growth opportunities, which can suppress their growth intentions. we thus expect that \u003cem\u003ecapital investment disruption\u003c/em\u003e is negatively associated with growth intentions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to working practices\u003c/em\u003e. When a crisis hits, SMEs often change their operation routines and processes in response to its effects, which can lead to either improved resilience or performance decline (Xiao \u0026amp; Hughes, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). For instance, the cancellation of signed purchase orders may compel SMEs to redeploy employees to new tasks and projects that could benefit to the business when the recovery comes. The ability of SMEs to make such effective adjustments rests on their capacity to mobilise and reconfigure necessary resources (Mittermaier et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, small firms generally operate under significant constraints, including shortages of skilled labour and limited access to external resources and facilities, which are often exacerbated during crisis, further reducing their capacity for adjustment (Shirokova et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Doern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, entrepreneurs of SMEs experiencing disruptions to working practices are less likely to anticipate a subsequent increase in revenue. As Gulati et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) argue, SMEs that fail to adapt adequately to new environment realities tend to take significantly longer to develop positive expectation about future growth. We thus expect that \u003cem\u003eworking practices disruption\u003c/em\u003e is negatively associated with entrepreneurs\u0026rsquo; growth intentions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to leadership training\u003c/em\u003e. In the absence of leadership training, entrepreneurs and senior managers are deprived of vital skills updates, which can undermine their strategic and operational competencies and, in turn, their growth intentions. Leadership training provides entrepreneurs/senior managers with foundational capabilities such as effective communication, strategic decision-making, and complex problem-solving (Franco \u0026amp; Matos, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We argue that these competencies enable managers to anticipate negative effects, reconfigure resources, and transform operations in response to disruptions, and thus increase entrepreneurs\u0026rsquo; confidence in their ability to cope with crisis-induced changes to their business and work environment. For instance, effective communication with key employees and business partners helps entrepreneurs to evaluate the potential of emerging opportunities and to assess their firm\u0026rsquo;s capability to realise them. Similarly, strong problems-solving skills enable managers to make difficult and timely decisions required to navigate turbulent conditions with greater confidence and effectiveness. Despite its importance and significance, few work have examined the role of leadership training in shaping SME growth intentions under challenging conditions (Miklian \u0026amp; Heolscher, 2022). Prior studies suggest the direct contribution of leadership to innovation and export performance (Dunne et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Freel, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) \u003cem\u003eand\u003c/em\u003e that the leadership training can have sustained positive effects on firms in overcoming exporting hurdle (Love \u0026amp; Roper, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When leadership training is disrupted, entrepreneurs and senior managers may overestimate the likelihood of failure, and become hesitate to pursue growth opportunities and reluctant to committing resources to projects that they feel ill-equipped to manage. We thus expect the leadership training disruption is negatively associated with growth intentions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to export\u003c/em\u003e. Export disruptions generate formidable barriers for SMEs seeking to access and compete in international markets, affecting their attitude towards future growth (Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, the UK\u0026rsquo;s new relationship with the EU, which leads to increased costs and paperwork complication, creates new barriers for the UK SMEs to export products or services to the EU market. The effects of such disruption vary considerably based on a firm\u0026rsquo;s level of internationalization and openness to trade, and broader uncertain trading conditions. The negative effects of Brexit are disproportionately felt by exporters and importers located in peripheral regions of the UK (Brown et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is partly because exporting itself serves as a critical mechanism for developing export capability (Gkypali at al. 2021); disruptions to export activities thus hinder capability development, particularly for firms in peripheral regions, amplifying the impact on their international performance. Through export practice, firms gain tacit knowledge and critical information about overseas markets and the value of their offering, which support more informed decisions on subsequent entrepreneurial activities. Furthermore, translating the knowledge into successful market entry requires more than just information; it also demands strategic vision and managerial expertise to re-configure a firm\u0026rsquo;s resource base, including managerial, marketing, and financial resources. Thus, entrepreneurs of SMEs experiencing a drop in goods sales are likely to anticipate reduced future business growth. This leads us to assume that export disruption is negatively associated with growth intentions.\u003c/p\u003e \u003cp\u003eThe above discussion suggests that entrepreneurs\u0026rsquo; ability to adapt to change conditions caused by disruptions to \u003cem\u003ecapital investment, working practices\u003c/em\u003e, export activities, and \u003cem\u003eleadership training\u003c/em\u003e is constrained. Therefore, we propose:\u003c/p\u003e \u003cp\u003eH1a: Disruptions \u003cem\u003eto capital investment, working practices\u003c/em\u003e, export activities, and \u003cem\u003eleadership training\u003c/em\u003e have negative and significant effects on SME growth intentions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to workforce\u003c/em\u003e. Access to a skilled workforce is critical for growth-orientated and innovative SMEs to operate at full capacity. Shortages of skilled workforce, coupled with raising labour costs, can undermine SMEs\u0026rsquo; ability to retain employees and maintain cost-efficient task delivery (Qamar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, SMEs are particularly vulnerable to workforce disruption in the immediate aftermath of a crisis. For instance, during the early debates on Brexit, public policy changes affecting EU immigration were anticipated to worsen labour market conditions for SMEs, particularly in the manufacturing and construction sector, where skilled labour shortages are more pronounced than in the service sector (Hopley, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, recent research shows that such workforce disruptions are often temporary. Across the UK, SMEs have mitigated labour shortage by employing EU nationals, who fill about 10% of manufacturing jobs (Cowling et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It indicates that deficiencies in skilled labour at the onset of a crisis can recover relatively quickly. Furthermore, SMEs may proactively respond by developing internal capacities, such as building a skilled workforce through targeted training and upskilling initiatives (Burford et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or increase their automation to reduce reliance on labour.\u003c/p\u003e \u003cp\u003eGiven these adaptive strategies, temporary workforce disruption does not necessarily undermine entrepreneurs\u0026rsquo; confidence in pursuing business expansion. Entrepreneurs may view such disruptions as manageable challenges rather than insurmountable obstacles. We therefore argue that workforce disruption, while significant, do not inherently reduce SMEs\u0026rsquo; growth intention, particularly when firms possess the capabilities to respond strategically.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDisruption to innovation\u003c/em\u003e. Innovation disruption refers to a pause in SMEs\u0026rsquo; ongoing innovation activities, during which they may be compelled to redirect their efforts toward crisis-driven innovation. In this sense, innovation is used as a resilience-building tool to mitigate the disruption effects of crises (Lien \u0026amp; Timmermans, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For SMEs, innovation activities are often undertaken by skilled employers and senior tech team members (Xiao \u0026amp; Ramsden, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) \u003cem\u003eand\u003c/em\u003e typically draw on publicly available external scientific knowledge (Freel, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) or created by partners undertaking R\u0026amp;D (Love \u0026amp; Roper, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This highlights the importance of knowledge-based resources and internal capabilities in sustaining entrepreneurial resilience and supporting recovery. Under particularly challenging conditions, SMEs shift from improvising products/services and launching the revised products to meet the future market demands, towards actively realising emerge business opportunities (Xiao \u0026amp; Hughes, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this regard, SMEs not only engage in intensive innovation but focus on the long-term value creation, with the expectation of achieving returns over the long-run (Lien \u0026amp; Timmermans, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, it is not evident that UK SMEs\u0026rsquo; access to external knowledge from the EU has been reduced by new relationship with the EU. Following this line of argument, innovation disruption can act as an enabler for organizational adaptation, influencing SMEs motivations and efforts to pursue long-term value creation.\u003c/p\u003e \u003cp\u003eThe discussion above indicates that SMEs exhibit a degree of resilience in the face of workforce and innovation disruptions. We thus propose:\u003c/p\u003e \u003cp\u003eH1b: \u003cem\u003eworkforce\u003c/em\u003e and \u003cem\u003einnovation\u003c/em\u003e disruptions have no significant effects on SMEs\u0026rsquo; growth intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The relationship between adaptive capabilities and growth intentions\u003c/h2\u003e \u003cp\u003eWe now discuss the roles of adaptive capabilities in shaping SME growth intentions when experiencing different disruptions, with a focus on the three specific dimensions introduced earlier.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInnovation capability\u003c/em\u003e, as a key dimension of adaptive capability, refers to a firm\u0026rsquo;s ability to continuously identify new knowledge and ideas as well as transform them into new/improved products, processes, and system (Autio et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, innovation capability supports firm\u0026rsquo;s adaptation to new and changing conditions. It can come at a variety of forms, and encompasses both the technological and non-technological dimensions of innovative activities and the potential for both radical and incremental changes (Love \u0026amp; Roper, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Literature posits that product and process innovation play a central role in the firm performance, and a positive association between innovation and productivity (Hall et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) \u003cem\u003eand\u003c/em\u003e between innovation and realised growth (indicated by employment and sales) holds in more stable environments (Freel, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross different types of disruption, innovation reflects firms\u0026rsquo; ability to respond effectively to diverse challenges while seizing new opportunities arising from a crisis (Taalbi, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A firm\u0026rsquo;s ability to innovate shapes creative responses, enabling it to address immediate problems while also pursuing opportunities that extend beyond the crisis (Greenstein et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Teece, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such innovation capability, which support the development of solutions to distinct challenges, is likely to enhance entrepreneurs\u0026rsquo; confidence in pursuing future business growth. The greater a firm\u0026rsquo;s innovation capacity, the higher its potential to increase the desirability of future growth (Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We therefore expect that \u003cem\u003einnovation capability\u003c/em\u003e is positively associated with the growth intentions across different disruption strands.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExport capability\u003c/em\u003e refers to a firm\u0026rsquo; ability to achieve competitive advantages and enhance export performance through the effective mobilisation of skills and resources (Efrat et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). When facing a range of disruptions (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), export capability, at its core, facilitates adaptation strategies by enabling firms to restructure its skills and resources to respond effectively to changing conditions. Knowledge and expertise gained from export and import practices strengthen SMEs\u0026rsquo; ability to assess the effects of disruptions and address the evolving needs of new customers. As a result, they are better positioned to make timely and informed strategic decisions. SMEs with the higher level of export capability are consequently better equipped to respond effectively to affected activities, particularly in their international operation. Strong export capability provides firms with knowledge, skills, and resource necessary to assess the impact of different disruptions on international activities, make contingencies to reduce the such effects, shorten the duration of declines in export growth, and adapt their offerings or processes to evolving market demands (Fang \u0026amp; Zou, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Helfat \u0026amp; Winter, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, export capability functions not only as a driver of international performance but as a critical component of organisational resilience in the post crisis period, supporting firms in restructuring their resources to mitigate the effects of different types of disruption. It is logical to expect that the strength of this relationship may vary across different disruption strands, as the way export capability is applied\u0026mdash;and the extent to which it mitigates or leverages the effects of disruption\u0026mdash;differs depending on the nature of each disruption. Basing on these analyses, we thus assume that \u003cem\u003eexport capability\u003c/em\u003e is positively associated growth intentions and the strength of this association may vary across different types of disruption.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTraining capability\u003c/em\u003e refers to a firm\u0026rsquo;s ability to update and enhance employees\u0026rsquo; skills and knowledge, ensuring that they remain effective in areas aligned with the firm\u0026rsquo;s core strengths and strategic mission (Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Through training, knowledge and expertise flow within a firm and among the members, while also incorporating specialised insights from external experts (Brown et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Outcomes of training include improved cognitive ability, increased self-efficacy, and updated role-specific skills and knowledge. Training thus plays a central role in the development of a firm\u0026rsquo;s broad capacity for growth, innovation, and resilience. Training helps SMEs to develop an internal pool of skilful workforce to compensate for shortages in the entrepreneurship ecosystem. For tech firms, training facilitates the advancements of collective technological expertise to achieve competitive advantages in the market, such collective expertise is critical to form new teams quickly to pursue emerging opportunities (Xiao \u0026amp; Hughes, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn times of crisis, training is, at its core, about equipping employees to address both challenges and new tasks with flexible, adaptive solutions. Training capability provides feedback that improves entrepreneurs\u0026rsquo; confidence in their employees\u0026rsquo; ability to perform new tasks effectively, thereby motivating them to pursue growth beyond the crisis. Similar to export capability, the way training capability is applied\u0026mdash;and the extent to which it mitigates or leverages the effects of disruption\u0026mdash;differs depending on the nature of each disruption. We thus expect that \u003cem\u003etraining capability\u003c/em\u003e is positively associated growth intentions, with the magnitude of these effects potentially differing across various types of disruption.\u003c/p\u003e \u003cp\u003eTaken together, we argue that growth intentions are cultivated by three dimensions of their adaptive capabilities that emphasize SMEs\u0026rsquo; flexibility and responsiveness, and the extent to which adaptive capabilities that leverage the effect of disruption may differ depending on the nature of each disruption. Specifically, innovation capability enables SMEs to develop solutions to crisis-induced problems through adaptive and innovative responses to changing conditions; export capability facilitates contingencies for affected export activities and effective exploration of new markets; and training capability prepares employees to perform new and emerging tasks and to manage unprecedent challenges effectively. Thus, we posit:\u003c/p\u003e \u003cp\u003eH2: Adaptive capabilities (i.e. \u003cem\u003eexport, innovate, and training capability\u003c/em\u003e) have positive effects on SME growth intentions, with the magnitude of these effects potentially differing across disruption strands.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data source and sample\u003c/h2\u003e \u003cp\u003eThe dataset for this study is drawn from waves 1 to 6 of LSBS, covering time period from 2015 to 2020 where firms experiencing Brexit-induced uncertainties. The sample was stratified by firm size, region, and industrial sector. The UK administrative Inter-Departmental Business Register was the source for sampling of registered businesses, while Dun and Bradstreet\u0026rsquo;s database was the source for sampling of unregistered businesses. The dataset provides information on a variety of disruptive strands, growth intentions, specific dynamic capabilities, firm characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variables\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Dependent variable\u003c/h2\u003e \u003cp\u003eOur interest is in growth intentions (\u003cem\u003eGROWTH\u003c/em\u003e) of SMEs beyond crisis. The dependent variable is a categorical variable that measures owners\u0026rsquo; expectation of turnover growth in the next 12 months from 2015. The variable is coded as 1 if a firm responded \u0026ldquo;increased\u0026rdquo;, and 0 if \u0026ldquo;decreased\u0026rdquo; or \u0026ldquo;stay the same\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Independent variable\u003c/h2\u003e \u003cp\u003e \u003cem\u003eDisruptive strands\u003c/em\u003e. The variable includes six Brexit-induced disruptive strands based on the following question in the LSBS dataset: \u0026ldquo;Whether plans of each of six strands over the next three years have been affected by Brexit\u0026rdquo;. \u003cem\u003eCapital investment disruption (CAPITAL)\u003c/em\u003e is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Capital investment\u0026rdquo;, 0 if otherwise; \u003cem\u003eWorkforce disruption (WORKFORCE)\u003c/em\u003e is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Increase the skills of the workforce\u0026rdquo;, 0 if otherwise; \u003cem\u003eLeadership disruption (LEADERSHIP)\u003c/em\u003e is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Increase the leadership capability of managers\u0026rdquo;, 0 if otherwise; \u003cem\u003eInnovation disruption (INNOVATION)\u003c/em\u003e is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Develop and launch new products/services\u0026rdquo;; \u003cem\u003eWorking practices disruption (PRACTICE)\u003c/em\u003e is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Introduce new working practices\u0026rdquo;; lastly, \u003cem\u003eExport disruption\u003c/em\u003e (EXPORT) is operationalised as 1 if a firm answered \u0026ldquo;Yes\u0026rdquo; to \u0026ldquo;Increase export sales or begin selling to new overseas markets\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAdaptive capabilities\u003c/em\u003e. The \u003cem\u003eadaptive capabilities\u003c/em\u003e variable is constructed to measure observable strategic activities, specifically \u003cem\u003eexport, innovation and training capabilities\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA measure of \u003cem\u003eexport capability\u003c/em\u003e (EXPCAP) is a dichotomous variable that takes the value 1 if a firm is an exporter, and 0 otherwise. An \u003cem\u003einnovation capability\u003c/em\u003e measure (INNOCAP) is a dichotomous variable that equals 1 if a firm has new to market innovation for the any three consecutive years during the period of 2015\u0026ndash;2020, and 0 otherwise. A measure of \u003cem\u003etraining capability\u003c/em\u003e (TRAINCAP) is operationalised as a dummy variable that takes the value 1 if the firm provided both formal and informal training in the past year, and 0 if otherwise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Control variable\u003c/h2\u003e \u003cp\u003eWe included control variables - a firm\u0026rsquo;s size and age, region, sector, and year in our analyses. Specifically, a firm\u0026rsquo;s size (SIZE) is indicated by the log form of employees. A firm\u0026rsquo;s age (AGE) is a categorical variable that takes the value 1 if firm is 0 to 5 years old (reference group), 2 if firm is 6 to 10 years old, 3 if firm is 11\u0026ndash;20 years old, or 4 if more than 20 years old. REGION is a categorical variable indicating the location where a business is operating in (in total, 12 regions included in the dataset). SECTOR is a categorical variable showing the sector in which a firm is operating (a total of 14 sectors included). YEAR is a categorical variable indicating the time period from 2015 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Empirical strategy\u003c/h2\u003e \u003cp\u003eWe adapt a random-effects probit model for testing hypotheses proposed earlier, as the dependent variable is a binary. This approach is preferred because it accounts for time invariant unobserved firm-specific effect and a random unobserved shock, unlike fixed-effects probit model (Bernard \u0026amp; Jensen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). the Wu-Hausman test failure to reject the null hypothesis, further supporting the use of the random-effects specification.\u003c/p\u003e \u003cp\u003eWe follow a \u003cem\u003elagged\u003c/em\u003e approach that enables us to overcome the problem of endogeneity partially (Almeida \u0026amp; Phene, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Endogeneity is a common problem in the research. Endogeneity occurs when the independent variable and the error term (which is omitted from the equation) is correlated. In this study, adaptive capability variables (i.e., innovation, export, and training) are considered endogenous to SMEs\u0026rsquo; growth intentions. We recognise that effects of disruptions and adaptive capabilities may require some time to manifest on the intentions.\u003c/p\u003e \u003cp\u003eWe specify the equation as:\u003c/p\u003e \u003cp\u003eGROWTH \u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e = ꞵ\u003csub\u003e0\u003c/sub\u003e + ꞵ\u003csub\u003e1\u003c/sub\u003e Disruptive strands \u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e + ꞵ\u003csub\u003e2\u003c/sub\u003e Adaptive capabilities \u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e + ꞵ\u003csub\u003e3\u003c/sub\u003e Controls \u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ϵ}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eDisruptive strands\u003c/em\u003e \u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e are a set of dummy variables indicating whether a firm indicates whether plans over the next three years have been affected by Brexit in the past year; \u003cem\u003eAdaptive capabilities\u003c/em\u003e \u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e is a set of three variables indicating whether a firm has exported services or goods overseas, introduced consistent new-to-market innovations, provided both informal and formal training to its managers and employees in the past year; Controls \u003csub\u003eit\u003c/sub\u003e is a vector for control variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e0\u003c/sub\u003e is a constant, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ϵ}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Addressing endogeneity\u003c/h2\u003e \u003cp\u003eTo further alleviate potential endogeneity between dynamic capabilities and SMEs\u0026rsquo; growth intentions, we apply propensity score matching (PSM) with a kernel estimator. ThePSM technique samples data that are not involved in the intervention to determine the likely outcomes for those that did participate, had they not been part of the intervention. The technique can address selection bias by eliminating systematic differences between the treated and control group. The treated group includes units received the treatment, whereas the control group include comparable firms that do not. A kernal-based matching method is used because it uses information on all available controls and downweighs distant observations, providing superior matching quality and balance between the treated and control groups\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003ePSM estimates the average treatment effect for the treated (ATT), caputring the effect of intervention on firms that receive it. We construct a set of dummy variables equal to 1 if a firm exports, is a serial innovator, or undertakes training, and 0 otherwise. This allows us to compare firms engaging in these activities with similar firms across the control variables, ensuring that observed differences in expected performance can be attributed to exporting, innovation, or training.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Findings","content":"\u003cp\u003eWe present the sample descriptive statistics, the correlation matrix for the variables; followed by the empirical results from random-effects probit regressions and propensity score matching. Due to the non-linear nature of our models, we report average marginal effects, indicating the average change in the probability of growth intentions when a firm possesses a specific characteristic.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the summary statistics for all variables included. The analysis includes 64,876 firm-level observations of SMEs\u0026rsquo; expected growth from 2015 to 2020. Within the sample, micro firms (fewer than 10 employees) make up 61.6% of all firms, small firms (11\u0026ndash;49 employees) constitute 23.3%, and medium-sized firms (50\u0026ndash;249 employees) represent 15.1%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics\u003csup\u003ez,2\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGROWTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpectation of turnover growth in next 12 months: =1 if firm expected turnover growth in the next 12 months; =0 if decrease or stay the firm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.491988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXPORT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about export over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1804267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3845871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital investment disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAPITAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about capital investment over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.317676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkforce disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWORKFORCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about workforce over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.266356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLEADERSHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about leadership training over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking practice disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRACTICE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about introducing new working practices over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.275486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInnovation disruptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINNOVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 Plan about introducing innovation over the next three years have been affected by Brexit; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.324906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXPCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 if firm exported goods or services in the past year; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.220588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.414646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRAINCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: =1 if firm provided both formal and informal training in the past year; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.233359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.4229732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInnovation capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINNOCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variable: = 1 if firm had introduced new to market innovation for three consecutive years; 0 if otherwise.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical variable: = 1 if 0\u0026ndash;5 years, = 2 if 6\u0026ndash;10 years, =\u0026thinsp;3 if 11\u0026ndash;20 years, = 4 if more than 20 years.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of employees (ln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.942692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.574279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.521461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector of firm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSECTOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical variable that indicates the sector of the business.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.061935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.128514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of firm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREGION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical variable that indicates the location of the business.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.815109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.709309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYEAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical variable that indicates the time period (2015\u0026ndash;2020).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2017.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.723682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003eSince the key variables of the sample are dummy variables, the mean of each dummy represents the percentage of observations where the variable equals 1. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that approximately 41.1% of firms anticipated turnover growth within the next 12 months from 2015 to 2020. Around 18% of firms indicated that their export plans for the next three years were affected by Brexit. Additionally, 12% of firms reported that their plans to introduce innovation were impacted, 11.4% noted that their capital investment plans were affected, 8.3% mentioned disruptions to working practices, and 7.7% experienced workforce disruptions. Lastly, 6.4% of firms reported disruptions to leadership training. Regarding adaptive capabilities, 22.1% of firms exported goods or services in the past year; 23.3% of firms provided both formal and informal training; and only 0.8% introduced new-to-market innovations for three consecutive years.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1. GROWTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. INNOCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0409*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. EXPCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1100*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0931*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. TRAINCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1255*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0440*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0705*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. CAPITAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0454*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0826*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. EXPORT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0399*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2519*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0478*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5209*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. INNOVATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0531*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0918*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6374*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5664*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. PRACTICE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0511*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0796*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5467*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4307*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6170*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. WORKFORCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0415*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0282*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0614*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5319*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4323*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5989*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6501*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. LEADERSHIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0416*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0564*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5487*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4311*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6071*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6809*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7315*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. SIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1538*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0083*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1344*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4722*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0374*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0438*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0342*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Pearson\u0026rsquo;s correlation matrix for both dichotomous and continuous variables. The table indicates that the correlation between disruptions to capital investment and growth intentions is positive and significant at 1% level. In contrast, disruptions to innovation, workplace practice, workforce, and leadership training are negatively and significantly correlated with growth intentions at the 1% level. The correlation between disruptions to export activities and growth intentions is negative but not significant. There is a strong correlation between adaptive capability variables (i.e. INNOCAP, EXPCAP, and TRAINCAP) and growth intentions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of disruptive strands on SMEs\u0026rsquo; growth intentions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPITAL \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0819\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPORT \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINNOVATION \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRACTICE \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0822\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWORKFORCE \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEADERSHIP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.114\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0896\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.103\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0765\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.115\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.102\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.125\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.176\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.185\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.174\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0359\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0297\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0509\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0410\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0431\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0298\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(7.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.217\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.151\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.166\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWholesale/ Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0998\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport/ Storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccommodation/ Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0969\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation/ Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial/ Real Estate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/ Scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.141\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative/ Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.160\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth/ Social Work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArts/ Entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Regression results\u003c/h2\u003e \u003cp\u003eThe starting point was to estimate the probability that entrepreneurs exhibit growth intentions when experiencing individual disruption strands. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the outcomes of models 1\u0026ndash;6, indicating that the extent to which individual disruption strands affect the intentions varies according to a strand. H1a and H1b are fully supported.\u003c/p\u003e \u003cp\u003eIt is found that disruption to \u003cem\u003ecapital investment\u003c/em\u003e plans (Model 1) decreases the likelihood of the intentions by 8.2% (β=-0.0819; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, when disruption to the introduction of new working practices occurs, the probability of the intentions is lowered by 8.2% (β=-0.0822; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Model 4). Disruption to leadership training plans (Model 6) reduces the likelihood of SMEs\u0026rsquo; growth intentions by 11.4% (β=-0.114; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These significant reductions in growth intentions associated with disruptions in \u003cem\u003eleadership training\u003c/em\u003e, \u003cem\u003eworking practices\u003c/em\u003e, \u003cem\u003eexport activities\u003c/em\u003e and \u003cem\u003ecapital investment\u003c/em\u003e suggest that SMEs perceive these disruption strands as beyond their controls affecting their confidence for future growth. Such a setback of the intentions is not temporal and takes years to recover if it can be reversed. In contrast, the results reveals that disruptions to (Model 2), \u003cem\u003einnovation\u003c/em\u003e (Model 3) and \u003cem\u003eworkforce\u003c/em\u003e (Model 5) have a negative but statistically insignificant effect on the intentions, meaning these disruptions are used by SMEs as a tool to either mitigate the negative effects or promote the internal efficiencies.\u003c/p\u003e \u003cp\u003eThe analysis also includes firm characteristics as control variables for SME growth intentions. Firm age (AGE) generally shows a significant and negative association with the intentions, as demonstrated in Models 1, 3, 4, 5 and 6. Model 1 shows that disruptions to capital investment negatively affect intentions across all firm ages, with established firms (β=-0.201; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), middle-aged firms (β=-0.149; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and young firms (β=-0.0896; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) being significantly impacted. Models 3\u0026ndash;6 (disruptions to innovation, working practices, workforce, and leadership training) yield similar results, indicating older firms are more conservative in their intentions. However, Model 2 (disruption to export activities) shows no significant relationship between firm age and growth intentions, suggesting external market factors are more influential. Additionally, Model 4 indicates young firms (6\u0026ndash;10 years) are less affected by working practices disruptions, showing better adaptability compared to older firms.\u003c/p\u003e \u003cp\u003eConsidering the size of a firm (SIZE), we find a consistent and positive relationship withgrowth intentions across all models (Models 1 to 6). This suggests that larger firms are more likely to anticipate growth even when facing various disruptions. the consistency of this effect indicates that larger firms are better able to handle disruption strands encountered and thus more confidence for future growth.\u003c/p\u003e \u003cp\u003eThe findings reveal significant sectoral differences in how disruptions affect growth intentions. Disruption to capital investment shows strong positive effects in manufacturing and information/communication, with moderate or slight effects in other sectors (i.e. financial/real estate, professional/scientific, and administrative/support sectors). Export disruption are positively correlated across multiple sectors, strongest in information/communication and weakest in health/social work and construction. Disruption to innovation shows no significant correlations, while. workplace disruption to positively affects only the financial/real estate sector. Workforce disruption are significant only in manufacturing and information/communication. leadership disruptions have positive influence in manufacturing, professional/scientific, and administrative/support, with the strongest impact in information/communication and the weakest in wholesale/retail.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of dynamic capabilities on SMEs\u0026rsquo; growth intentions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eINNOCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0736\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAINCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0523\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u003csub\u003et\u0026minus;1\u003c/sub\u003e \u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.101\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0745\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.159\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.162\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-10.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-10.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-7.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.194\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-14.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-15.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-10.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0513\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0482\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(19.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(18.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(12.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0595\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0678\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWholesale/ Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport/ Storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccommodation/ Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation/ Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0923\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0752\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial/ Real Estate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/ Scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0479\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0592\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative/ Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0659\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0631\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0645\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0668\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth/ Social Work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0678\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArts/ Entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe then move to model econometrically the correlation between specific adaptive capabilities and growth intentions in disruptive environment. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that the three adaptive capabilities are significantly and positively correlated with SMEs\u0026rsquo; growth intentions. Model 7 shows that possessed innovation capability significantly enhances the likelihood of SMEs\u0026rsquo; growth intentions by 13.6% (β\u0026thinsp;=\u0026thinsp;0.136; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, Model 8 indicates that export capability held boosts the probability of growth intentions by 7.4% (β\u0026thinsp;=\u0026thinsp;0.0736; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, Model 9 demonstrates that training capability held increases the likelihood of growth intentions by 5.2% (β\u0026thinsp;=\u0026thinsp;0.0523; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results underscore the importance of adaptive capabilities in fostering growth intentions among SMEs in times of crisis.\u003c/p\u003e \u003cp\u003eSimilar to the results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, firm age (AGE) is significantly and negatively associated with SMEs' growth intentions, as shown in Models 7, 8, and 9. Conversely, firm size (SIZE) positively correlates with the intentions, as indicated in Models 7, 8, and 9.\u003c/p\u003e \u003cp\u003eThe analysis reveals that innovation capability positively affects growth intentions in manufacturing, administrative/support, and information/communication sectors. Export capability positively impacts growth intentions in information/communication and administrative/support sectors. Training capability strongly links to growth intentions in the information/communication sector, with slight positive correlations in manufacturing, professional/scientific, and administrative/support sectors. Conversely, education and health/social work sectors are negatively associated with growth intentions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of disruptive strands and dynamic capabilities on SMEs\u0026rsquo; growth intentions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPITAL\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0930\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPORT \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0759\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINNOVATION \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRACTICE \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWORKFORCE \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEADERSHIP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.133\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINNOCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.167\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0944\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0918\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0859\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.113\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAINCAP \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0592\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0914\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u003csub\u003et\u0026minus;1\u003c/sub\u003e \u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.114\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0802\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.129\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.132\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.112\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.112\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.180\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.158\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.178\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize \u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0207\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0504\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0396\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0406\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0347\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWholesale/ Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.282\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport/ Storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccommodation/ Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.119\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation/ Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.221\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.170\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.263\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial/ Real Estate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/ Scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.131\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative/ Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth/ Social Work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArts/ Entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.190\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear dummies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of firms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModels 10\u0026ndash;15 combine disruptive strands and adaptive capability variables to analyse their impact on growth intentions (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The aim is to examine how adaptive capabilities affect SMEs\u0026rsquo; growth intentions across different disruption strands. The results support H2.\u003c/p\u003e \u003cp\u003eAcross these models, the effect of innovation capability on growth intention is more positive and significant with disruptions to capital investment (β\u0026thinsp;=\u0026thinsp;0.222; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), export activities (β\u0026thinsp;=\u0026thinsp;0.179; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and workforce (β\u0026thinsp;=\u0026thinsp;0.178; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than with disruptions to innovation activities (β\u0026thinsp;=\u0026thinsp;0.146; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), leadership training (β\u0026thinsp;=\u0026thinsp;0.167; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and workplace practices (β\u0026thinsp;=\u0026thinsp;0.145; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). The effect of export capability is significant and positive in the context of capital investment disruptions (β\u0026thinsp;=\u0026thinsp;0.0944; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), innovation disruptions (β\u0026thinsp;=\u0026thinsp;0.0918; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), workplace practice disruptions (β\u0026thinsp;=\u0026thinsp;0.117; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), workforce disruptions (β\u0026thinsp;=\u0026thinsp;0.0859; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and leadership training disruptions (β\u0026thinsp;=\u0026thinsp;0.113; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but not export disruptions. The effect of training capability is slightly significant in the presence of disruptions to capital investment plans (β\u0026thinsp;=\u0026thinsp;0.0592; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and export activities (β\u0026thinsp;=\u0026thinsp;0.0914; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), but not in the context of disruptions to innovation, workplace practice, workforce, and leadership training.\u003c/p\u003e \u003cp\u003eModel 10 shows that disruptions to capital investment reduce SMEs' growth intentions by 9.3% (β=-0.0093; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, Model 11 indicates that disruptions to export activities decrease SMEs' growth intentions by 7.6% (β=-0.0759; p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Model 13 reveals that disruptions to plans for new workplace practices lower the likelihood of SMEs' growth intentions by 12.2% (β=-0.122; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, Model 15 demonstrates that disruption to leadership training decrease the likelihood of SMEs' growth intentions by 13.3% (β=-0.133; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). It is noteworthy that the negative effects observed in these models are more pronounced than those reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Models 12 and 14, which examine the impacts of disruptions to innovation and workforce, respectively, do not show significant effects. This finding aligns with the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, the results for the control variables are largely consistent with those in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Specifically, firm age (AGE) is generally found to be significantly and negatively associated with growth intentions. Firm size (SIZE) demonstrates a significant and positive effect on growth intentions under various disruptions, except for disruptions to export activities.\u003c/p\u003e \u003cp\u003eThe findings reveal sector-specific effects of disruptions on growth intentions. Capital investment disruptions show slightly significant positive associations in manufacturing, accommodation/food, professional/scientific, and administrative/support sectors, with a strong positive effect in the information/communication sector. Disruptions to export activities show moderately significant positive in manufacturing, wholesale, transport/storage, and professional/scientific sectors, with slightly significant positive effects in construction and administrative, and strongly positive in information/communication. Disruptions to innovation activities show a slightly significant negative relationship only in the 'other service' sector. Workplace disruptions show no effects. Workforce disruptions are significant in the information/communication and administrative/support. Leadership training disruptions are strong in the information/communication sector, with slightly positive in professional/scientific and administrative/support.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e about here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Propensity Score Matching.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eATT\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003ez,2\u003c/sup\u003e Summary statistics based on the full sample.\u003c/p\u003e \u003cp\u003e\u003cb\u003eS.E.\u003c/b\u003e\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT-stat\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINNOCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAINCAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also apply PSM to further alleviate potential endogeneity for the role of dynamic capabilities in shaping the intentions. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the average treatment effect statistics for the treated firms. The analysis reveals that all the treatments namely, being an exporter, being a serial innovator, and providing both informal and formal training \u0026ndash; significantly enhance the probability of SME growth intentions.\u003c/p\u003e \u003cp\u003eIn column 1, the data shows that firms with export capability are 8.1% more likely to anticipate growth compared to those without such capabilities. Column 2 highlights that innovation capability increases the likelihood of growth intentions by 24.5%, indicating a substantial positive impact. Column 3 demonstrates that training capability, which includes both informal and formal training, boosts the probability of growth intentions by 2.5%. These findings further underscore the importance of export, innovation, and training capability held by SMEs in improving the entrepreneurs\u0026rsquo; confidence for future growth beyond crisis.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion and Conclusion","content":"\u003cp\u003eThis study set out to examine how SME growth intentions are shaped by adaptative capabilities across individual disruption strands, using longitudinal data on the UK SMEs. The examined period 2015\u0026ndash;2020 was when the UK SMEs were challenged to cope with the Brexit. The findings from our analysis suggest that growth intentions in the post-crisis period are neither static nor uniformly constrained by external shocks. Instead, SME entrepreneurs\u0026rsquo; perceptions of crisis conditions as both constraining and enabling entrepreneurial growth depend on how disruptions are interpreted and enacted, leading to rational adjustments in growth intentions. In this regard, disruption does not operate solely as a limiting force; rather, certain forms of disruption can activate adaptive capabilities and stimulate opportunity-seeking behaviour.\u003c/p\u003e \u003cp\u003eSpecifically, disruptions \u003cem\u003eto capital investment, work practices, export, and leadership training\u003c/em\u003e \u0026mdash;each of which directly constrain resource access, operational stability, and strategic direction\u0026mdash;tend to negatively affect entrepreneurs\u0026rsquo; growth attitudes in SMEs. In contrast, this pattern does not hold for disruptions to \u003cem\u003einnovation and workforce\u003c/em\u003e. This highlights the dual nature of crisis contexts, in which constraints and opportunities coexist, and where entrepreneurial responses are shaped by sensemaking processes and strategic enactment.\u003c/p\u003e \u003cp\u003eFrom a crisis perspective in entrepreneurship research, this distinction can be understood by differentiating between disruption types that constrain core resource orchestration and those that allow for adaptive recombination. Disruptions \u003cem\u003eto capital investment, work practices, export, and leadership training\u003c/em\u003e are more likely to be perceived as externally imposed constrains that fall beyond the immediate control of entrepreneurs, limiting their capacity to enable adaptive responses, thereby weakening expectations of future growth. By contrast, disruptions to \u003cem\u003einnovation and workforce\u003c/em\u003e do not exhibit the same negative pattern, as these domains are inherently more flexible and amendable to recombination. Such disruption may instead prompt entrepreneurs to reconfigure knowledge, skills, and routines in response to changing conditions.\u003c/p\u003e \u003cp\u003eThese findings complement the extant literature, which has predominantly focused on integrated crisis (Cowling et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Doern et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shepherd \u0026amp; Williams, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Branzei \u0026amp; Fathallah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and on how growth intentions are shaped by entrepreneurial experience in relatively stable environments (Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By unpacking the distinct effects of individual disruption strands, we advance our understanding of growth intentions beyond the traditional treatment of crisis as a monolithic condition. In so doing, we contribute to the growing body of literature on crisis in entrepreneurship (Doern et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shepherd \u0026amp; Williams, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Branzei \u0026amp; Fathallah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which has primarily examined the relationship between integrated crises and actual growth. Our findings instead highlight the importance of growth intentions, showing that SME entrepreneurs\u0026rsquo; responses to crises\u0026mdash;and their adjustments in growth intentions\u0026mdash;are shaped how disruptions are interpreted and enacted.\u003c/p\u003e \u003cp\u003eWe also contribute to the stream of entrepreneurship research adopting adaptive capabilities perspective (Teece, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Soluk et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) by demonstrating that three dimensions of adaptive capacities shaping growth intentions, and that the strength of their effects varies depending on both the type of capability and the type of disruption. These findings conceptualise adaptive capabilities as multidimensional mechanisms whose influence on growth intentions is contingent upon their alignment with specific disruption contexts.\u003c/p\u003e \u003cp\u003eSpecifically, the three dimensions of adaptive capabilities shape growth intentions by enabling different forms of resource orchestration\u0026mdash;such as reconfiguration, knowledge recombination, and opportunity recognition. However, their effectiveness depends on how well they match the demands imposed by particular types of disruption. \u003cem\u003eInnovation capability\u003c/em\u003e emerges as a general-purpose adaptive mechanism, underpinning consistently high growth intentions across all disruption strands, albeit with varying effect sizes. Innovation activities\u0026mdash;such as developing new products, and improving processes\u0026mdash;enhance entrepreneurs\u0026rsquo; ability to sense and respond to environment changes. Through these activities, entrepreneurs build experiential knowledge, and develop adaptive routines, and strengthen opportunity recognition, enabling them to perceive disruptions more controllable. This reinforces self-efficacy and confidence in pursuing growth, even under adverse conditions. In line with dynamic capabilities theory, innovation capability not only supports effective responses to immediate challenges but also facilitates proactive adaptation, thereby sustaining longer-term strategic orientation and growth prospects (Teece, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, export \u003cem\u003ecapability\u003c/em\u003e operates as a context-dependent adaptive mechanism. While it is generally associated with stronger growth intention, its positive effect weakens under export-related disruptions. This suggests that when disruptions directly target the domain in which the capability is embedded, its effectiveness is temporarily constrained. Entrepreneurs may recognise that compensatory strategies\u0026mdash;such as identifying and entering alternative markets\u0026mdash;require time and resources, thereby dampening immediate growth expectations. Nonetheless, under other disruption strands, export capability remains valuable by enabling market diversification, knowledge acquisition, and strategic flexibility.\u003c/p\u003e \u003cp\u003eTraining capability, in turn, reflects a more indirect and temporally lagged adaptive mechanism, with limited influence on growth intentions across most disruption strands. Its effect becomes salient primarily in the context of capital investment and export disruptions, where skill development and knowledge upgrading are more directly linked to addressing specific constraints. The generally weak impact of training capability may stem from its less immediate and more ambiguous feedback loop; investments in training often yield delayed and uncertain returns, making it less effective in shaping short-term growth intentions during crisis conditions (Miocevic, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, these patterns highlight the heterogeneous nature of environmental shocks and underscores the importance of capability\u0026ndash;disruption alignment in explaining how SMEs strategically adjust their growth intentions in crisis contexts. Adaptive capabilities are not uniformly beneficial; rather, their influence varies because each capability dimension addresses distinct constraints and opportunities. The strength of their effects on growth intentions reflects a contingency logic, where entrepreneurs selectively activate and interpret different capabilities in response to specific disruption strands. This perspective highlights that growth intentions emerge not only from the presence of adaptive capacities but from their context-sensitive deployment, reinforcing the idea that crisis conditions amplify the differentiated value of distinct capability dimensions.\u003c/p\u003e \u003cp\u003ePrior studies in more stable environments have identified the mechanisms and conditions under which dynamic capabilities positively influence realised growth outcomes (Jantunen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hernandez-Linares et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We extend this research by demonstrating how SMEs map specific disruption experiences onto particular dimensions of adaptive capabilities to inform and adjust their growth intentions. In doing so, we show that adaptive capability is not uniformly enacted but selectively mobilised depending on disruption type. Capabilities that are more readily deployable and reconfigurable\u0026mdash;such as innovation and export capabilities\u0026mdash;play a more prominent role in shaping growth intentions under disruption, as they enable firms to both \u0026ldquo;do different things\u0026rdquo; and \u0026ldquo;do things differently\u0026rdquo; in response to changing environments (Branzei \u0026amp; Fathallah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, capabilities with more indirect or delayed effects, such as training, contribute less to intention formation.\u003c/p\u003e \u003cp\u003eOur work has important practical implications. Entrepreneurs, managers, and advisors of SMEs are increasingly required to address managerial challenges arising from recurrent disruptions embodied in the entrepreneurial process (Branzei \u0026amp; Fathallah, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miklian \u0026amp; Hoelscher; \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst, our longitudinal evidence shows the conditions under which SMEs can still expect to grow their business beyond crisis. SMEs holding \u003cem\u003einnovation capability\u003c/em\u003e exhibit a 24.5% higher likelihood of growth intentions, while those with \u003cem\u003eexport capability\u003c/em\u003e show an 8.1% higher likelihood compared to firms lacking these capabilities. These findings demonstrate the importance of developing \u003cem\u003einnovation capability\u003c/em\u003e to improve entrepreneurs\u0026rsquo; confidence in their ability to pursue growth under crisis conditions. They also have practical implications for shaping strategic actions and investment focus, encouraging SME entrepreneurs and managers to continuously develop specific dimensions of adaptive capabilities that support the identification of opportunities and manage threats beyond crisis (Kukkamala \u0026amp; Koporcic, 2024).\u003c/p\u003e \u003cp\u003eSecond, our findings indicate that SMEs experiencing \u003cem\u003einnovation and workforce disruption\u003c/em\u003e do not lower their growth aspiration unlike disruption in other areas such as \u003cem\u003ecapital investment, leadership training\u003c/em\u003e, \u003cem\u003eexport\u003c/em\u003e, and \u003cem\u003eworking practice\u003c/em\u003e. This suggests that entrepreneurs and managers of SMEs could benefit from bespoke support that target most impactful disruption strands. By focusing on individual disruptions, SMEs can develop greater confident in their ability to respond effectively to changing environments, thus facilitating the timely and efficient pursuit of growth opportunities.\u003c/p\u003e \u003cp\u003eThe findings of this study have strong implications for policymakers, serving as a basis for policy actions aimed at helping SMEs emerge from crises with minimal damage, which is critical for long-term success. The results highlight that disruptions to \u003cem\u003ecapital investment, leadership training, export, and working practices\u003c/em\u003e have negative effects on growth intentions. This suggests that targeted support measures\u0026mdash;beyond access to external finance (Cowling et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u0026mdash;such as programs focused on \u003cem\u003eleadership training, export facilitation, and working practice\u003c/em\u003e, could effectively motivate entrepreneurs of SMEs to pursue growth beyond crisis. Such support is likely to enhance SMEs confidence in their post-crisis growth intentions. Furthermore, the findings reveal that entrepreneurial intentions are negatively associated with firm age but positively correlated with firm size under most disruptions, with exception of \u003cem\u003eexport disruption.\u003c/em\u003e This -has significant policy implications for governments seeking to encourage SMEs growth in disruptive environments. Policymakers could consider promoting new forms of collaborations among SMEs, such as online platforms that include SMEs, bespoke government schemes, and organisations providing targeted business support. By facilitating access to resources, knowledge, and networks tailored to specific disruption contexts, these measures can strengthen SMEs\u0026rsquo; capacity to adapt and maintain growth intentions in times of crisis.\u003c/p\u003e \u003cp\u003eOur study documents that \u003cem\u003einnovation and export capability\u003c/em\u003e are positively associated with growth intentions across almost all individual disruptions, highlighting the enduring relevance of dynamic capabilities in shaping growth intentions under rapidly changing conditions. Future research could build on this insight by examining the conditions under which different dimensions of adaptive capabilities (for instance, sensing, learning, and integrating capability) affect growth intentions of SMEs operating in disruptive environments. Such work would further clarify how capability heterogeneity and context interact to influence entrepreneurial decision-making during crises.\u003c/p\u003e \u003cp\u003eWe also acknowledge potential overlap between certain disruption strands and adaptive capabilities under examination. For example, export disruption is likely to be more relevant to firms possessing established export capability, while innovation disruption may disproportionately affect firms with higher innovation capability. This overlap stems from the interdependence between exposure and capability\u0026mdash;firms that operate more actively in a particular domain are simultaneously more capable and more vulnerable to disruption in that area. Consequently, the interpretation of results requires cautions, as stronger observed relationships may partly reflect differential exposure rather than purely capability effects. Nonetheless, distinguishing between these domains remains meaningful, as the experience of disruption and the capability to respond represent distinct mechanisms within the dynamic capabilities framework (Teece, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Helfat and Winter, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHaving broadly established the association between adaptive capabilities and growth intention across different types of disruption, an important and interesting question is how SMEs ensure that adaptive capabilities remain useful and relevant for future conditions. The observed negative effects of entrepreneur\u0026rsquo;s income on growth intentions (Freel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight a limitation of our study, as we did not observe entrepreneur attributes. Future research could deepen our understanding of crisis in entrepreneurship by examining how women-owned SMEs build and deploy specific dimensions of dynamic capabilities to meet the demands of the disruptive environments.\u003c/p\u003e \u003cp\u003eA further concern lies in the operationalization of adaptive capabilities. training capability is measured based on the provision of training, yet the frequency or mere presence of training provides limited insight into whether firms can effectively reconfigure skills, redeploy knowledge, or adapt routines in response to environmental shocks. Similarly, innovation capability is operationalized as serial new-to-market innovations, which reflects observable outcomes rather than the underlying processes that enable adaptation. In this study, we employ these measures to capture patterns of resource deployment rather than dynamic capabilities in the sense articulated by Teece (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and other scholars. Our findings thus provide valuable evidence on the alignment between certain capabilities and growth intentions, and the strength of the relations varies depending on capability dimension and disruption type.\u003c/p\u003e \u003cp\u003eFuture research could take longitudinal or process-based approaches that track capability enactment in real time could provide richer insight into how SMEs dynamically adapt to crises and which mechanisms most effectively translate capabilities into intentional and strategic growth outcomes. Such studies would help unpack the micro-foundations of adaptive capabilities and clarify how specific processes, rather than outcomes alone, shape entrepreneurial decision-making under disruptive conditions\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDisclosure\u003c/strong\u003e \u003cp\u003e \u003cb\u003estatement\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLX contributes wrote and reviewed the main manuscript text RZ prepared tables and drafted findings\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida, P., \u0026amp; Phene, A. (2004). 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Founder expertise, strategic choices, formation, and survival of high-tech SMEs in China: A resource‐substitution approach. \u003cem\u003eJournal of Small Business Management\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 892\u0026ndash;911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jsbm.12230\u003c/span\u003e\u003cspan address=\"10.1111/jsbm.12230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e A control group is constructed by matching each treated unit with a non-treated unit of similar characteristics.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For dichotomous and continuous variables.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Reference group: firms aged 0\u0026ndash;5 years.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Reference group: firms aged 0\u0026ndash;5 years.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Reference group: firms aged 0\u0026ndash;5 years.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Standard error.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SMEs, growth intentions, crisis, disruption strands, dynamic capabilities, and entrepreneurship","lastPublishedDoi":"10.21203/rs.3.rs-9293921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9293921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines how growth intentions are shaped by adaptive capabilities across different types of disruption strands, using a sample of UK small and medium-sized enterprises (SMEs) observed for a five-year aftermath of Brexit. Our findings show significant heterogeneity in how different disruption strands are perceived by SME entrepreneurs and how these, in turn, influence growth intentions. Specifically, disruptions to \u003cem\u003ecapital investment, leadership training, export, and working practice\u003c/em\u003e are perceived as exogenous threats beyond entrepreneurs\u0026rsquo; control, leading to lower growth intentions. In contrast, disruptions to \u003cem\u003einnovation and workforce\u003c/em\u003e do not appear to reduce growth intentions, meaning that SMEs may leverage these disruptions to mitigate the negative effects or enhance internal efficiency.\u003c/p\u003e \u003cp\u003eThe role of adaptive capabilities is also differentiated. \u003cem\u003eInnovation capability\u003c/em\u003e emerges as a general-purpose adaptive mechanism, underpinning consistently high growth intentions across all disruption strands. \u003cem\u003eExport capability\u003c/em\u003e is generally associated with stronger growth intention, although its positive effect weakens under export-related disruptions. \u003cem\u003eTraining capability\u003c/em\u003e shows a limited effect on growth intentions, except disruptions to investment and export. Overall, the findings suggest that growth intentions are shaped by adaptive capabilities through different forms of resource orchestration, the effectiveness of which depends on their alignment with specific disruption types. These insights contribute to the literature on SMEs and crisis in entrepreneurship research and offer important implications for both research and practice.\u003c/p\u003e","manuscriptTitle":"SMEs’ Adaptive Capabilities and Growth Intentions: Evidence across Different Disruption Types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 18:12:35","doi":"10.21203/rs.3.rs-9293921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d17b93cf-fdd3-4d1a-8c38-a0c6d85cca50","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T18:12:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 18:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9293921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9293921","identity":"rs-9293921","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}