Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector

Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector Abdulkareem Awwad, Abdellatef Anouze, Elizabeth Cudney This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7456495/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines how pharmaceutical manufacturing companies in emerging markets reconfigure internal resources and dynamic capabilities to navigate crises. The study identifies internal and external antecedents of organizational decline based on the Resource-Based View (RBV) and Dynamic Capabilities (DC) frameworks. It evaluates the effectiveness of turnaround strategies under varying levels of competitive intensity. A sequential multi-method design combined Random Forest (RF), a machine learning method for detecting nonlinear interactions and ranking predictor importance, with Structural Equation Modeling (SEM) to validate hypothesized causal relationships. Data were collected from pharmaceutical firms in Jordan, and the analysis proceeded in three phases: identifying drivers of decline, modeling strategic responses, and testing mediation and moderation effects. Findings reveal that external pressures, particularly economic downturns and supply chain disruptions, are the primary sources of decline, with internal factors such as labor constraints and operational inefficiencies also significant. Both categories of antecedents shaped firms’ recovery actions. Organizational agility emerged as a key enabler of effective responses, particularly in highly competitive environments. Turnaround strategies mediated the relationship between crisis antecedents and firm performance, while competitive intensity moderated these relationships. The study contributes a theoretically grounded and empirically validated framework for organizational recovery in high-risk, resource-constrained environments. It advances disaster risk research by integrating RBV and DC perspectives and applying RF and SEM methodologies. Practically, the study provides guidance for managers and policymakers in pharmaceuticals and other critical industries on realigning strategies and building agility to withstand systemic disruptions and prolonged uncertainty. Turnaround strategies causes of corporate decline competitive intensity crisis pharmaceutical sector Jordan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In an increasingly volatile global environment marked by compounded disruptions, today’s business ecosystem is characterized by instability that transcends conventional economic cycles. From the health-induced rupture of the COVID-19 pandemic to the ongoing geopolitical instability driven by the Russian-Ukrainian conflict (Donthu & Gustafsson, 2020 ; Kahveci et al., 2025 ; Verma & Gustafsson, 2020 ), and the unilateral imposition of tariffs by the Trump administration (Contractor, 2025 ), firms are facing unprecedented strategic and operational threats. These threats not only erode firm performance through resource constraints and market contraction but also expose critical weaknesses in managerial agility and strategic foresight. Consequently, firms are no longer disrupted occasionally; disruption has become a systemic condition that is frequently, nonlinearly, and often politically induced. Firms should be agile, adapt quickly, and reconfigure their resources to sustain competitiveness (Atanassova et al., 2025 ). This has led to a transformed academic and practical focus on turnaround strategies as essential tools for firm sustainable survival and renewal (Barker & Duhaime, 1997 ; Bruton et al., 2001 ; Stoiber et al., 2025 ; Xia & Donzé, 2024 ). Although a substantial body of literature addresses turnaround processes during the crisis (Barker III, et al., 2024; Stoiber, et al., 2025 ), fundamental questions persist: How do firms in crisis effectively reorient their resources and capabilities to regain viability? Which strategic pathways yield optimal performance under constrained and uncertain conditions? These questions are made more pressing by the lack of consensus on the efficacy of specific turnaround strategies (Bhattacharya et al., 2024 ; Fang & Yuan, 2025 ; Cater & Schwab, 2008 ). This challenge becomes more evident in developing countries, where structural limitations further constrain effective crisis response. The lack of empirical research in these contexts, institutional weaknesses, and complex stakeholder environments often reduce managerial autonomy and limit the ability of firms to respond adaptively. They are further reducing managerial freedom and restricting the scope for timely and effective turnaround efforts during periods of crisis. This study responds to these gaps by adopting the Resource-Based View (RBV) and Dynamic Capabilities (DC) perspectives to examine how firms mobilize and reconfigure resources under crisis conditions. While RBV offers a foundational lens for understanding how firms leverage valuable, rare, inimitable, and non-substitutable (VRIN) resources for competitive advantage (Barney, 1991 ), it is the DC framework that provides the necessary dynamism to account for how firms adapt these resources in rapidly changing environments. Specifically, this research positions organizational agility as dynamic capabilities enabling effective turnaround in crisis contexts. To operationalize these theoretical insights into practical understanding, the study turns to the pharmaceutical manufacturing sector. This context presents a unique opportunity to explore how firms apply RBV and DC in a real crisis. Hence, this study moves beyond abstract theorization to examine how internal agility and external pressures shape strategic responses. Therefore, this operationalization enables a focused, evidence-based investigation grounded in the realities of a vital yet underexamined industry. Focusing on the underexplored pharmaceutical manufacturing sector, this study develops and empirically tests a predictive-analytic model that identifies the internal and external antecedents of decline/crisis and their influence on the selection and success of turnaround strategies. Although this sector plays a critical role in maintaining healthcare system functionality, it remains underexplored in empirical crisis management research (Latonen et al., 2025 ). Notably, no theory-based studies have examined its response to a crisis such as the COVID-19 pandemic. Therefore, this study contributes to theory by integrating positive enablers (e.g., agility) and negative stressors (e.g., resource depletion, market loss) into a cohesive framework grounded in RBV and DC theory. The model is operationalized through a sequential multi-method research design combining Random Forest (RF) machine learning techniques (Liébana-Cabanillas et al., 2017 ) and Structural Equation Modeling (SEM). While SEM validates causal pathways among constructs, RF accommodates complex nonlinear interactions and ranks the relative importance of predictors with high precision. By advancing this dual-method approach, the study makes two key methodological contributions: (1) it enhances the robustness and generalizability of turnaround strategy models in crisis settings, and (2) it demonstrates the value of machine learning in strategic management research, an emerging frontier that demands further academic attention (Scott & Walczak, 2009 ). Furthermore, this study responds to the most recent call for research by Hamdi et al. ( 2024 ) to examine pharmaceutical operations management beyond the immediate context of COVID-19. Empirically, the study sheds light on strategic responses in emerging markets, where institutional constraints, resource scarcity, and stakeholder entanglements complicate standard turnaround prescriptions. Hence, this study contributes to the strategic management literature by advancing a theoretically grounded and empirically validated framework that guides managers, particularly in resource-constrained, crisis-prone environments, in selecting and executing turnaround strategies that are both contextually sensitive and capability-driven. In doing so, it offers actionable insights for pharmaceutical firms seeking to recalibrate their strategic posture in the face of systemic shocks and sustained uncertainty. The following sections are structured beginning with a theoretical background, literature review, and hypothesis development. Then a presentation of data collection and analysis methods follows. The next section discusses the main results, while the conclusion and main practical and theoretical implications are presented in the final section. 2. Theoretical background, literature review, and development of hypotheses Foundations of Corporate Decline and Turnaround Organizational vulnerability has grown more acute in the face of systemic disruptions. Pandemics, financial volatility, geopolitical instability, and climate-related shocks increasingly challenge firms’ resilience and threaten long-term viability and survival (Prayag et al., 2024 ). Corporate decline, in this context, is not simply a temporary setback but a progressive deterioration in resource effectiveness and strategic fit, often resulting in prolonged underperformance (Cameron et al., 1988 ). Hence, understanding the decline, particularly within the complex pharmaceutical sector, requires grounding in established strategic management theory. The RBV provides a useful lens, emphasizing that sustainable competitive advantage arises from internal resources that are VRIN (Barney, 1991 ). Firms lacking such VRIN resources are more vulnerable to environmental dynamism and less capable of strategic renewal (Serra et al., 2013 ). Consequently, maintaining relevance under turbulent conditions depends on the continuous alignment of internal resource configurations with external opportunities and constraints (Barney, 1991 ). Failure to maintain VRIN resources constitutes a primary pathway to competitive disadvantage and decline. Dynamic environments demand more than static resource holding; hence, possessing VRIN resources alone is insufficient in volatile environments. Organizational capabilities, the organizational processes through which resources are mobilized and reconfigured, are critical in enabling adaptive responses (Newbert, 2008 ). Therefore, the dynamic capabilities view (DCV) extends the RBV by positioning adaptability and strategic responsiveness as central to organizational resilience. In times of crisis, such capability enables firms to proactively anticipate and reactively respond to crises, driving necessary innovation and sustainable reconfiguration. Thus, effective turnaround strategies must be rooted not only in static resource evaluations but in dynamic renewal mechanisms that enable firms to respond proactively and reactively to a crisis. While the literature offers no agreement on which turnaround strategies are most effective, the choice is widely recognized as contingent on the cause and severity of decline. Among the few structured frameworks available, Hofer’s framework could be one of the few guidelines for such selections. Hofer ( 1980 ) categorized turnaround strategies based on a firm’s position relative to its break-even point. Hofer proposed four main approaches as illustrated in Fig. 1 , which include revenue generation, market or product refocusing, cost reduction, and asset downsizing. The first two reflect strategic repositioning, while the latter are oriented toward operational efficiency. Revenue generation and product/market refocusing strategies involve expanding sales or repositioning offerings; for example, entering new therapeutic markets or modifying existing formulations. Cost-cutting (i.e., productivity improvement) and asset reduction strategies aim to enhance internal efficiency through streamlining research and development (R&D), divesting underperforming units, or restructuring the workforce. Source: Adapted from Hofer ( 1980 , p. 27) and Pandit ( 2000 ) Firms operating below break-even (blocks A, B, C) require targeted interventions. Moderate decline often calls for a combination of cost containment and revenue expansion (blocks B and C). Whereas severe decline, where firms operate below fixed cost thresholds (block A), may necessitate asset liquidation alongside more aggressive cost and revenue strategies. Empirical evidence suggests efficiency-focused strategies (cost and asset reduction) are particularly critical for stabilizing performance and ensuring survival, especially when internal factors drive the decline (Rico et al., 2021; Tao et al., 2020 ). Furthermore, as decline severity intensifies, reliance shifts towards more drastic asset-reduction strategies over mere cost-cutting (Howard, 2006 ). Hence, understanding the causes of decline is vital for effective strategy selection. Early work distinguished between strategic causes and operating causes (Schendel & Patton, 1976 ; Schendel et al., 1976 ). Heany ( 1985 ) further categorized causes as: Internal: Stemming from within the firm (e.g., inefficient production, weak innovation pipeline, poor financial controls). External: Originating in the firm’s environment (e.g., adverse regulatory changes, intense competitive pressure, economic recession). Therefore, it becomes evident that navigating corporate crisis requires an integrated RBV-DCV framework to navigate organizational crisis. Firms must mobilize both strategic resources and dynamic capabilities to execute effective turnaround strategies. Understanding whether the drivers of decline are internal or external, strategic or operational, enables firms to design turnaround strategies that restore alignment, responsiveness, and resilience. Building on this foundation, the current study applies these frameworks to the pharmaceutical manufacturing sector, which is an area of critical importance yet limited empirical attention, to examine how firms restructure resource configurations and renew capabilities to recover from crisis. Internal causes of corporate decline and turnaround strategy Internal causes are those controllable factors inside the firm, including financial management, operational efficiency, and marketing performance. Although these causes could be sources of a firm’s competitive advantage if managed well, they become critical vulnerabilities that erode firm performance over time. Previous literature identified several internal contributors to decline, including poor governance structures, operational inefficiencies, weak financial controls, and deteriorating product quality (Jas & Skelcher, 2005 ; Marwa & Zairi, 2008 ; Trahms et al., 2013 ). These deficiencies compromise the firm’s ability to sustain VRIN resources, leaving it exposed to performance deterioration and reduced strategic resilience. The loss or underutilization of such resources directly increases the firm’s susceptibility to internal crises, especially under external stress. Unlike external drivers, such as market shocks or policy volatility, that emerge from the macro environment, internal causes reflect breakdowns in the firm’s operational core (Santana et al., 2019 ). These are often rooted in mismanagement, outdated capabilities, or structural inefficiencies. Panicker and Manimala ( 2015 ) and Kamel ( 2005 ) highlight how weaknesses in key functional areas, including decision-making, quality assurance, and innovation, contribute to organizational decline. Similarly, Trahms et al. ( 2013 ) emphasize that internal decline is often the outcome of accumulated failures across management systems, firm architecture, and resource allocation. Understanding internal causes is therefore essential for designing effective turnaround strategies. It allows for targeted interventions that address the root of the problem, rather than its symptoms. Within crisis contexts, internally driven decline can intensify if left unaddressed, further limiting the firm’s capacity to respond adaptively. Consequently, identifying and correcting internal deficiencies becomes a critical step toward restoring organizational health and long-term viability. External causes of corporate decline and turnaround strategy Few studies have drawn a clear distinction between performance decline caused by external environment (industry-wide) factors (Barker & Duhaime, 1997 ; Pearce & Robbins, 1993 ). Whereas recognizing this distinction is essential, as it shapes both the urgency and nature of strategic recovery. External disruptions typically have emerged from two primary trajectories: sudden (unpredictable) and gradual (predictable). Sudden disruptions, such as geopolitical tensions or pandemics, impose severe pressures that often overwhelm existing operational buffers. Conversely, gradual disruptions, such as technological obsolescence or regulatory evolution, often align with historical patterns and allow for delayed recognition (Hayne, 2022 ). In both situations, organizational flexibility is verified by the speed and scale of response. These external disturbances from critical resource scarcities to systemic logistics collapse not only threaten operational continuity but also impact customer trust and disrupt long-term strategic planning. Therefore, when firms fail to effectively respond to these compounding pressures, the risk becomes entrenched, culminating in financial distress and strategic decline (Ivanovic et al., 2025 ). Seminal work by Schendel et al. ( 1976 ), Slatter ( 1984 ), and Grinyer and McKiernan (1990) highlighted this interdependence between external threats and internal. Subsequent contributions affirm this critical interplay as core vulnerabilities that determine crisis outcomes (e.g., Pearce & Robbins, 1993 , 2008 ; Scherrer, 2003 ; Solnet et al., 2010 ). While others validate the persistent relevance of these dynamics across diverse contexts (Awwad, 2011 ; Liang et al., 2018 ; Nyatsumba & Pooe, 2023 ; Barker et al., 2024 ). This means that corporate decline is not only a consequence of external or internal disruption, but also a result of organizational preparedness and strategic agility. Accordingly, effective turnaround strategies must be grounded in a dual diagnostic lens, one that interrogates both the external shock and the internal vulnerabilities it exposes. Identifying environmental triggers alongside structural or managerial deficiencies allows for a more coherent and targeted recovery response. In sectors marked by high uncertainty and systemic interdependence, such as pharmaceutical production and critical infrastructure, this integrated approach is not optional; it is essential for resilience, operational continuity, and the restoration of long-term performance. Turnaround strategies In the face of growing economic uncertainty and recurring disruptions, several theoretical models and approaches to turnaround strategies have been discussed (for further detail, see Schoenberg et al., 2013 ). Some of them focus on the factors that accelerate the change of turnaround strategy, while others focus on the factors that restrict a turnaround (Bradley et al., 2011 ) and how the turnaround occurred (Panicker & Manimala, 2015 ). Despite this extensive research, no agreement exists on which turnaround strategy is better (Trahms et al., 2013 ; Cater & Schwab, 2008 ). Table 1 summarizes these strategies with their actions. Hofer ( 1980 ) classified turnaround strategies into two main categories: strategic and operating turnarounds. Strategic turnarounds involve changes in a firm’s business model or market focus, typically aimed at decreasing assets, increasing revenue, reducing costs, or combining these actions. Operating turnarounds, on the other hand, focus on improving internal processes and efficiencies. Pearce and Robbins ( 2008 ) further refined the turnaround process into two distinct phases: retrenchment and recovery. Retrenchment addresses the immediate symptoms of decline, such as high costs or excessive overhead, by cutting expenses, streamlining operations, and reducing asset bases (Pearce & Robbins, 1993 ; Awwad, 2011 ; Francis & Desai, 2005 ). Recovery strategies then target the root causes of decline, seeking to reposition the firm competitively through marketing initiatives, innovation, and corporate-level strategic improvements (Rasheed, 2005 ; Pearce & Robbins, 2008 ). Building on these foundational strategies, recent research has expanded the concept of turnaround to encompass organizational resilience, a proactive and adaptive capacity essential in today’s volatile, uncertain, complex, and ambiguous (VUCA) environment. Joussen et al. (2024) propose that resilience-building can be framed through six DC dimensions: engage (building partnerships), anticipate (forecasting risks), cope (crisis management), transform (driving innovation), sustain (ensuring continuity), and grow (leveraging disruption for advantage). Further advancing this approach, Zhuang and Zhang ( 2025 ) argue that effective recovery from corporate decline now increasingly depends on resource orchestration and digital transformation. Through the adoption of digital tools, firms can enhance data sharing, optimize processes, cut operational costs, and improve cross-functional coordination. These capabilities enable firms not only to respond swiftly to disruptions but also to build strategic flexibility, strengthen partnerships, and reconfigure internal resources in ways that support long-term resilience and sustainable growth. Table 1 Actions of turnaround strategies Turnaround strategy Turnaround actions Revenue generation - Raising product prices - Increasing cash discounts to customers - Loosening customer credit criteria - Market share domination Product/market refocusing - Elimination of unprofitable products, customers, channels of distribution, sales regions, or sales representatives Asset reduction - Immediate cash flow - Major retrenchment - Divestiture “strategic cures” - Liquidation of inventory, equipment, or physical plant - Divestiture of a subsidiary, product line, or holdings Cost-cutting/ productivity improvement - Cost-cutting, such as reducing expenses (e.g., marketing and sales costs) - Operating cures - Greater plant capacity utilization Source: Adapted from Gowen III and Tallon (2002) and Hofer ( 1980 ) El-Haddad and Zaki ( 2023 ) highlight that firm resilience and long-term performance are closely tied to investments in research and development, along with the adoption of modern technologies. In the same vein, Grover and Karplus ( 2021 ) found that firms applying solid management practices, like setting clear targets, regularly evaluating performance, rewarding employees, and improving operational efficiency, are more capable of handling crises and recovering from business decline. El-Haddad and Zaki ( 2023 ) further explain that a firm’s ability to survive and grow depends on two key dimensions: (1) its structural characteristics, and (2) its behavioural choices. Behavioural factors relate to how well a firm manages its internal processes, fosters innovation, adopts advanced technologies, and invests in employee development. These actions are essential for firms to reshape their business models and build greater resilience during economic disruptions. Similarly, the empirical results of Saraiva et al. ( 2024 ) suggest that adopting a strategic entrepreneurial posture, together with an organic organizational structure characterized by flexibility, decentralization, and results-driven management, positively contributes to the recovery process from organizational decline. However, the effective selection of a turnaround strategy depends largely on accurately and precisely diagnosing the causes of decline. Therefore, we proposed the following hypothesis: H1 There is a statistically significant relationship between internal causes and the turnaround strategies that will be used to recover from decline. H2 There is a statistically significant relationship between external causes and the turnaround strategies that will be used to recover from decline. Organizational agility and turnaround strategy In the face of a VUCA environment, organizational agility empowers firms to stay responsive by dynamically adjusting their resource configurations, harnessing essential competitive capabilities such as speed, flexibility, and innovation, and applying established best practices in knowledge-driven contexts (Hu & Wang, 2025 ). Change serves as the primary catalyst for agility (Yusuf et al., 1999 ). Organizational agility embodies a firm’s strategic capability to navigate change successfully through recognizing, understanding, reacting to, capitalizing on, and proactively shaping change (Yusuf et al., 1999 ). However, the degree of uncertainty under which the firm operates and the variety of products, processes, and activities it must cope with determine the need for flexibility and agility (Slack, 1987 ). In other words, dynamic environments become more complex and turbulent when experiencing rapid changes. Such environments, however, make organizations face considerable challenges when matching the changes in quantity and speed with what a firm should have in resources and capabilities to thrive in turbulent business environments. However, different types of uncertainty and changes demand different capabilities for effective organizational responses (Purvis, et al., 2014 ). In this sense, Zhang and Sharifi ( 2000 ) developed a conceptual model for implementing agility in industry, suggesting that a firm needs agile capabilities, such as competency, responsiveness, flexibility, and quickness, to cope with changes and pressures resulting from the business environment (i.e., competition, marketplace, technology, and customer requirements). Similarly, Sherehiy et al. ( 2007 ) indicated that agility has seven main attributes, namely: responsiveness, flexibility, and adaptability; integration and low complexity; high quality and customized products; speed; mobilization of core competencies; and culture of change. However, Fayezi et al. ( 2017 ) also support the argument that organizational agility comprises the following dimensions: responsiveness, quickness, flexibility, adaptiveness, proactiveness, cooperation, and information system/technology. It can be concluded that a firm will be under the threats of environmental changes, such as an accelerated rate of process and product innovation, global competition, and increasingly demanding customers, technological innovation, and digital revolution (Margherita et al., 2021 ), unless it has the needed level of agility to master and accommodate these changes (Zhang & Sharifi, 2000 ). Moreover, agility adds value not only to the company but also to the customers. In this context, Sanatigar et al. ( 2017 ) indicated that organizational agility adds value to the customer by enabling a firm to respond to changing customer needs. In summary, in unpredictable environments, organizational agility (OA) is essential as it enhances decision-making and reduces uncertainty. This, in turn, enables businesses to effectively respond to changing demands, where adaptability under pressure becomes a key driver of success (Yao et al., 2025 ). Based on these arguments, it can be concluded that agility provides a firm with quick reactions to changes in marketing needs while dealing reactively. However, if the firm is proactive, agility results in faster and shorter response times to changing environmental conditions. This idea is consistent with Agarwal et al. ( 2007 ), who indicated that supply chain agility depends on cost minimization, quality improvement, new product introduction, delivery speed, lead-time reduction, and customer satisfaction. Therefore, we can hypothesize the following: H3 A statistically significant relationship exists between organizational agility and the adopted turnaround strategy to recover from decline. Competition intensity and turnaround strategy There is no doubt that competition primarily determines what turnaround strategies a firm can use to deal with different situations of decline. Contingency theorists address the importance of the strategic fit of a firm’s strategy with its business environment. In declining periods, firms should quickly switch to the proper turnaround actions in a changing business environment. Competition intensity leads to multiple customer choices (Yang et al., 2012 ), which may leave a firm in a difficult position unless it is agile enough to accommodate the changing needs and customer preferences. Organizations need agility to respond quickly to corporate decline, and when they fail to respond may result in complete failure (Serra et al., 2013 ). Therefore, the big challenge that may be faced is the link between a decline and a turnaround strategy appropriate for the severity of the situation (Awwad, 2011 ). Tang and Chen ( 2020 ) indicate that operating in a highly competitive industry could reduce a firm’s profitability, leading to corporate decline. More specifically, the results of environmental scanning and the severity of decline guide a firm to select between strategic and operating responses or both. Short-term efficiency responses are necessary if the priority is to stabilize the firm, but strategic turnarounds are helpful if the remedial procedure is to expand into the market and offer new products (Barker & Duhaime, 1997 ; Abebi, 2013 ). Therefore, hypercompetitive markets drive declining firms to implement turnaround actions more swiftly than their competitors to recover quickly because late or slow responses will result in more costs and a difficult situation. Therefore, we hypothesize the following: H4 Under a highly competitive intensity (CI), the associations between internal causes and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened. H5 Under a high CI, the associations between external causes and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened. H6 Under a high CI, the associations between organizational agility and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened. Turnaround strategies and their outcomes Although turnaround strategy outcomes require time to take effect and show inevitable performance fluctuations, it is imperative to recognize the limitations of a one-year evaluation period, as suggested by Morrow et al. (2008). Such shortness may inadequately capture the comprehensive effectiveness of corporate turnaround strategies. Scholars often advocate for a more protracted observation period (Barker III et al., 2024) spanning two to three consecutive years, allowing for a more robust assessment of firm performance. Conversely, the delineation of metrics for assessing the outcomes of turnaround strategies, thereby facilitating the objective identification of leading competitors, has been addressed in scholarly discourse (Alamro et al., 2018 ). Prior academic inquiry has ventured into certain assumptions regarding the performance of turnaround strategies (Harker & Sharma, 1999 ; Sudarsanam & Lai, 2001 ). Kesner and Dalton ( 1994 ) advocate measuring turnaround performance through a sustained variation in revenue over four consecutive years, whereas Bo-Wei and Tzu-Hsiang ( 2017 ) propose an increase in net profit over three consecutive years. Robbins and Pearce ( 1992 ) offer an alternative perspective, defining successful turnaround performance as the attainment of above-average return on sales (ROS) and return on investment (ROI), persisting over two consecutive years after turnaround intervention. Consistent with later arguments, this study chose the most widely used measure of turnaround performance: the averages of return on assets (ROA), ROS, and ROI for two consecutive years to measure turnaround performance. Previous studies have used these measures (Hambrick & Schecter, 1983 ; Davis, 1993 ). Multiple measures can provide greater validity (Venkatraman & Grant, 1986 ). Therefore, this study proposes the following hypothesis: H7a-d Employing different turnaround strategies leads to different turnaround outcomes. The theoretical framework in Fig. 2 is proposed considering the previously proposed hypothesis. 3. Methods Research Design In responding to the research aim of understanding the antecedents of corporate decline and evaluating the effectiveness of turnaround strategies, this study adopts a robust, three-stage methodological framework (Fig. 3 ). It integrates both qualitative and quantitative approaches. Stage 1: Literature Review This stage involves an in-depth literature review. This review encompassed peer-reviewed journals and aimed to collate a comprehensive list of the main sources (internal and external) contributing to firms’ decline. Then these sources are taken to the second stage to select the most significant causes of decline employing RF analysis. Stage 2: Variable Selection Using RF Analysis The identified sources of decline (factors) were employed as inputs for the RF analysis. RF is a supervised ensemble learning method to systematically identify and prioritise the key variables associated with organisational decline. It can handle high-dimensional data, accommodate complex interactions, and generate interpretable variable importance measures, thereby addressing limitations inherent in traditional linear techniques. The RF model was trained to evaluate the importance of each factor in predicting firm decline. Factor importance was assessed using the mean decrease in impurity (MDI) metric, which measures the contribution of each factor to the homogeneity of the nodes and leaves in the ensemble of trees. Factors exhibiting the highest MDI scores were selected for further analysis. Stage 3: Structural Modeling Using SmartPLS 4.0 This stage utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0. Building upon the empirically derived determinants from RF, the third stage seeks to evaluate the causal pathways through which these determinants influence the performance outcomes of implemented turnaround strategies. This integrative approach ensures methodological triangulation, enhances explanatory power, and supports the derivation of nuanced insights into the dynamics of corporate decline and recovery. 4. Data Analysis and Results Sampling and Data Collection This study focuses on the Jordanian pharmaceutical manufacturing industry, which significantly contributes to the Jordanian economy, as most of its production (around 80%) is exported to more than 70 countries, such as Europe and the United States. The pharmaceutical industry in Jordan is considered the most advanced in the Middle East region and worldwide. According to DNB (2021) 1 , there are 28 pharmaceutical manufacturing companies in Jordan; these include large companies, such as Hikma Pharmaceuticals; medium companies, such as Jordan Sweden Medical & Sterilization; and small companies, such as Jordan River Pharmaceutical Industries. The first pharmaceutical factory was founded in 1962 in Jordan, and the industry has grown significantly since then. The proposed theoretical model was tested through a survey of the Jordanian pharmaceutical sector for two reasons. First, the pharmaceutical industry was considered one of the most critical sectors during the COVID-19 pandemic. Therefore, identifying the impact of the pandemic on this sector may guide policymakers toward more evidence-based planning to overcome the accompanying challenges (Ayati et al., 2020 ). Second, although focusing on a single sector in a specific context could limit the implications for the general business, it will ensure high internal validity (Wong et al., 2011 ). The original survey draft was sent to six strategic and operations management professors to ascertain the content and face validity of the questionnaire. Then, a pilot study of 35 MBA students was conducted to evaluate the clarity, wording, relevance, and time spent on completion. Data Description The questionnaires were distributed only to respondents who identified as pharmaceutical practitioners and managers. The researchers distributed the survey randomly to managers during January and March 2023. Of the 250 distributed questionnaires, only 168 were usable, with a response rate of 67%. The empirical findings indicate clear variation in the respondents’ characteristics, as shown in Table 2 . The sample comprised 63% male respondents, with around 68% aged between 20 and 40, 21% between 40 and 50, and the rest older than 50. Around 3% of respondents held a doctorate, 23% held a master’s degree, and 74% held a bachelor’s degree. Regarding the managerial level, 33% of respondents were line supervisors, 50% were operations managers, 10% were middle managers, and the rest were senior managers. Finally, 65% of respondents had experience in the same position for less than ten years, and only 14% had 15 years of experience or more. Table 2 Characteristics of respondents Characteristics of respondents Frequency Percentage Characteristics of respondents Frequency Percentage Gender Experience Male 106 63% Less than five years 23 14% Female 62 37% 5 to less than ten years 86 51% Total 168 100% 10 to less than 15 years 35 21% Age 15 years and above 24 14% 20 to less than 30 years 73 43% Total 168 100% 30 to less than 40 years 42 25% Managerial Level 40 to less than 50 years 35 21% Supervisors 56 33% 50 years and above 18 11% Operational Manager 83 50% Total 168 100% Middle Managers 17 10% Education Senior Managers 12 7% Bachelor’s degree 125 74% Total 168 100% Master’s degree 38 23% Doctorate 5 3% Total 168 100% Variable measurements All survey items of independent variables were adapted from prior research and were measured through managerial perceptions. A seven-point Likert scale was used to assess the agreement level for each item, anchored at strongly agree (7) and strongly disagree (1). The 48 items employed were adapted from past studies (Appendix Table AI): Internal causes (19 items) and external causes (14 items) of the corporate decline scale that initially are based on Pearce and Robbins ( 1993 ) and Jas and Skelcher ( 2005 ). Organizational agility scale that includes four items based on Cegarra-Navarro et al. ( 2016 ). One dependent variable, namely, turnaround strategies (four items), was based on Gowen III and Tallon (2002); one moderator, namely, the intensity of competition (four items); and one outcome (three items) based on Venkatraman and Grant ( 1986 ). Study one: Identifying and establishing the key drivers of corporate decline The total number of internal and external causes in the original survey was 33 items. However, one of the problems faced by researchers was associated with the large number of variables in models concerning over-parametrization (Eynaud et al., 2013 ). Simplifying models that reduce complexity may be an interesting issue. Therefore, employing statistical methods that simplify a model according to data would be interesting. Hence, the need for a statistical method to identify how the models’ outputs are linked to their inputs appears to select the best model that accurately predicts the dependent variable (Ellies-Oury et al., 2019). Unfortunately, researchers used to omit variables to deal with this problem (Anouze, 2010 ; Emrouznejad et al., 2010 ). Several traditional statistical models, such as stepwise regression or Lasso logistic regression, can be used to predict the dependent variable. However, a common problem with these models occurs whenever the number of observations ( n ) is small concerning the number of predictors ( p ). Further, stepwise regression or Lasso logistic regression, for instance, selects covariates in a parametric linear regression model (Ellies-Oury et al., 2019). Therefore, which variables are relevant to be included is a topic of debate (Bou-Hamad et al., 2017 ). Identifying the most critical variables fills the gap in predicting the dependent variable. This study introduced RF, a powerful data-driven tool for variable selection, to determine the most critical causes of corporate decline (variables). RF can deal with linear and nonlinear relationships (Anouze & Bou-Hamad, 2021 ; Bou-Hamad et al., 2022 ). Breiman ( 2001 ) introduced RF as an ensemble of classification and regression trees (Breiman, 2001 ). For each tree in the ensemble, RF uses some input data to predict the output based on averaging (the case of regression) or majority voting (the case of classification) (Antipov & Pokryshevskaya, 2012 ). Then, it combines many individual decision trees to provide a final prediction. Therefore, the 33 causes of corporate decline are reduced before modeling using the RF tool to identify the best variables to include in the model. Variable selection aims to remove irrelevant, redundant variables and is unlikely to improve model accuracy (Chowdhury & Turin, 2020 ). Thus, using RF helps to find a small number of variables sufficient for a good parsimonious prediction concerning the dependent variable (Breiman, 2001 ). The RF algorithm has not yet been employed to estimate the proper turnaround strategy during troubling times, such as redesigning the process or product and eliminating unprofitable products. RF was computed using SPSS Modeler 18 software, where the original data is split into validation and training data sets, and the result is presented in Table 3 . Table 4 shows the variable importance and the normalized importance of each cause. The critical measure was the percentage of increase in the mean squared error. The economic slowdown is the most important cause that predicts firm decline, with an importance of 0.75, followed by increased supplier power and labour problems, with an importance of 0.690 and 0.665, respectively. The lowermost important causes are inadequate production equipment, falling demand for an industry’s products, board-management conflict, and international expansion, with the importance of 0.040, 0.020, 0.009, and 0.006, respectively. Four out of the top five causes are external causes, which is reasonable during a crisis such as the COVID-19 pandemic. Table 3 Independent variable importance list Code Independent Variable Importance Normalized Importance Ext_01 Economic slowdown 0.750 100.0% Ext_02 Increased supplier power 0.690 92.0% Int_01 Labour problems 0.665 88.6% Ext_05 Inappropriate channels of distribution 0.282 37.6% Ext_03 Consumer, regulatory, and economic changes 0.234 31.2% Ext_04 Global financial crisis 0.220 29.4% Int_03 Input price increase 0.207 27.6% Int_07 Lack of capital funds 0.183 24.4% Int_02 Failure to innovate 0.146 19.4% Int_08 Inadequate financing 0.145 19.3% Ext_09 Entrance of new domestic competitors into the industry 0.139 18.5% Ext_10 Changing technology 0.135 18.1% Ext_06 Increase in customer complaints 0.132 17.6% Int_05 Inadequate research and development 0.122 16.3% Int_04 Failure to control costs 0.108 14.4% Ext_08 Entrance of new foreign competitors into the industry 0.101 13.4% Int_09 Too much debt 0.094 12.5% Ext_07 Loss of market share 0.092 12.2% Int_06 Poor management of information systems 0.084 11.2% Int_11 Inadequate understanding of customers’ needs 0.078 10.4% Int_18 Marketing the wrong product and in the wrong markets 0.075 10.0% Int_13 Dysfunctional organizational culture 0.073 9.8% Int_10 High-cost structure 0.072 9.5% Int_12 Displacement by competition 0.063 8.4% Ext_14 Inadequate understanding of customer needs 0.058 7.8% Int_19 Plant expansion 0.055 7.3% Int_14 Problems with teamwork 0.048 6.4% Int_16 Aging production techniques 0.043 5.7% Ext_12 Inadequate production equipment 0.040 5.4% Ext_11 Falling demand for an industry’s products 0.020 2.6% Int_17 Board-management conflict 0.009 1.3% Ext_13 International expansion 0.006 0.7% For ease of reading, these measures are illustrated in Fig. 4 , where the y-axis represents the normalized importance, and the x-axis represents the internal and external causes. Figure 4 and Table 3 show that the most important external causes of firm decline are economic slowdown, increased supplier power, inappropriate distribution channels, consumer, regulatory, and economic changes, and the global financial crisis. At the same time, the five most important internal causes are labour problems, input price increases, lack of capital funds, failure to innovate, and inadequate financing. Therefore, to simplify the proposed model and include only the most important causes, this research included any cause with a normalized importance above 20%. Accordingly, five external and three internal causes are included in the final model and taken for further analysis. Study two: Predict turnaround strategy outcomes Testing the multivariate analysis assumptions Several statistical tests, including linearity, normality, and multicollinearity, were conducted for the collected data, as shown in Table 4 . The maximum value of skewness was 0.87 (˂ ± 1), while the maximum value of kurtosis was 1.17 (˂ ± 2), which suggests that non-normality was not excessive. Likewise, the single-factor model exhibited a significantly worse fit, indicating that common method bias is not a serious concern. Meanwhile, the shared variance of the common latent factor was 19.67%, indicating the non-existence of common method variance. The variance inflation factor (VIF) test of scale items for multicollinearity showed that the VIF was 8.62 and the smallest tolerance was 1.27, much less than the common cut-off threshold of 10. The sample size was 168, which is adequate for SEM (Hair et al., 2010 ). Table 4 Descriptive statistics for research variables Variable Mean Std Dev Skewness Kurtosis Internal causes of corporate decline 5.25 0.98 0.54 0.82 External causes of corporate decline 5.97 0.83 0.87 0.92 Organizational agility 5.07 1.10 0.65 1.17 Competitive intensity 4.86 0.78 0.73 1.10 Turnaround strategies 6.10 0.96 0.83 0.89 Construct validity and reliability Several tests were used to measure construct validity and reliability. The goodness-of-fit index (GFI) was used to measure each construct’s unidimensionality. Based on this test, a value greater than the 0.90 threshold indicates good unidimensionality among the constructs (Prajogo & Hong, 2008 ). Table 5 shows that the composite reliability (CR) values ranged from 0.853 to 0.918, with all the construct loadings significant at p < 0.001. Moreover, all constructs’ average variance extracted (AVE) exceeded the value of 0.50. The maximum shared squared variance (MSV) and average shared squared variance (ASV) were less than the AVE. This means that all the constructs fulfilled the requirements of discriminant and convergent validity; hence, both the reliability and validity of the constructs for the measurement model were successfully verified. Table 5 Construct unidimensionality, reliability, and convergent validity 2 CR AVE MSV MaxR(H) Int Cause Ext Cause Org Agile Inte Comp Turn Strat IC 0.897 0.560 0.663 0.640 0.824 EC 0.918 0.636 0.429 0.478 0.644 0.732 OA 0.869 0.542 0.487 0.385 0.730 0.692 0.767 CI 0.853 0.682 0.203 0.510 0.540 0.584 0.320 0.727 Turn Strat 0.866 0.564 0.501 0.153 0.435 0.431 0.642 0.453 0.751 IC : Internal causes of corporate decline EC : External causes of corporate decline OA : Organizational agility CI : Competitive intensity Turn Strat : Turnaround strategies CR = ( \(\:\frac{\sum\:{{(\lambda\:}_{i})}^{2}}{\sum\:{{(\lambda\:}_{i})}^{2}+\:\sum\:{\delta\:}_{i}}\) ), ( \(\:{\lambda\:}_{i}\) = standardized factor loadings, i = observed variables , \(\:{\delta\:}_{i}\) = error variance). AVE = ( \(\:\frac{\sum\:{{(\lambda\:}_{i})}^{2}}{n}\) ), (i = 1...n , \(\:{\lambda\:}_{i}\) = standardized factor loadings, i = observed variables). MSV : Maximum shared squared variance Measurement model-confirmatory factor analysis To test the fitness of the data to the measurement model, several goodness-of-fit measures were assessed, such as normed chi-square (χ 2 / df ), comparative fit index (CFI), GFI, standardized root mean residual (SRMR), root mean square error of approximation (RMSEA), normed fit index (NFI), incremental fit index (IFI), and Tucker-Lewis index (TLI). The results showed that all these measures exceeded the recommended thresholds (Hu & Bentler, 1999 ) ( X 2 /df = 3.2, p = 0.00; CFI = 0.95; GFI = 0.915; RMSEA = 0.045; SRMR = 0.031; NFI = 0.93; IFI = 0.95; and TLI = 0.94). This indicates that the measurement model fits the data well. Similar to the measurement model, GIF measures were used to measure the fit of the structural model. As Table 5 shows, all the measures ( X 2 /df = 2.95, p = 0.00; CFI = 0.965; GFI = 0.923; RMSEA = 0.043; SRMR = 0.029; NFI = 0.943; IFI = 0.965; and TLI = 0.954) exceeded the recommended thresholds. Consequently, the results of structural model tests indicate a good fit with the collected data. Hypothesis testing Three stages were used to test the proposed hypotheses: direct effects (H1-H3), mediation effects (H4-H6), and moderated mediation effects (H7). An SEM was run using AMOS version 27 to test the proposed hypotheses; Table 6 and Fig. 5 illustrate the results. Table 7 shows a significant positive impact of internal causes on revenue generation strategy that is not significant ( β = 0.137, p > 0.001). Nevertheless, the positive effects of the internal causes on product/market refocusing were significant ( β = 0.04, p < 0.001). Likewise, the positive effects of the internal causes on asset reduction and productivity improvement strategies were significant ( β = 0.06, p < 0.001 and β = 0.03, p < 0.05, respectively), which partially support H1. About the impact of external causes on turnaround strategies, Table 6 shows a significant positive impact of external causes on revenue generation and product/market refocusing strategies ( β = 0.432, p < 0.001 and β = 0.198, p < 0.01, respectively). Similarly, the positive effects of the external causes on the asset reduction strategy were significant ( β = 0.05, p < 0.01). However, the positive effects of the external causes on productivity improvement strategies were not significant, which partially supports H2. Table 6 Structural parameter estimates (standardized coefficients) Step 1: Main effect β t SE Result H1: Independent variable (IC) R 2 0.47 Revenue Generation 0.14 1.64 0.05 Product/Market Refocusing 0.04*** 3.65 0.04 Asset Reduction 0.06*** 3.86 0.05 Productivity Improvement 0.03*** 4.43 0.05 Partially Supported H2: Independent variable (EC) R 2 0.53 Revenue Generation 0.43*** 4.93 0.05 Product/Market Refocusing 0.20** 2.76 0.08 Asset Reduction 0.05*** 3.05 0.05 Productivity Improvement 0.12 1 .60 0.07 Partially Supported H3: Independent variable (OA) R 2 0.54 Revenue Generation 0.25*** 5.85 0.05 Product/Market Refocusing 0.42*** 5.70 0.08 Asset Reduction 0.05** 3.09 0.06 Productivity Improvement 0.37** 1.98 0.06 Fully Supported IC : Internal causes EC : External causes Turn : Turnaround strategy CI : Competitive Intensity OA : Organization Agility *** Significant at α ≤ 0.001 ** Significant at α ≤ 0.05 Finally, the impact of organizational agility on turnaround strategies showed a significant positive impact on revenue generation strategy ( β = 0.247, p < 0.001). It also significantly impacted product/market refocusing ( β = 0.420, p < 0.001). Likewise, asset reduction and productivity improvement strategies were also significant ( β = 0.052, p < 0.05 and β = 0.371, p < 0.05, respectively), fully supporting H3. This structural model explains 47% (R 2 = 0.47) to 54% (R 2 = 0.54) of the variance, which is above the cut-off value of 10% (Falk & Miller, 1992 ). Testing Mediation Effects To test the mediating effects of turnaround strategies, the study focused on the significance of the indirect effects (Osei-Frimpong et al., 2020 ; Zhao et al., 2010 ). A bootstrapping-based mediation test was computed with a two-tailed significance from 5,000 bootstrapping runs using the PROCESS macro in SPSS (Hayes, 2013 ). The 5,000 replicates of the data set were chosen to estimate the coefficients and confidence intervals corrected to 95%. Table 7 shows that the three dependent variables directly affect turnaround strategy and the lower-level confidence interval (LLCI) and upper-level confidence interval (ULCI). Results revealed that the relationship between internal causes and performance was significant after including the turnaround strategy as a mediator (indirect effect = 0.183, SE = 0.031, LLCI: 0.062, and ULCI: 0.410). This means the turnaround strategy fully mediates the relationship between internal causes and firm performance. The PROCESS macro was executed again to test the impact of external causes and organizational agility on firm performance, mediated by turnaround strategy. Table 7 Bootstrapped indirect effects on turnaround performance Paths and effect Indirect effects SE LLCI ULCI IC ◊ Turn ◊ Turnaround strategy outcomes 0.183 0.031 0.062 0.410 EC ◊ Turn ◊ Turnaround strategy outcomes 0.281 0.057 0.023 0.317 OA ◊ Turn ◊ Turnaround strategy outcomes 0.196 0.023 0.049 0.235 The results remained significant (indirect effect = 0.281, SE = 0.057, LLCI: 0.023, ULCI: 0.317; indirect effect = 0.196, SE =, LLCI: 0.049, ULCI: 0.235, respectively). This result suggests that the turnaround strategy significantly mediates the relationships between internal and external causes and organizational agility and performance. Moderation test This study proposed CI as the moderating variable. Hence, the aim is to identify whether the relationships proposed in the baseline model (Fig. 1 ) remain the same or invariant across different CI groups (i.e., low CI and high CI groups). To examine the contingency effects of CI, the original dataset was divided into two groups: the high CI group ( n = 72) and the low CI group ( n = 96) based on the average scores of centralized constructs (i.e., 5.429) (Tse et al., 2021 ). A multi-group analysis for internal and external causes and organization agility dimensions was computed to examine the impacts under the influence of low and high CIs. This procedure aligns with the guidelines of Marsh et al. ( 1998 ) and Tse et al. ( 2021 ). The findings of the multi-group and structural path analyses are illustrated in Table 8 . Internal causes and turnaround strategies under low and high CIs (H4) Panel A of the table shows both the path estimates and X 2 statistics. The result showed that there are significant differences in the X 2 statistics (∆ X 2 = 15.14, ∆ df = 15, p < 0.001) between the baseline model and the constrained model 3 . This result suggested the variance of the model under low and high CIs. Thus, the next step is to test the equality of the paths between the low and high CI groups. The significant X 2 difference (∆ X 2 at p -value < 0.05) indicated the moderating effect of CI. The results showed that the relationship between the internal causes of corporate decline and revenue generation was significant but invariant in terms of its strengths under low and high CIs (∆ X 2 = 4.21, p < 0.05), which supports H4a. The results further indicated that the internal causes-assets reduction relationship was significant under low ( β = 0.32, p < 0.01) and high CIs ( β = 0.76, p < 0.001). Based on a significant difference in the X 2 statistics (∆ X 2 = 4.13, p < 0.05) and the difference in β , we concluded that the internal causes-assets reduction relationship was strengthened under a high CI, which supports H4c. However, the path from internal causes to product/market refocusing (∆ X 2 = 1.56, p > 0.05) is insignificant. In contrast, the path from internal causes to productivity improvement had a significant X 2 difference (∆ X 2 = 4.19, p < 0.05), suggesting a contingency effect, hence rejecting H4b and accepting H5d. Table 8 Multi-group analysis for CI Models X 2 df X 2 /df Δ X 2 Δ df X 2 difference test High CI β ( t -value) Low CI β ( t -value) Hypotheses Panel A: Multi-group analysis for hypothesis H4 1. Baseline Model 645.76 361 1.789 2. Constrained Model 660.9 376 1.758 15.14 15 p < 0.01 3. Constrained Paths (Competitive intensity) 3a: IC ◊ RG 649.97 362 1.795 4.21 1 p < 0.05 0.54 (3.76)*** 0.68 (3.87)*** Supported 3b: IC ◊ PM/R 647.32 362 1.788 1.56 1 Insignificant 0.13 (2.89)** 0.09 (2.93)** Not supported 3c: IC ◊ AR 649.89 362 1.795 4.13 1 p < 0.05 0.76 (3.12)** 0.32 (4.09)*** Supported 3d: IC ◊ PI 649.95 362 1.795 4.19 1 p < 0.05 0.71 (4.79)*** 0.57 (3.91)*** Supported Panel B: Multi-group analysis for hypothesis H5 1. Baseline Model 668.05 406 1.645 2. Constrained Model 744.31 456 1.632 76.26 p < 0.01 3. Constrained Paths (Competitive intensity) 3a: EC ◊ RG 672.61 362 1.858 4.56 1 p < 0.05 0.74 (2.31)* 0.62 (3.13)** Supported 3b: EC ◊ PM/R 672.85 362 1.859 4.80 1 p < 0.05 0.63 (3.53)*** 0.68 (2.79)** Supported 3c: EC ◊ AR 671.98 362 1.856 3.93 1 p < 0.05 0.71 (2.23)* 0.76 (3.43)*** Supported 3d: EC ◊ PI 672.32 362 1.857 4.27 1 p < 0.05 0.51 (3.78)*** 0.60 (2.46)* Supported Panel C: Multi-group analysis for hypothesis H6 1. Baseline Model 660.78 400 1.652 2. Constrained Model 665.14 458 1.468 4.36 p < 0.01 3. Constrained Paths (competitive intensity) 3a: OA ◊ RG 666.97 362 1.842 6.19 1 p < 0.05 0.74 (4.76)*** 0.67 (2.87)** Supported 3b: OA ◊ PM/R 664.87 362 1.837 4.09 1 p < 0.05 0.73 (2.98)** 0.49 (3.94)*** Supported 3c: OA ◊ AR 664.72 362 1.836 3.94 1 p < 0.05 0.76 (4.72)*** 0.32 (2.59)** Supported 3d: OA ◊ PI 665.97 362 1.840 5.19 1 p < 0.05 0.71 (3.39)*** 0.56 (2.91)* Supported RG = Revenue Generation AR = Asset Reduction CI = Competitive Intensity *** Significant at α ≤ 0.001 PM/R = Product/Market Refocusing PI = Productivity Improvement ** Significant at α ≤ 0.05 * Significant at α ≤ 0.1 External causes and turnaround strategies under low and high CIs (H5) The above analysis is repeated for the external causes of corporate decline and turnaround strategies. Panel B of Table 8 shows significant differences in the X 2 statistics (∆ X 2 = 76.26, p < 0.01) between the constrained and baseline models. The relationship between the external causes of corporate decline and revenue generation strategy was significant under low CI groups ( β = 0.62, p < 0.05) and high ones ( β = 0.74, p < 0.1). The difference in β value suggests that this relationship was strengthened under a high CI, which supports H5a. Likewise, the relationship between the external causes to product/market refocusing found a significant X 2 difference (∆ X 2 = 4.80, p 0.05). This suggests a contingency effect, lending support to H5b and H5c. The external causes and productivity improvement relationship was significant under low ( β = 0.60, p < 0.1) and highly competitive intensity groups ( β = 0.51, p 0.05) suggested variance of the path across low and high CIs. These results suggested that the external causes-productivity improvement relationship was strengthened under a high CI, which supports H5d. Organization agility and turnaround strategies under low and high CIs (H6) Panel C of Table 8 shows that the difference in the X 2 statistics was significant (∆ X 2 = 4.36, df = 58, p < 0.01) between the constrained and baseline models. The relationship between organization agility and revenue generation (∆ X 2 = 6.19, p < 0.05) showed a significant X 2 difference between low and high CI groups, supporting H6a. Furthermore, the results showed a significant positive relationship between organization agility-product/market refocusing and agility-assets reduction. The X 2 differences were significant (∆ X 2 = 4.09, p < 0.05 and ∆ X 2 = 3.94, p < 0.05, respectively), which supports H6b and H6c. Finally, the X 2 difference was found significant between the low and high CI groups (∆ X 2 = 5.19, p < 0.05) for the path from organization agility to productivity improvement; the coefficients of this path were significant too for both low, competitive intensity groups ( β = 0.56, p < 0.1) and high ( β = 0.71, p < 0.001). The differences in β value indicated that the organization’s agility-productivity improvement relationship was strengthened when the CI was high, which supports H6d. 5. Discussion The importance of the turnaround strategy’s role in performance has been highlighted in the previous literature. However, the paucity of research is that an integrated analysis of corporate decline causes, organizational agility, turnaround strategies, and turnaround performance to survive during the crisis remains unexplored. This study proposed a model exploring the role of corporate decline causes and organizational agility as a predictor of turnaround strategy and performance. The first key finding is that the most important causes of corporate decline are economic slowdown, increased supplier power, and labour problems. Although there are many reasons behind corporate decline, these causes are associated with a sharp decline in corporate performance. The lowermost important causes are inadequate production equipment, falling demand for an industry’s products, board-management conflict, and international expansion. A firm may reduce redundant investments during an economic slowdown to remain competitive or survive (Luan et al., 2013 ). Insufficient investment may threaten a firm’s survival in the short term, while decreases in capital expenditures may damage its future development. Second, although pharmaceutical supply chains did not collapse under the pressure of COVID-19, the pandemic revealed serious weaknesses (Flynn, 2021 ; Wu et al., 2024 ). These weaknesses include increasing supplier power, long manufacturing lead times, and unpredictable demand, which are likely to cause problems for pharmaceutical manufacturing companies. Lastly, labour problems seem to be an important cause of the corporation’s decline. While the effect of the pandemic on labour varies across sectors, there is an experience of sudden and drastic labour reductions in some sectors (Luckstead et al., 2021 ). Employees may consider these changes unacceptable and reduce productivity, leading to corporate decline. The next key finding is about understanding how the causes of corporate decline could affect the selection of turnaround strategies and performance. The result shows that pharmaceutical managers adopted various strategies to reverse that decline. Some of these strategies have a short-term focus, whereas others have a long-term strategic nature. In response to the internal causes, managers choose product/market refocusing and productivity improvement. In contrast, when considering the external causes, managers will, in general, utilize different turnaround strategies, such as revenue generation, asset reduction, and productivity improvement strategies. This finding aligns with Hayne ( 2022 ), who emphasized that formal accounting tools can enhance efficiency and reduce costs when responding to organizational crises. However, since these tools are often slow to adapt and deeply embedded in established routines, informal accounting practices may prove to be a more agile and responsive option during times of crisis. Additionally, Bhattacharya et al. ( 2024 ) supported the idea that marketing capabilities help a firm recover, particularly when the source of distress is firm-specific. R&D capabilities, on the other hand, contribute to recovery only when industry-wide factors drive the distress. However, managers employed all the proposed turnaround strategies to enhance organizational agility. When faced with performance decline, they often opted for revenue generation strategies to improve efficiency and profitability, thereby sustaining operations (Lai & Sudarsanam, 1997 ). These findings are consistent with those of Rios-Rodríguez et al. ( 2023 ) and Thomas and Douglas ( 2024 ), as well as with the contingency theory and the suggestions of Hofer ( 1980 ). Hofer emphasized that the severity of a crisis is a critical contextual factor in determining the effectiveness of turnaround strategies. For less severe crises, modest changes, such as cost pruning through productivity improvement and product/market refocusing, may suffice for recovery. In contrast, more severe crises demand more substantial transformations, such as asset reduction or market reorientation. Bhattacharya et al. ( 2024 ) also find that the appropriate deployment of dynamic capabilities strongly influences retrenchment strategies, aiding in firm survival, and that recovery depends on the nature of the distress. This is further supported by prior studies emphasizing the value of strategic actions. Tangpong et al. ( 2015 ) found that firms implementing retrenchment actions early have a higher likelihood of successful turnaround. Arora ( 2016 ) highlighted the role of financially linked independent directors in facilitating recovery, and Rico et al. (2021) showed that deep cost retrenchment enhances survival. Bhattacharya et al. ( 2024 ) reinforced the importance of dynamic capabilities, noting that marketing capabilities are particularly effective when distress is firm-specific, whereas R&D capabilities are more useful during industry-wide downturns. The impact of organizational agility on turnaround strategies shows a significant positive impact on revenue generation and product/market refocusing strategies. Similarly, the positive effects of the organization’s agility on asset reduction and productivity improvement strategies are significant, too. This means that during the crisis, organizational agility becomes more significant (Keremah & Monday, 2020 ). In this context, Doz ( 2020 ) stressed that strategic agility assists managers in avoiding “rigidity traps” by moving to prohibit organizational recession and orienting towards more operational flexibility. Kale et al. ( 2019 ) and Vaillant and Lafuente ( 2019 ) found that agile organizations respond more quickly to exploit the opportunities of the work environment and improve the efficiency of resources by restructuring them in harmony with ambient conditions (AlTaweel & Al-Hawary, 2021 ). This result aligns with the empirical findings of Yao et al. ( 2025 ), who highlighted the role of organizational agility in enhancing job satisfaction. Their study showed that organizational agility helps organizations to accommodate changes related to corporate decline by promoting adaptability, collaboration, and resilience. 6. Conclusion This research aims to shed light on the causes of corporate decline and the main implemented turnaround strategies to survive during the crisis, particularly in the Jordanian pharmaceutical sector. It also aims to orient researchers and practitioners on how to face severe survival-threatening situations in the future. To reach this end, this study integrated two powerful tools, RF and SEM. The conjoined analysis of RF-SEM provides significant methodological contributions from a statistical point of view, thus offering a holistic understanding. Such a study helps managers formulate appropriate strategies, leading to a proper turnaround strategy, reducing corporate costs, and generating more revenue. More specifically, this study intended to answer the following main questions: 1) Are the employed turnaround strategies effective in increasing the likelihood of turnaround? 2) Are those strategies suitable for the pharmaceutical manufacturing sector? Moreover, 3) Does competitive intensity shape the effectiveness of turnaround strategies during a crisis? Therefore, the findings of this study provide the following theoretical contributions. First, it demonstrates the benefits of conceptualizing turnaround strategies and their outcomes as multidimensional constructs. The proposed model is novel in exploring the role of turnaround strategies as a mediator and competition intensity as the moderator between causes of decline, organization agility, and turnaround performance. Second, unlike previously published research, which conceptualizes turnaround strategies and their outcomes as unidimensional constructs, this study allows us to comprehensively understand the causes of corporate decline, turnaround strategies, and performance relationships at the dimension levels. Third, from an analytical method point of view, this study integrates two statistical methods (RF and SEM). This integration offers an in-depth analysis of the collected data and thus provides more helpful information. At the managerial level, the study highlights several valuable lessons for survival during a crisis. First, although researchers have recently increased interest in organizational decline, there is a lack of managerial aptitude for arranging successful turnaround strategies to revive organizations (Bodolica & Spraggon, 2021 ). Hence, this study bridges this gap in the decline literature. In addition, out of the 33 causes introduced in this study, only five seem to be the most important, mainly external. This finding is significant for managers to understand the leading causes and, accordingly, formulate effective strategies. Further, since successful turnaround strategies can build a good reputation and image for declining firms after experiencing declining situations, this study proposes that managers should be prepared for future environmental crises. While doing so, they must know the strategy tools used (Linden, 2021 ). Finally, to survive the crisis, managers performed well by implementing suitable turnaround strategies, which led many companies to revisit their business strategies and competitive objectives, including agility. At least two main limitations open the opportunities for new research. 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Technology Analysis & Strategic Management , 1–14. https://doi.org/10.1080/09537325.2025.2464038 Footnotes ) https://www.dnb.com/business-directory/company-information.pharmaceutical-manufacturing.jo.html ) These measures are calculated based on Gaskin (2016) Excel StatTools. Gaskin, J. (2016). Excel StatTools. Excel Stats Tools Package. http://statwiki.kolobkreations.com ) In the structural model, parameters are varied freely across the two competitive intensity groups, while, in the constrained model the structural parameters constrained to be equal across the two competitive intensity groups. 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. 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05:31:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2737715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7456495/v1/c8332976-63ef-4317-822c-c11a24d414da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn an increasingly volatile global environment marked by compounded disruptions, today\u0026rsquo;s business ecosystem is characterized by instability that transcends conventional economic cycles. From the health-induced rupture of the COVID-19 pandemic to the ongoing geopolitical instability driven by the Russian-Ukrainian conflict (Donthu \u0026amp; Gustafsson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kahveci et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Verma \u0026amp; Gustafsson, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the unilateral imposition of tariffs by the Trump administration (Contractor, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), firms are facing unprecedented strategic and operational threats. These threats not only erode firm performance through resource constraints and market contraction but also expose critical weaknesses in managerial agility and strategic foresight. Consequently, firms are no longer disrupted occasionally; disruption has become a systemic condition that is frequently, nonlinearly, and often politically induced. Firms should be agile, adapt quickly, and reconfigure their resources to sustain competitiveness (Atanassova et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This has led to a transformed academic and practical focus on turnaround strategies as essential tools for firm sustainable survival and renewal (Barker \u0026amp; Duhaime, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Bruton et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Stoiber et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xia \u0026amp; Donz\u0026eacute;, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough a substantial body of literature addresses turnaround processes during the crisis (Barker III, et al., 2024; Stoiber, et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), fundamental questions persist: How do firms in crisis effectively reorient their resources and capabilities to regain viability? Which strategic pathways yield optimal performance under constrained and uncertain conditions? These questions are made more pressing by the lack of consensus on the efficacy of specific turnaround strategies (Bhattacharya et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fang \u0026amp; Yuan, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cater \u0026amp; Schwab, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This challenge becomes more evident in developing countries, where structural limitations further constrain effective crisis response. The lack of empirical research in these contexts, institutional weaknesses, and complex stakeholder environments often reduce managerial autonomy and limit the ability of firms to respond adaptively. They are further reducing managerial freedom and restricting the scope for timely and effective turnaround efforts during periods of crisis.\u003c/p\u003e\u003cp\u003eThis study responds to these gaps by adopting the Resource-Based View (RBV) and Dynamic Capabilities (DC) perspectives to examine how firms mobilize and reconfigure resources under crisis conditions. While RBV offers a foundational lens for understanding how firms leverage valuable, rare, inimitable, and non-substitutable (VRIN) resources for competitive advantage (Barney, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), it is the DC framework that provides the necessary dynamism to account for how firms adapt these resources in rapidly changing environments. Specifically, this research positions organizational agility as dynamic capabilities enabling effective turnaround in crisis contexts.\u003c/p\u003e\u003cp\u003eTo operationalize these theoretical insights into practical understanding, the study turns to the pharmaceutical manufacturing sector. This context presents a unique opportunity to explore how firms apply RBV and DC in a real crisis. Hence, this study moves beyond abstract theorization to examine how internal agility and external pressures shape strategic responses. Therefore, this operationalization enables a focused, evidence-based investigation grounded in the realities of a vital yet underexamined industry.\u003c/p\u003e\u003cp\u003eFocusing on the underexplored pharmaceutical manufacturing sector, this study develops and empirically tests a predictive-analytic model that identifies the internal and external antecedents of decline/crisis and their influence on the selection and success of turnaround strategies. Although this sector plays a critical role in maintaining healthcare system functionality, it remains underexplored in empirical crisis management research (Latonen et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, no theory-based studies have examined its response to a crisis such as the COVID-19 pandemic. Therefore, this study contributes to theory by integrating positive enablers (e.g., agility) and negative stressors (e.g., resource depletion, market loss) into a cohesive framework grounded in RBV and DC theory. The model is operationalized through a sequential multi-method research design combining Random Forest (RF) machine learning techniques (Li\u0026eacute;bana-Cabanillas et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Structural Equation Modeling (SEM). While SEM validates causal pathways among constructs, RF accommodates complex nonlinear interactions and ranks the relative importance of predictors with high precision.\u003c/p\u003e\u003cp\u003eBy advancing this dual-method approach, the study makes two key methodological contributions: (1) it enhances the robustness and generalizability of turnaround strategy models in crisis settings, and (2) it demonstrates the value of machine learning in strategic management research, an emerging frontier that demands further academic attention (Scott \u0026amp; Walczak, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, this study responds to the most recent call for research by Hamdi et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to examine pharmaceutical operations management beyond the immediate context of COVID-19. Empirically, the study sheds light on strategic responses in emerging markets, where institutional constraints, resource scarcity, and stakeholder entanglements complicate standard turnaround prescriptions. Hence, this study contributes to the strategic management literature by advancing a theoretically grounded and empirically validated framework that guides managers, particularly in resource-constrained, crisis-prone environments, in selecting and executing turnaround strategies that are both contextually sensitive and capability-driven. In doing so, it offers actionable insights for pharmaceutical firms seeking to recalibrate their strategic posture in the face of systemic shocks and sustained uncertainty.\u003c/p\u003e\u003cp\u003eThe following sections are structured beginning with a theoretical background, literature review, and hypothesis development. Then a presentation of data collection and analysis methods follows. The next section discusses the main results, while the conclusion and main practical and theoretical implications are presented in the final section.\u003c/p\u003e"},{"header":"2. Theoretical background, literature review, and development of hypotheses","content":"\u003cp\u003e\u003cb\u003eFoundations of Corporate Decline and Turnaround\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOrganizational vulnerability has grown more acute in the face of systemic disruptions. Pandemics, financial volatility, geopolitical instability, and climate-related shocks increasingly challenge firms\u0026rsquo; resilience and threaten long-term viability and survival (Prayag et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Corporate decline, in this context, is not simply a temporary setback but a progressive deterioration in resource effectiveness and strategic fit, often resulting in prolonged underperformance (Cameron et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Hence, understanding the decline, particularly within the complex pharmaceutical sector, requires grounding in established strategic management theory.\u003c/p\u003e\u003cp\u003eThe RBV provides a useful lens, emphasizing that sustainable competitive advantage arises from internal resources that are VRIN (Barney, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Firms lacking such VRIN resources are more vulnerable to environmental dynamism and less capable of strategic renewal (Serra et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consequently, maintaining relevance under turbulent conditions depends on the continuous alignment of internal resource configurations with external opportunities and constraints (Barney, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Failure to maintain VRIN resources constitutes a primary pathway to competitive disadvantage and decline.\u003c/p\u003e\u003cp\u003eDynamic environments demand more than static resource holding; hence, possessing VRIN resources alone is insufficient in volatile environments. Organizational capabilities, the organizational processes through which resources are mobilized and reconfigured, are critical in enabling adaptive responses (Newbert, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, the dynamic capabilities view (DCV) extends the RBV by positioning adaptability and strategic responsiveness as central to organizational resilience. In times of crisis, such capability enables firms to proactively anticipate and reactively respond to crises, driving necessary innovation and sustainable reconfiguration. Thus, effective turnaround strategies must be rooted not only in static resource evaluations but in dynamic renewal mechanisms that enable firms to respond proactively and reactively to a crisis.\u003c/p\u003e\u003cp\u003eWhile the literature offers no agreement on which turnaround strategies are most effective, the choice is widely recognized as contingent on the cause and severity of decline. Among the few structured frameworks available, Hofer\u0026rsquo;s framework could be one of the few guidelines for such selections. Hofer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) categorized turnaround strategies based on a firm\u0026rsquo;s position relative to its break-even point. Hofer proposed four main approaches as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which include revenue generation, market or product refocusing, cost reduction, and asset downsizing. The first two reflect strategic repositioning, while the latter are oriented toward operational efficiency. Revenue generation and product/market refocusing strategies involve expanding sales or repositioning offerings; for example, entering new therapeutic markets or modifying existing formulations. Cost-cutting (i.e., productivity improvement) and asset reduction strategies aim to enhance internal efficiency through streamlining research and development (R\u0026amp;D), divesting underperforming units, or restructuring the workforce.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Adapted from Hofer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1980\u003c/span\u003e, p. 27) and Pandit (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eFirms operating below break-even (blocks A, B, C) require targeted interventions. Moderate decline often calls for a combination of cost containment and revenue expansion (blocks B and C). Whereas severe decline, where firms operate below fixed cost thresholds (block A), may necessitate asset liquidation alongside more aggressive cost and revenue strategies.\u003c/p\u003e\u003cp\u003eEmpirical evidence suggests efficiency-focused strategies (cost and asset reduction) are particularly critical for stabilizing performance and ensuring survival, especially when internal factors drive the decline (Rico et al., 2021; Tao et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, as decline severity intensifies, reliance shifts towards more drastic asset-reduction strategies over mere cost-cutting (Howard, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Hence, understanding the causes of decline is vital for effective strategy selection. Early work distinguished between strategic causes and operating causes (Schendel \u0026amp; Patton, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Schendel et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Heany (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) further categorized causes as:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eInternal: Stemming from within the firm (e.g., inefficient production, weak innovation pipeline, poor financial controls).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExternal: Originating in the firm\u0026rsquo;s environment (e.g., adverse regulatory changes, intense competitive pressure, economic recession).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTherefore, it becomes evident that navigating corporate crisis requires an integrated RBV-DCV framework to navigate organizational crisis. Firms must mobilize both strategic resources and dynamic capabilities to execute effective turnaround strategies. Understanding whether the drivers of decline are internal or external, strategic or operational, enables firms to design turnaround strategies that restore alignment, responsiveness, and resilience. Building on this foundation, the current study applies these frameworks to the pharmaceutical manufacturing sector, which is an area of critical importance yet limited empirical attention, to examine how firms restructure resource configurations and renew capabilities to recover from crisis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternal causes of corporate decline and turnaround strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInternal causes are those controllable factors inside the firm, including financial management, operational efficiency, and marketing performance. Although these causes could be sources of a firm\u0026rsquo;s competitive advantage if managed well, they become critical vulnerabilities that erode firm performance over time. Previous literature identified several internal contributors to decline, including poor governance structures, operational inefficiencies, weak financial controls, and deteriorating product quality (Jas \u0026amp; Skelcher, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Marwa \u0026amp; Zairi, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Trahms et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These deficiencies compromise the firm\u0026rsquo;s ability to sustain VRIN resources, leaving it exposed to performance deterioration and reduced strategic resilience. The loss or underutilization of such resources directly increases the firm\u0026rsquo;s susceptibility to internal crises, especially under external stress.\u003c/p\u003e\u003cp\u003eUnlike external drivers, such as market shocks or policy volatility, that emerge from the macro environment, internal causes reflect breakdowns in the firm\u0026rsquo;s operational core (Santana et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These are often rooted in mismanagement, outdated capabilities, or structural inefficiencies. Panicker and Manimala (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Kamel (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) highlight how weaknesses in key functional areas, including decision-making, quality assurance, and innovation, contribute to organizational decline. Similarly, Trahms et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) emphasize that internal decline is often the outcome of accumulated failures across management systems, firm architecture, and resource allocation.\u003c/p\u003e\u003cp\u003eUnderstanding internal causes is therefore essential for designing effective turnaround strategies. It allows for targeted interventions that address the root of the problem, rather than its symptoms. Within crisis contexts, internally driven decline can intensify if left unaddressed, further limiting the firm\u0026rsquo;s capacity to respond adaptively. Consequently, identifying and correcting internal deficiencies becomes a critical step toward restoring organizational health and long-term viability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExternal causes of corporate decline and turnaround strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFew studies have drawn a clear distinction between performance decline caused by external environment (industry-wide) factors (Barker \u0026amp; Duhaime, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pearce \u0026amp; Robbins, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Whereas recognizing this distinction is essential, as it shapes both the urgency and nature of strategic recovery. External disruptions typically have emerged from two primary trajectories: sudden (unpredictable) and gradual (predictable). Sudden disruptions, such as geopolitical tensions or pandemics, impose severe pressures that often overwhelm existing operational buffers. Conversely, gradual disruptions, such as technological obsolescence or regulatory evolution, often align with historical patterns and allow for delayed recognition (Hayne, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In both situations, organizational flexibility is verified by the speed and scale of response. These external disturbances from critical resource scarcities to systemic logistics collapse not only threaten operational continuity but also impact customer trust and disrupt long-term strategic planning. Therefore, when firms fail to effectively respond to these compounding pressures, the risk becomes entrenched, culminating in financial distress and strategic decline (Ivanovic et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Seminal work by Schendel et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), Slatter (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), and Grinyer and McKiernan (1990) highlighted this interdependence between external threats and internal. Subsequent contributions affirm this critical interplay as core vulnerabilities that determine crisis outcomes (e.g., Pearce \u0026amp; Robbins, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Scherrer, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Solnet et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While others validate the persistent relevance of these dynamics across diverse contexts (Awwad, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nyatsumba \u0026amp; Pooe, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barker et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis means that corporate decline is not only a consequence of external or internal disruption, but also a result of organizational preparedness and strategic agility. Accordingly, effective turnaround strategies must be grounded in a dual diagnostic lens, one that interrogates both the external shock and the internal vulnerabilities it exposes. Identifying environmental triggers alongside structural or managerial deficiencies allows for a more coherent and targeted recovery response. In sectors marked by high uncertainty and systemic interdependence, such as pharmaceutical production and critical infrastructure, this integrated approach is not optional; it is essential for resilience, operational continuity, and the restoration of long-term performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTurnaround strategies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the face of growing economic uncertainty and recurring disruptions, several theoretical models and approaches to turnaround strategies have been discussed (for further detail, see Schoenberg et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Some of them focus on the factors that accelerate the change of turnaround strategy, while others focus on the factors that restrict a turnaround (Bradley et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and how the turnaround occurred (Panicker \u0026amp; Manimala, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite this extensive research, no agreement exists on which turnaround strategy is better (Trahms et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cater \u0026amp; Schwab, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes these strategies with their actions.\u003c/p\u003e\u003cp\u003eHofer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) classified turnaround strategies into two main categories: strategic and operating turnarounds. Strategic turnarounds involve changes in a firm\u0026rsquo;s business model or market focus, typically aimed at decreasing assets, increasing revenue, reducing costs, or combining these actions. Operating turnarounds, on the other hand, focus on improving internal processes and efficiencies. Pearce and Robbins (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) further refined the turnaround process into two distinct phases: retrenchment and recovery. Retrenchment addresses the immediate symptoms of decline, such as high costs or excessive overhead, by cutting expenses, streamlining operations, and reducing asset bases (Pearce \u0026amp; Robbins, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Awwad, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Francis \u0026amp; Desai, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Recovery strategies then target the root causes of decline, seeking to reposition the firm competitively through marketing initiatives, innovation, and corporate-level strategic improvements (Rasheed, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pearce \u0026amp; Robbins, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding on these foundational strategies, recent research has expanded the concept of turnaround to encompass organizational resilience, a proactive and adaptive capacity essential in today\u0026rsquo;s volatile, uncertain, complex, and ambiguous (VUCA) environment. Joussen et al. (2024) propose that resilience-building can be framed through six DC dimensions: engage (building partnerships), anticipate (forecasting risks), cope (crisis management), transform (driving innovation), sustain (ensuring continuity), and grow (leveraging disruption for advantage).\u003c/p\u003e\u003cp\u003eFurther advancing this approach, Zhuang and Zhang (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argue that effective recovery from corporate decline now increasingly depends on resource orchestration and digital transformation. Through the adoption of digital tools, firms can enhance data sharing, optimize processes, cut operational costs, and improve cross-functional coordination. These capabilities enable firms not only to respond swiftly to disruptions but also to build strategic flexibility, strengthen partnerships, and reconfigure internal resources in ways that support long-term resilience and sustainable growth.\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\u003eActions of turnaround strategies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnaround strategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTurnaround actions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenue generation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Raising product prices\u003c/p\u003e\u003cp\u003e- Increasing cash discounts to customers\u003c/p\u003e\u003cp\u003e- Loosening customer credit criteria\u003c/p\u003e\u003cp\u003e- Market share domination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct/market refocusing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Elimination of unprofitable products, customers, channels of distribution, sales regions, or sales representatives\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsset reduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Immediate cash flow\u003c/p\u003e\u003cp\u003e- Major retrenchment\u003c/p\u003e\u003cp\u003e- Divestiture \u0026ldquo;strategic cures\u0026rdquo;\u003c/p\u003e\u003cp\u003e- Liquidation of inventory, equipment, or physical plant\u003c/p\u003e\u003cp\u003e- Divestiture of a subsidiary, product line, or holdings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCost-cutting/\u003c/p\u003e\u003cp\u003eproductivity improvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e- Cost-cutting, such as reducing expenses (e.g., marketing and sales costs)\u003c/p\u003e\u003cp\u003e- Operating cures\u003c/p\u003e\u003cp\u003e- Greater plant capacity utilization\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\u003e\u003cem\u003eSource: Adapted from Gowen III and Tallon (2002) and\u003c/em\u003e Hofer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1980\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eEl-Haddad and Zaki (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight that firm resilience and long-term performance are closely tied to investments in research and development, along with the adoption of modern technologies. In the same vein, Grover and Karplus (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that firms applying solid management practices, like setting clear targets, regularly evaluating performance, rewarding employees, and improving operational efficiency, are more capable of handling crises and recovering from business decline. El-Haddad and Zaki (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further explain that a firm\u0026rsquo;s ability to survive and grow depends on two key dimensions: (1) its structural characteristics, and (2) its behavioural choices. Behavioural factors relate to how well a firm manages its internal processes, fosters innovation, adopts advanced technologies, and invests in employee development. These actions are essential for firms to reshape their business models and build greater resilience during economic disruptions. Similarly, the empirical results of Saraiva et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggest that adopting a strategic entrepreneurial posture, together with an organic organizational structure characterized by flexibility, decentralization, and results-driven management, positively contributes to the recovery process from organizational decline.\u003c/p\u003e\u003cp\u003eHowever, the effective selection of a turnaround strategy depends largely on accurately and precisely diagnosing the causes of decline. Therefore, we proposed the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eThere is a statistically significant relationship between internal causes and the turnaround strategies that will be used to recover from decline.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eThere is a statistically significant relationship between external causes and the turnaround strategies that will be used to recover from decline.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOrganizational agility and turnaround strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the face of a VUCA environment, organizational agility empowers firms to stay responsive by dynamically adjusting their resource configurations, harnessing essential competitive capabilities such as speed, flexibility, and innovation, and applying established best practices in knowledge-driven contexts (Hu \u0026amp; Wang, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Change serves as the primary catalyst for agility (Yusuf et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Organizational agility embodies a firm\u0026rsquo;s strategic capability to navigate change successfully through recognizing, understanding, reacting to, capitalizing on, and proactively shaping change (Yusuf et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, the degree of uncertainty under which the firm operates and the variety of products, processes, and activities it must cope with determine the need for flexibility and agility (Slack, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). In other words, dynamic environments become more complex and turbulent when experiencing rapid changes. Such environments, however, make organizations face considerable challenges when matching the changes in quantity and speed with what a firm should have in resources and capabilities to thrive in turbulent business environments. However, different types of uncertainty and changes demand different capabilities for effective organizational responses (Purvis, et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this sense, Zhang and Sharifi (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) developed a conceptual model for implementing agility in industry, suggesting that a firm needs agile capabilities, such as competency, responsiveness, flexibility, and quickness, to cope with changes and pressures resulting from the business environment (i.e., competition, marketplace, technology, and customer requirements). Similarly, Sherehiy et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) indicated that agility has seven main attributes, namely: responsiveness, flexibility, and adaptability; integration and low complexity; high quality and customized products; speed; mobilization of core competencies; and culture of change. However, Fayezi et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also support the argument that organizational agility comprises the following dimensions: responsiveness, quickness, flexibility, adaptiveness, proactiveness, cooperation, and information system/technology.\u003c/p\u003e\u003cp\u003eIt can be concluded that a firm will be under the threats of environmental changes, such as an accelerated rate of process and product innovation, global competition, and increasingly demanding customers, technological innovation, and digital revolution (Margherita et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), unless it has the needed level of agility to master and accommodate these changes (Zhang \u0026amp; Sharifi, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, agility adds value not only to the company but also to the customers. In this context, Sanatigar et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicated that organizational agility adds value to the customer by enabling a firm to respond to changing customer needs. In summary, in unpredictable environments, organizational agility (OA) is essential as it enhances decision-making and reduces uncertainty. This, in turn, enables businesses to effectively respond to changing demands, where adaptability under pressure becomes a key driver of success (Yao et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on these arguments, it can be concluded that agility provides a firm with quick reactions to changes in marketing needs while dealing reactively. However, if the firm is proactive, agility results in faster and shorter response times to changing environmental conditions. This idea is consistent with Agarwal et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who indicated that supply chain agility depends on cost minimization, quality improvement, new product introduction, delivery speed, lead-time reduction, and customer satisfaction. Therefore, we can hypothesize the following:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eA statistically significant relationship exists between organizational agility and the adopted turnaround strategy to recover from decline.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCompetition intensity and turnaround strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere is no doubt that competition primarily determines what turnaround strategies a firm can use to deal with different situations of decline. Contingency theorists address the importance of the strategic fit of a firm\u0026rsquo;s strategy with its business environment. In declining periods, firms should quickly switch to the proper turnaround actions in a changing business environment. Competition intensity leads to multiple customer choices (Yang et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which may leave a firm in a difficult position unless it is agile enough to accommodate the changing needs and customer preferences. Organizations need agility to respond quickly to corporate decline, and when they fail to respond may result in complete failure (Serra et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, the big challenge that may be faced is the link between a decline and a turnaround strategy appropriate for the severity of the situation (Awwad, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Tang and Chen (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicate that operating in a highly competitive industry could reduce a firm\u0026rsquo;s profitability, leading to corporate decline. More specifically, the results of environmental scanning and the severity of decline guide a firm to select between strategic and operating responses or both. Short-term efficiency responses are necessary if the priority is to stabilize the firm, but strategic turnarounds are helpful if the remedial procedure is to expand into the market and offer new products (Barker \u0026amp; Duhaime, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Abebi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, hypercompetitive markets drive declining firms to implement turnaround actions more swiftly than their competitors to recover quickly because late or slow responses will result in more costs and a difficult situation. Therefore, we hypothesize the following:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003eUnder a highly competitive intensity (CI), the associations between internal causes and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003cp\u003eUnder a high CI, the associations between external causes and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e\u003cp\u003eUnder a high CI, the associations between organizational agility and product/market refocusing, productivity improvement, revenue generation, and asset reduction will be strengthened.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTurnaround strategies and their outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough turnaround strategy outcomes require time to take effect and show inevitable performance fluctuations, it is imperative to recognize the limitations of a one-year evaluation period, as suggested by Morrow et al. (2008). Such shortness may inadequately capture the comprehensive effectiveness of corporate turnaround strategies. Scholars often advocate for a more protracted observation period (Barker III et al., 2024) spanning two to three consecutive years, allowing for a more robust assessment of firm performance.\u003c/p\u003e\u003cp\u003eConversely, the delineation of metrics for assessing the outcomes of turnaround strategies, thereby facilitating the objective identification of leading competitors, has been addressed in scholarly discourse (Alamro et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Prior academic inquiry has ventured into certain assumptions regarding the performance of turnaround strategies (Harker \u0026amp; Sharma, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sudarsanam \u0026amp; Lai, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Kesner and Dalton (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) advocate measuring turnaround performance through a sustained variation in revenue over four consecutive years, whereas Bo-Wei and Tzu-Hsiang (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) propose an increase in net profit over three consecutive years. Robbins and Pearce (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) offer an alternative perspective, defining successful turnaround performance as the attainment of above-average return on sales (ROS) and return on investment (ROI), persisting over two consecutive years after turnaround intervention.\u003c/p\u003e\u003cp\u003eConsistent with later arguments, this study chose the most widely used measure of turnaround performance: the averages of return on assets (ROA), ROS, and ROI for two consecutive years to measure turnaround performance. Previous studies have used these measures (Hambrick \u0026amp; Schecter, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Davis, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Multiple measures can provide greater validity (Venkatraman \u0026amp; Grant, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH7a-d\u003c/strong\u003e\u003cp\u003eEmploying different turnaround strategies leads to different turnaround outcomes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe theoretical framework in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is proposed considering the previously proposed hypothesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003e\u003cb\u003eResearch Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn responding to the research aim of understanding the antecedents of corporate decline and evaluating the effectiveness of turnaround strategies, this study adopts a robust, three-stage methodological framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It integrates both qualitative and quantitative approaches.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStage 1: Literature Review\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis stage involves an in-depth literature review. This review encompassed peer-reviewed journals and aimed to collate a comprehensive list of the main sources (internal and external) contributing to firms\u0026rsquo; decline. Then these sources are taken to the second stage to select the most significant causes of decline employing RF analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStage 2: Variable Selection Using RF Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe identified sources of decline (factors) were employed as inputs for the RF analysis. RF is a supervised ensemble learning method to systematically identify and prioritise the key variables associated with organisational decline. It can handle high-dimensional data, accommodate complex interactions, and generate interpretable variable importance measures, thereby addressing limitations inherent in traditional linear techniques. The RF model was trained to evaluate the importance of each factor in predicting firm decline. Factor importance was assessed using the mean decrease in impurity (MDI) metric, which measures the contribution of each factor to the homogeneity of the nodes and leaves in the ensemble of trees. Factors exhibiting the highest MDI scores were selected for further analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStage 3: Structural Modeling Using SmartPLS 4.0\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis stage utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0. Building upon the empirically derived determinants from RF, the third stage seeks to evaluate the causal pathways through which these determinants influence the performance outcomes of implemented turnaround strategies.\u003c/p\u003e\u003cp\u003eThis integrative approach ensures methodological triangulation, enhances explanatory power, and supports the derivation of nuanced insights into the dynamics of corporate decline and recovery.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. Data Analysis and Results","content":"\u003cp\u003e\u003cb\u003eSampling and Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study focuses on the Jordanian pharmaceutical manufacturing industry, which significantly contributes to the Jordanian economy, as most of its production (around 80%) is exported to more than 70 countries, such as Europe and the United States. The pharmaceutical industry in Jordan is considered the most advanced in the Middle East region and worldwide. According to DNB (2021)\u003csup\u003e1\u003c/sup\u003e, there are 28 pharmaceutical manufacturing companies in Jordan; these include large companies, such as Hikma Pharmaceuticals; medium companies, such as Jordan Sweden Medical \u0026amp; Sterilization; and small companies, such as Jordan River Pharmaceutical Industries. The first pharmaceutical factory was founded in 1962 in Jordan, and the industry has grown significantly since then.\u003c/p\u003e\u003cp\u003eThe proposed theoretical model was tested through a survey of the Jordanian pharmaceutical sector for two reasons. First, the pharmaceutical industry was considered one of the most critical sectors during the COVID-19 pandemic. Therefore, identifying the impact of the pandemic on this sector may guide policymakers toward more evidence-based planning to overcome the accompanying challenges (Ayati et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, although focusing on a single sector in a specific context could limit the implications for the general business, it will ensure high internal validity (Wong et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe original survey draft was sent to six strategic and operations management professors to ascertain the content and face validity of the questionnaire. Then, a pilot study of 35 MBA students was conducted to evaluate the clarity, wording, relevance, and time spent on completion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Description\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe questionnaires were distributed only to respondents who identified as pharmaceutical practitioners and managers. The researchers distributed the survey randomly to managers during January and March 2023. Of the 250 distributed questionnaires, only 168 were usable, with a response rate of 67%.\u003c/p\u003e\u003cp\u003eThe empirical findings indicate clear variation in the respondents\u0026rsquo; characteristics, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sample comprised 63% male respondents, with around 68% aged between 20 and 40, 21% between 40 and 50, and the rest older than 50. Around 3% of respondents held a doctorate, 23% held a master\u0026rsquo;s degree, and 74% held a bachelor\u0026rsquo;s degree. Regarding the managerial level, 33% of respondents were line supervisors, 50% were operations managers, 10% were middle managers, and the rest were senior managers. Finally, 65% of respondents had experience in the same position for less than ten years, and only 14% had 15 years of experience or more.\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\u003eCharacteristics of respondents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics of respondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCharacteristics of respondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eExperience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLess than five years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 to less than ten years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 to less than 15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20 to less than 30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 to less than 40 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eManagerial Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40 to less than 50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSupervisors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOperational Manager\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle Managers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSenior Managers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23%\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoctorate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3%\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariable measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll survey items of independent variables were adapted from prior research and were measured through managerial perceptions. A seven-point Likert scale was used to assess the agreement level for each item, anchored at strongly agree (7) and strongly disagree (1). The 48 items employed were adapted from past studies (Appendix Table AI):\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInternal causes (19 items) and external causes (14 items) of the corporate decline scale that initially are based on Pearce and Robbins (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Jas and Skelcher (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrganizational agility scale that includes four items based on Cegarra-Navarro et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eOne dependent variable, namely, turnaround strategies (four items), was based on Gowen III and Tallon (2002); one moderator, namely, the intensity of competition (four items); and one outcome (three items) based on Venkatraman and Grant (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy one: Identifying and establishing the key drivers of corporate decline\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe total number of internal and external causes in the original survey was 33 items. However, one of the problems faced by researchers was associated with the large number of variables in models concerning over-parametrization (Eynaud et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Simplifying models that reduce complexity may be an interesting issue. Therefore, employing statistical methods that simplify a model according to data would be interesting. Hence, the need for a statistical method to identify how the models\u0026rsquo; outputs are linked to their inputs appears to select the best model that accurately predicts the dependent variable (Ellies-Oury et al., 2019). Unfortunately, researchers used to omit variables to deal with this problem (Anouze, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Emrouznejad et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral traditional statistical models, such as stepwise regression or Lasso logistic regression, can be used to predict the dependent variable. However, a common problem with these models occurs whenever the number of observations (\u003cem\u003en\u003c/em\u003e) is small concerning the number of predictors (\u003cem\u003ep\u003c/em\u003e). Further, stepwise regression or Lasso logistic regression, for instance, selects covariates in a parametric linear regression model (Ellies-Oury et al., 2019). Therefore, which variables are relevant to be included is a topic of debate (Bou-Hamad et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Identifying the most critical variables fills the gap in predicting the dependent variable.\u003c/p\u003e\u003cp\u003eThis study introduced RF, a powerful data-driven tool for variable selection, to determine the most critical causes of corporate decline (variables). RF can deal with linear and nonlinear relationships (Anouze \u0026amp; Bou-Hamad, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bou-Hamad et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Breiman (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) introduced RF as an ensemble of classification and regression trees (Breiman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For each tree in the ensemble, RF uses some input data to predict the output based on averaging (the case of regression) or majority voting (the case of classification) (Antipov \u0026amp; Pokryshevskaya, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Then, it combines many individual decision trees to provide a final prediction.\u003c/p\u003e\u003cp\u003eTherefore, the 33 causes of corporate decline are reduced before modeling using the RF tool to identify the best variables to include in the model. Variable selection aims to remove irrelevant, redundant variables and is unlikely to improve model accuracy (Chowdhury \u0026amp; Turin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, using RF helps to find a small number of variables sufficient for a good parsimonious prediction concerning the dependent variable (Breiman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The RF algorithm has not yet been employed to estimate the proper turnaround strategy during troubling times, such as redesigning the process or product and eliminating unprofitable products. RF was computed using SPSS Modeler 18 software, where the original data is split into validation and training data sets, and the result is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the variable importance and the normalized importance of each cause. The critical measure was the percentage of increase in the mean squared error. The economic slowdown is the most important cause that predicts firm decline, with an importance of 0.75, followed by increased supplier power and labour problems, with an importance of 0.690 and 0.665, respectively.\u003c/p\u003e\u003cp\u003eThe lowermost important causes are inadequate production equipment, falling demand for an industry\u0026rsquo;s products, board-management conflict, and international expansion, with the importance of 0.040, 0.020, 0.009, and 0.006, respectively. Four out of the top five causes are external causes, which is reasonable during a crisis such as the COVID-19 pandemic.\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\u003eIndependent variable importance list\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=\"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\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndependent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNormalized Importance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic slowdown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncreased supplier power\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabour problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInappropriate channels of distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer, regulatory, and economic changes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal financial crisis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInput price increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLack of capital funds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFailure to innovate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate financing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEntrance of new domestic competitors into the industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChanging technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncrease in customer complaints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate research and development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFailure to control costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEntrance of new foreign competitors into the industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eToo much debt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoss of market share\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor management of information systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate understanding of customers\u0026rsquo; needs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarketing the wrong product and in the wrong markets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDysfunctional organizational culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-cost structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisplacement by competition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate understanding of customer needs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlant expansion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems with teamwork\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAging production techniques\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate production equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFalling demand for an industry\u0026rsquo;s products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInt_17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard-management conflict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExt_13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational expansion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7%\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\u003eFor ease of reading, these measures are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, where the y-axis represents the normalized importance, and the x-axis represents the internal and external causes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the most important external causes of firm decline are economic slowdown, increased supplier power, inappropriate distribution channels, consumer, regulatory, and economic changes, and the global financial crisis. At the same time, the five most important internal causes are labour problems, input price increases, lack of capital funds, failure to innovate, and inadequate financing. Therefore, to simplify the proposed model and include only the most important causes, this research included any cause with a normalized importance above 20%. Accordingly, five external and three internal causes are included in the final model and taken for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy two: Predict turnaround strategy outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTesting the multivariate analysis assumptions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral statistical tests, including linearity, normality, and multicollinearity, were conducted for the collected data, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The maximum value of skewness was 0.87 (˂ \u0026plusmn; 1), while the maximum value of kurtosis was 1.17 (˂ \u0026plusmn; 2), which suggests that non-normality was not excessive. Likewise, the single-factor model exhibited a significantly worse fit, indicating that common method bias is not a serious concern. Meanwhile, the shared variance of the common latent factor was 19.67%, indicating the non-existence of common method variance. The variance inflation factor (VIF) test of scale items for multicollinearity showed that the VIF was 8.62 and the smallest tolerance was 1.27, much less than the common cut-off threshold of 10. The sample size was 168, which is adequate for SEM (Hair et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\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\u003eDescriptive statistics for research variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd Dev\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal causes of corporate decline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExternal causes of corporate decline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganizational agility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompetitive intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurnaround strategies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\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\u003e\u003cb\u003eConstruct validity and reliability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral tests were used to measure construct validity and reliability. The goodness-of-fit index (GFI) was used to measure each construct\u0026rsquo;s unidimensionality. Based on this test, a value greater than the 0.90 threshold indicates good unidimensionality among the constructs (Prajogo \u0026amp; Hong, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the composite reliability (CR) values ranged from 0.853 to 0.918, with all the construct loadings significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Moreover, all constructs\u0026rsquo; average variance extracted (AVE) exceeded the value of 0.50. The maximum shared squared variance (MSV) and average shared squared variance (ASV) were less than the AVE. This means that all the constructs fulfilled the requirements of discriminant and convergent validity; hence, both the reliability and validity of the constructs for the measurement model were successfully verified.\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\u003eConstruct unidimensionality, reliability, and convergent validity\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMSV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaxR(H)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInt Cause\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExt Cause\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOrg Agile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eInte Comp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTurn Strat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.824\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.732\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.767\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.727\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurn Strat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.751\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIC\u003c/b\u003e: Internal causes of corporate decline \u003cb\u003eEC\u003c/b\u003e: External causes of corporate decline\u003c/p\u003e\u003cp\u003e\u003cb\u003eOA\u003c/b\u003e: Organizational agility \u003cb\u003eCI\u003c/b\u003e: Competitive intensity\u003c/p\u003e\u003cp\u003e\u003cb\u003eTurn Strat\u003c/b\u003e: Turnaround strategies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003eCR\u003c/b\u003e \u003cem\u003e= (\u003c/em\u003e\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\sum\\:{{(\\lambda\\:}_{i})}^{2}}{\\sum\\:{{(\\lambda\\:}_{i})}^{2}+\\:\\sum\\:{\\delta\\:}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003e), (\u003c/em\u003e\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003e= standardized factor loadings, i = observed variables\u003c/em\u003e,\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003e= error variance).\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003eAVE\u003c/b\u003e \u003cem\u003e= (\u003c/em\u003e\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\sum\\:{{(\\lambda\\:}_{i})}^{2}}{n}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003e), (i = 1...n\u003c/em\u003e,\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003e= standardized factor loadings, i = observed variables).\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003eMSV\u003c/b\u003e: \u003cem\u003eMaximum shared squared variance\u003c/em\u003e\u003c/sub\u003e\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\u003e\u003cb\u003eMeasurement model-confirmatory factor analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo test the fitness of the data to the measurement model, several goodness-of-fit measures were assessed, such as normed chi-square (χ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e), comparative fit index (CFI), GFI, standardized root mean residual (SRMR), root mean square error of approximation (RMSEA), normed fit index (NFI), incremental fit index (IFI), and Tucker-Lewis index (TLI).\u003c/p\u003e\u003cp\u003eThe results showed that all these measures exceeded the recommended thresholds (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.2, p\u0026thinsp;=\u0026thinsp;0.00; CFI\u0026thinsp;=\u0026thinsp;0.95; GFI\u0026thinsp;=\u0026thinsp;0.915; RMSEA\u0026thinsp;=\u0026thinsp;0.045; SRMR\u0026thinsp;=\u0026thinsp;0.031; NFI\u0026thinsp;=\u0026thinsp;0.93; IFI\u0026thinsp;=\u0026thinsp;0.95; and TLI\u0026thinsp;=\u0026thinsp;0.94). This indicates that the measurement model fits the data well. Similar to the measurement model, GIF measures were used to measure the fit of the structural model. As Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows, all the measures (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.95, p\u0026thinsp;=\u0026thinsp;0.00; CFI\u0026thinsp;=\u0026thinsp;0.965; GFI\u0026thinsp;=\u0026thinsp;0.923; RMSEA\u0026thinsp;=\u0026thinsp;0.043; SRMR\u0026thinsp;=\u0026thinsp;0.029; NFI\u0026thinsp;=\u0026thinsp;0.943; IFI\u0026thinsp;=\u0026thinsp;0.965; and TLI\u0026thinsp;=\u0026thinsp;0.954) exceeded the recommended thresholds. Consequently, the results of structural model tests indicate a good fit with the collected data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHypothesis testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThree stages were used to test the proposed hypotheses: direct effects (H1-H3), mediation effects (H4-H6), and moderated mediation effects (H7). An SEM was run using AMOS version 27 to test the proposed hypotheses; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate the results.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows a significant positive impact of internal causes on revenue generation strategy that is not significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.137, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001). Nevertheless, the positive effects of the internal causes on product/market refocusing were significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, the positive effects of the internal causes on asset reduction and productivity improvement strategies were significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively), which partially support H1.\u003c/p\u003e\u003cp\u003eAbout the impact of external causes on turnaround strategies, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows a significant positive impact of external causes on revenue generation and product/market refocusing strategies (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.432, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.198, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively). Similarly, the positive effects of the external causes on the asset reduction strategy were significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the positive effects of the external causes on productivity improvement strategies were not significant, which partially supports H2.\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\u003eStructural parameter estimates (standardized coefficients)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep 1: Main effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1: Independent variable (IC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenue Generation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct/Market Refocusing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsset Reduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProductivity Improvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003ePartially Supported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH2: Independent variable (EC)\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenue Generation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct/Market Refocusing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsset Reduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProductivity Improvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 .60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003ePartially Supported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH3: Independent variable (OA)\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRevenue Generation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct/Market Refocusing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsset Reduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProductivity Improvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eFully Supported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIC\u003c/b\u003e: Internal causes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEC\u003c/b\u003e: External causes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTurn\u003c/b\u003e: Turnaround strategy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCI\u003c/b\u003e: Competitive Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOA\u003c/b\u003e: Organization Agility\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e*** Significant at α\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e** Significant at α\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/em\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, the impact of organizational agility on turnaround strategies showed a significant positive impact on revenue generation strategy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.247, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It also significantly impacted product/market refocusing (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.420, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, asset reduction and productivity improvement strategies were also significant (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.371, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively), fully supporting H3. This structural model explains 47% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.47) to 54% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.54) of the variance, which is above the cut-off value of 10% (Falk \u0026amp; Miller, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTesting Mediation Effects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo test the mediating effects of turnaround strategies, the study focused on the significance of the indirect effects (Osei-Frimpong et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A bootstrapping-based mediation test was computed with a two-tailed significance from 5,000 bootstrapping runs using the PROCESS macro in SPSS (Hayes, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The 5,000 replicates of the data set were chosen to estimate the coefficients and confidence intervals corrected to 95%. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that the three dependent variables directly affect turnaround strategy and the lower-level confidence interval (LLCI) and upper-level confidence interval (ULCI). Results revealed that the relationship between internal causes and performance was significant after including the turnaround strategy as a mediator (indirect effect\u0026thinsp;=\u0026thinsp;0.183, SE\u0026thinsp;=\u0026thinsp;0.031, LLCI: 0.062, and ULCI: 0.410). This means the turnaround strategy fully mediates the relationship between internal causes and firm performance. The PROCESS macro was executed again to test the impact of external causes and organizational agility on firm performance, mediated by turnaround strategy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBootstrapped indirect effects on turnaround performance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaths and effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndirect effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLLCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eULCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIC \u0026loz; Turn \u0026loz; Turnaround strategy outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC \u0026loz; Turn \u0026loz; Turnaround strategy outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOA \u0026loz; Turn \u0026loz; Turnaround strategy outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.235\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\u003eThe results remained significant (indirect effect\u0026thinsp;=\u0026thinsp;0.281, SE\u0026thinsp;=\u0026thinsp;0.057, LLCI: 0.023, ULCI: 0.317; indirect effect\u0026thinsp;=\u0026thinsp;0.196, SE =, LLCI: 0.049, ULCI: 0.235, respectively). This result suggests that the turnaround strategy significantly mediates the relationships between internal and external causes and organizational agility and performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModeration test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study proposed CI as the moderating variable. Hence, the aim is to identify whether the relationships proposed in the baseline model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) remain the same or invariant across different CI groups (i.e., low CI and high CI groups). To examine the contingency effects of CI, the original dataset was divided into two groups: the high CI group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72) and the low CI group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96) based on the average scores of centralized constructs (i.e., 5.429) (Tse et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA multi-group analysis for internal and external causes and organization agility dimensions was computed to examine the impacts under the influence of low and high CIs. This procedure aligns with the guidelines of Marsh et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Tse et al. (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The findings of the multi-group and structural path analyses are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternal causes and turnaround strategies under low and high CIs (H4)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePanel A of the table shows both the path estimates and \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics. The result showed that there are significant differences in the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.14, ∆\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the baseline model and the constrained model\u003csup\u003e3\u003c/sup\u003e. This result suggested the variance of the model under low and high CIs. Thus, the next step is to test the equality of the paths between the low and high CI groups. The significant \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e at \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated the moderating effect of CI.\u003c/p\u003e\u003cp\u003eThe results showed that the relationship between the internal causes of corporate decline and revenue generation was significant but invariant in terms of its strengths under low and high CIs (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which supports H4a. The results further indicated that the internal causes-assets reduction relationship was significant under low (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and high CIs (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on a significant difference in the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the difference in \u003cem\u003eβ\u003c/em\u003e, we concluded that the internal causes-assets reduction relationship was strengthened under a high CI, which supports H4c. However, the path from internal causes to product/market refocusing (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) is insignificant. In contrast, the path from internal causes to productivity improvement had a significant \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a contingency effect, hence rejecting H4b and accepting H5d.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-group analysis for CI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\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=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e /df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eΔ\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eΔ \u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHigh CI\u003c/p\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (\u003cem\u003et\u003c/em\u003e-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLow CI\u003c/p\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (\u003cem\u003et\u003c/em\u003e-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHypotheses\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003ePanel A: Multi-group analysis for hypothesis H4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. \u003cb\u003eBaseline Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e645.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. \u003cb\u003eConstrained Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e660.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e15.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e3. \u003cb\u003eConstrained Paths (Competitive intensity)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3a: IC \u0026loz; RG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e649.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54 (3.76)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.68 (3.87)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3b: IC \u0026loz; PM/R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e647.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.13 (2.89)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.09 (2.93)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eNot supported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3c: IC \u0026loz; AR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e649.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.76 (3.12)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.32 (4.09)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3d: IC \u0026loz; PI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e649.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.71 (4.79)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.57 (3.91)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePanel B: Multi-group analysis for hypothesis H5\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. \u003cb\u003eBaseline Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e668.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. \u003cb\u003eConstrained Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e744.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e3. \u003cb\u003eConstrained Paths (Competitive intensity)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3a: EC \u0026loz; RG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e672.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.74 (2.31)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.62 (3.13)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3b: EC \u0026loz; PM/R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e672.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.63 (3.53)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.68 (2.79)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3c: EC \u0026loz; AR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e671.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.71 (2.23)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.76 (3.43)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3d: EC \u0026loz; PI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e672.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.51 (3.78)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.60 (2.46)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePanel C: Multi-group analysis for hypothesis H6\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. \u003cb\u003eBaseline Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e660.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. \u003cb\u003eConstrained Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e665.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e3. \u003cb\u003eConstrained Paths (competitive intensity)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3a: OA \u0026loz; RG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e666.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.74 (4.76)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.67 (2.87)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3b: OA \u0026loz; PM/R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e664.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.73 (2.98)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.49 (3.94)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3c: OA \u0026loz; AR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e664.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.76 (4.72)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.32 (2.59)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3d: OA \u0026loz; PI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e665.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.71 (3.39)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.56 (2.91)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRG\u0026thinsp;=\u0026thinsp;Revenue Generation\u003c/p\u003e\u003cp\u003eAR\u0026thinsp;=\u0026thinsp;Asset Reduction\u003c/p\u003e\u003cp\u003eCI\u0026thinsp;=\u0026thinsp;Competitive Intensity\u003c/p\u003e\u003cp\u003e*** Significant at α\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003ePM/R\u0026thinsp;=\u0026thinsp;Product/Market Refocusing\u003c/p\u003e\u003cp\u003ePI\u0026thinsp;=\u0026thinsp;Productivity Improvement\u003c/p\u003e\u003cp\u003e** Significant at α\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e* Significant at α\u0026thinsp;\u0026le;\u0026thinsp;0.1\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\u003e\u003cb\u003eExternal causes and turnaround strategies under low and high CIs (H5)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe above analysis is repeated for the external causes of corporate decline and turnaround strategies. Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows significant differences in the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;76.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the constrained and baseline models. The relationship between the external causes of corporate decline and revenue generation strategy was significant under low CI groups (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high ones (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1). The difference in \u003cem\u003eβ\u003c/em\u003e value suggests that this relationship was strengthened under a high CI, which supports H5a. Likewise, the relationship between the external causes to product/market refocusing found a significant \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the path from external causes to assets reduction found a significant \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This suggests a contingency effect, lending support to H5b and H5c.\u003c/p\u003e\u003cp\u003eThe external causes and productivity improvement relationship was significant under low (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and highly competitive intensity groups (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A significant difference in the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) suggested variance of the path across low and high CIs. These results suggested that the external causes-productivity improvement relationship was strengthened under a high CI, which supports H5d.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOrganization agility and turnaround strategies under low and high CIs (H6)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePanel C of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the difference in the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistics was significant (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.36, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the constrained and baseline models. The relationship between organization agility and revenue generation (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed a significant \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference between low and high CI groups, supporting H6a.\u003c/p\u003e\u003cp\u003eFurthermore, the results showed a significant positive relationship between organization agility-product/market refocusing and agility-assets reduction. The \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e differences were significant (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively), which supports H6b and H6c. Finally, the \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e difference was found significant between the low and high CI groups (∆\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the path from organization agility to productivity improvement; the coefficients of this path were significant too for both low, competitive intensity groups (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and high (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The differences in \u003cem\u003eβ\u003c/em\u003e value indicated that the organization\u0026rsquo;s agility-productivity improvement relationship was strengthened when the CI was high, which supports H6d.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe importance of the turnaround strategy\u0026rsquo;s role in performance has been highlighted in the previous literature. However, the paucity of research is that an integrated analysis of corporate decline causes, organizational agility, turnaround strategies, and turnaround performance to survive during the crisis remains unexplored. This study proposed a model exploring the role of corporate decline causes and organizational agility as a predictor of turnaround strategy and performance.\u003c/p\u003e\u003cp\u003eThe first key finding is that the most important causes of corporate decline are economic slowdown, increased supplier power, and labour problems. Although there are many reasons behind corporate decline, these causes are associated with a sharp decline in corporate performance. The lowermost important causes are inadequate production equipment, falling demand for an industry\u0026rsquo;s products, board-management conflict, and international expansion. A firm may reduce redundant investments during an economic slowdown to remain competitive or survive (Luan et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Insufficient investment may threaten a firm\u0026rsquo;s survival in the short term, while decreases in capital expenditures may damage its future development.\u003c/p\u003e\u003cp\u003eSecond, although pharmaceutical supply chains did not collapse under the pressure of COVID-19, the pandemic revealed serious weaknesses (Flynn, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These weaknesses include increasing supplier power, long manufacturing lead times, and unpredictable demand, which are likely to cause problems for pharmaceutical manufacturing companies. Lastly, labour problems seem to be an important cause of the corporation\u0026rsquo;s decline. While the effect of the pandemic on labour varies across sectors, there is an experience of sudden and drastic labour reductions in some sectors (Luckstead et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Employees may consider these changes unacceptable and reduce productivity, leading to corporate decline.\u003c/p\u003e\u003cp\u003eThe next key finding is about understanding how the causes of corporate decline could affect the selection of turnaround strategies and performance. The result shows that pharmaceutical managers adopted various strategies to reverse that decline. Some of these strategies have a short-term focus, whereas others have a long-term strategic nature.\u003c/p\u003e\u003cp\u003eIn response to the internal causes, managers choose product/market refocusing and productivity improvement. In contrast, when considering the external causes, managers will, in general, utilize different turnaround strategies, such as revenue generation, asset reduction, and productivity improvement strategies. This finding aligns with Hayne (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who emphasized that formal accounting tools can enhance efficiency and reduce costs when responding to organizational crises. However, since these tools are often slow to adapt and deeply embedded in established routines, informal accounting practices may prove to be a more agile and responsive option during times of crisis. Additionally, Bhattacharya et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) supported the idea that marketing capabilities help a firm recover, particularly when the source of distress is firm-specific. R\u0026amp;D capabilities, on the other hand, contribute to recovery only when industry-wide factors drive the distress.\u003c/p\u003e\u003cp\u003eHowever, managers employed all the proposed turnaround strategies to enhance organizational agility. When faced with performance decline, they often opted for revenue generation strategies to improve efficiency and profitability, thereby sustaining operations (Lai \u0026amp; Sudarsanam, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These findings are consistent with those of Rios-Rodr\u0026iacute;guez et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Thomas and Douglas (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as with the contingency theory and the suggestions of Hofer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Hofer emphasized that the severity of a crisis is a critical contextual factor in determining the effectiveness of turnaround strategies. For less severe crises, modest changes, such as cost pruning through productivity improvement and product/market refocusing, may suffice for recovery. In contrast, more severe crises demand more substantial transformations, such as asset reduction or market reorientation. Bhattacharya et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also find that the appropriate deployment of dynamic capabilities strongly influences retrenchment strategies, aiding in firm survival, and that recovery depends on the nature of the distress. This is further supported by prior studies emphasizing the value of strategic actions. Tangpong et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that firms implementing retrenchment actions early have a higher likelihood of successful turnaround. Arora (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlighted the role of financially linked independent directors in facilitating recovery, and Rico et al. (2021) showed that deep cost retrenchment enhances survival. Bhattacharya et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reinforced the importance of dynamic capabilities, noting that marketing capabilities are particularly effective when distress is firm-specific, whereas R\u0026amp;D capabilities are more useful during industry-wide downturns.\u003c/p\u003e\u003cp\u003eThe impact of organizational agility on turnaround strategies shows a significant positive impact on revenue generation and product/market refocusing strategies. Similarly, the positive effects of the organization\u0026rsquo;s agility on asset reduction and productivity improvement strategies are significant, too. This means that during the crisis, organizational agility becomes more significant (Keremah \u0026amp; Monday, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, Doz (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) stressed that strategic agility assists managers in avoiding \u0026ldquo;rigidity traps\u0026rdquo; by moving to prohibit organizational recession and orienting towards more operational flexibility. Kale et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Vaillant and Lafuente (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that agile organizations respond more quickly to exploit the opportunities of the work environment and improve the efficiency of resources by restructuring them in harmony with ambient conditions (AlTaweel \u0026amp; Al-Hawary, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This result aligns with the empirical findings of Yao et al. (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who highlighted the role of organizational agility in enhancing job satisfaction. Their study showed that organizational agility helps organizations to accommodate changes related to corporate decline by promoting adaptability, collaboration, and resilience.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis research aims to shed light on the causes of corporate decline and the main implemented turnaround strategies to survive during the crisis, particularly in the Jordanian pharmaceutical sector. It also aims to orient researchers and practitioners on how to face severe survival-threatening situations in the future. To reach this end, this study integrated two powerful tools, RF and SEM. The conjoined analysis of RF-SEM provides significant methodological contributions from a statistical point of view, thus offering a holistic understanding. Such a study helps managers formulate appropriate strategies, leading to a proper turnaround strategy, reducing corporate costs, and generating more revenue. More specifically, this study intended to answer the following main questions: 1) Are the employed turnaround strategies effective in increasing the likelihood of turnaround? 2) Are those strategies suitable for the pharmaceutical manufacturing sector? Moreover, 3) Does competitive intensity shape the effectiveness of turnaround strategies during a crisis?\u003c/p\u003e\u003cp\u003eTherefore, the findings of this study provide the following theoretical contributions. First, it demonstrates the benefits of conceptualizing turnaround strategies and their outcomes as multidimensional constructs. The proposed model is novel in exploring the role of turnaround strategies as a mediator and competition intensity as the moderator between causes of decline, organization agility, and turnaround performance. Second, unlike previously published research, which conceptualizes turnaround strategies and their outcomes as unidimensional constructs, this study allows us to comprehensively understand the causes of corporate decline, turnaround strategies, and performance relationships at the dimension levels. Third, from an analytical method point of view, this study integrates two statistical methods (RF and SEM). This integration offers an in-depth analysis of the collected data and thus provides more helpful information.\u003c/p\u003e\u003cp\u003eAt the managerial level, the study highlights several valuable lessons for survival during a crisis. First, although researchers have recently increased interest in organizational decline, there is a lack of managerial aptitude for arranging successful turnaround strategies to revive organizations (Bodolica \u0026amp; Spraggon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, this study bridges this gap in the decline literature. In addition, out of the 33 causes introduced in this study, only five seem to be the most important, mainly external. This finding is significant for managers to understand the leading causes and, accordingly, formulate effective strategies. Further, since successful turnaround strategies can build a good reputation and image for declining firms after experiencing declining situations, this study proposes that managers should be prepared for future environmental crises. While doing so, they must know the strategy tools used (Linden, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, to survive the crisis, managers performed well by implementing suitable turnaround strategies, which led many companies to revisit their business strategies and competitive objectives, including agility.\u003c/p\u003e\u003cp\u003eAt least two main limitations open the opportunities for new research. First, this study includes specific variables that affect turnaround strategies and their performance; therefore, further research would be conducted (e.g., leadership or corporate strategy). Such an extension may strengthen the conclusions reported here. Organizational solid leadership is essential for any successful transformation (Al-Emadi \u0026amp; Anouze, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, a comparative study in other countries with similar conditions would significantly enhance the currently available knowledge.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.A. performed the literature review. A.A. performed the statistical analysis. E.C. formalized the study motivation and discussion. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbebi, M. (2013). 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Turnaround or crisis: digital transformation and enterprise financial distress in economic downturns?- the moderating effects of slack resources. \u003cem\u003eTechnology Analysis \u0026amp; Strategic Management\u003c/em\u003e, 1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09537325.2025.2464038\u003c/span\u003e\u003cspan address=\"10.1080/09537325.2025.2464038\" 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) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dnb.com/business-directory/company-information.pharmaceutical-manufacturing.jo.html\u003c/span\u003e\u003cspan address=\"https://www.dnb.com/business-directory/company-information.pharmaceutical-manufacturing.jo.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e) These measures are calculated based on Gaskin (2016) Excel StatTools. Gaskin, J. (2016). Excel StatTools. Excel Stats Tools Package. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://statwiki.kolobkreations.com\u003c/span\u003e\u003cspan address=\"http://statwiki.kolobkreations.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e) In the structural model, parameters are varied freely across the two competitive intensity groups, while, in the constrained model the structural parameters constrained to be equal across the two competitive intensity groups.\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":"Turnaround strategies, causes of corporate decline, competitive intensity, crisis, pharmaceutical sector, Jordan","lastPublishedDoi":"10.21203/rs.3.rs-7456495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7456495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how pharmaceutical manufacturing companies in emerging markets reconfigure internal resources and dynamic capabilities to navigate crises. The study identifies internal and external antecedents of organizational decline based on the Resource-Based View (RBV) and Dynamic Capabilities (DC) frameworks. It evaluates the effectiveness of turnaround strategies under varying levels of competitive intensity. A sequential multi-method design combined Random Forest (RF), a machine learning method for detecting nonlinear interactions and ranking predictor importance, with Structural Equation Modeling (SEM) to validate hypothesized causal relationships. Data were collected from pharmaceutical firms in Jordan, and the analysis proceeded in three phases: identifying drivers of decline, modeling strategic responses, and testing mediation and moderation effects. Findings reveal that external pressures, particularly economic downturns and supply chain disruptions, are the primary sources of decline, with internal factors such as labor constraints and operational inefficiencies also significant. Both categories of antecedents shaped firms\u0026rsquo; recovery actions. Organizational agility emerged as a key enabler of effective responses, particularly in highly competitive environments. Turnaround strategies mediated the relationship between crisis antecedents and firm performance, while competitive intensity moderated these relationships. The study contributes a theoretically grounded and empirically validated framework for organizational recovery in high-risk, resource-constrained environments. It advances disaster risk research by integrating RBV and DC perspectives and applying RF and SEM methodologies. Practically, the study provides guidance for managers and policymakers in pharmaceuticals and other critical industries on realigning strategies and building agility to withstand systemic disruptions and prolonged uncertainty.\u003c/p\u003e","manuscriptTitle":"Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-06 07:37:22","doi":"10.21203/rs.3.rs-7456495/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":"427ea7ee-6b11-4f88-adce-c0219a4a3a8e","owner":[],"postedDate":"September 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T05:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-06 07:37:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7456495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7456495","identity":"rs-7456495","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}