When Technological Pressure Is No Longer Merely a Burden: How Technological Overload and Technological Complexity Empower Employee Productivity

When Technological Pressure Is No Longer Merely a Burden: How Technological Overload and Technological Complexity Empower Employee Productivity | 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 Article When Technological Pressure Is No Longer Merely a Burden: How Technological Overload and Technological Complexity Empower Employee Productivity Chung-Pin Wu, Chor-Sum Au-Yeung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8759770/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract As digital transformation deepens, technostress has become a critical issue influencing employees’ work processes, yet prior research has largely emphasized its negative consequences. Integrating the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress, this study examines how technostress is transformed into IT enabled productivity. Study 1 uses fuzzy set qualitative comparative analysis to show that high IT enabled productivity arises from specific configurations of technostress creators rather than a single stressor, with techno overload and techno complexity as core conditions. Building on these findings, Study 2 applies structural equation modeling and demonstrates that techno overload directly enhances IT enabled productivity, whereas techno complexity operates indirectly through IT self efficacy and IT autonomy. Importantly, IT autonomy serves as a stable mediating mechanism across both stressors. Overall, the findings indicate that technostress can generate positive performance outcomes when appraised as a challenge and supported by key psychological resources. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Technostress IT-enabled Productivity Challenge–Hindrance Stressor Framework Transactional Theory of Stress fuzzy-set qualitative comparative analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction The concept of stress was initially used to describe a set of physiological and psychological responses that individuals experience when facing adverse external environments or stimuli, with its core lying in the interaction between external demands and individual resources (Ghasemi et al., 2024 ). With the extensive penetration of information technology into work contexts, sources of stress have gradually extended from traditional job demands to technology use itself. Accordingly, the concept of technostress creators has been proposed, referring to stress experiences induced by technological characteristics, modes of technology use, and technological environments (Nastjuk et al., 2024 ). These stressors include techno overload, techno invasion, techno complexity, techno insecurity, and techno uncertainty (Tarafdar et al., 2007 ). Wang and Zhao ( 2023 ) conceptualize technostress as a key component of the dark side of technology, arguing that it leads to outcomes such as exhaustion, job dissatisfaction, and performance decline. Unfortunately, this stream of research has largely overlooked the possibility that technostress can also facilitate employees’ work processes and serve as a tool that helps stimulate employees’ work capabilities (Huang and Gursoy, 2024 ). This perspective is supported by a growing body of research. Gerdiken et al. ( 2021 ), through a meta-analysis, demonstrate a significant positive relationship between technostress creators and work engagement, suggesting that technostress does not necessarily result in negative outcomes. Furthermore, studies by Li and Wang ( 2021 ) and Tarafdar et al. ( 2019 ) indicate that technostress may be accompanied by positive outcomes such as efficiency improvement and work-related innovation. Based on these recent studies, this research seeks to challenge the traditional assumption that stress inevitably produces only negative effects. To explain these divergences in prior findings, this study adopts the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress as its theoretical foundations to reexamine the effects of technostress on employees. Challenge stressors refer to stress inducing demands that, although eliciting stress responses, are simultaneously perceived by individuals as motivating forces that facilitate growth, learning, and achievement at work (González Morales and Neves, 2015). When situations are appraised as opportunities for control, gain, or personal development, stress may be transformed into positive energy rather than being perceived solely as a threat or loss (Jex, 1998 ). In prior technostress research, technostress has commonly been treated as a second order construct with its effects examined at an aggregate level, or scholars have focused only on isolated technostress creators, resulting in both experiential and nominal complexity (Brooks et al., 2017 ; Califf et al., 2020 ; Chandra et al., 2019 ). However, such approaches may lead to information loss or the inclusion of less salient technostress factors, thereby biasing the validity or parsimony of the resulting models (Nimako and Ntim, 2013 ). To address the potential limitations of prior research approaches, this study treats technostress creators as first order conditions in Study 1 and employs fuzzy set qualitative comparative analysis to emphasize how different technostress creators form multiple configurations and how these configurations lead to IT enabled productivity, thereby capturing the characteristics of equifinality and causal complexity. After identifying configurational patterns of technostress creators in Study 1, Study 2 shifts its focus to explaining why these technostress creators are able to generate IT enabled productivity, with particular attention to the role of cognition. By integrating the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress, this study responds to the misconception in prior research that technostress is inherently negative and empirically examines the applicability of the Challenge Hindrance Stressor Framework in technological contexts. Accordingly, the research objectives of this study are as follows: To identify configurations of technostress creators that lead to IT enabled productivity To examine the mechanisms through which technostress creators enhance IT enabled productivity To integrate the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress to strengthen theoretical extension 2. Study 1 2.1 Technostress Creators and IT Enabled Productivity Technostress is a specific form of stress that originates from the context of information technology use. Technostress refers to the stress experience that arises when individuals are unable to effectively cope with the demands of computer and information technology use within organizations (Nastjuk et al., 2024 ). This stress does not stem solely from technology itself but from the combined load created by technology and job demands (Srivastava et al., 2015 ). In highly digitalized organizational environments, technostress has become a critical issue shaping individuals’ work experiences and performance, and its mechanisms and outcomes are no longer limited to negative effects but instead exhibit a more complex pattern. Within technostress research, the five technostress creator categories proposed by Tarafdar et al. ( 2007 ) provide an essential foundation for understanding the structure of technostress. Techno-overload refers to a state in which individuals are compelled by technology to work faster and handle more tasks with greater intensity, although large volumes of information can also expand possibilities for decision making and action (Ragu Nathan et al., 2008). Techno-invasion reflects the intrusion of technology into personal time and space, thereby blurring the boundaries between work and private life (Nastjuk et al., 2024 ; Tarafdar et al., 2007 ). Techno-complexity refers to the difficulties individuals face when understanding and operating emerging or highly complex technologies, whereby higher usage thresholds may lead to feelings of insufficient capability (Ragu Nathan et al., 2008; Yuan et al., 2023 ). Techno-insecurity involves concerns about future employment, as individuals may continuously perceive threats of replacement due to technological innovation and believe that constant upgrading of technological skills is necessary to maintain competitiveness. Such conditions may even inhibit knowledge sharing behaviors (Tarafdar et al., 2011 ). Techno-uncertainty arises in environments characterized by continuous technological change and updates, in which users find it difficult to establish stable technological experience bases and remain in prolonged states of adaptation and relearning (Ragu Nathan et al., 2008; Tarafdar et al., 2007 ). Compared with viewing technostress solely as a source of exhaustion and burden, information systems research has increasingly emphasized the potential positive implications of technostress under specific conditions. IT enabled productivity is a key concept that captures this positive outcome and is defined by Su et al. ( 2024 ) as the extent to which information technology enhances employee productivity. This implies that technology is not merely a tool but can exert empowering effects on individual performance by supporting information processing and strengthening work capabilities. Accordingly, technostress creators do not necessarily equate to productivity decline. Instead, their impact depends critically on how individuals appraise and respond to technology related demands. From the perspective of challenge stressors, technostress creators may be perceived as challenges that promote growth and capability demonstration rather than as pure hindrances in specific contexts (González Morales and Neves, 2015). Although techno-overload increases work intensity, it may simultaneously prompt individuals to improve information processing efficiency and task prioritization capabilities (Hurbean et al., 2022 ). While techno-invasion blurs work boundaries, it also provides greater work flexibility and real time responsiveness, making technology an important resource that supports task completion (Wang et al., 2020 ). Techno-complexity requires individuals to invest in learning and adaptation, and when this process is appraised as an opportunity for capability development, it may enhance perceptions of technological mastery and strengthen technology enabled empowerment effects (Ciarli et al., 2021 ). Techno-insecurity may be transformed into motivation for proactive learning and skill upgrading rather than passive defensive stress responses (Malik et al., 2022 ). Techno-uncertainty may encourage individuals to maintain heightened vigilance and learning orientation to adapt to rapidly changing technological environments (Chakraborty et al., 2025 ). Therefore, within the theoretical framework of challenge stressors, the relationship between technostress creators and IT enabled productivity is not unidirectionally negative but instead entails conditional and contextual positive potential that warrants attention and examination at both structural and psychological levels. Based on these considerations, this study conducts fsQCA analysis as illustrated in Fig. 1 . 2.2 Research Design and Method To examine the multiple configurations of different technostress creators and the diverse pathways leading to a common outcome (Xia et al., 2024 ), this study adopts fsQCA to investigate how these technostress creators jointly shape employees’ IT enabled productivity. 2.2.1 Measurement development The measurement scales for the constructs in this study were developed based on existing literature. The five dimensions of technostress were measured using the scale adapted from Chen ( 2015 ). Specifically, techno-overload was measured with six items, techno-invasion with three items, techno-complexity with five items, techno-insecurity with five items, and techno-uncertainty with four items. IT enabled productivity was measured using the four-item scale developed by Tarafdar et al. ( 2007 ). For the translation of the scales, three scholars proficient in both Chinese and English were invited to conduct revisions using the back translation method. All items were measured using a six-point Likert scale ranging from 1 strongly disagree to 6 strongly agree. 2.2.2 Sample This study focuses on employees working in the financial industry as the research sample. As the financial sector increasingly relies on technology for managing customer data, administrative operations, and related tasks, employees are required to confront technological advancements in order to enhance their performance. Accordingly, Study 1 collected data from 250 financial industry employees between January and February 2025. After excluding responses with missing values and straight-line answering patterns, a total of 206 valid samples were retained for Study 1. 2.2.3 Calibration Consistent with prior literature, the calibration of condition attributes and outcome attributes represents the first step in the fsQCA procedure (Fiss, 2011 ; Ragin, 2009 ). Calibration involves assigning membership scores to raw variables in order to transform them into fuzzy sets. Based on Boolean logic, raw data must be converted into a set ranging from 0 to 1. After calibration, attributes are classified as full membership, intersection, or full non membership according to specified thresholds. Full membership, intersection, and full non membership refer to different degrees of a case’s membership within a particular set or condition. Following Greckhamer and Gur ( 2021 ), the direct calibration method was applied, with the 90th and 10th percentiles of each attribute used as the thresholds for full membership and full non membership, respectively, and the median selected as the crossover point for intersection. In addition, a constant of 0.001 was added to scores equal to 0.5 to avoid ambiguous cases (Fiss, 2011 ; Ragin, 2009 ). 2.2.4 Analysis of necessity The analysis of necessity aims to examine whether any necessary conditions exist for the outcome. The fsQCA approach employs consistency and coverage to assess reliability (Greckhamer et al., 2018 ). In necessity analysis, high consistency indicates that a fuzzy set accurately represents the data, whereas high coverage indicates that the fuzzy set sufficiently covers the relevant elements within the data space (Ragin, 2009 ). Consistent with prior research (Furnari et al., 2021 ), a consistency threshold of 0.9 is applied for identifying necessary conditions. Table 1 presents the necessity of single conditions for high IT enabled productivity and the absence of IT enabled productivity. As none of the conditions reach the consistency threshold of 0.90, it is concluded that no single condition constitutes a necessary condition for either high IT enabled productivity or the absence of IT enabled productivity. Table 1 Test of necessity for single conditions Conditions High IT-enabled Productivity Absence of IT-enabled Productivity Consistency Coverage Consistency Coverage Techno-Overload 0.761 0.735 0.601 0.584 Not-high Techno-Overload 0.569 0.586 0.728 0.754 Techno-Invasion 0.552 0.578 0.711 0.749 Not-high Techno-Invasion 0.760 0.723 0.599 0.574 Techno-Complexity 0.688 0.706 0.578 0.597 Not-high Techno-Complexity 0.607 0.588 0.715 0.698 Techno-Insecurity 0.585 0.568 0.735 0.718 Not-high Techno-Insecurity 0.710 0.727 0.558 0.575 Techno-Uncertainty 0.578 0.567 0.764 0.754 Not-high Techno-Uncertainty 0.749 0.760 0.561 0.573 2.2.5 Analysis of sufficiency This study analyzes sufficient conditions based on the truth table algorithm. Following the guidelines of Fiss ( 2011 ), the frequency threshold is set at 3 and the consistency threshold is set at 0.8. In addition, the proportional reduction in inconsistency is applied with a threshold of 0.7 to ensure that configurations do not simultaneously appear in both the presence and absence of the outcome (Pappas and Woodside, 2021 ). The analysis yields seven distinct configurations. In line with prior conventions, the intermediate solution is reported, representing an optimal balance between the complex and parsimonious solutions (Fiss, 2011 ). Using standard notation, the presence of a condition is indicated by ●, whereas the absence of a condition is indicated by ⊕. Table 2 Configurations of IT-enabled Productivity Config 1 Config 2 Config 3 Config 4 Config 5 Config 6 Config 7 Techno-Overload ● ● ● Techno-Invasion ⊕ ⊕ ⊕ Techno-Complexity ● ● ● Techno-Insecurity ⊕ ⊕ ⊕ Techno-Uncertainty ⊕ Consistency 0.760 0.837 0.814 0.813 0.791 0.786 0.791 Raw coverage 0.749 0.604 0.600 0.639 0.568 0.586 0.581 Unique coverage 0.069 0.015 0.013 0.016 0.006 0.004 0.010 Overall solution consistency 0.704 Overall solution coverage 0.907 2.4 Integration and Implications of Study 1 Findings The fsQCA results of Study 1 deepen theoretical understanding of the effects of technostress on IT enabled productivity and facilitate integration with the Challenge Hindrance Stressor Framework. The findings of Study 1 indicate that high IT enabled productivity does not arise from the comprehensive presence of all technostress creators but rather from the joint effects of specific technostress configurations, particularly techno-overload and techno-complexity. These stressors are most likely to be appraised by individuals as controllable and developmentally meaningful challenge stressors, thereby being transformed into positive forces that promote IT enabled empowerment and work effectiveness. This finding addresses prior research limitations that either oversimplified technostress creators or treated them with excessive complexity. Accordingly, building on the fsQCA results of Study 1, Study 2 further investigates the mechanisms through which techno-overload and techno-complexity contribute to IT enabled productivity. 3. Study 2 Building on the findings of Study 1, this study identifies techno-overload and techno-complexity as presence conditions for IT enabled productivity, and therefore Study 2 conducts an in-depth examination of the mechanisms underlying their effects. According to Yuan et al. ( 2023 ), stress does not arise directly from stressors themselves but rather depends on individuals’ cognitive appraisals. Accordingly, Study 2 focuses on self-efficacy and autonomy to explain how technostress is transformed into IT enabled productivity. 3.1 Research Framework and Hypothesis Development 3.1.1 The Effects of Techno-Overload and Techno-Complexity on IT Enabled Productivity Ongoing digital transformation has led to the introduction of information technology across diverse work contexts. Whether these transformations function as forces that enhance or inhibit employee performance does not depend solely on the objective characteristics of technology itself but is more deeply rooted in individuals’ subjective cognitive appraisal of stressors. To understand how technostress influences employees’ IT enabled productivity, this study adopts the TTS as its core theoretical foundation to explain how technostress creators affect employee performance through cognitive appraisal processes. The TTS posits that stress does not arise directly from environmental stimuli but instead originates from individuals’ subjective evaluations of the meaning of stressors within specific contexts (Lazarus and Folkman, 1984 ). When individuals encounter job demands, they first engage in primary appraisal to determine whether the demand is relevant to their well-being and to categorize it as a potential challenge or hindrance. When a demand is perceived as an opportunity for learning, growth, or achievement, a challenge appraisal is formed. In contrast, when it is perceived as obstructing goal attainment or causing loss, a hindrance appraisal emerges (LePine et al., 2016 ). These appraisal outcomes subsequently influence judgments regarding available resources and shape coping strategies and behavioral responses. The TTS is particularly suitable for explaining stress experiences in technology and information systems contexts (Lee et al., 2016 ). In digitalized work environments, employees face stressors that extend beyond increased workload to include requirements for learning and adapting to new technologies. When such technostress creators are appraised as challenges, they may stimulate motivation and engagement, thereby enhancing job performance. Conversely, when they are appraised as hindrances, they may undermine work effectiveness (Bermes, 2021 ). Findings from Study 1 indicate that techno-overload and techno-complexity are the key dimensions most directly influencing employees’ work processes. Although information systems can provide large volumes of information, they may also generate information overload, requiring employees to invest greater cognitive resources in filtering and integrating information in order to extract relevant content (Savolainen, 2007 ). According to the TTS, when employees appraise such heightened technological demands as contributing to improved work efficiency and professional capability, techno-overload may be perceived as a challenge stressor that encourages more active use of information technology to accomplish tasks, thereby enhancing IT enabled productivity. When technological tools are highly functional but characterized by high usage thresholds, employees may experience frustration, anxiety, and even feelings of incompetence (Yuan et al., 2023 ). However, the Transactional Theory of Stress emphasizes that such negative experiences are not inevitable. When employees appraise techno-complexity as an opportunity for capability development and skill accumulation, they may engage in proactive learning and deepen technological competencies, thereby strengthening the empowering effects of information technology on work productivity (LePine et al., 2016 ). Accordingly, employees who appraise digital challenges as challenge stressors tend to be more supportive of digital transformation and to exhibit higher levels of engagement and performance (Liu et al., 2024 ). Based on the TTS, the following hypotheses are proposed. H1: Techno-overload has a positive effect on employees’ IT enabled productivity. H2: Techno-complexity has a positive effect on employees’ IT enabled productivity. 3.1.2 The Mediating Role of IT Self Efficacy Self-efficacy is widely regarded as a critical psychological resource that facilitates the attainment of specific task goals (Bandura, 1977 ), buffers the negative effects of stress, promotes individuals’ adaptation to organizational change, and serves as a key factor influencing a variety of stress related outcomes (Saidy et al., 2022 ). In the context of information technology transformation, IT self-efficacy reflects employees’ beliefs in their capabilities to learn, operate, and utilize technological tools to accomplish work tasks. The TTS posits that when individuals encounter stressors, they first perceive their presence and intensity and subsequently engage in cognitive appraisal to determine the significance of the stressors for their personal well-being (Ma et al., 2021 ). This appraisal process can also be viewed as a core internal resource through which stress is transformed into motivational energy (Yu et al., 2018 ). Accordingly, when employees appraise technology related demands as challenges, they perceive these demands as facilitating personal growth (LePine et al., 2016 ), thereby enhancing IT self-efficacy. With respect to techno-overload, although it represents a condition in which technology compels employees to work more and at a faster pace (Tarafdar et al., 2007 ), under a challenge appraisal employees may view high volumes of information and intensified work demands as opportunities to hone technological capabilities and improve efficiency. In such cases, successful experiences in coping with techno-overload may instead strengthen employees’ confidence in their technological abilities and increase IT self-efficacy. Based on this reasoning, the following hypothesis is proposed. H3: Techno-overload has a positive effect on IT self-efficacy. Similarly, although techno-complexity may elicit frustration and anxiety (Yuan et al., 2023 ), the TTS emphasizes that when employees appraise complex technologies as challenges for learning and professional development, the process of overcoming techno-complexity itself becomes an important source of self-efficacy (LePine et al., 2016 ). Accordingly, this study posits that under challenge appraisal conditions, techno-complexity may also strengthen employees’ IT self-efficacy. Based on this reasoning, the following hypothesis is proposed. H4: Techno-complexity has a positive effect on IT self-efficacy. In addition, employees with higher IT self-efficacy are more likely to adopt problem focused coping strategies, proactively explore technological functionalities, and effectively integrate technology to support work processes (Yazdanmehr et al., 2023 ), thereby enhancing IT enabled productivity. When employees believe that they can effectively utilize technology to accomplish tasks, technology is more likely to function as an empowering tool rather than a source of stress. Based on this reasoning, the following hypothesis is proposed. H5: IT self-efficacy has a positive effect on IT enabled productivity. Based on the TTS and the hypotheses H3 to H5, this study posits that when stressors are appraised as opportunities that facilitate growth and performance, IT self-efficacy is enhanced and can be translated into higher levels of IT enabled productivity. Accordingly, the following mediating hypothesis is proposed. H6: IT self-efficacy mediates the positive relationship between techno-overload and IT enabled productivity. H7: IT self-efficacy mediates the positive relationship between techno-complexity and IT enabled productivity. 3.1.3 The Mediating Role of IT Autonomy Autonomy refers to the extent to which individuals are able to organize their work, determine execution procedures, and retain control over how their work is carried out (Wu et al., 2023 ). In technological contexts, IT autonomy can be understood as the degree of control and discretion employees perceive over the selection of technological tools, modes of use, and adjustments to work processes when using information technology to accomplish tasks. It is regarded as an important psychological and structural resource that influences employees’ technology use behaviors and performance outcomes (Xavier and Korunka, 2025 ). According to the TTS, when individuals encounter stressors, they first perceive their objective presence and intensity and subsequently engage in cognitive appraisal to evaluate their significance for personal well being (Ma et al., 2021 ). This appraisal process not only determines whether stressors are perceived as challenges or hindrances but also shapes individuals’ judgments of available resources, thereby influencing subsequent coping strategies and behavioral responses (Yu et al., 2018 ). Within this framework, autonomy can be viewed as a critical resource that enables employees to respond to technology related work demands in more flexible and proactive ways.。 With respect to techno-overload, although it reflects situations in which technology compels employees to work more and at a faster pace (Tarafdar et al., 2007 ; Ragu Nathan et al., 2008), under challenge appraisal conditions employees may view heightened technological demands as opportunities to demonstrate professional competence and optimize work processes (Sawhney et al., 2025 ). When organizations allow employees to adjust how they use technology and regulate their work pace, techno-overload may instead strengthen perceptions of IT autonomy, leading employees to feel that they can harness technology to support their work rather than being dominated by it. Accordingly, this study proposes the following hypothesis. H8: Techno-overload has a positive effect on IT autonomy. Techno-complexity represents the difficulties employees face in understanding and operating emerging technologies (Ragu Nathan et al., 2008; Yuan et al., 2023 ). Although techno-complexity is often regarded as a source of stress, the TTS emphasizes that its effects depend on individuals’ cognitive appraisals. When employees appraise complex technologies as challenges for learning and growth rather than as constraints on action, the process of overcoming techno-complexity may encourage more proactive exploration of technological functionalities and a stronger demand for flexibility and decision latitude in technology use (Brivio et al., 2018 ), thereby enhancing perceptions of IT autonomy. Based on this reasoning, the following hypothesis is proposed. H9: Techno-complexity has a positive effect on IT autonomy. In addition, autonomy is regarded as an important job design characteristic that promotes work motivation, satisfaction, and performance (de Vargas Pinto et al., 2023 ). In information technology contexts, when employees perceive higher levels of IT autonomy, they are more likely to use technology in flexible and innovative ways to support task completion, enhance the fit between technology and work processes, and thereby strengthen IT enabled productivity. Accordingly, this study proposes the following hypothesis. H10: IT autonomy has a positive effect on IT enabled productivity. Based on the TTS and the hypotheses H8 to H10, this study posits that techno-overload and techno-complexity influence IT enabled productivity through IT autonomy. When technostress creators are appraised as opportunities, employees’ sense of control and autonomy is enhanced, thereby promoting more effective use of information technology and higher productivity outcomes. Accordingly, the following mediating hypotheses are proposed. H11: IT autonomy mediates the positive relationship between techno-overload and IT enabled productivity. H12: IT autonomy mediates the positive relationship between techno-complexity and IT enabled productivity. 3.2 Research Methods and Data Analysis Procedures In Study 2, this research employs structural equation modeling to examine the mechanisms through which techno-overload and techno-complexity influence IT enabled productivity, incorporating IT self-efficacy and IT autonomy as key mediating variables. This approach clarifies how technostress jointly shapes employees’ IT enabled productivity through multiple pathways. 3.2.1 Sample and Procedure The sample for the current study included employees from financial institutions. Data were collected between March and June 2025. A total of 500 questionnaires were distributed, and 424 valid responses were obtained after excluding those with incomplete data or straight-line responses. Descriptive statistics were used to describe the sample's demographic characteristics. The majority of the sample participants were male (62.5%), while the remaining participants (37.5%) were female. Regarding age, the largest proportion of respondents was born between 1966 and 1980 (51.4%), followed by those born between 1981 and 1995 (29%). The largest group of respondents had attained either a college degree (44.6%) or a graduate degree or equivalent (25.5%). The majority of respondents worked in medium- to large-sized companies, with companies of more than 100 employees accounting for more than half of the sample. Regarding respondents’ jobs, the largest group held general staff positions, followed by management positions. 42.7% of the sample reported working for more than 21 years in their current job. 3.2.2 Instrument The measurement instruments for all constructs in this study were developed based on existing literature. The scales for techno-overload and techno-complexity follow the measurement approach used in Study 1 to ensure consistency in construct operationalization across the two research stages. IT autonomy and IT self-efficacy were each measured using three items adapted from the technology control belief scale proposed by Fishbein and Ajzen ( 2011 ), reflecting individuals’ perceived control and capability in technology use contexts. All scale items were measured using a six-point Likert scale ranging from 1 strongly disagree to 6 strongly agree. For the translation procedure, three scholars proficient in both Chinese and English were invited to conduct back translation and revisions to ensure semantic equivalence and content validity. 3.2.3 Data Analysis In this research study, SEM will be used for data analysis, with AMOS 24.0 as the analytical tool, to examine the relationships among the variables of interest. As a first step, a confirmatory factor analysis will be conducted to assess the reliability and validity of the measurement scales for each construct. Following the confirmatory factor analysis, structural equation modelling will be completed using path analysis to test all proposed direct effects of each hypothesis. The bootstrap method developed by Hayes ( 2018 ) will also be employed to test for mediating effects by performing 5,000 resamples to compute the overall significance of each indirect effect within the structural pathways. 3.2.4 Common Method Bias Data were collected using a questionnaire survey, and therefore common method bias may arise for various reasons, including consistency motives and social desirability (Podsakoff et al., 2003 ). Harman’s single factor test was conducted to assess common method bias. The results of the unrotated exploratory factor analysis indicate that eight factors have eigenvalues greater than 1, and the largest factor accounts for 29.731 percent of the total variance, which is below the 40 percent threshold. Accordingly, common method bias does not pose a serious concern in this study. 3.3. Results 3.3.1. Measurement Model Before conducting measurement model analysis, this study first examined the univariate normality of the sample data. The results indicate that the skewness values of all items range from − 0.546 to 0.538 and the kurtosis values range from − 0.986 to -0.143. The absolute values all meet the criteria recommended by Kline ( 2023 ), with skewness less than 2 and kurtosis less than 2, indicating that the data satisfy the assumption of univariate normality and are suitable for SEM analysis. Subsequently, confirmatory factor analysis was conducted to examine the convergent validity of the measurement model. According to the criteria suggested by Hair et al. ( 2017 ), convergent validity is established when standardized factor loadings exceed 0.6, composite reliability is greater than 0.7, and average variance extracted exceeds 0.5. As shown in Table 3 , except for item TOV1 in the techno-overload construct, which was removed due to a factor loading below 0.6, all remaining items meet the recommended thresholds for factor loadings, composite reliability, and average variance extracted. These results indicate that the latent variables in this study demonstrate satisfactory internal consistency and convergent validity. Table 3 Confirmatory Factor Analysis and Scale Reliability Items Unstd. S.E. t p Std. α CR AVE Techno-Overload (TOV) .850 .853 .539 TOV1 1 .554 TOV2 1.053 .088 11.985 < .001 .719 TOV3 1.111 .073 15.311 < .001 .824 TOV4 .949 .072 13.237 < .001 .699 TOV5 1.129 .077 14.674 < .001 .781 TOV6 .949 .079 11.985 < .001 .631 Techno-Complexity (TC) .900 .902 .651 TC1 1.000 .820 TC2 1.094 .049 22.157 < .001 .899 TC3 1.004 .050 20.011 < .001 .834 TC4 .793 .055 14.536 < .001 .657 TC5 .943 .050 18.994 < .001 .804 IT Self-Efficacy (CAP) .858 .861 .674 CAP1 1 < .001 .763 CAP2 1.132 .067 16.882 < .001 .874 CAP3 1.084 .066 16.508 < .001 .822 IT Autonomy (AUT) .865 .869 .689 AUT1 1 .750 AUT2 1.233 .071 17.371 < .001 .902 AUT3 1.160 .069 16.838 < .001 .831 IT-Enabled Productivity (PRO) .936 .934 .781 PRO1 1 .864 PRO2 1.072 .041 26.010 < .001 .908 PRO3 1.032 .041 25.065 < .001 .890 PRO4 1.065 .044 24.094 < .001 .872 Note: *Item TOV1 was deleted because its standardized factor loading was below 0.6; the values of α, CR, and AVE are the results calculated after the deletion. Next, this study examines the discriminant validity of the measurement model using the Fornell-Larcker criterion proposed by Hair et al. ( 2017 ). Discriminant validity is established when the square root of the AVE for each construct exceeds its Pearson correlations with other constructs. As shown in Table 4 , the square roots of AVE for all constructs, presented on the diagonal, range from 0.734 to 0.884 and are all greater than the corresponding inter construct correlation coefficients shown in the lower triangle. These results indicate that the measurement scales demonstrate satisfactory discriminant validity. In addition, following the recommendation of Kline ( 2023 ), constructs can be considered distinct when the absolute values of inter construct correlations are below 0.85. In this study, the correlation coefficients among constructs range from 0.181 to 0.419, all well below the suggested threshold, further supporting adequate discriminant validity among the constructs and indicating good construct validity of the measurement model. Table 4 Discriminant Validity Assessment Construct Mean Standard deviation AVE Discriminant validity TOV TC CAP AUT PRO Techno-Overload (TOV) 4.125 .927 .539 .734 Techno-Complexity (TC) 3.728 1.132 .651 .347 .807 IT Self-Efficacy (CAP) 4.430 .941 .674 .181 .276 .821 IT Autonomy (AUT) 4.149 1.061 .689 .220 .188 .368 .830 IT-Enabled Productivity (PRO) 4.446 .996 .781 .301 .277 .323 .419 .884 Note: AVE༚average variance extracted The bold values on the diagonal for discriminant validity represent the square roots of AVE, and the lower triangle represents Pearson correlations. 3.3.2. Structural Model The results of the model fit analysis indicate that the Bollen–Stine corrected chi square value is 199.199 with 161 degrees of freedom, yielding a normed chi square ratio of 1.237. This value falls within the recommended range of 1 to 3, indicating a good overall model fit. Other fit indices further support the adequacy of the model, with GFI = 0.963, AGFI = 0.947, RMSEA = 0.024, SRMR = 0.067, TLI (NNFI) = 0.991, CFI = 0.993, IFI = 0.993, Hoelter’s N (CN) = 343.192, Gamma hat = 0.996, and McDonald’s NCI = 0.956. All indices meet or exceed the recommended thresholds, demonstrating excellent model fit (West et al., 2012 ). As shown in Table 5 and Fig. 2 , techno-overload has a significant positive effect on IT enabled productivity (β = 0.194, p = 0.001), supporting Hypothesis H1. However, the direct effect of techno-complexity on IT enabled productivity is not significant (β = 0.096, p = 0.115), and thus Hypothesis H2 is not supported. The effect of techno-overload on IT self-efficacy is not significant (β = 0.108, p = 0.107), leading to the rejection of Hypothesis H3. In contrast, techno-complexity exhibits a significant positive effect on IT self-efficacy (β = 0.249, p = 0.001), supporting Hypothesis H4. In addition, IT self-efficacy has a significant positive effect on IT enabled productivity (β = 0.166, p = 0.003), supporting Hypothesis H5. The mediation analysis for IT self-efficacy indicates that IT self-efficacy does not mediate the relationship between techno-overload and IT enabled productivity (β = 0.018, p = 0.059), and therefore Hypothesis H6 is not supported. However, IT self-efficacy demonstrates a significant mediating effect in the relationship between techno-complexity and IT enabled productivity (β = 0.041, p = 0.002), supporting Hypothesis H7. On the other hand, both techno-overload (β = 0.209, p = 0.003) and techno-complexity (β = 0.130, p = 0.046) exert significant positive effects on IT autonomy, supporting Hypotheses H8 and H9, respectively. IT autonomy also has a significant positive effect on IT enabled productivity (β = 0.326, p < 0.001), supporting Hypothesis H10. Mediation analysis further shows that IT autonomy plays a significant mediating role in the relationships between techno-overload and IT enabled productivity (β = 0.068, p = 0.003) and between techno-complexity and IT enabled productivity (β = 0.042, p = 0.025), thereby supporting Hypotheses H11 and H12. Table 5 Path Coefficients and Significances. Hypothesis Std. Unstd. S.E. Bias-Corrected 95% CI p Lower Upper H1: Techno-Overload → IT-Enabled Productivity .194 .212 .060 .071 .357 .001 H2: Techno-Complexity→ IT-Enabled Productivity .096 .079 .043 − .019 .180 .115 H3: Techno-Overload → IT Self-Efficacy .108 .105 .058 − .026 .245 .107 H4: Techno-Complexity → IT Self-Efficacy .249 .183 .043 .080 .289 .001 H5: IT Self-Efficacy → IT-Enabled Productivity .166 .185 .057 .059 .319 .003 H6: Techno-Overload → IT Self-Efficacy → IT-Enabled Productivity .018 .020 .014 − .001 .057 .059 H7: Techno-Complexity → IT Self-Efficacy → IT-Enabled Productivity .041 .034 .016 .010 .076 .002 H8: Techno-Overload → IT Autonomy .209 .220 .064 .074 .377 .003 H9: Techno-Complexity → IT Autonomy .130 .102 .045 .002 .210 .046 H10: IT Autonomy → IT-Enabled Productivity .326 .340 .054 .209 .483 < .001 H11: Techno-Overload → IT Autonomy → IT-Enabled Productivity .068 .041 .022 .010 .100 .003 H12: Techno-Complexity → IT Autonomy → IT-Enabled Productivity .042 .019 .012 .002 .051 .025 Note: Bootstrapping = 5,000 3.4. Integration and Implications of Study 2 Findings The TTS posits that stress does not originate from situations themselves but rather from individuals’ subjective cognitive appraisals of stressors (Ma et al., 2021 ). When employees encounter digital work demands such as techno-overload and techno-complexity, they first evaluate whether these demands constitute challenges or hindrances and then conduct secondary appraisal based on available resources, which in turn shapes employee behavior (Ma et al., 2021 ; Yu et al., 2018 ). The findings of Study 2 show that techno-complexity indirectly enhances IT enabled productivity through increased IT self-efficacy and IT autonomy. This indicates that some employees appraise complex technological demands as challenge stressors with growth potential (LePine et al., 2016 ), thereby stimulating learning motivation and resource mobilization capabilities. These results are consistent with prior studies that view digital challenges as opportunities for development (Liu et al., 2024 ). In contrast, although techno-overload directly increases IT enabled productivity, it does not exert an indirect effect through self-efficacy. This suggests that high workload conditions may simultaneously embody both opportunities and burdens, aligning with the perspective that challenge stressors are not entirely benign (Bermes, 2021 ). Moreover, autonomy plays a stable mediating role across both types of technostress creators, indicating that when employees perceive greater control over actions and decision making, they are better able to transform technostress into positive performance related experiences (Lartey, 2021). 4. Discussion 4.1 Theoretical contributions This study makes three theoretical contributions to the literature on technostress and IT enabled productivity. First, Study 1 identifies key configurational conditions through which technostress creators give rise to IT enabled productivity by applying fsQCA, thereby addressing the limitations of prior research that has predominantly explained technostress effects using single variables and linear relationships. Compared with approaches that incorporate excessive conditions and risk model complexity and estimation bias (Nimako and Ntim, 2013 ), the use of necessity and sufficiency analyses provides a more parsimonious and explanatory theoretical structure. This approach helps reduce bias arising from overly simplified or overly complex models and deepens the theoretical positioning of technostress creators in IT enabled productivity research. Second, this study explains why technostress can generate positive performance outcomes from the perspective of employees’ subjective cognitive appraisals of stress. According to the Transactional Theory of Stress, the effects of stress are not determined by stressors themselves but depend on individuals’ cognitive appraisals of stressors and assessments of available resources (Ma et al., 2021 ). Study 2 shows that techno-complexity indirectly enhances IT enabled productivity through IT self-efficacy and IT autonomy, whereas techno-overload follows a different pathway, indicating asymmetry in the effects of technostress. These findings suggest that when employees appraise technological demands as challenges with growth potential rather than as hindrances, technostress can be transformed into a positive force that promotes IT enabled empowerment and performance, thereby addressing gaps in the technostress literature regarding mechanisms underlying positive outcomes. Third, this study theoretically emphasizes the complementary relationship between the CHSF and TTS rather than treating them as redundant explanatory frameworks. The CHSF primarily indicates that the same stressor may be appraised as a challenge or a hindrance, leading to different motivational and performance consequences (Bermes, 2021 ; Zhang et al., 2014), but provides relatively limited explanation of how such appraisals are formed and how stress is translated into concrete performance outcomes. In contrast, the TTS offers a comprehensive psychological process explaining how individuals conduct primary and secondary appraisals to evaluate the meaning of stressors and available resources, which subsequently shape behavior (Ma et al., 2021 ). By integrating both theories, this study embeds the challenge or hindrance nature highlighted by the CHSF into the cognitive appraisal and resource mobilization processes articulated by the TTS, thereby explaining how technostress is transformed into IT enabled productivity under specific psychological conditions. The findings further indicate that IT self-efficacy and IT autonomy, as key psychological and job related resources, constitute the core mechanisms linking challenge appraisals to positive performance outcomes. In doing so, this study fills a theoretical gap in the CHSF regarding how stress becomes a source of motivational drive and underscores the critical complementary role of TTS in technostress research. 4.2 Managerial implications This study offers important implications for human resource management and technology implementation in highly digitalized financial institutions. First, the findings indicate that techno-complexity does not inherently suppress performance. Its positive effect on IT enabled productivity is realized through IT self-efficacy, highlighting the critical role of employees’ beliefs in their technological capabilities during digital transformation. Accordingly, financial institutions should not focus solely on technology adoption or demand rapid adaptation but should instead systematically cultivate employee self-efficacy. Through structured technological training and technical support, organizations can enhance employees’ sense of control over technology and strengthen its empowering effects. In addition, this study finds that IT autonomy plays a stable and significant mediating role in the relationships between techno-overload and IT enabled productivity as well as between techno-complexity and IT enabled productivity. These results suggest that autonomy is a key factor in transforming technostress into performance enhancing outcomes. Financial institutions should therefore preserve flexibility in employees’ technology use, such as allowing discretion in the selection of supportive tools, to increase perceptions of control and responsibility. In doing so, technology can be transformed from a source of pressure into an empowering resource that supports performance. 4.3 Research Limitations and Future Research Directions The current investigation provides empirical evidence for the relationships among technostress, psychological resources, and IT-enabled productivity; however, it has several limitations. The use of cross-sectional survey data restricts the researcher’s ability to make causal inferences as they can only examine relationships between variables; therefore, future studies could use longitudinal or panel designs to assess how technostress and psychological resources change over time. Also, the sample included only employees in the financial sector; therefore, while this restricts confounding variables associated with the nature of each industry, it does not allow for generalizing the findings to other industries. To establish the broader applicability of the proposed model, future studies should include participants from multiple industries. A third limitation of this research is that the sample is composed entirely of participants in the financial sector; therefore, future research could include participants from other industries to determine whether the components of technostress creators that lead to IT-enabled productivity differ in those contexts. Finally, data collection was conducted using self-reported questionnaires, which could introduce common-method bias. Future studies should include objective performance measures and/or multiple data sources to improve the reliability and external validity of the findings. 5. Conclusion This study integrates fsQCA and SEM to examine the effects of technostress on IT enabled productivity, revealing its nonlinear and asymmetric mechanisms. The fsQCA results of Study 1 indicate that high IT enabled productivity does not arise from a single form of technostress but rather from combinations of multiple technostress conditions, with techno-overload and techno-complexity playing particularly critical roles. The SEM results of Study 2 show that techno-overload directly enhances IT enabled productivity, suggesting that high technological demands in financial contexts may be appraised as performance-oriented challenges. In contrast, the positive effects of techno-complexity emerge only through the mediation of psychological resources such as IT self-efficacy and IT autonomy. Among these, IT autonomy demonstrates a stable mediating role across different technostress creators, indicating that perceived control and decision flexibility are key factors in transforming technostress into productivity. Accordingly, technostress in highly digitalized financial environments is not inherently negative, and its effects depend on the type of stressor and how employees cognitively appraise technological demands. Declarations Author Contribution C.P.W. wrote the main manuscript text, prepared figures 1-2 and data collection. C.S.A.Y. responsible for data analysisAll authors reviewed the manuscript. Data availability: All the data are included in the manuscript. Competing interests: The author(s) declare no competing interests. Ethical statements: This research was approved and cleared by the Quantitative Analysis and Research Association (No. 1130901001). Informed consent statement: This study employed non-interventional methods. All participants were fully informed of the study purpose, the use of their data for academic research, and the assurance of anonymity. Participation involved no foreseeable risks to the participants. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8759770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583978496,"identity":"7fe136e5-fa7d-4d1b-8d9e-d7bc35c4a4b5","order_by":0,"name":"Chung-Pin Wu","email":"","orcid":"","institution":"National Yunlin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chung-Pin","middleName":"","lastName":"Wu","suffix":""},{"id":583978497,"identity":"5ecd8356-8ba9-4c5b-b6a5-74f32a880862","order_by":1,"name":"Chor-Sum Au-Yeung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFADZsaGAxIVIAZzA5Fa2JsbD1icgeglUgvP8eYDlW0gFgEt/P2Hn0nzVBzOk49IbDhwc15tNH87UMuPim04tUjcSDOT5jlzuNjwRmLDwZnbjufOOMzYwNhz5jZOLQYSDGbSvG2HEzfOSGw4LLntWG4DUAszYxseLfzHv0nz/oNq+TvnWO58gloYcoC2NBxOnM9zsOGAZENN7gZCWiRu5BRbzjmWnriBvREYL8cO5G4EajmIzy/8/cc33nhTY504v5n98QeJmrrceecPH3zwowK3FhBg4gG58ACYfRhMHsCrHggYfwAJ+QYwu46Q4lEwCkbBKBiBAABsjWWE05pdCgAAAABJRU5ErkJggg==","orcid":"","institution":"I-Shou University","correspondingAuthor":true,"prefix":"","firstName":"Chor-Sum","middleName":"","lastName":"Au-Yeung","suffix":""}],"badges":[],"createdAt":"2026-02-02 02:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8759770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8759770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101749890,"identity":"15a77bf5-cf8e-48c9-9648-80d24c4070dd","added_by":"auto","created_at":"2026-02-03 09:59:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66188,"visible":true,"origin":"","legend":"\u003cp\u003eStudy 1 Framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8759770/v1/2998fd02874795c850c1401e.png"},{"id":101749889,"identity":"fca74dd7-a029-4185-ad3d-7e812fd170f3","added_by":"auto","created_at":"2026-02-03 09:59:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24882,"visible":true,"origin":"","legend":"\u003cp\u003eStudy 2 Framework\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8759770/v1/94cabeb5037b7a7d9f732e41.png"},{"id":101749887,"identity":"53180e7d-becb-4f3e-b11e-1695e34639c2","added_by":"auto","created_at":"2026-02-03 09:59:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24805,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Theoretical Framework. Note. The values are standardized regression coefficients. Dotted lines indicate insignificance.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8759770/v1/40c361a044a4ed6642b5939a.png"},{"id":101754424,"identity":"3c3082fa-3290-46b2-bc9b-33d859b06bf4","added_by":"auto","created_at":"2026-02-03 10:42:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1399377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8759770/v1/369f1333-6f64-4cbe-a47b-0890ec6886a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Technological Pressure Is No Longer Merely a Burden: How Technological Overload and Technological Complexity Empower Employee Productivity","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe concept of stress was initially used to describe a set of physiological and psychological responses that individuals experience when facing adverse external environments or stimuli, with its core lying in the interaction between external demands and individual resources (Ghasemi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the extensive penetration of information technology into work contexts, sources of stress have gradually extended from traditional job demands to technology use itself. Accordingly, the concept of technostress creators has been proposed, referring to stress experiences induced by technological characteristics, modes of technology use, and technological environments (Nastjuk et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These stressors include techno overload, techno invasion, techno complexity, techno insecurity, and techno uncertainty (Tarafdar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Wang and Zhao (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conceptualize technostress as a key component of the dark side of technology, arguing that it leads to outcomes such as exhaustion, job dissatisfaction, and performance decline. Unfortunately, this stream of research has largely overlooked the possibility that technostress can also facilitate employees’ work processes and serve as a tool that helps stimulate employees’ work capabilities (Huang and Gursoy, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This perspective is supported by a growing body of research. Gerdiken et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), through a meta-analysis, demonstrate a significant positive relationship between technostress creators and work engagement, suggesting that technostress does not necessarily result in negative outcomes. Furthermore, studies by Li and Wang (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Tarafdar et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicate that technostress may be accompanied by positive outcomes such as efficiency improvement and work-related innovation. Based on these recent studies, this research seeks to challenge the traditional assumption that stress inevitably produces only negative effects.\u003c/p\u003e \u003cp\u003eTo explain these divergences in prior findings, this study adopts the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress as its theoretical foundations to reexamine the effects of technostress on employees. Challenge stressors refer to stress inducing demands that, although eliciting stress responses, are simultaneously perceived by individuals as motivating forces that facilitate growth, learning, and achievement at work (González Morales and Neves, 2015). When situations are appraised as opportunities for control, gain, or personal development, stress may be transformed into positive energy rather than being perceived solely as a threat or loss (Jex, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In prior technostress research, technostress has commonly been treated as a second order construct with its effects examined at an aggregate level, or scholars have focused only on isolated technostress creators, resulting in both experiential and nominal complexity (Brooks et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Califf et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chandra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, such approaches may lead to information loss or the inclusion of less salient technostress factors, thereby biasing the validity or parsimony of the resulting models (Nimako and Ntim, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address the potential limitations of prior research approaches, this study treats technostress creators as first order conditions in Study 1 and employs fuzzy set qualitative comparative analysis to emphasize how different technostress creators form multiple configurations and how these configurations lead to IT enabled productivity, thereby capturing the characteristics of equifinality and causal complexity. After identifying configurational patterns of technostress creators in Study 1, Study 2 shifts its focus to explaining why these technostress creators are able to generate IT enabled productivity, with particular attention to the role of cognition. By integrating the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress, this study responds to the misconception in prior research that technostress is inherently negative and empirically examines the applicability of the Challenge Hindrance Stressor Framework in technological contexts. Accordingly, the research objectives of this study are as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify configurations of technostress creators that lead to IT enabled productivity\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine the mechanisms through which technostress creators enhance IT enabled productivity\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo integrate the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress to strengthen theoretical extension\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e\u003c/ol\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"2. Study 1","content":"\u003ch2\u003e2.1 Technostress Creators and IT Enabled Productivity\u003c/h2\u003e\u003cp\u003eTechnostress is a specific form of stress that originates from the context of information technology use. Technostress refers to the stress experience that arises when individuals are unable to effectively cope with the demands of computer and information technology use within organizations (Nastjuk et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This stress does not stem solely from technology itself but from the combined load created by technology and job demands (Srivastava et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In highly digitalized organizational environments, technostress has become a critical issue shaping individuals’ work experiences and performance, and its mechanisms and outcomes are no longer limited to negative effects but instead exhibit a more complex pattern.\u003c/p\u003e\u003cp\u003eWithin technostress research, the five technostress creator categories proposed by Tarafdar et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) provide an essential foundation for understanding the structure of technostress. Techno-overload refers to a state in which individuals are compelled by technology to work faster and handle more tasks with greater intensity, although large volumes of information can also expand possibilities for decision making and action (Ragu Nathan et al., 2008). Techno-invasion reflects the intrusion of technology into personal time and space, thereby blurring the boundaries between work and private life (Nastjuk et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tarafdar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Techno-complexity refers to the difficulties individuals face when understanding and operating emerging or highly complex technologies, whereby higher usage thresholds may lead to feelings of insufficient capability (Ragu Nathan et al., 2008; Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Techno-insecurity involves concerns about future employment, as individuals may continuously perceive threats of replacement due to technological innovation and believe that constant upgrading of technological skills is necessary to maintain competitiveness. Such conditions may even inhibit knowledge sharing behaviors (Tarafdar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Techno-uncertainty arises in environments characterized by continuous technological change and updates, in which users find it difficult to establish stable technological experience bases and remain in prolonged states of adaptation and relearning (Ragu Nathan et al., 2008; Tarafdar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared with viewing technostress solely as a source of exhaustion and burden, information systems research has increasingly emphasized the potential positive implications of technostress under specific conditions. IT enabled productivity is a key concept that captures this positive outcome and is defined by Su et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as the extent to which information technology enhances employee productivity. This implies that technology is not merely a tool but can exert empowering effects on individual performance by supporting information processing and strengthening work capabilities. Accordingly, technostress creators do not necessarily equate to productivity decline. Instead, their impact depends critically on how individuals appraise and respond to technology related demands.\u003c/p\u003e\u003cp\u003eFrom the perspective of challenge stressors, technostress creators may be perceived as challenges that promote growth and capability demonstration rather than as pure hindrances in specific contexts (González Morales and Neves, 2015). Although techno-overload increases work intensity, it may simultaneously prompt individuals to improve information processing efficiency and task prioritization capabilities (Hurbean et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While techno-invasion blurs work boundaries, it also provides greater work flexibility and real time responsiveness, making technology an important resource that supports task completion (Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Techno-complexity requires individuals to invest in learning and adaptation, and when this process is appraised as an opportunity for capability development, it may enhance perceptions of technological mastery and strengthen technology enabled empowerment effects (Ciarli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Techno-insecurity may be transformed into motivation for proactive learning and skill upgrading rather than passive defensive stress responses (Malik et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Techno-uncertainty may encourage individuals to maintain heightened vigilance and learning orientation to adapt to rapidly changing technological environments (Chakraborty et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, within the theoretical framework of challenge stressors, the relationship between technostress creators and IT enabled productivity is not unidirectionally negative but instead entails conditional and contextual positive potential that warrants attention and examination at both structural and psychological levels. Based on these considerations, this study conducts fsQCA analysis as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003e2.2 Research Design and Method\u003c/h2\u003e\u003cp\u003eTo examine the multiple configurations of different technostress creators and the diverse pathways leading to a common outcome (Xia et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), this study adopts fsQCA to investigate how these technostress creators jointly shape employees’ IT enabled productivity.\u003c/p\u003e\u003ch2\u003e2.2.1 Measurement development\u003c/h2\u003e\u003cp\u003eThe measurement scales for the constructs in this study were developed based on existing literature. The five dimensions of technostress were measured using the scale adapted from Chen (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Specifically, techno-overload was measured with six items, techno-invasion with three items, techno-complexity with five items, techno-insecurity with five items, and techno-uncertainty with four items. IT enabled productivity was measured using the four-item scale developed by Tarafdar et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For the translation of the scales, three scholars proficient in both Chinese and English were invited to conduct revisions using the back translation method. All items were measured using a six-point Likert scale ranging from 1 strongly disagree to 6 strongly agree.\u003c/p\u003e\u003ch2\u003e2.2.2 Sample\u003c/h2\u003e\u003cp\u003eThis study focuses on employees working in the financial industry as the research sample. As the financial sector increasingly relies on technology for managing customer data, administrative operations, and related tasks, employees are required to confront technological advancements in order to enhance their performance. Accordingly, Study 1 collected data from 250 financial industry employees between January and February 2025. After excluding responses with missing values and straight-line answering patterns, a total of 206 valid samples were retained for Study 1.\u003c/p\u003e\u003ch2\u003e2.2.3 Calibration\u003c/h2\u003e\u003cp\u003eConsistent with prior literature, the calibration of condition attributes and outcome attributes represents the first step in the fsQCA procedure (Fiss, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ragin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Calibration involves assigning membership scores to raw variables in order to transform them into fuzzy sets. Based on Boolean logic, raw data must be converted into a set ranging from 0 to 1. After calibration, attributes are classified as full membership, intersection, or full non membership according to specified thresholds. Full membership, intersection, and full non membership refer to different degrees of a case’s membership within a particular set or condition. Following Greckhamer and Gur (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the direct calibration method was applied, with the 90th and 10th percentiles of each attribute used as the thresholds for full membership and full non membership, respectively, and the median selected as the crossover point for intersection. In addition, a constant of 0.001 was added to scores equal to 0.5 to avoid ambiguous cases (Fiss, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ragin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003e2.2.4 Analysis of necessity\u003c/h2\u003e\u003cp\u003eThe analysis of necessity aims to examine whether any necessary conditions exist for the outcome. The fsQCA approach employs consistency and coverage to assess reliability (Greckhamer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In necessity analysis, high consistency indicates that a fuzzy set accurately represents the data, whereas high coverage indicates that the fuzzy set sufficiently covers the relevant elements within the data space (Ragin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consistent with prior research (Furnari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), a consistency threshold of 0.9 is applied for identifying necessary conditions. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the necessity of single conditions for high IT enabled productivity and the absence of IT enabled productivity. As none of the conditions reach the consistency threshold of 0.90, it is concluded that no single condition constitutes a necessary condition for either high IT enabled productivity or the absence of IT enabled productivity.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eTest of necessity for single conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh IT-enabled Productivity\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAbsence of IT-enabled Productivity\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Overload\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-high Techno-Overload\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Invasion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-high Techno-Invasion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Complexity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-high Techno-Complexity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Insecurity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-high Techno-Insecurity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Uncertainty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot-high Techno-Uncertainty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.2.5 Analysis of sufficiency\u003c/h2\u003e\u003cp\u003eThis study analyzes sufficient conditions based on the truth table algorithm. Following the guidelines of Fiss (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), the frequency threshold is set at 3 and the consistency threshold is set at 0.8. In addition, the proportional reduction in inconsistency is applied with a threshold of 0.7 to ensure that configurations do not simultaneously appear in both the presence and absence of the outcome (Pappas and Woodside, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The analysis yields seven distinct configurations. In line with prior conventions, the intermediate solution is reported, representing an optimal balance between the complex and parsimonious solutions (Fiss, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Using standard notation, the presence of a condition is indicated by ●, whereas the absence of a condition is indicated by ⊕.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eConfigurations of IT-enabled Productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfig 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfig 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfig 3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConfig 4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfig 5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConfig 6\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConfig 7\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Overload\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Invasion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Complexity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Insecurity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Uncertainty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⊕\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw coverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall solution consistency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall solution coverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.4 Integration and Implications of Study 1 Findings\u003c/h2\u003e\u003cp\u003eThe fsQCA results of Study 1 deepen theoretical understanding of the effects of technostress on IT enabled productivity and facilitate integration with the Challenge Hindrance Stressor Framework. The findings of Study 1 indicate that high IT enabled productivity does not arise from the comprehensive presence of all technostress creators but rather from the joint effects of specific technostress configurations, particularly techno-overload and techno-complexity. These stressors are most likely to be appraised by individuals as controllable and developmentally meaningful challenge stressors, thereby being transformed into positive forces that promote IT enabled empowerment and work effectiveness. This finding addresses prior research limitations that either oversimplified technostress creators or treated them with excessive complexity. Accordingly, building on the fsQCA results of Study 1, Study 2 further investigates the mechanisms through which techno-overload and techno-complexity contribute to IT enabled productivity.\u003c/p\u003e"},{"header":"3. Study 2","content":"\u003cp\u003eBuilding on the findings of Study 1, this study identifies techno-overload and techno-complexity as presence conditions for IT enabled productivity, and therefore Study 2 conducts an in-depth examination of the mechanisms underlying their effects. According to Yuan et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), stress does not arise directly from stressors themselves but rather depends on individuals\u0026rsquo; cognitive appraisals. Accordingly, Study 2 focuses on self-efficacy and autonomy to explain how technostress is transformed into IT enabled productivity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Framework and Hypothesis Development\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 The Effects of Techno-Overload and Techno-Complexity on IT Enabled Productivity\u003c/h2\u003e \u003cp\u003eOngoing digital transformation has led to the introduction of information technology across diverse work contexts. Whether these transformations function as forces that enhance or inhibit employee performance does not depend solely on the objective characteristics of technology itself but is more deeply rooted in individuals\u0026rsquo; subjective cognitive appraisal of stressors. To understand how technostress influences employees\u0026rsquo; IT enabled productivity, this study adopts the TTS as its core theoretical foundation to explain how technostress creators affect employee performance through cognitive appraisal processes.\u003c/p\u003e \u003cp\u003eThe TTS posits that stress does not arise directly from environmental stimuli but instead originates from individuals\u0026rsquo; subjective evaluations of the meaning of stressors within specific contexts (Lazarus and Folkman, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). When individuals encounter job demands, they first engage in primary appraisal to determine whether the demand is relevant to their well-being and to categorize it as a potential challenge or hindrance. When a demand is perceived as an opportunity for learning, growth, or achievement, a challenge appraisal is formed. In contrast, when it is perceived as obstructing goal attainment or causing loss, a hindrance appraisal emerges (LePine et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These appraisal outcomes subsequently influence judgments regarding available resources and shape coping strategies and behavioral responses.\u003c/p\u003e \u003cp\u003eThe TTS is particularly suitable for explaining stress experiences in technology and information systems contexts (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In digitalized work environments, employees face stressors that extend beyond increased workload to include requirements for learning and adapting to new technologies. When such technostress creators are appraised as challenges, they may stimulate motivation and engagement, thereby enhancing job performance. Conversely, when they are appraised as hindrances, they may undermine work effectiveness (Bermes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFindings from Study 1 indicate that techno-overload and techno-complexity are the key dimensions most directly influencing employees\u0026rsquo; work processes. Although information systems can provide large volumes of information, they may also generate information overload, requiring employees to invest greater cognitive resources in filtering and integrating information in order to extract relevant content (Savolainen, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to the TTS, when employees appraise such heightened technological demands as contributing to improved work efficiency and professional capability, techno-overload may be perceived as a challenge stressor that encourages more active use of information technology to accomplish tasks, thereby enhancing IT enabled productivity.\u003c/p\u003e \u003cp\u003eWhen technological tools are highly functional but characterized by high usage thresholds, employees may experience frustration, anxiety, and even feelings of incompetence (Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the Transactional Theory of Stress emphasizes that such negative experiences are not inevitable. When employees appraise techno-complexity as an opportunity for capability development and skill accumulation, they may engage in proactive learning and deepen technological competencies, thereby strengthening the empowering effects of information technology on work productivity (LePine et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accordingly, employees who appraise digital challenges as challenge stressors tend to be more supportive of digital transformation and to exhibit higher levels of engagement and performance (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the TTS, the following hypotheses are proposed.\u003c/p\u003e \u003cp\u003eH1: Techno-overload has a positive effect on employees\u0026rsquo; IT enabled productivity.\u003c/p\u003e \u003cp\u003eH2: Techno-complexity has a positive effect on employees\u0026rsquo; IT enabled productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 The Mediating Role of IT Self Efficacy\u003c/h2\u003e \u003cp\u003eSelf-efficacy is widely regarded as a critical psychological resource that facilitates the attainment of specific task goals (Bandura, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), buffers the negative effects of stress, promotes individuals\u0026rsquo; adaptation to organizational change, and serves as a key factor influencing a variety of stress related outcomes (Saidy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of information technology transformation, IT self-efficacy reflects employees\u0026rsquo; beliefs in their capabilities to learn, operate, and utilize technological tools to accomplish work tasks. The TTS posits that when individuals encounter stressors, they first perceive their presence and intensity and subsequently engage in cognitive appraisal to determine the significance of the stressors for their personal well-being (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This appraisal process can also be viewed as a core internal resource through which stress is transformed into motivational energy (Yu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, when employees appraise technology related demands as challenges, they perceive these demands as facilitating personal growth (LePine et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thereby enhancing IT self-efficacy.\u003c/p\u003e \u003cp\u003eWith respect to techno-overload, although it represents a condition in which technology compels employees to work more and at a faster pace (Tarafdar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), under a challenge appraisal employees may view high volumes of information and intensified work demands as opportunities to hone technological capabilities and improve efficiency. In such cases, successful experiences in coping with techno-overload may instead strengthen employees\u0026rsquo; confidence in their technological abilities and increase IT self-efficacy. Based on this reasoning, the following hypothesis is proposed.\u003c/p\u003e \u003cp\u003eH3: Techno-overload has a positive effect on IT self-efficacy.\u003c/p\u003e \u003cp\u003eSimilarly, although techno-complexity may elicit frustration and anxiety (Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the TTS emphasizes that when employees appraise complex technologies as challenges for learning and professional development, the process of overcoming techno-complexity itself becomes an important source of self-efficacy (LePine et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accordingly, this study posits that under challenge appraisal conditions, techno-complexity may also strengthen employees\u0026rsquo; IT self-efficacy. Based on this reasoning, the following hypothesis is proposed.\u003c/p\u003e \u003cp\u003eH4: Techno-complexity has a positive effect on IT self-efficacy.\u003c/p\u003e \u003cp\u003eIn addition, employees with higher IT self-efficacy are more likely to adopt problem focused coping strategies, proactively explore technological functionalities, and effectively integrate technology to support work processes (Yazdanmehr et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby enhancing IT enabled productivity. When employees believe that they can effectively utilize technology to accomplish tasks, technology is more likely to function as an empowering tool rather than a source of stress. Based on this reasoning, the following hypothesis is proposed.\u003c/p\u003e \u003cp\u003eH5: IT self-efficacy has a positive effect on IT enabled productivity.\u003c/p\u003e \u003cp\u003eBased on the TTS and the hypotheses H3 to H5, this study posits that when stressors are appraised as opportunities that facilitate growth and performance, IT self-efficacy is enhanced and can be translated into higher levels of IT enabled productivity. Accordingly, the following mediating hypothesis is proposed.\u003c/p\u003e \u003cp\u003eH6: IT self-efficacy mediates the positive relationship between techno-overload and IT enabled productivity.\u003c/p\u003e \u003cp\u003eH7: IT self-efficacy mediates the positive relationship between techno-complexity and IT enabled productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 The Mediating Role of IT Autonomy\u003c/h2\u003e \u003cp\u003eAutonomy refers to the extent to which individuals are able to organize their work, determine execution procedures, and retain control over how their work is carried out (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In technological contexts, IT autonomy can be understood as the degree of control and discretion employees perceive over the selection of technological tools, modes of use, and adjustments to work processes when using information technology to accomplish tasks. It is regarded as an important psychological and structural resource that influences employees\u0026rsquo; technology use behaviors and performance outcomes (Xavier and Korunka, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the TTS, when individuals encounter stressors, they first perceive their objective presence and intensity and subsequently engage in cognitive appraisal to evaluate their significance for personal well being (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This appraisal process not only determines whether stressors are perceived as challenges or hindrances but also shapes individuals\u0026rsquo; judgments of available resources, thereby influencing subsequent coping strategies and behavioral responses (Yu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Within this framework, autonomy can be viewed as a critical resource that enables employees to respond to technology related work demands in more flexible and proactive ways.。\u003c/p\u003e \u003cp\u003eWith respect to techno-overload, although it reflects situations in which technology compels employees to work more and at a faster pace (Tarafdar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ragu Nathan et al., 2008), under challenge appraisal conditions employees may view heightened technological demands as opportunities to demonstrate professional competence and optimize work processes (Sawhney et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When organizations allow employees to adjust how they use technology and regulate their work pace, techno-overload may instead strengthen perceptions of IT autonomy, leading employees to feel that they can harness technology to support their work rather than being dominated by it. Accordingly, this study proposes the following hypothesis.\u003c/p\u003e \u003cp\u003eH8: Techno-overload has a positive effect on IT autonomy.\u003c/p\u003e \u003cp\u003eTechno-complexity represents the difficulties employees face in understanding and operating emerging technologies (Ragu Nathan et al., 2008; Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although techno-complexity is often regarded as a source of stress, the TTS emphasizes that its effects depend on individuals\u0026rsquo; cognitive appraisals. When employees appraise complex technologies as challenges for learning and growth rather than as constraints on action, the process of overcoming techno-complexity may encourage more proactive exploration of technological functionalities and a stronger demand for flexibility and decision latitude in technology use (Brivio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), thereby enhancing perceptions of IT autonomy. Based on this reasoning, the following hypothesis is proposed.\u003c/p\u003e \u003cp\u003eH9: Techno-complexity has a positive effect on IT autonomy.\u003c/p\u003e \u003cp\u003eIn addition, autonomy is regarded as an important job design characteristic that promotes work motivation, satisfaction, and performance (de Vargas Pinto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In information technology contexts, when employees perceive higher levels of IT autonomy, they are more likely to use technology in flexible and innovative ways to support task completion, enhance the fit between technology and work processes, and thereby strengthen IT enabled productivity. Accordingly, this study proposes the following hypothesis.\u003c/p\u003e \u003cp\u003eH10: IT autonomy has a positive effect on IT enabled productivity.\u003c/p\u003e \u003cp\u003eBased on the TTS and the hypotheses H8 to H10, this study posits that techno-overload and techno-complexity influence IT enabled productivity through IT autonomy. When technostress creators are appraised as opportunities, employees\u0026rsquo; sense of control and autonomy is enhanced, thereby promoting more effective use of information technology and higher productivity outcomes. Accordingly, the following mediating hypotheses are proposed.\u003c/p\u003e \u003cp\u003eH11: IT autonomy mediates the positive relationship between techno-overload and IT enabled productivity.\u003c/p\u003e \u003cp\u003eH12: IT autonomy mediates the positive relationship between techno-complexity and IT enabled productivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Methods and Data Analysis Procedures\u003c/h2\u003e \u003cp\u003eIn Study 2, this research employs structural equation modeling to examine the mechanisms through which techno-overload and techno-complexity influence IT enabled productivity, incorporating IT self-efficacy and IT autonomy as key mediating variables. This approach clarifies how technostress jointly shapes employees\u0026rsquo; IT enabled productivity through multiple pathways.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Sample and Procedure\u003c/h2\u003e \u003cp\u003eThe sample for the current study included employees from financial institutions. Data were collected between March and June 2025. A total of 500 questionnaires were distributed, and 424 valid responses were obtained after excluding those with incomplete data or straight-line responses. Descriptive statistics were used to describe the sample's demographic characteristics. The majority of the sample participants were male (62.5%), while the remaining participants (37.5%) were female. Regarding age, the largest proportion of respondents was born between 1966 and 1980 (51.4%), followed by those born between 1981 and 1995 (29%). The largest group of respondents had attained either a college degree (44.6%) or a graduate degree or equivalent (25.5%). The majority of respondents worked in medium- to large-sized companies, with companies of more than 100 employees accounting for more than half of the sample. Regarding respondents\u0026rsquo; jobs, the largest group held general staff positions, followed by management positions. 42.7% of the sample reported working for more than 21 years in their current job.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Instrument\u003c/h2\u003e \u003cp\u003eThe measurement instruments for all constructs in this study were developed based on existing literature. The scales for techno-overload and techno-complexity follow the measurement approach used in Study 1 to ensure consistency in construct operationalization across the two research stages. IT autonomy and IT self-efficacy were each measured using three items adapted from the technology control belief scale proposed by Fishbein and Ajzen (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), reflecting individuals\u0026rsquo; perceived control and capability in technology use contexts. All scale items were measured using a six-point Likert scale ranging from 1 strongly disagree to 6 strongly agree. For the translation procedure, three scholars proficient in both Chinese and English were invited to conduct back translation and revisions to ensure semantic equivalence and content validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eIn this research study, SEM will be used for data analysis, with AMOS 24.0 as the analytical tool, to examine the relationships among the variables of interest. As a first step, a confirmatory factor analysis will be conducted to assess the reliability and validity of the measurement scales for each construct. Following the confirmatory factor analysis, structural equation modelling will be completed using path analysis to test all proposed direct effects of each hypothesis. The bootstrap method developed by Hayes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) will also be employed to test for mediating effects by performing 5,000 resamples to compute the overall significance of each indirect effect within the structural pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Common Method Bias\u003c/h2\u003e \u003cp\u003eData were collected using a questionnaire survey, and therefore common method bias may arise for various reasons, including consistency motives and social desirability (Podsakoff et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Harman\u0026rsquo;s single factor test was conducted to assess common method bias. The results of the unrotated exploratory factor analysis indicate that eight factors have eigenvalues greater than 1, and the largest factor accounts for 29.731 percent of the total variance, which is below the 40 percent threshold. Accordingly, common method bias does not pose a serious concern in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Results\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Measurement Model\u003c/h2\u003e \u003cp\u003eBefore conducting measurement model analysis, this study first examined the univariate normality of the sample data. The results indicate that the skewness values of all items range from \u0026minus;\u0026thinsp;0.546 to 0.538 and the kurtosis values range from \u0026minus;\u0026thinsp;0.986 to -0.143. The absolute values all meet the criteria recommended by Kline (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with skewness less than 2 and kurtosis less than 2, indicating that the data satisfy the assumption of univariate normality and are suitable for SEM analysis. Subsequently, confirmatory factor analysis was conducted to examine the convergent validity of the measurement model. According to the criteria suggested by Hair et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), convergent validity is established when standardized factor loadings exceed 0.6, composite reliability is greater than 0.7, and average variance extracted exceeds 0.5. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, except for item TOV1 in the techno-overload construct, which was removed due to a factor loading below 0.6, all remaining items meet the recommended thresholds for factor loadings, composite reliability, and average variance extracted. These results indicate that the latent variables in this study demonstrate satisfactory internal consistency and convergent validity.\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\u003eConfirmatory Factor Analysis and Scale Reliability\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTechno-Overload (TOV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.554\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.719\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.824\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.699\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.781\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOV6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.631\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTechno-Complexity (TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.820\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.899\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.834\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.657\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.804\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIT Self-Efficacy (CAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.763\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.874\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.822\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIT Autonomy (AUT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.750\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.902\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.831\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIT-Enabled Productivity (PRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.864\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.908\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.890\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.872\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: *Item TOV1 was deleted because its standardized factor loading was below 0.6; the values of α, CR, and AVE are the results calculated after the deletion.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, this study examines the discriminant validity of the measurement model using the Fornell-Larcker criterion proposed by Hair et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Discriminant validity is established when the square root of the AVE for each construct exceeds its Pearson correlations with other constructs. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the square roots of AVE for all constructs, presented on the diagonal, range from 0.734 to 0.884 and are all greater than the corresponding inter construct correlation coefficients shown in the lower triangle. These results indicate that the measurement scales demonstrate satisfactory discriminant validity. In addition, following the recommendation of Kline (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), constructs can be considered distinct when the absolute values of inter construct correlations are below 0.85. In this study, the correlation coefficients among constructs range from 0.181 to 0.419, all well below the suggested threshold, further supporting adequate discriminant validity among the constructs and indicating good construct validity of the measurement model.\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\u003eDiscriminant Validity Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eDiscriminant validity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePRO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Overload (TOV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.734\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechno-Complexity (TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.807\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT Self-Efficacy (CAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.821\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT Autonomy (AUT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.830\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT-Enabled Productivity (PRO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.884\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: AVE༚average variance extracted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e The bold values on the diagonal for discriminant validity represent the square roots of AVE, and the lower triangle represents Pearson correlations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Structural Model\u003c/h2\u003e \u003cp\u003eThe results of the model fit analysis indicate that the Bollen\u0026ndash;Stine corrected chi square value is 199.199 with 161 degrees of freedom, yielding a normed chi square ratio of 1.237. This value falls within the recommended range of 1 to 3, indicating a good overall model fit. Other fit indices further support the adequacy of the model, with GFI\u0026thinsp;=\u0026thinsp;0.963, AGFI\u0026thinsp;=\u0026thinsp;0.947, RMSEA\u0026thinsp;=\u0026thinsp;0.024, SRMR\u0026thinsp;=\u0026thinsp;0.067, TLI (NNFI)\u0026thinsp;=\u0026thinsp;0.991, CFI\u0026thinsp;=\u0026thinsp;0.993, IFI\u0026thinsp;=\u0026thinsp;0.993, Hoelter\u0026rsquo;s N (CN)\u0026thinsp;=\u0026thinsp;343.192, Gamma hat\u0026thinsp;=\u0026thinsp;0.996, and McDonald\u0026rsquo;s NCI\u0026thinsp;=\u0026thinsp;0.956. All indices meet or exceed the recommended thresholds, demonstrating excellent model fit (West et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, techno-overload has a significant positive effect on IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.194, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), supporting Hypothesis H1. However, the direct effect of techno-complexity on IT enabled productivity is not significant (β\u0026thinsp;=\u0026thinsp;0.096, p\u0026thinsp;=\u0026thinsp;0.115), and thus Hypothesis H2 is not supported. The effect of techno-overload on IT self-efficacy is not significant (β\u0026thinsp;=\u0026thinsp;0.108, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107), leading to the rejection of Hypothesis H3. In contrast, techno-complexity exhibits a significant positive effect on IT self-efficacy (β\u0026thinsp;=\u0026thinsp;0.249, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), supporting Hypothesis H4. In addition, IT self-efficacy has a significant positive effect on IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.166, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), supporting Hypothesis H5. The mediation analysis for IT self-efficacy indicates that IT self-efficacy does not mediate the relationship between techno-overload and IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059), and therefore Hypothesis H6 is not supported. However, IT self-efficacy demonstrates a significant mediating effect in the relationship between techno-complexity and IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), supporting Hypothesis H7. On the other hand, both techno-overload (β\u0026thinsp;=\u0026thinsp;0.209, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and techno-complexity (β\u0026thinsp;=\u0026thinsp;0.130, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) exert significant positive effects on IT autonomy, supporting Hypotheses H8 and H9, respectively. IT autonomy also has a significant positive effect on IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.326, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting Hypothesis H10. Mediation analysis further shows that IT autonomy plays a significant mediating role in the relationships between techno-overload and IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.068, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and between techno-complexity and IT enabled productivity (β\u0026thinsp;=\u0026thinsp;0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), thereby supporting Hypotheses H11 and H12.\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\u003ePath Coefficients and Significances.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnstd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBias-Corrected 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1: Techno-Overload \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2: Techno-Complexity\u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3: Techno-Overload \u0026rarr; IT Self-Efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4: Techno-Complexity \u0026rarr; IT Self-Efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5: IT Self-Efficacy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6: Techno-Overload \u0026rarr; IT Self-Efficacy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7: Techno-Complexity \u0026rarr; IT Self-Efficacy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8: Techno-Overload \u0026rarr; IT Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9: Techno-Complexity \u0026rarr; IT Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10: IT Autonomy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH11: Techno-Overload \u0026rarr; IT Autonomy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH12: Techno-Complexity \u0026rarr; IT Autonomy \u0026rarr; IT-Enabled Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNote: Bootstrapping\u0026thinsp;=\u0026thinsp;5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Integration and Implications of Study 2 Findings\u003c/h2\u003e \u003cp\u003eThe TTS posits that stress does not originate from situations themselves but rather from individuals\u0026rsquo; subjective cognitive appraisals of stressors (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When employees encounter digital work demands such as techno-overload and techno-complexity, they first evaluate whether these demands constitute challenges or hindrances and then conduct secondary appraisal based on available resources, which in turn shapes employee behavior (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The findings of Study 2 show that techno-complexity indirectly enhances IT enabled productivity through increased IT self-efficacy and IT autonomy. This indicates that some employees appraise complex technological demands as challenge stressors with growth potential (LePine et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thereby stimulating learning motivation and resource mobilization capabilities. These results are consistent with prior studies that view digital challenges as opportunities for development (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, although techno-overload directly increases IT enabled productivity, it does not exert an indirect effect through self-efficacy. This suggests that high workload conditions may simultaneously embody both opportunities and burdens, aligning with the perspective that challenge stressors are not entirely benign (Bermes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, autonomy plays a stable mediating role across both types of technostress creators, indicating that when employees perceive greater control over actions and decision making, they are better able to transform technostress into positive performance related experiences (Lartey, 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 \u003cem\u003eTheoretical contributions\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study makes three theoretical contributions to the literature on technostress and IT enabled productivity. First, Study 1 identifies key configurational conditions through which technostress creators give rise to IT enabled productivity by applying fsQCA, thereby addressing the limitations of prior research that has predominantly explained technostress effects using single variables and linear relationships. Compared with approaches that incorporate excessive conditions and risk model complexity and estimation bias (Nimako and Ntim, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the use of necessity and sufficiency analyses provides a more parsimonious and explanatory theoretical structure. This approach helps reduce bias arising from overly simplified or overly complex models and deepens the theoretical positioning of technostress creators in IT enabled productivity research.\u003c/p\u003e \u003cp\u003eSecond, this study explains why technostress can generate positive performance outcomes from the perspective of employees\u0026rsquo; subjective cognitive appraisals of stress. According to the Transactional Theory of Stress, the effects of stress are not determined by stressors themselves but depend on individuals\u0026rsquo; cognitive appraisals of stressors and assessments of available resources (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Study 2 shows that techno-complexity indirectly enhances IT enabled productivity through IT self-efficacy and IT autonomy, whereas techno-overload follows a different pathway, indicating asymmetry in the effects of technostress. These findings suggest that when employees appraise technological demands as challenges with growth potential rather than as hindrances, technostress can be transformed into a positive force that promotes IT enabled empowerment and performance, thereby addressing gaps in the technostress literature regarding mechanisms underlying positive outcomes.\u003c/p\u003e \u003cp\u003eThird, this study theoretically emphasizes the complementary relationship between the CHSF and TTS rather than treating them as redundant explanatory frameworks. The CHSF primarily indicates that the same stressor may be appraised as a challenge or a hindrance, leading to different motivational and performance consequences (Bermes, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., 2014), but provides relatively limited explanation of how such appraisals are formed and how stress is translated into concrete performance outcomes. In contrast, the TTS offers a comprehensive psychological process explaining how individuals conduct primary and secondary appraisals to evaluate the meaning of stressors and available resources, which subsequently shape behavior (Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By integrating both theories, this study embeds the challenge or hindrance nature highlighted by the CHSF into the cognitive appraisal and resource mobilization processes articulated by the TTS, thereby explaining how technostress is transformed into IT enabled productivity under specific psychological conditions. The findings further indicate that IT self-efficacy and IT autonomy, as key psychological and job related resources, constitute the core mechanisms linking challenge appraisals to positive performance outcomes. In doing so, this study fills a theoretical gap in the CHSF regarding how stress becomes a source of motivational drive and underscores the critical complementary role of TTS in technostress research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Managerial implications\u003c/h2\u003e \u003cp\u003eThis study offers important implications for human resource management and technology implementation in highly digitalized financial institutions. First, the findings indicate that techno-complexity does not inherently suppress performance. Its positive effect on IT enabled productivity is realized through IT self-efficacy, highlighting the critical role of employees\u0026rsquo; beliefs in their technological capabilities during digital transformation. Accordingly, financial institutions should not focus solely on technology adoption or demand rapid adaptation but should instead systematically cultivate employee self-efficacy. Through structured technological training and technical support, organizations can enhance employees\u0026rsquo; sense of control over technology and strengthen its empowering effects.\u003c/p\u003e \u003cp\u003eIn addition, this study finds that IT autonomy plays a stable and significant mediating role in the relationships between techno-overload and IT enabled productivity as well as between techno-complexity and IT enabled productivity. These results suggest that autonomy is a key factor in transforming technostress into performance enhancing outcomes. Financial institutions should therefore preserve flexibility in employees\u0026rsquo; technology use, such as allowing discretion in the selection of supportive tools, to increase perceptions of control and responsibility. In doing so, technology can be transformed from a source of pressure into an empowering resource that supports performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Research Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThe current investigation provides empirical evidence for the relationships among technostress, psychological resources, and IT-enabled productivity; however, it has several limitations. The use of cross-sectional survey data restricts the researcher\u0026rsquo;s ability to make causal inferences as they can only examine relationships between variables; therefore, future studies could use longitudinal or panel designs to assess how technostress and psychological resources change over time. Also, the sample included only employees in the financial sector; therefore, while this restricts confounding variables associated with the nature of each industry, it does not allow for generalizing the findings to other industries. To establish the broader applicability of the proposed model, future studies should include participants from multiple industries. A third limitation of this research is that the sample is composed entirely of participants in the financial sector; therefore, future research could include participants from other industries to determine whether the components of technostress creators that lead to IT-enabled productivity differ in those contexts. Finally, data collection was conducted using self-reported questionnaires, which could introduce common-method bias. Future studies should include objective performance measures and/or multiple data sources to improve the reliability and external validity of the findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study integrates fsQCA and SEM to examine the effects of technostress on IT enabled productivity, revealing its nonlinear and asymmetric mechanisms. The fsQCA results of Study 1 indicate that high IT enabled productivity does not arise from a single form of technostress but rather from combinations of multiple technostress conditions, with techno-overload and techno-complexity playing particularly critical roles. The SEM results of Study 2 show that techno-overload directly enhances IT enabled productivity, suggesting that high technological demands in financial contexts may be appraised as performance-oriented challenges. In contrast, the positive effects of techno-complexity emerge only through the mediation of psychological resources such as IT self-efficacy and IT autonomy. Among these, IT autonomy demonstrates a stable mediating role across different technostress creators, indicating that perceived control and decision flexibility are key factors in transforming technostress into productivity. Accordingly, technostress in highly digitalized financial environments is not inherently negative, and its effects depend on the type of stressor and how employees cognitively appraise technological demands.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.P.W. wrote the main manuscript text, prepared figures 1-2 and data collection. C.S.A.Y. responsible for data analysisAll authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003eData availability: All the data are included in the manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003eEthical statements: This research was approved and cleared by the Quantitative Analysis and Research Association (No. 1130901001).\u003c/p\u003e\n\u003cp\u003eInformed consent statement: This study employed non-interventional methods. All participants were fully informed of the study purpose, the use of their data for academic research, and the assurance of anonymity. Participation involved no foreseeable risks to the participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. 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Inf Technol People 38(2):787\u0026ndash;826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/ITP-08-2022-0639\u003c/span\u003e\u003cspan address=\"10.1108/ITP-08-2022-0639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Technostress, IT-enabled Productivity, Challenge–Hindrance Stressor Framework, Transactional Theory of Stress, fuzzy-set qualitative comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-8759770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8759770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs digital transformation deepens, technostress has become a critical issue influencing employees\u0026rsquo; work processes, yet prior research has largely emphasized its negative consequences. Integrating the Challenge Hindrance Stressor Framework and the Transactional Theory of Stress, this study examines how technostress is transformed into IT enabled productivity. Study 1 uses fuzzy set qualitative comparative analysis to show that high IT enabled productivity arises from specific configurations of technostress creators rather than a single stressor, with techno overload and techno complexity as core conditions. Building on these findings, Study 2 applies structural equation modeling and demonstrates that techno overload directly enhances IT enabled productivity, whereas techno complexity operates indirectly through IT self efficacy and IT autonomy. Importantly, IT autonomy serves as a stable mediating mechanism across both stressors. Overall, the findings indicate that technostress can generate positive performance outcomes when appraised as a challenge and supported by key psychological resources.\u003c/p\u003e","manuscriptTitle":"When Technological Pressure Is No Longer Merely a Burden: How Technological Overload and Technological Complexity Empower Employee Productivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 09:59:14","doi":"10.21203/rs.3.rs-8759770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-10T13:32:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T13:16:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T07:14:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-02T02:33:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c28f8c2b-63c2-4a26-9ce5-e1759467f910","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62122087,"name":"Physical sciences/Mathematics and computing"},{"id":62122088,"name":"Biological sciences/Psychology"},{"id":62122089,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-27T12:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 09:59:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8759770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8759770","identity":"rs-8759770","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}