Reframing Technostress for Organizational Resilience: The Mediating Role of Techno-Eustress in the Performance of Accounting and Financial Reporting Professionals

Abstract

This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies (e.g., ERP systems, digital audit tools). The data collected through a convenience sampling method was analyzed using SPSS 27 and SmartPLS 4 software. The findings reveal that the direct effect of technostress on job performance is not significant; however, this stress indirectly contributes to performance through techno-eustress. In this study, techno-eustress refers to the cognitive appraisal of technology-related demands as development-enhancing challenges rather than threats. This concept is theoretically grounded in the broader eustress framework, which views stressors as potentially motivating and growth-promoting when positively interpreted. The model is based on Cognitive Evaluation Theory, the Job Demands–Resources Model, and Self-Determination Theory. This study demonstrates that digital transformation can promote not only operational improvements but also organizational resilience by enhancing employees’ psychological resources and adaptive capacities. By highlighting the mediating role of techno-eustress, this research offers a nuanced perspective on how human-centered cognitive mechanisms can strategically support performance and sustainability in the face of technological disruption—an increasingly relevant area for organizations striving to thrive amid uncertainty.

1. Introduction

Today, the digital transformation process not only refers to a set of technological infrastructure changes in businesses but also significantly impacts many organizational components, from business processes to decision making and human resources applications. Emerging technologies such as artificial intelligence, data analytics, automation systems, and cloud-based software solutions enhance the operational efficiency of businesses, while simultaneously challenging traditional models of organizational resilience by transforming how employees interact with their work environment and technology [1]. As organizations increasingly rely on digital systems to sustain competitiveness and manage uncertainty, this transformation introduces not only technical but also psychological and organizational resilience challenges.

In the context of organizational resilience, digital transformation requires not only adapting to technical innovation but also developing strategic capabilities that enable long-term survival, employee well-being, and adaptive capacity under uncertainty. This transformation entails not only technical but also cognitive and emotional restructuring processes, particularly for professionals in knowledge-intensive fields like accounting and finance [2,3]. In this context, the integration of digital technologies into business life is radically transforming not only organizational structures but also the individual experiences of employees. Constant interaction with technological systems in information-intensive environments requires not only technical skills from employees but also mental adaptability, emotional resilience, and the capacity for continuous learning. One of the most important theoretical frameworks developed in the literature to understand the psychological and behavioral effects of this transformation on individuals is the concept of technostress [4]. Technostress is a concept that refers to the mental and emotional pressure individuals experience due to their use of information systems (ISs) within an organizational context. This type of stress arises from employees’ constant interactions with technology, the digital demands they face in business processes, and their attempts to adapt to information technologies. Technostress encompasses the stress reactions an individual encounters in response to factors such as complexity, rapid change, constant accessibility, and the ongoing need to process new information within the digital systems utilized in business processes and in the behavioral contexts they engage with in the business environment [5,6,7]. Research shows that technostress, which employees have experienced intensely in recent years, extends beyond the individual effects of work–life conflict, fatigue, anxiety, and burnout; it also significantly influences organizational outcomes such as job performance, satisfaction, engagement, and motivation [4,6,8,9,10,11,12,13,14,15]. However, traditional approaches to technology-based stress often propose a framework suggesting that this stress only results in negative psychological consequences. Recent studies indicate that technological stressors do not affect every individual in the same way and that these factors do not necessarily lead to negative experiences [16,17].

This raises a critical issue for organizational resilience research: how some individuals and organizational units are able to transform digital stress into a source of strength and adaptability. It has been observed that individuals with high self-regulation skills, openness to learning, and strong psychological resilience can evaluate technology-related demands as opportunities for personal development and professional growth rather than burdens. In line with recent advances in positive organizational scholarship and resilience theory, a growing body of literature highlights techno-eustress—a concept referring to positive perceptions of technological demands—as a critical mediator in this process [18]. This concept is particularly relevant to organizational resilience frameworks, as it reflects how cognitive and emotional appraisal mechanisms can drive adaptive behaviors in the face of digital disruption. Techno-eustress occurs when individuals, instead of developing a perception of threat from technological changes, view these changes as opportunities to cope, adapt, and even enhance performance. This positive stress approach also aligns with positive psychology, the Job Demands–Resources Model [19], and cognitive evaluation theories [20]; it is considered an important factor that activates employees’ psychosocial resources during the technological transformation process.

While Cognitive Evaluation Theory suggests that an individual’s response to environmental stimuli depends on whether they perceive these stimuli as threats or opportunities, the Job Demands–Resources Model argues that the effect of demands in the work environment on performance is determined by the psychological and organizational resources an individual possesses. In line with this theoretical framework, it can be predicted that technostress may not directly impact employee performance but rather do so indirectly, depending on how an individual evaluates this stress—especially when perceived as techno-eustress. Indeed, some employees may reframe (evaluate) technological pressures and demands as opportunities for learning, pathways for developing mastery, and tools for achieving personal success. Such reframing mechanisms are crucial for building an organizational culture of resilience, where individuals are empowered to respond adaptively to continuous change.

Although extensive research has been conducted on the individual and organizational consequences of technostress [6,21], theoretical and empirical models that explain the impact of this form of stress on performance through a positive resilience-based mechanism such as techno-eustress are quite limited. Moreover, little is known about how this mechanism contributes to strategic organizational responses under digital disruption—a topic directly relevant to strategic resilience and adaptive capacity. Studies examining groups like financial reporting specialists who work with high digital intensity are particularly insufficient. This lack of focus on high-tech professional environments restricts the development of targeted strategic management approaches aimed at enhancing human resilience in the digital era.

This study aims to address the impact of technostress on employee performance indirectly through techno-eustress; it seeks to reveal how individuals’ perceptions of technological demands can significantly influence their capacity to perform and adapt. The research makes three main contributions in this context:

• It redefines technostress as a dual-valence construct, reflecting both risk and opportunity, and aligns this duality with organizational resilience theory.
• It contributes to the organizational resilience literature by testing techno-eustress as a cognitive–motivational mechanism enhancing employee functioning under stress.
• It offers practical implications for building resilient workforces by integrating digital transformation strategies with human-centered resilience practices.

In this respect, the study not only explains the psychological effects at the individual level; it also reveals how employee experiences and organizational structures are transformed during the digitalization process and how current management models should be reshaped. In particular, it shows that technology-related stress can be reinterpreted as a strategic resource when properly managed—thereby contributing to a resilient organizational culture Furthermore, it highlights that employee-centered adaptation strategies are essential for redesigning digital transformation policies and fostering human capability during periods of uncertainty. In this context, the research clearly reveals how understanding the effects of digital transformation at the individual level during technological disruption is crucial for integrating emerging technologies into organizational practices and restructuring management models for long-term resilience.

2. Literature Review

Technostress, Techno-Eustress, and Performance

In today’s business environment, the integration of advancing technology and digital tools into professional life creates both opportunities and new types and sources of stress for employees. At this point, technostress refers to the psychological tension and challenges experienced by employees while adapting to information and communication technologies and the new situations they bring. It emerges as a widespread and significant type of stress, mainly addressed in five key dimensions in the literature [4].

The first of these dimensions, techno-overload, arises from the acceleration of work pace due to technological tools and the added time pressure associated with the obligation to perform multiple tasks simultaneously. Employees experience an increased workload as a result of the pressure and expectation to remain constantly online and respond instantly through digital platforms in their work-related connections. Techno-invasion relates to the fact that the boundaries between employees’ work and private lives are increasingly violated in this pressured environment, leading to infringements of the private sphere and blurred boundaries. Employees can be reached outside of work via mobile devices, email, and other business communication applications, triggering a sense of constant “readiness”. Techno-complexity is the feeling of inadequacy stemming from the complexity of technological systems and related business processes, which can be difficult to understand, complicated, and not user-friendly. This complexity escalates daily as employees face pressure to adapt to new systems, learn updated requirements, and keep up with developments, all while lacking adequate technical knowledge. Techno-insecurity refers to the fear of job loss due to technological inadequacy and the complexity of this process. Techno-uncertainty denotes the feeling of unpredictability concerning the future brought about by these changes and the need to integrate into a rapidly evolving system. These five dimensions illustrate that technostress is not merely a physical phenomenon; it is also multi-layered, encompassing cognitive, emotional, and behavioral aspects. More importantly, they highlight how digital transformation challenges the psychological resilience of employees and the adaptive capacity of organizations. These dimensions should be assessed alongside organizational policies regarding technology use, digital proficiency levels, and individual coping strategies.

Technostress has various effects on employees and organizations. The technology-related stress experienced by employees at the individual level includes increased burnout, job dissatisfaction, anxiety, role conflicts, and mental fatigue [11,21,22]. Factors such as technological complexity, increased workload, and pressure to stay online challenge employees’ perception of control over their work processes, thereby increasing psychological tension. Additionally, this situation can trigger adverse outcomes that may lead to intentions to quit [23,24].

When examining its effects at the organizational level, some studies indicate that technostress diminishes organizational commitment, decreases productivity, and leads to higher absenteeism [4,25]. In this respect, technostress not only affects individual well-being but also poses a strategic threat to organizational resilience and performance continuity, particularly in knowledge-intensive sectors. Constant interaction with technology and the excessive cognitive load they face in their job roles affect the entire range of organizational processes, from the individual to the organizational level.

When considering the process of Technostress, which impacts both organizational and individual performance, through the lens of Cognitive Evaluation Theory [20], individuals’ initial evaluations of environmental stimuli are viewed as threats or, in some cases, opportunities due to changing conditions. Following this evaluation, individuals respond appropriately by assessing their resources while navigating these variables. In this context, the interaction between stressors and performance varies based on how employees perceive and assess these stressors.

Furthermore, within the framework of the Job Demands–Resources Model [19], when technological demands are high but employees lack personal and organizational resources, education, social support, or technical infrastructure, they may experience burnout or low performance. Conversely, when adequate resources are made available within both the Cognitive Evaluation Theory and the Job Demands–Resources Model, these demands can enhance an individual’s motivation and yield positive outcomes.

The extant literature reveals differing results regarding the relationship between technostress and performance. For example, Tarafdar et al. [4] emphasized that technostress creators (e.g., technology overload and complexity) negatively impact employees’ performance, particularly in information technology-intensive jobs. Califf et al. [17] demonstrated that technostress is linked to employee burnout and low performance within health information systems. Additionally, Ali-Hassan et al. [26] stated that technology can positively influence job performance by enhancing social capital. Furthermore, Saleem et al. [27], in their study on university instructors during the COVID-19 period, noted that technostress can have positive effects on performance, especially when employees receive training and possess high creative self-efficacy. On the other hand, Yao and Wang [28], in their study of university students, reported a significant relationship between technostress and academic performance. Taken together, these findings suggest that technostress functions as a complex construct whose impact on performance is mediated by psychological perception, organizational context, and support mechanisms—factors directly relevant to strategic management for organizational resilience. Although these studies contribute valuable insights, the contrasting findings regarding the effect of technostress on performance—such as the negative impact reported by Tarafdar et al. (2007) [4] and the positive relationship found by Saleem et al. (2021) [27]—highlight a need for further theoretical clarification. These discrepancies can be attributed to various factors, including how individuals cognitively appraise technology-related demands and the availability of contextual resources. For instance, while some employees may perceive technology as overwhelming and intrusive, others—especially those with higher digital competence or creative self-efficacy—may view the same demands as developmental challenges. Organizational variables such as leadership support, training access, and role clarity also shape whether technostress is experienced as a threat or an opportunity. By proposing techno-eustress as a mediating mechanism, this study aims to offer a coherent explanation for these divergent outcomes, suggesting that the performance impact of technostress depends significantly on employees’ subjective interpretations and the broader organizational context.

Although the traditional view of stress has often been linked to negative outcomes in the business world, recent studies have shown that stress can sometimes enhance individual performance without being harmful. At this point, the concept of “eustress” emerges as a positive type of stress and serves as an important mediating variable. Eustress is defined as individuals perceiving the challenges they face as opportunities for growth and harboring positive feelings toward stress [29]. When evaluated through the lens of Cognitive Evaluation Theory [20] and the Job Demands–Resources Model [19], eustress arises when individuals see environmental demands as challenges rather than threats. This mindset boosts individuals’ sense of self-efficacy and enhances their motivation. In the limited existing literature, it has been noted that eustress improves employees’ job performance, enhances job satisfaction, and positively impacts performance; this effect is associated with employees receiving training and possessing high levels of creative self-efficacy [27,30]. These results underscore the potential of positive stress (techno-eustress) as a resilience-enabling mechanism, especially in high-tech, uncertainty-prone organizational environments.

Financial reporting and accounting professionals face significant stress in their work lives due to intensive information processing, frequently changing regulations, and high performance expectations [31,32,33]. However, empirical studies examining the effects of eustress on the performance of finance and accounting professionals are quite limited in the literature. For example, a study conducted by Bonache [34] on French certified public accountants and their trainees emphasized that the perception of environmental uncertainty affects performance through motivation, but this relationship needs further examination in the context of eustress.

A bibliometric analysis by Rodrigues et al. [32] revealed that studies on job stress among accounting professionals are limited and that there is a lack of research specifically addressing the relationship between stress and performance with variables such as eustress. Given the strategic role of finance and accounting professionals in organizational decision making and risk assessment, understanding how techno-eustress influences their performance is also critical for resilience-oriented management approaches. In particular, revealing how eustress acts as a mediating mechanism in the relationship between technostress and job performance can highlight the strategic importance of the human factor in digital transformation processes and resilience-oriented management approaches.

The study aims to test whether the technological pressures brought about by digitalization can be perceived by employees not as a threat but as a development opportunity and whether this perception can positively affect employee performance. Conducting the study with a sample of finance and accounting professionals will also contribute to understanding the sectoral reflections of stress.

Besides Cognitive Evaluation Theory and the Job Demands–Resources Model, the framework of Self-Determination Theory also contributes to the interpretation of this study’s findings. This theory suggests that individuals are more inclined to engage positively with external demands—such as technological challenges—when their core psychological needs are fulfilled. Specifically, when the work environment supports employees’ sense of autonomy, fosters their competence, and encourages meaningful social connections, individuals are more likely to perceive stressors as manageable and potentially motivating. This perspective aligns with resilience research, emphasizing that intrinsic motivation and self-determination are central to sustained adaptability under disruption. In this regard, the transformation of technostress into eustress may depend significantly on how well these psychological needs are met, ultimately leading to increased motivation and improved job performance.

In line with this theoretical background, empirical findings, and calls, the following hypotheses were developed for the research model depicted in Figure 1 to be tested:

H1: 

Technostress has a significant effect on employees’ job performance.

H2: 

Technostress significantly affects the level of eustress.

H3: 

Techno-eustress has a positive effect on employees’ job performance.

H4: 

Techno-eustress plays a mediating role in the relationship between technostress and job performance.

3. Materials and Methods

3.1. Research Design

This research was conducted to examine the mediating role of techno-eustress in the effect of technostress on individual job performance among accounting and financial reporting professionals. The research tests an explanatory model based on a quantitative research method. The theoretical framework draws from Cognitive Evaluation Theory, Self-Determination Theory and the Job Demands–Resources Model. These theories suggest that how individuals evaluate demands in the work environment is crucial in explaining both negative and positive job outcomes.

3.2. Sampling and Data Collection Process

The study sample consists of accounting and financial reporting experts working in small and medium-sized enterprises. The convenience sampling technique was employed as the sampling method. The criteria for selecting participants included having at least one year of professional experience and actively working with digital accounting systems. Data collection occurred over two weeks, between April and May 2024. Participants received an online survey form, and the data were gathered via the online survey platform. Prior to the study, ethical approval was obtained from the Kocaeli University Social and Human Sciences Ethics Board (Decision no. 2025/06 dated 22 April 2025), and voluntary participation was secured from participants through an informed consent form. A total of 392 individuals participated in the study, but four surveys were excluded due to significant deficiencies. Thus, the sample consists of 388 professionals working in accounting and financial reporting. This study specifically focused on professionals working in small and medium-sized enterprises (SMEs), as these environments often represent settings where digital transformation is actively underway but organizational resources may be more constrained. While convenience sampling was adopted, the sample includes diverse occupational roles—such as accountants, reporting specialists, CPAs, and managers—which supports internal variability within the group. This contextual focus aligns with the study’s aim to explore technostress and techno-eustress dynamics in settings where digital demands are high but support structures may differ from those of large corporations. Although broader generalizability is limited, the findings provide meaningful insights into digitally intensive but resource-constrained professional contexts.

Examining the occupational distribution of the participants shows that 28.4% are accounting specialists or staff, 21.9% are finance and reporting specialists, 43.8% are CPAs and financial advisors, and 5.9% are accounting managers or senior managers. When assessed by age distribution, a significant portion of the sample consists of participants aged 25–34 (48.5%) and those aged 35–44 (26.3%). Of the participants, 56.2% are male and 43.8% are female. Regarding marital status, 54.1% of the participants are married, while 45.9% are single. Data on education level indicates that the majority of the sample comprises undergraduate level (77.3%).

3.3. Measurement Tools

The data collection tools utilized in this study were developed by adapting measurement instruments that have previously been validated and proven reliable in the literature. The measurement form primarily consists of questions regarding the demographic information of the participants (age, gender, educational status, position, etc.), as well as statements designed to assess the levels of technostress, techno-eustress, and job performance, which are the key variables of the study. Detailed information about the measurement tools is as follows:

Technostress: In line with the theoretical framework of the study, the Job Demands–Resources Model [19] and Cognitive Evaluation Theory [20], the technostress variable was assessed through two sub-dimensions: techno-overload and techno-invasion. This approach is frequently preferred in the literature and is supported by empirical findings indicating that these two dimensions significantly impact both psychological and performance outcomes [13,35]. A comprehensive literature review on technostress highlighted that the techno-overload (83.3%) and techno-invasion (74.1%) dimensions, which are key components of technostress, are primarily discussed in relation to work performance and health-related outcomes [11,36]. Techno-overload refers to situations where technology exerts pressure on employees by increasing their workload, while techno-invasion pertains to situations where technology blurs the boundaries between work and personal life, instilling a sense of obligation to remain constantly connected. The technostress scale employed in this study was adapted from a measurement tool developed by Tarafdar et al. [4] and has been utilized in numerous recent studies. The techno-overload dimension was evaluated using five items, while the techno-invasion dimension was assessed with four items. Sample items include: “Technological tools force me to do more work in less time” and “Thanks to technology, my work is invading my private life”.

Techno-Eustress: The “Techno-Eustress Creators” scale developed by Tarafdar, Stich, Maier, and Laumer [37] was used in the study to measure the factors that lead individuals to evaluate technological demands positively. This scale consists of four fundamental dimensions: techno-mastery, techno-autonomy, techno-relatedness, and techno-enrichment. Each dimension reflects the elements that enable individuals to perceive technology as an opportunity for development and learning. The scale was developed within the framework of Self-Determination Theory and positive psychology, aiming to measure experiences that support intrinsic motivation and enhance positive interactions with technology in work processes. Sample scale items include: The work-related IT applications and devices I use challenge and motivate me in a positive way, such as “Make my work methods more effective” and “Make it easier for me to exchange ideas on work issues with many of my colleagues.” The scale items focus on positive cognitive and emotional outcomes, such as improved work performance, increased autonomy in decision-making processes, more effective communication with colleagues, and enhanced professional development through technology. In this context, the scale is a valid measurement tool that can be utilized to analyze the relationship between individuals working in knowledge-intensive sectors such as accounting and finance, and to evaluate the effects of this relationship on performance.

Job Performance: The scale used to evaluate the individual job performance levels of employees in the study was developed by Kirkman and Rosen [38] and applied by Sigler and Pearson [39]. The adaptation process to Turkish was conducted by Çöl [40], and the reliability and validity of the scale were assessed. The scale is structured with a one-dimensional framework and consists of four items. Example items include “I more than achieve my job goals” and “I am sure that I more than meet the standards in the service quality I provide”. This scale, designed as a Likert type, aims to measure employees’ effectiveness and task-oriented competence in their job processes. As a performance evaluation tool, this scale provides valuable insights into the task success of professionals, especially in goal-oriented fields such as accounting and finance that require effective time management.

All statements were rated using a 5-point Likert-type scale (1 = Strongly Disagree, 5 = Strongly Agree). Validity studies for the language were conducted during the scale adaptations, and the statements were translated into Turkish with the help of expert opinions.

3.4. Data Analysis Process

The data analysis process was conducted in two main stages following the structural equation modeling approach. In the first stage, the measurement model was evaluated. In this context, the internal consistency of the structures was analyzed through the outer loadings of the variables and composite reliability. The convergent validity of the scales was tested using the Average Variance Extracted (AVE) values. For the discriminant validity analysis, both the HTMT ratios and the Fornell–Larcker criterion were considered. Additionally, to evaluate the risk of multicollinearity, the Variance Inflation Factor (VIF) values were calculated for each predictor variable, confirming that all structure VIF values met the criterion of <5.

In the second stage, structural model analysis was conducted according to the assumed theoretical model. In this stage, the path coefficients (β), statistical significance levels (p-value), and t-values in the model were tested using the 5000-sample bootstrap method. The explanatory power of the model was evaluated with R² (coefficient of determination) values, indicating the amount of variance explained by the variables. Additionally, the effect size of each independent structure in the model on the dependent structures was measured using f² coefficients; the predictive validity of the model was analyzed with the Q² (Stone–Geisser coefficient).

Additionally, a mediation analysis was conducted to determine the effects of the mediator variable within the model. In this context, it was tested whether the effect of technostress on individual job performance occurred indirectly through techno-eustress. Both indirect and direct effects were evaluated together to reveal whether there was partial or full mediation in these relationships. The findings of this analysis support both the structural consistency and conceptual validity of the model.

4. Results

4.1. Measurement Model Analysis

Various statistical analyses were conducted to test the reliability and validity properties of the measurement model used in the study. Before proceeding to the testing of the structural model, it was examined whether the latent variables designed to measure the theoretical structures were adequately represented by the observed items.

In this context, first, outer loading coefficients that show the relationship between each observed variable and its corresponding structure were evaluated. In accordance with the generally accepted limits in the literature, loading values of 0.708 and above were considered strong; loadings between 0.40 and 0.70 were assessed for inclusion in the model based on the average variance explained (AVE) and composite reliability (CR) values of the relevant structure. Indicators below 0.40 were excluded from the analysis due to their insufficient representation power on the structure [41,42]. In line with this approach, the model’s adequacy at the measurement level was tested meticulously, and only theoretically meaningful and statistically valid indicators were included. Thus, before moving on to structural equation modeling, an attempt was made to verify that the measurement model was based on reliable foundations.

The results of the first-order analysis are detailed in

Table 1. Measurement model analysis results.

Items	Factor Loadings	CR	AVE	VIF
TA1	0.764	0.899	0.691	1.698
TA2	0.822			1.971
TA3	0.902			2.244
TA4	0.832			2.078
TE1	0.854	0.900	0.693	1.820
TE2	0.800			1.999
TE3	0.854			2.290
TE4	0.820			2.191
TM1	0.883	0.940	0.795	2.473
TM2	0.911			3.381
TM3	0.900			3.308
TM4	0.873			2.607
TR1	0.815	0.893	0.677	1.824
TR2	0.894			2.699
TR3	0.728			1.547
TR4	0.844			2.079
P1	0.797	0.929	0.768	1.900
P2	0.927			3.460
P3	0.895			3.157
P4	0.880			2.585
TO1	0.783	0.898	0.638	1.695
TO2	0.817			2.113
TO3	0.864			2.121
TO4	0.726			1.707
TO5	0.798			1.960
TI1	0.693	0.850	0.587	1.354
TI2	0.787			1.664
TI3	0.732			1.336
TI4	0.843			1.881

TM: techno-mastery, TA: techno-autonomy, TR: techno-relatedness, TE: techno-enrichment, TO: techno-overload, TI: techno-invasion, P: performance.

:

To test the construct validity of the measurement model in this study, outer loading values, composite reliability coefficients (CR), average variance explained (AVE), and multicollinearity (VIF) analyses were conducted. These statistical indicators were thoroughly examined to assess the measurement integrity of the constructs.

First, the composite reliability (CR) value for each latent variable was considered. According to the approach suggested by Hair et al. [41], a CR value above 0.70 indicates sufficient structural reliability. Based on the analysis findings, the CR coefficients for all constructs range from 0.850 to 0.940, revealing that the constructs exhibit extreme reliability in terms of internal consistency. The highest CR value was found in the Techno-Mastery (TM) construct at 0.940, while the lowest value was recorded in the Techno-Invasion (TI) construct at 0.850. It is clear that all constructs meet the threshold value.

Convergent validity was tested using the AVE (Average Variance Extracted) coefficient proposed by Fornell and Larcker [42]. AVE values of 0.50 and above indicate that the constructs adequately reflect the concepts they represent. According to the findings, all constructs exceeded this threshold; AVE values ranged from 0.587 to 0.795. This result supports the conclusion that all latent variables in the model are conceptually valid.

The outer loading coefficients (factor loadings) of the observed variables are generally at an acceptable level. As frequently emphasized in the literature [41], loading coefficients of 0.708 and above indicate high representativeness. The fact that all indicator loading coefficients included in the model exceed this limit implies that each item adequately represents its own structure. Notably, the loadings in the TM and TA structures are particularly above 0.80. Only the loading coefficient of the TI1 item is very close to this threshold, at 0.693, but it is still at an acceptable level. Additionally, the presence of a multicollinearity problem in the model was evaluated using the Variance Inflation Factor (VIF) values. Hair et al. [41] stated that multicollinearity does not pose a problem if the VIF values are below 5. In the analysis, the VIF values for all indicators ranged from 1.336 to 3.460, indicating that there is no risk of multicollinearity in the model.

Discriminant validity was assessed using the Fornell–Larcker criterion and HTMT ratios. According to the Fornell and Larcker [42] approach, the square root of the AVE for each construct should be higher than its correlations with other constructs. The analysis results in

Table 2. Fornell–Larcker criterion Analysis Results.

Construct	1	2	3	4	5	6	7
TA	0.832
TE	0.622	0.832
TM	0.634	0.573	0.892
TR	0.590	0.636	0.536	0.823
P	0.591	0.695	0.651	0.573	0.876
TO	0.191	0.170	0.185	0.296	0.158	0.799
TI	0.183	0.152	0.197	0.254	0.192	0.649	0.766

TM: Techno-mastery, TA: Techno-autonomy, TR: Techno-relatedness, TE: Techno-enrichment, TO: Techno-overload, TI: Techno-invasion, P: Performance.

showed that this condition was met for all constructs, confirming that conceptual separation was successfully achieved in the measurement model. Additionally, HTMT ratios were examined as another test of discriminant validity, and as seen in

Table 3. HTMT Criteria Analysis Results.

Construct	1	2	3	4	5	6	7
TA
TE	0.742
TM	0.727	0.642
TR	0.710	0.726	0.599
P	0.699	0.801	0.718	0.663
TO	0.206	0.176	0.196	0.340	0.162
TI	0.209	0.175	0.231	0.313	0.227	0.794

TM: Techno-mastery, TA: Techno-autonomy, TR: Techno-relatedness, TE: Techno-enrichment, TO: Techno-overload, TI: Techno-invasion, P: Performance.

, all values remained below the recommended threshold of 0.90 [43]. This result indicates that conceptual separation was achieved between the constructs.

4.2. Second-Order Measurement Model Analysis

In this study, both techno-eustress and technostress variables were modeled as reflective-reflective second-order structures, considering their theoretical integrity and sub-dimensional structures. Techno-eustress comprises four sub-dimensions (techno-mastery, techno-autonomy, techno-enrichment, and techno-relatedness) associated with individuals’ positive perceptions of technology. This structure reflects the psychological resources that individuals experience when using technology within the framework of self-determination theory [44]. Likewise, the technostress variable was modeled through the techno-invasion and techno-overload sub-dimensions that represent technology-related pressure and stress factors. These two sub-structures are defined in the literature as the interference of technological demands in individuals’ living spaces and the creation of an excessive burden [4].

The second-level constructs included in this study (e.g., technostress and techno-eustress) were modeled using the Two-Stage Approach since they correspond to multidimensional conceptual constructs. In this method, the first stage involved running the measurement models related to the sub-dimensions with the PLS algorithm to obtain the latent variable scores of each construct. In the second stage, these scores served as indicators of the second-level construct and were included in the structural model analysis [45,46]. The two-stage approach is particularly recommended in situations where the number of sub-dimensions is significant, and the repetition of indicators creates difficulties for the model. This method maintains the parsimony principle of the model while enabling a more precise structural analysis by preventing the propagation of measurement errors.

In the modeling process, the technostress structure was created by combining the techno-invasion and techno-overload sub-dimensions, while the techno-eustress structure was formed by integrating the mastery, autonomy, enrichment, and relatedness sub-dimensions. The representational power of each sub-dimension was evaluated separately, and afterward, the latent scores obtained were included in the measurement of the second-level structure. This approach allowed for the analysis of the second-level concepts in a measurementally consistent and theoretically holistic way.

At this stage, the validity and reliability of the second-order measurement model were evaluated through external loading values, composite reliability (CR), average variance explained (AVE), and multicollinearity (VIF) statistics. According to the threshold values suggested by Hair et al. [41], CR values for all constructs ranged from 0.876 to 0.930, providing structural reliability (

Table 4. Second-order measurement model analysis results.

Items	Factor Loadings	CR	AVE	VIF
TM	0.816	0.891	0.671	1.849
TR	0.801			1.713
TA	0.824			1.999
TE	0.834			1.884
P1	0.819	0.930	0.768	1.900
P2	0.914			3.460
P3	0.895			3.157
P4	0.875			2.585
TO	0.934	0.876	0.780	1.495
TI	0.830			1.495

TM: techno-mastery, TA: techno-autonomy, TR: techno-relatedness, TE: techno-enrichment, TO: techno-overload, TI: techno-invasion, P: performance.

). AVE coefficients also ranged from 0.671 to 0.780, meeting the Fornell & Larcker [42] criterion (Table 4). The fact that the factor loadings of all indicators were above 0.80 indicated that convergent validity was supported. In addition, all VIF values were below 3.46, indicating that there was no multicollinearity problem in the model (Table 4).

The discriminant validity of the model was examined using the Fornell–Larcker and HTMT criteria. Upon reviewing

Table 5. Fornell–Larcker criterion analysis results.

Construct	1	2	3
Eustress	0.819
Performance	0.759	0.877
Technostress	0.290	0.189	0.883

, it was observed that the square root of the AVE for each construct in the Fornell–Larcker analysis was greater than its correlations with other constructs (e.g., eustress = 0.819 > 0.759 and 0.290). This indicates that conceptual separation between constructs was achieved. Furthermore, as shown in

Table 6. HTMT criteria analysis results.

Construct	1	2	3
Eustress
Performance	0.872
Technostress	0.350	0.231

, the HTMT ratios remained below the recommended limit of 0.90 for all construct pairs (e.g., eustress—p = 0.872; technostress–p = 0.231), confirming that there is no significant conceptual overlap in the model and that discriminant validity was established.

In the second-order measurement model analyses, it was observed that the external loadings met the recommended levels; the CR and AVE values provided structural validity; the Fornell–Larcker and HTMT criteria confirmed discriminant validity; and the VIF values did not indicate a risk of multicollinearity. These findings demonstrate that the measurement model is both theoretically and statistically sufficient before proceeding to the structural model analysis.

4.3. Structural Model Analysis and Hypothesis Testing

Within the scope of the structural model, directional relationships and effect sizes between variables were tested. Model estimates were conducted using the SmartPLS 4 program, and the statistical significance of the parameters was assessed using the 5000-sample bootstrapping method. The analysis results were interpreted based on the theoretical foundation of the research in

Table 7. Structural model analysis results.

Path	β	Mean	sd	t Value	p Values	Hypothesis Result
techno-eustress -> performance	0.769	0.770	0.029	26.969	0.000	Supported
technostress -> techno-eustress	0.290	0.293	0.054	5.395	0.000	Supported
technostress -> performance	−0.034	−0.035	0.040	0.855	0.393	Supported—Full mediation observed
Indirect effect
technostress -> techno-eustress -> performance	0.223	0.226	0.044	5.105	0.000	Supported
Total effect
technostress -> performance	0.189	0.191	0.058	3.251	0.001

p < 0.05.

.

According to the analysis results, the effect of techno-eustress on job performance is quite strong and significant (β = 0.769, t = 26.969, p < 0.001). This finding shows that employees who experience technology positively exhibit higher job performance. In particular, positive perceptions, such as learning from technology, developing mastery, or gaining autonomy, were found to impact job results significantly. Moreover, the impact of technostress on techno-eustress is also positive and significant (β = 0.290, t = 5.395, p < 0.001). This finding indicates that some employees view technological pressure and demands as opportunities for development rather than as threats. In this context, the process of positive reframing (reappraisal) of the stressor is activated.

On the other hand, the direct effect of technostress on performance was not found to be significant (β = –0.034, t = 0.855, p = 0.393). This finding indicates that technological stress does not directly harm an individual’s performance; rather, its effect is indirect. Conversely, the total effect of technostress (β = 0.189, t = 0.191, p < 0.001) is positive due to the significant indirect effect. This suggests that although the direct relationship is weak or insignificant, it contributes to a substantial holistic effect on performance through techno-eustress. In the indirect effect analysis, it was observed that the impact of technostress on performance became significant only through techno-eustress (β = 0.223, t = 5.105, p < 0.001). This finding underscores that the mediating role of techno-eustress is significant and strong. Furthermore, the lack of significance in the direct effect, combined with a high and significant indirect effect, indicates that full mediation exists in this relationship.

In summary, while technology-related stress factors do not directly affect employee performance, when these stressors are transformed into a positive experience, an increase in performance is observed. This indicates that it is essential for these pressures to be reframed in a developmental manner for employees to thrive in a high-stress technology environment.

As a result, as shown in Figure 2, given the significance of the total effect in H1 (total effect of technostress on performance (β = 0.189, t = 0.191, p < 0.001)), all hypotheses tested within the model were supported.

The explanatory power of the structural model was assessed using the R² coefficients of the dependent variables. Accordingly, R² = 0.084 was calculated for the eustress construct, and R² = 0.577 for performance (p). According to the threshold values suggested by Hair et al. [41], R² ≥ 0.25 indicates an acceptable level of explanatory power. In this context, while the performance variable is explained to a high extent by the model, the explanatory power for techno-eustress is more limited.

The effect sizes between the constructs were evaluated using f² coefficients according to Cohen’s classification [47]. The findings indicate that the effect in the direction of techno-eustress → performance is very large (f² = 1.280), the effect of technostress → techno-eustress is small to medium (f² = 0.092, p = 0.018), while the direct effect of technostress → performance is negligible (f² = 0.003, p = 0.755). These results demonstrate that almost all the variance in the performance variable is explained through techno-eustress.

The model’s predictive power was tested using Stone–Geisser Q² coefficients. Positive Q² values were obtained for both constructs (eustress = 0.075; performance = 0.028), indicating that the model has predictive ability [41].

5. Discussion

The findings of this study reveal that the impact of technology-related stress (technostress) on employee performance can vary significantly based on how individuals perceive this stress. In particular, significant increases in performance levels are seen in individuals who view technological demands as development opportunities rather than threats. This supports the idea that cognitive framing mechanisms are not only personal coping strategies but also critical levers for building organizational resilience in digitally intensive work environments.

The first finding of the study, which highlights the positive and significant effect of Technostress on techno-eustress (β = 0.290, p < 0.001), concretely demonstrates that some employees perceive technological pressure and demands as development opportunities rather than threats. Although this effect size is moderate, it is statistically robust and aligns with prior findings in organizational behavior research, where even modest relationships can reflect meaningful psychological mechanisms. In digital work environments, small but consistent cognitive shifts can significantly influence employees’ ability to adapt and transform stress into growth-oriented outcomes. This situation aligns with the primary and secondary evaluation processes of Cognitive Evaluation Theory. Employees initially assess a situation they encounter in their life dynamics as a threat or an opportunity, and then they expand their evaluations by reviewing their coping resources [20]. Furthermore, from a positive psychology perspective, this finding shows that individuals can positively reframe stressful situations and transform them into personal development and learning opportunities [48]. This reframing process is a cornerstone of psychological resilience—one of the foundational components of organizational resilience in the face of digital transformation. In knowledge-intensive sectors, experts such as financial reporting specialists and accountants are rapidly and directly affected by the digital transformation process, facing constantly updated ERP applications, digital audit tools, and related changes in applications and regulations. Intensive use of technology triggers a high level of technostress [6,7]. However, some employees view this stressful situation as an opportunity for development and learning rather than perceiving it as a problem or threat.

A study conducted specifically on accounting specialists in the accumulated literature indicates that technology and the new situations it introduces are interpreted differently; not every stressor is perceived at the same level by the manager or expert accountant. Technostress can be viewed as an element that may yield positive results in terms of personal skill acquisition rather than negative psychological effects [49]. Moreover, Nascimento et al. [50] found that employees who use information technologies intensively can restructure technology-related stressors and transform the climate created by this situation into a factor that enhances productivity and job satisfaction. A large-scale study involving employees in the finance sector determined that the development of positive reappraisal strategies by employees reduced the technology-related stress they experienced while increasing their job satisfaction and learning tendencies [51]. Indeed, the results of the current study also support the literature. These findings collectively show that when an employee cognitively reappraises stressors positively, their capacity to cope with stress is strengthened, which can positively impact performance. This psychological mechanism can be viewed as a micro-level driver of macro-level organizational adaptability.

One finding from the study shows that employees’ perception of technology as a positive experience significantly increases their job performance (β = 0.769, p < 0.001). This result reveals that when technological demands are viewed not only as challenges but also as opportunities for development and motivation, substantial gains in individual productivity can be realized. These findings align with both Self-Determination Theory [44] and Cognitive Evaluation Theory [20]. According to Self-Determination Theory, fulfilling individuals’ needs for autonomy, competence, and social connection enhances intrinsic motivation and, consequently, performance. Cognitive Evaluation Theory describes the psychological effects of a situation based on individual perception, highlighting that a positive perception will lead to positive outcomes. This phenomenon is particularly evident in knowledge-intensive sectors. It has been observed that technostress, which rises with the digitalization of business processes among employees in technology-intensive sectors, enhances performance by integrating technology-induced changes into these processes, fostering a perception that it improves operations, and assessing it as an opportunity for learning, self-efficacy, information systems (IS) literacy, and creative problem-solving [7]. In this regard, techno-eustress can be interpreted not just as an individual coping outcome but also as a potential strategic capability contributing to an organization’s resilience culture. In a study conducted on financial reporting and accounting professionals, it was determined that when the technological demands and stress caused by continuous digitalization in business processes were positively managed through professional commitment, job performance and audit quality among the experts improved [52]. The study’s results support the literature.

Another important finding from the study is that the direct effect of technostress on job performance is not statistically significant (β = –0.034, p = 0.393). This suggests that technological stress may affect employee performance indirectly rather than directly. When evaluated within the framework of the Job Demands–Resources Model, this finding suggests that the impact of job demands (e.g., technological stress) on performance depends on the resources (e.g., supportive leadership, autonomy) available to the individual [19]. This aligns with the broader strategic management view that resilience emerges through the interaction between external demands and internal resource mobilization. Individuals working in knowledge-intensive sectors, such as financial reporting specialists and accountants, often encounter high levels of technostress due to the constant changes in technological tools and software. However, instead of perceiving these technological changes as a threat, some individuals may view them as opportunities for professional growth and learning. This positive reframing enhances employees’ ability to navigate stressful situations and positively influences their job performance. For example, a study conducted by Boyer-Davis [49] examined how accounting and financial reporting professionals respond to technological stressors and found significant differences between managers and non-managers. This finding indicates that organizational support and leadership style are crucial in mitigating the effects of technological stress. Similarly, the literature includes studies that identify both positive and negative effects when examining the relationship between technostress and performance [4,53,54,55]. For instance, Li et al. [56] demonstrate that techno-overload positively impacts teachers’ performance. Furthermore, Penado Abilleira et al. [57] report that technostress does not predict performance for employees who use ICT intensively, such as academics. At this point, it is clear that the nature of the relationship between technostress and performance, particularly the varying results, may depend on various individual and organizational factors, making it essential to explore potential mediating variables in this effect.

The most notable finding of the study is that techno-eustress serves as a full mediator, highlighting the intricate relationship between technostress and performance. This suggests that the impact of technostress on job performance is not significant through direct means; instead, it occurs only through the influence of Techno-eustress. This indicates that resilience is not a passive buffer against stress but an active cognitive–emotional process that reinterprets and transforms stress into performance-enhancing outcomes. In this context, employees can transform stressors into factors that enhance their performance by positively reframing technological pressures and demands, such as viewing them as opportunities for mastery, autonomy, and personal growth. This aligns with Cognitive Evaluation Theory, which posits that individual evaluation processes shape stress. Additionally, the positive psychology approach contends that stressful experiences can be reinterpreted and turned into opportunities for growth, thereby supporting the theoretical foundations of this study’s findings. Nascimento and his colleagues [50] conducted a study involving higher education teachers that yielded similar results. They found that when technostress is interpreted positively by employees, it can lead to eustress, which in turn enhances job satisfaction and performance. Furthermore, the research highlights that technostress experiences, particularly among healthcare workers who face increasing digitalization and a stressful work environment, can boost job satisfaction through positive experiences (techno eustress). This increase in satisfaction may reduce their intention to leave their jobs [17]. Additionally, Yu and his colleagues [58] noted that technostress caused by techno-overload—another significant aspect of technostress—can enhance productivity and facilitate creative problem-solving when perceived as eustress through positive cognitive appraisal. The findings of their study align with existing literature. They clearly indicate that individuals working in knowledge-intensive professions, such as accounting and financial reporting, navigate technological stress through cognitive and emotional filters. As a result, the impact on performance outcomes occurs indirectly rather than directly.

In conclusion, this study demonstrates that the negative effects of technological stress are not unavoidable. Rather, the way individuals cognitively appraise digital demands plays a transformative role in shaping performance outcomes. Techno-eustress, as a cognitive–emotional reframing process, emerges as a strategic enabler of psychological and organizational resilience in an era of accelerating digital change. Therefore, it is important to recognize that the integration of technology into the workplace presents not only risks but also opportunities.

The concept of techno-eustress, which refers to the positive aspects of technology use, can contribute constructively to managerial effectiveness, human sustainability, and the development of resilient organizational cultures. This finding offers practical implications for organizations seeking to leverage digital transformation as a resilience-building opportunity rather than as a threat to workforce well-being and productivity.

6. Conclusions

This study examined the impact of technological transformation on the psychological experiences and job performance of employees in knowledge-intensive sectors from a holistic perspective. The findings reveal that technology-related stress does not inherently diminish job performance; rather, its impact is shaped by how employees cognitively interpret and respond to these pressures. When perceived as techno-eustress, digital demands can lead to increased motivation, engagement, and productivity. This suggests that technostress is not solely a disruptive force but, rather, a strategic psychological dynamic that, if reframed, can enhance both individual functioning and organizational outcomes.

A key contribution of this study is the finding that techno-eustress not only has a significant positive effect on performance but also serves as a full mediating mechanism in the relationship between technostress and performance. This indicates that employees are not solely negatively impacted by technological pressures; they can also leverage these stressors to create opportunities for personal growth, learning, and increased productivity. When examined through the lens of Cognitive Evaluation Theory and Self-Determination Theory, this finding highlights that employees’ perceptions of stressful situations and their coping strategies play a crucial role in determining job performance.

Moreover, the findings reinforce the importance of addressing not only technological infrastructure but also the cognitive and emotional capacities of employees. In this context, the implementation of structured interventions such as cognitive reframing techniques, resilience-building programs, and positive psychology practices becomes critical. Additionally, this study addresses a significant gap in the literature by focusing on groups that engage intensively with information technologies, such as accounting and financial reporting specialists. This research offers valuable insights into how stress affects performance, particularly given the high level of digitalization in their work processes. In this respect, the study assesses the impact of digitalization on management models, not only at the level of institutional structures but also within the context of the psychological relationships that individuals form with technology. The findings reveal that emerging technologies are reshaping employee experiences, and this transformation is decisive in basic organizational outcomes, such as performance.

Ultimately, this research argues for the integration of human-centered design principles into strategic digital transformation efforts. Technology adaptation must be supported not only through hardware and software improvements but also by cultivating the psychological flexibility and resilience of the workforce. Recognizing the strategic significance of employee perception and mental well-being will be vital for building sustainable, adaptive, and resilient organizations in the age of technological disruption.

6.1. Theoretical and Practical Implications

This study makes significant contributions both theoretically and practically. Theoretically, this study explores the effects of technological stress on employees, not only viewing it as a negative experience but also as a dynamic that can lead to positive outcomes, depending on how individuals subjectively evaluate the situation. It provides empirical findings that support the idea that perceiving a stimulus as either a “threat” or an “opportunity” influences individual outcomes within the framework of Cognitive Evaluation Theory [20].

The study’s full mediation model makes a significant contribution to the literature by demonstrating that the impact of technology-related stress on performance is not direct; it occurs only through positive reevaluation processes, referred to as techno-eustress. Additionally, the research examines how technological stress interacts with individuals’ basic psychological needs—autonomy, competence, and relatedness—within the context of Self-Determination Theory [44]. It shows that techno-eustress can serve as a valuable tool to support these needs. In this context, a theoretical framework has been presented that suggests evaluating interaction with technology not only in terms of workload and cognitive strain, but also considering the individual’s subjective motivation processes. This mediating mechanism offers a theoretical advancement by reframing stress not as a binary positive or negative condition, but as a contextually modifiable construct shaped by appraisal processes. By introducing techno-eustress into the conversation, the study opens a pathway for integrating stress research with positive organizational scholarship and resilience theory, enhancing the conceptual depth of the technostress literature.

The study’s findings, particularly in demonstrating that technological stress has a reconstructable conceptual framework, suggest that the concept of techno-eustress can be moved to a more central position in business life theories [59]. In addition, the full mediation effect demonstrated in the study makes a significant original contribution to previous studies that aim to explain the technostress-performance relationship. In the existing literature, contradictory findings are frequently reported regarding the direction of this relationship [7,17]. Some studies have reported direct adverse effects of technostress [6], while others have reported positive or no effects [50,57,60]. In this context, the model presented in the current study provides an integrative framework to explain theoretical inconsistencies by revealing the psychological mediation mechanism behind these differences.

The research also has significant practical contributions. The findings of this research indicate that interventions that support the positive reframing of stress faced by employees during technological transformation processes, rather than an absolute reduction in this stress, can yield more effective results. Especially in knowledge-based sectors, such as accounting and financial reporting, which require high digital integration, individuals who evaluate technological demands as development opportunities significantly increase their job performance. In this context, organizations’ digital transformation strategies should consider not only technology investments but also individual resources that shape the ways employees cope with stress [7,50]. In this context, organizations’ human resources policies should include not only technical skills training but also programs based on positive psychological interventions (e.g., cognitive restructuring, resilience training) because such practices can increase motivational and productivity outcomes by facilitating employees to see technology-related stress factors as a means of learning and mastery development rather than as threats. This suggestion is also directly consistent with the predictions of Cognitive Evaluation Theory regarding the manageability of stress [20].

Furthermore, these findings offer actionable insights for management practice. Organizations can deliberately cultivate environments that promote techno-eustress through leadership behaviors that encourage autonomy, digital upskilling opportunities, and a psychologically safe climate for experimentation. In doing so, they can transform technology-related pressure into a motivational force that drives innovation, adaptive performance, and long-term strategic agility.

Finally, the findings suggest that the success of digital transformation hinges not only on technological investment but also on the quality of human experience in the workplace. Employee perceptions, emotional responses, and psychological capacities should be considered strategic assets in the implementation of any transformation initiative. Promoting techno-eustress as a target organizational mindset offers a new pathway for aligning digital transformation with resilience, sustainability, and strategic agility.

6.2. Research Limitations and Future Directions

This study has certain methodological and contextual limitations. Firstly, the study employed a cross-sectional design, and therefore, the temporal transitivity of causal relationships between variables could not be assessed. Thus, the findings should be interpreted at a relational level rather than a causal one. The use of longitudinal designs in future studies will provide essential contributions, particularly in revealing the temporal evolution of technology-related stress and the sustainability of techno-eustress perception.

Secondly, the sample group is limited to professionals in accounting and financial reporting. This limitation affects the generalizability of the findings at the sector level. However, it is believed that this occupational group, which works in knowledge-intensive jobs, provides a significant starting point for representing a professional field where technology-related stress is experienced intensely. Future studies should aim to expand the scope to include diverse sectors, allowing for comparative resilience analysis across industries with varying levels of digital maturity and stress exposure. In particular, industry-specific characteristics—such as task automation, regulatory pressure, or organizational digital culture—may shape how technostress and techno-eustress are experienced and interpreted. Discussing these factors will improve the contextual richness and generalizability of future findings.

Moreover, this study focused solely on individual-level cognitive appraisal processes. However, organizational factors (e.g., leadership style, technology infrastructure, organizational culture) can also significantly influence the perception of technological stress. Future research should consider developing multi-level theoretical models that explore the interaction between individual psychological resources (e.g., resilience, self-efficacy, psychological capital) and organizational enablers (e.g., supportive leadership, flexibility, learning-oriented culture). These models would provide a more comprehensive view of how strategic management practices can buffer or amplify the effects of technostress on employee and organizational outcomes. In addition, the current model focused exclusively on techno-eustress as the mediating variable. Other plausible mediators—such as psychological capital or technical self-efficacy—were not included and may offer complementary or competing explanations. Future studies are encouraged to examine these alternatives to enrich the theoretical scope and explanatory power of technostress research.

In addition, future studies should examine the moderating or mediating roles of organizational variables within the technostress–performance link. For instance, leadership style—especially transformational or resilience-focused leadership—may influence how stress is framed and managed at both individual and team levels. Similarly, the presence of strategic HR practices that foster learning agility, psychological safety, and employee empowerment could significantly contribute to building organizational resilience capabilities in the face of digital transformation.

Lastly, deeper exploration of personal psychological resources—such as psychological capital, adaptive performance, or trait resilience—can enrich the understanding of why some employees thrive under digital stress while others struggle. This would help inform the development of resilience-oriented training programs, aimed not only at mitigating stress but also at turning it into a strategic asset for both individual development and organizational competitiveness.