Why do Employees (not) Support Enterprise Digital Transformation? A Transactional Theory of Stress Perspective

Abstract

Employee support is a vital component for enterprise digital transformation, and a clearer understanding of its mechanism is crucial to the success of such transformation. Drawing upon the transactional theory of stress, we examine why employees (not) support enterprise digital transformation. A field study of 121 employees in a company undergoing digital transformation demonstrates that challenge and hindrance appraisals are two countervailing mechanisms that mediate the relationship between enterprise digital transformation and employee digital transformation support. The results further show that employees’ regulatory foci moderate the indirect effect of enterprise digital transformation on employee digital transformation support through their challenge or hindrance appraisal of the digital transformation. Specifically, employees with a higher promotion (prevention) focus are more likely to engage in challenge (hindrance) appraisal and support (not support) enterprise digital transformation. Our research has theoretical and managerial implications for employee management for enterprise digital transformation.

Keywords

employee digital transformation support enterprise digital transformation challenge appraisal hindrance appraisal promotion focus prevention focus

Enterprise digital transformation refers to a change in an enterprise’s usage of digital technologies to establish a novel digital business model that facilitates the creation and appropriation of additional value for the enterprise (Verhoef et al., 2021). It has become a vital trend in the development of enterprises (Eller et al., 2020). According to the International Data Corporation, enterprise digital transformation expenditure is projected to near $3.9 trillion by 2027, exhibiting a robust five-year compound annual growth rate of 16.1%¹. Given the prevalence and significance of enterprise digital transformation, scholars have increasingly paid attention to this topic and explored its consequences (Fischer et al., 2018; Li et al., 2018; Vial, 2019; Wang et al., 2020). Indeed, studies have shown that the successful implementation of digital transformation brings positive changes to enterprises (Warner & Wäger, 2019; Wessel et al., 2021), such as the promotion of enterprise performance (Guo & Xu, 2021) and the enhancement of enterprise innovation (Wen et al., 2022).

Despite such fruitful findings, previous studies have largely ignored the significant role of employees in facilitating the success of enterprise digital transformation. Indeed, several emerging conceptual papers have urged empirical investigations on this aspect (e.g., Cetindamar Kozanoglu & Abedin, 2021; Hanelt et al., 2021). This perspective is also consistent with Kim et al. (2014), which has highlighted that the strategic endeavors of upper echelons can significantly benefit from the impetus and grassroots innovations introduced by employees. Hence, one necessity for the successful implementation of top-down strategies such as enterprise digital transformation is employees’ support (Li et al., 2016; Meske & Junglas, 2021; Schneider & Sting, 2020). Given these considerations, it is pivotal to clarify why employees (not) support enterprise digital transformation, which refers to the extent to which employees approve and promote enterprise digital transformation in the workplace (Fedor et al., 2006; Venus et al., 2019).

Moreover, enterprise digital transformation may have double-edged sword effects on employees, making whether employee support it or not unclear. On the one hand, the change from traditional technology to information technology helps employees streamline work procedures and promote work efficiency (Mahmood et al., 2001). Digital transformation also has the potential to optimize employee workflows and overall work experiences, liberating them from tedious and repetitive tasks (Meske & Junglas, 2021; Vom Brocke et al., 2018). Hence, employees may support digital transformation due to such potential benefits. On the other hand, digital transformation may force employees to learn new knowledge, which imposes higher job requirements on their working capabilities (Mahmood et al., 2001). Meanwhile, the digital technologies introduced by digital transformation may replace some simple and repetitive tasks. This change can lead to the replacement of some positions or the evolution of job content, which places employees at a risk of losing their jobs (Kolade & Owoseni, 2022). In addition, employees’ social exchange relationships may also be altered by digital transformation because the basis for reciprocity (information and knowledge) has been modified, which further complicates social relationships in the workplace (Van Der Schaft et al., 2022). In this regard, employees may not support digital transformation because of its potential costs.

Our research thus builds a contingent dual-path model to clarify why employees (not) support enterprise digital transformation, on the basis of transactional theory of stress (Lazarus & Folkman, 1984). According to this theory, when employees encounter stressful events relevant to themselves, they would firstly form cognitive appraisals toward the stressor and then determine how they would cope with such stressor (Biggs et al., 2017; Webster et al., 2011). Digital transformation can be seen as a potential stressor relevant to employees because it attaches new requirements to employees’ job responsibilities, working characteristics, working styles, and other aspects (Bautista et al., 2018; Mukerjee et al., 2021; Muylaert et al., 2022). In this regard, employees may take the form of challenge appraisal (“an individual’s subjective interpretation that the demands have a potential for personal gain, growth, development, and well-being”; LePine et al., 2016, p. 10) or hindrance appraisal (“an individual’s subjective interpretation that the demands have a potential to result in personal loss, constraints, or harm”; LePine et al., 2016, p. 10) toward enterprise digital transformation and subsequently react in different ways. Specifically, we expect that employees who develop a challenge appraisal toward enterprise digital transformation believe it to be an opportunity for their growth, thereby enhancing digital transformation support. On the contrary, a hindrance appraisal of enterprise digital transformation is likely to make employees consider digital transformation as a potential risk, thereby leading to a decline in digital transformation support.

Furthermore, it is important to investigate the contingency that determines the extent to which employees develop a challenge or hindrance appraisal of digital transformation. Drawing upon the transactional theory of stress, we identify employees’ promotion focus and prevention focus as vital moderators (Brockner & Higgins, 2001; Kark & Van Dijk, 2007). This theory holds that individuals’ cognitive evaluation of stressors and coping styles are influenced by their characteristics (Lazarus & Folkman, 1984), especially those related to self-regulation, because such characteristics affect individuals’ perception of stress (Byron et al., 2018). Regulatory foci, as relatively stable and chronic traits (Higgins & Spiegel, 2004), are important individual characteristics related to self-regulation (Brockner & Higgins, 2001) and are key moderators in stress appraisal processes (Sacramento et al., 2013). There are two types of regulatory foci: promotion focus and prevention focus. Promotion focus is a self-regulatory focus in which people’s growth and development needs dominate, such that they attempt to align with their ideal selves, which then increases the salience of potential gains to be attained (Brockner & Higgins, 2001). Prevention focus is a self-regulatory focus in which people’s security and safety needs dominate, such that they align with their ought-to selves, and this intensifies the salience of potential losses to be avoided (Brockner & Higgins, 2001). Accordingly, we argue that employees with a promotion or prevention focus are likely to develop different appraisals of enterprise digital transformation (Brockner & Higgins, 2001; Crum et al., 2013), resulting in differences in digital transformation support. Indeed, empirical evidence has revealed that employees with different regulatory foci show obvious behavioral differences in the context of organizational change (Tseng & Kang, 2008).

Our research makes three main contributions. First, taking digital transformation as a whole, we explore the effect of enterprise digital transformation on employees’ attitudes and behaviors. Previous studies have focused on the macro-level impacts of digital transformation on enterprises, such as enterprise innovation (Liang & Li, 2022; Wen et al., 2022), enterprise value (Ma et al., 2022), and enterprise performance (Guo & Xu, 2021). However, limited empirical studies have paid attention to the effects of digital transformation on employees, even though with some conceptual calls (Cetindamar Kozanoglu & Abedin, 2021; Hanelt et al., 2021). Building on such work, this study explains why employees (not) support enterprise digital transformation. Our research is among the first to make a clear theoretical and empirical exploration of the relationship between enterprise digital transformation and employee digital transformation support, thereby enriching the nomological network of digital transformation consequences.

Second, we identify two distinctive stress appraisals (challenge and hindrance) as mediators that explain how enterprise digital transformation promotes or inhibits employee digital transformation support. Previous studies on the mediating variables of enterprise digital transformation mainly considered macro mechanisms such as environmental protection cognition (Xie et al., 2022), ambidextrous innovation (Ma et al., 2022), and research and development (R&D) capability (Liang & Li, 2022). Among the few studies focusing on the micro-level impact of enterprise digital transformation, Van Der Schaft et al. (2022) drew on construal-level theory and dynamic change perspectives to explore employee attitudes toward digital transformation as explaining mechanisms. They highlighted the importance of a social exchange lens in digital transformation knowledge because it represents a deep structural change and can lead to the failure of change. By contrast, our study unpacks challenge and hindrance appraisals as mediators to explain the double-edged effects of enterprise digital transformation on employee digital transformation support, thereby contributing to the scholarly understanding of why digital transformation receives more or less support from employees.

Third, we identify employees’ regulatory foci as an important boundary condition. Among the limited research available, previous studies have mainly explored the moderating effects of age, gender, and tenure on the relationship between digital technology application and employees’ attitudes and behaviors (Tong et al., 2021; Wang et al., 2020). Although these studies help us understand the influences of demographics, further exploration of other critical individual characteristics is warranted to analyze the contingencies of digital transformation’s consequences on employees. In this vein, our research advances the digital transformation literature by introducing employees’ self-regulation characteristics to explore the contingency for the relationship between digital technology and employees’ attitudes and behaviors.

Theory and Hypotheses

Enterprise Digital Transformation

Digital transformation has become a major trend for the development of enterprises (Eller et al., 2020). In the process of digital transformation, enterprises adopt a combination of information, computing, communication, and connectivity technologies to bring about significant changes in their attributes and achieve overall improvement (Warner & Wäger, 2019; Wessel et al., 2021). Indeed, existing studies, predominantly centering on the macro level, demonstrate the positive effects of enterprise digital transformation on optimizing resource allocation, facilitating collaboration efficiency, improving decision-making accuracy, creating customer value, and reshaping business ecology (Fischer et al., 2018; Vial, 2019; Wang et al., 2020).

Recently, emerging studies have paid attention to the significant role of employees in the context of enterprise digital transformation (e.g., Cetindamar Kozanoglu & Abedin, 2021; Meske & Junglas, 2021; Schneider & Sting, 2020). Scholars have argued that employees are impacted by digital transformation and firms need to account for the digitalization of employees (for a review, see Hanelt et al., 2021). Cetindamar Kozanoglu and Abedin (2021) have theoretically discussed digital literacy of employees as a multi-dimensional organizational affordance in promoting enterprise digital transformation. Meske and Junglas (2021) have found that the expected work design characteristics have important influences on employees’ attitude towards digital transformation. Schneider and Sting (2020) have discerned five distinct cognitive frames—utilitarian, functional, anthropocentric, traditional, and playful—that shape employee attitudes toward digitalization-induced Industry 4.0. These studies indicate the criticality of directly examining the relationship between enterprise digital transformation and employee digital transformation support, given the pivotal role employees play in the digital transformation process.

Indeed, digital transformation brings about a series of changes to employees via technology, workflow, and interpersonal relationships, which significantly affects employees’ support toward digital transformation. In terms of technology, the widespread application of new technologies such as big data analysis, social media, mobile technology, and cloud computing will change the way businesses operate (Verhoef et al., 2021). In terms of workflow, digital transformation changes traditional offline work and promote the emergence of remote work and virtual teams. This new way of working poses significant challenges for employees (Yan et al., 2022; Zeike et al., 2019). Further, as new technologies are applied and developed, employees may need to rely on and interact with talents occupying relevant skills and experience, which may affect their interpersonal relationships at work (Van Der Schaft et al., 2022).

Transactional Theory of Stress

The transactional theory of stress holds that individuals assess stressors based on personal experiences, which in turn influences their appraisal of available resources and shapes their subsequent coping strategies (Lazarus & Folkman, 1984). The stressor concept is crucial to this theory; it is a factor or experience that exerts pressure on individuals. In a process called “primary appraisal” (Lazarus & Folkman, 1984; Prem et al., 2017), individuals perceive and interpret the pressure they experience. Through this process, individuals would firstly assess relevance of the stressor; they would not have subsequent attitudinal or behavioral reactions if the stressor deemed irrelevant to themselves. Conversely, if deemed relevant, employees may carry out different types of cognitive appraisals toward the same stressor at the stage of primary appraisal (LePine et al., 2016; Webster et al., 2011). Stressors viewed as having potential rewards (e.g., recognition and praise), opportunities for mastery, and growth are termed challenge appraisals, while those perceived as threatening one’s well-being by impeding goal attainment and development are classified as hindrance appraisals (Biggs et al., 2017; Lazarus & Folkman, 1984). In the “secondary appraisal” process, individuals would assess their resources to help determine how they will cope with the stressor and ultimately behave in a certain way based on their cognitive evaluation of the stressor (Lazarus & Folkman, 1984; Prem et al., 2017).

Based on the transactional theory of stress, we propose that enterprise digital transformation represents a stressor relevant to employees because it may dramatically change employees’ workflow, job responsibilities, work characteristics, working styles, and interpersonal relationships (Kaltiainen et al., 2020; Muylaert et al., 2022; Sacramento et al., 2013). Moreover, when employees make a primary appraisal of digital transformation, they may develop a challenge or hindrance appraisal. When employees make a secondary appraisal, they assess their resources to determine how they will cope with enterprise digital transformation. They either support or not support enterprise digital transformation as a coping strategy against digital transformation as a work stressor (Lazarus & Folkman, 1984; LePine et al., 2016).

Enterprise Digital Transformation and Employees’ Stress Appraisals

Employees may view enterprise digital transformation as a challenge stressor and offer support for three reasons. First, enterprise digital transformation brings mobile devices, information systems based on cloud computing, and other technologies to the work environment, all of which can free employees from the burdens of redundant tasks they used to perform, such that they can spend more time and energy solving important and innovative problems (Bautista et al., 2018). Second, enterprise digital transformation eliminates the need for employees to work onsite all the time and increases their telecommuting opportunities. Consequently, time flexibility for employees increases, and they perceive greater job autonomy (Gajendran & Harrison, 2007). Finally, the digital enterprise culture and the leadership and information interaction modes may continuously provide employees with rich information and knowledge, helping them generate better ideas and solutions at work and facilitate their growth (Gajendran & Harrison, 2007).

Conversely, employees may also develop a hindrance appraisal of enterprise digital transformation and not support it. First, a digital work system is characterized by comprehensive coverage, immediate feedback, and high interaction frequency, all of which make uninterrupted work connection possible and improve enterprises’ and leaders’ control over employees. Thus, employees’ perceived job autonomy may be reduced and they may resist the enterprise’s digital control (Dettmers et al., 2016; Kellogg et al., 2020). Second, previous studies have shown that “telecommuting” and “virtual teams” have become the norm under a digital enterprise culture. This new working environment reduces employees’ sense of group belongingness and result in role ambiguity, causing confusion and planting obstacles in their organizational identification (Petriglieri et al., 2018). Finally, enterprise digital transformation leads to the replacement of some positions or the evolution of work content, requires new job skills, and may even bring about the risk of unemployment (Kolade & Owoseni, 2022). All of these may cause employees to feel threatened and not support digital transformation.

The Moderating Role of Regulatory Foci

Based on the core of transactional theory of stress (Lazarus & Folkman, 1984; LePine et al., 2016; Webster et al., 2011), we further clarify how regulatory foci affect the extent to which employees develop challenge or hindrance appraisals. Specifically, employees with higher promotion focus are eager to grow and develop, and are committed to pursue their ideals, hopes, and aspirations (Brockner & Higgins, 2001). Therefore, when faced with enterprise digital transformation, they tend to regard it as an opportunity for self-improvement (Crum et al., 2013). They try their best to adapt to the changes brought about by digital transformation, such as a more challenging job content and a higher level of professional requirements (Zeike et al., 2019). For example, in the face of these new job challenges, they strive to find solutions. They are also more willing to invest time and energy to adapt to such changes, and they take practical actions to for self-improvement (LePine et al., 2016). Moreover, setbacks and failures do not impair the self-efficacy of employees with a higher promotion focus (LePine et al., 2016), but instead help them recognize the facts, find their purpose, summarize their experience, and improve their own digital professional skills (Webster et al., 2011). Thus, enterprise digital transformation positively influences employees with higher promotion focus and elicit challenge appraisal. By contrast, employees with lower promotion focus are less likely to pursue growth and development (Brockner & Higgins, 2001). Thus, they are less willing to accept and adapt to the job changes brought by digital transformation and are more easily defeated by the difficulties and setbacks resulting from digital transformation (Brockner & Higgins, 2001; LePine et al., 2016). Consequently, employees with lower promotion focus are less likely to make a challenge appraisal of digital transformation. Therefore, we propose:

Hypothesis 1

Employees’ promotion focus moderates the relationship between enterprise digital transformation and challenge appraisal, such that this relationship is more positive for employees with higher (vs. lower) promotion focus.

Meanwhile, employees with higher prevention focus are driven by security needs, as they only want to complete their tasks and avoid uncertainty and challenges (Brockner & Higgins, 2001). Thus, they are more likely to consider enterprise digital transformation as a risk and have a sense of insecurity (Crum et al., 2013). They are anxious about the new job requirements and lower perceived job autonomy brought about by enterprise digital transformation and regard these changes as a threat to past achievements (Fugate & Soenen, 2018). Such sensitivity to threats may cause drastic psychological fluctuations or even trigger a major crisis, thereby leading employees to experience more negative emotions and regard digital transformation as an obstacle to their development (Brockner & Higgins, 2001; Crum et al., 2013). In addition, these negative emotions are likely to worsen over time as employees fail to keep up with their team’s and colleagues’ progress (Brockner & Higgins, 2001; Muylaert et al., 2022). Thus, enterprise digital transformation is more likely to be seen as an obstacle by employees with higher prevention focus and elicit hindrance appraisal. By contrast, employees with lower prevention focus are less likely to avoid uncertainties and challenges (Brockner & Higgins, 2001). The latter tend to not believe that the high job requirements brought by digital transformation are unacceptable and feel less nervous (Brockner & Higgins, 2001). Thus, they are less likely to regard digital transformation as a risk and make a hindrance appraisal (Crum et al., 2013). Accordingly, we argue the following:

Hypothesis 2

Employees’ prevention focus moderates the relationship between enterprise digital transformation and hindrance appraisal, such that this relationship is more positive for employees with higher (vs. lower) prevention focus.

The Impacts on Employee Digital Transformation Support

According to the transactional theory of stress (Lazarus & Folkman, 1984; LePine et al., 2016), employees’ type of stress cognitive appraisal will influence their coping style in terms of digital transformation support. Specifically, we propose that the challenge appraisal of enterprise digital transformation enhances employee digital transformation support. The transactional theory of stress holds that when employees make a challenge appraisal of digital transformation, they regard the additional job requirements brought by digital transformation as valuable opportunities to realize personal growth and development and improve revenue. Therefore, these employees work hard and spend more time and energy to deal with the new job requirements (Lazarus & Folkman, 1984; LePine et al., 2016) and support digital transformation, accordingly. In addition, employees who make a challenge appraisal are more likely to capitalize on the job opportunities brought by enterprise digital transformation (LePine et al., 2016), such as job autonomy and technology empowerment (Bartsch et al., 2020). Therefore, they tend to pay more attention to the positive aspects of digital transformation and recognize its benefits, thus promoting employee digital transformation support. Finally, employees who make a challenge appraisal of digital transformation eventually enhance their work performance (LePine et al., 2005, 2016). They are more willing to do additional work and make extra effort to facilitate the success of the digital transformation, which means they support the digital transformation (Herold et al., 2008; Kim et al., 2010). Therefore, we propose:

Hypothesis 3

Employees’ challenge appraisal of enterprise digital transformation is positively related to employee digital transformation support.

Meanwhile, we argue that employees’ hindrance appraisal of enterprise digital transformation inhibits employee digital transformation support. First, employees who make a hindrance appraisal of enterprise digital transformation believe that the work changes and additional job requirements brought by the digital transformation will bring them potential losses, restrict them, and even damage their interests (Bartsch et al., 2020; LePine et al., 2005; Webster et al., 2011). Consequently, they tend to reduce their perceived negative feelings by avoiding such changes and expressing negative emotions and attitudes (Lazarus & Folkman, 1984; LePine et al., 2016), which leads to a reduction in their digital transformation support. Second, employees who make a hindrance appraisal of digital transformation focus on its negative aspects such as high organizational control and information overload, but ignore its positive effects (LePine et al., 2016). Thus, they are less likely to support such a transformation. Third, hindrance appraisal may deteriorate employee work performance (Lazarus & Folkman, 1984; LePine et al., 2016), as employees become uncomfortable with the new requirements brought by digital transformation and are reluctant to make extra effort (LePine et al., 2016). Such employees are less likely to support digital transformation (Oreg, 2003; Venus et al., 2019). Therefore, we suggest:

Hypothesis 4

Employees’ hindrance appraisal of enterprise digital transformation is negatively related to employee digital transformation support.

Moderated Mediation Model

Based on the hypotheses discussed above, we propose a moderated mediation model (Figure 1). As a stressor, enterprise digital transformation may lead to challenge and hindrance appraisals, depending on employees’ regulatory foci. Specifically, employees with a higher promotion focus will regard the transformation as an opportunity for growth and development, causing them to make a challenge appraisal and support digital transformation. On the contrary, those with a higher prevention focus will consider the transformation a risk and threat to their security, leading them to make a hindrance appraisal and reduce digital transformation support. In summary, we propose the following:

Figure 1.

Research model.

Hypothesis 5

Employees’ promotion focus moderates the mediating effect of enterprise digital transformation on employee digital transformation support through challenge appraisal, such that this mediation effect is more positive for employees with higher (vs. lower) promotion focus.

Hypothesis 6

Employees’ prevention focus moderates the mediating effect of enterprise digital transformation on employee digital transformation support through hindrance appraisal, such that this mediation effect is more negative for employees with higher (vs. lower) prevention focus.

Method

Sample and Procedures

The sample consisted of 121 employees from an A-share listed enterprise in Beijing, China. It is a leading IT enterprise which provides services for data centers in more than 150 cities in China. Due to the enterprise’s fast development, the centralized management style of all processes was unable to meet the requirements for flexibility and efficiency. In response to this management mode challenge, top managers promoted a digital transformation strategy and officially launched the bidding digital management system in the bidding department, hoping to improve its work efficiency in all processes such as application, stamp usage, revision, and approval through digital transformation. Indeed, the data extracted from the real workflow records of the enterprise’s management system showed that the time taken for the entire process was significantly shorter after digital transformation.

With the enterprise head’s consent, we obtained a roster of bidding-department employees who were willing to participate in the study along with their email addresses from the human resource (HR) department. Subsequently, we sent emails to the participants to introduce our study’s purpose and procedures. They were assured that their responses would remain confidential and secure from third parties (including enterprise managers) and that such responses would only be used for academic research. To minimize common method variance (Podsakoff et al., 2003), we conducted a multi-time field survey. To match the data collected at different time points, a unique identification code (ID), known only to the researchers and participants, was randomly generated for each employee.

Before conducting the survey, we used G*Power software to calculate the required sample size. Given that no previous study had examined hypotheses similar to ours, the requested effect size was unknown. We chose a t test and adopted linear multiple regression in G*Power. Following the work of Curchod et al. (2019), the effect size was set to medium (f² = 0.15) and the alpha was set to 0.05. The calculation required 89 participants, and the statistical power was around 0.95. Thus, our sample met this requirement.

At Time 1, we sent online questionnaires to 300 employees and invited them to evaluate their perceived enterprise digital transformation, promotion focus, and prevention focus. At this time point, we collected 185 completed responses with a response rate of 62%. At Time 2, after a three-month interval, we asked employees who participated in the Time-1 survey to assess their type of stress appraisal for digital transformation (challenge and hindrance appraisal) and their digital transformation support. At this time point, we obtained 127 completed responses, yielding a response rate of 69%. After obtaining the participants’ consent, we obtained their demographic information from the HR department, namely, their gender, education level, tenure, and rank. Finally, we gave 15 RMB (almost 2USD) to participants who completed the two-wave survey, as a reward for their support.

After matching the two-wave data, 121 responses were obtained, which exceeded the minimum sample size (89 responses). Analysis of the t test showed no significant differences (p > 0.05) in gender, age, educational level, tenure, and rank between the participants who completed the two-wave survey and those who did not, indicating that the sample loss was random. Among the final 121 employees, 31.0% were male, 95.1% had a bachelor’s degree or above, 90% were primary employees and primary managers, and average tenure was 6.83 years (SD = 3.83).

Measures

Given that all the scales were originally in English, they were translated into Chinese following the translation and back-translation method (Brislin, 1986). Unless otherwise noted, all variables were measured using a seven-point Likert-type scale (1 = “strongly disagree”; 7 = “strongly agree”).

Enterprise Digital Transformation

Enterprise digital transformation was measured by a three-item scale from Eller et al. (2020). Employees were asked to evaluate the extent of digital transformation in their company, compared with that in other companies from the same industry. A sample item is “What is your assessment of your own digitalization compared to the industry?” and “What is your assessment of information and communication technology use?” A seven-point Likert-type scale was used (1 = “far lower than peer companies”; 7 = “far higher than peer companies”). The Cronbach’s alpha (α) for this measure was 0.94.

Promotion and Prevention Focus

We measured trait regulatory focus using the scale developed by Lockwood et al. (2002). The scale contains 18 items corresponding to employees’ promotion and prevention foci (nine items for each). A sample item for promotion focus is “I frequently imagine how I will achieve my hopes and aspirations” (α = 0.95). A sample item for prevention focus is “In general, I am focused on preventing negative events in my life” (α = 0.90).

Challenge and Hindrance Appraisals

We adopted the Cognitive Appraisal of Stress Scale from LePine et al. (2016), which is based on the scales developed by Lazarus and Folkman (1984) and LePine et al. (2005). The scale contains six items corresponding to challenge and hindrance appraisal (three items for each). A sample item for challenge appraisal is “Working to fulfill the demands of enterprise digital transformation helps improve my personal growth and well-being” (α = 0.71). Meanwhile, a sample item for hindrance appraisal is “Working to fulfill the demands of enterprise digital transformation thwarts my personal growth and well-being” (α = 0.95).

Employee Digital Transformation Support

We measured employee digital transformation support by adapting the four-item Organizational Change Support Scale from Fedor et al. (2006). Sample items are “I am doing whatever I can to help enterprise digital transformation be successful” and “I have tried (or intend to try) to convince colleagues to support enterprise digital transformation” (α = 0.93).

Control Variables

Previous studies have demonstrated that gender and job tenure affect employees’ attitudes and behaviors during organizational change (Madsen et al., 2005), while educational level and rank influence the type of cognitive appraisal of stress (LePine et al., 2016). Therefore, we controlled for these demographic variables to eliminate their potential impact on the results of the data analysis. Notably, all results were consistent with or without these controls.

Results

Descriptive Statistics

Table 1 shows the means, standard deviations, correlations, and reliability values of all the study variables.

Table 1.

Means, Standard Deviations, Correlations, and Reliability of Key Variables.

Variables	Mean	SD	1	2	3	4	5	6	7	8	9	10
1. Gender	0.31	0.37
2. Education	3.01	0.33	.32***(<.001)
3. Rank	1.44	0.68	.18^*(.049)	.47***(<.001)
4. Tenure	6.83	3.83	.21^*(.02)	.27**(.003)	.50***(<.001)
5. Enterprise digital transformation	5.27	0.96	.11 (.23)	.16 (.08)	.14 (.13)	−.06 (.48)	.94
6. Promotion focus	5.52	0.89	.18 (.052)	.07 (.42)	.14 (.12)	−.03 (.75)	.47***(<.001)	.95
7. Prevention focus	4.39	1.12	.10 (.25)	.01 (.94)	−.12 (.20)	.11 (.24)	.09 (.30)	.32***(<.001)	.90
8. Challenge appraisal	5.49	0.92	.18^*(.04)	.06 (.51)	.08 (.39)	.02 (.82)	.34***(<.001)	.34***(<.001)	.03 (.73)	.71
9. Hindrance appraisal	3.20	1.78	−.10 (.28)	−.07 (.43)	−.11 (.25)	.07 (.44)	.08 (.36)	−.05 (.56)	.29**(.0011)	.13 (.16)	.95
10. Employee digital transformation support	5.87	0.97	.09 (.31)	.18^*(.047)	.08 (.41)	−.01 (.88)	.19^*(.04)	.32***(<.001)	.05 (.58)	.45***(<.001)	−.15 (.09)	.93

Note. N = 121. Variables 1–4 were provided by the HR department. Variables 5–7 were reported by the participants at Time 1. Variables 8–10 were reported by the participants at Time 2. For gender, male = 1, female = 0; for education, middle school = 1, high school = 2, bachelor = 3, master = 4, Ph.D. = 5; for rank, primary employee = 1, primary manager = 2, middle manager = 3, senior manager = 4; for tenure, the unit is “year.” Cronbach’s alphas are bolded and marked on the diagonal. Exact p-values are in parentheses next to the correlation coefficients. *p < .05, **p < .01, ***p < .001.

Confirmatory Factor Analyses

Before testing the hypotheses, we conducted confirmatory factor analyses to test the discriminant validity of the six key variables (enterprise digital transformation, promotion focus, prevention focus, challenge appraisal, hindrance appraisal, and employee digital transformation support). Item parceling was used because these variables contain too many items relative to the sample size, which would greatly impair the fit indices (Little et al., 2013). Specifically, regarding promotion and prevention focus, we created three parcels, each based on the item-to-construct-balance approach (Williams et al., 2009). Table 2 shows that the six-factor model fitted the data well (χ² (137) = 273.79, p < 0.001; RMSEA = 0.09; CFI = 0.94; TLI = 0.92; SRMR = 0.06) and was better compared to other alternative models. Thus, the six key variables in our research were distinct.

Table 2.

Model Fit Results of Confirmatory Factor Analyses.

Model	χ ²	df	Δχ²	Δdf	RMSEA	CFI	TLI	SRMR
Six-factor model
EDT, PO, PE, CA, HA, EDTS	273.79***	137	--	--	.09	.94	.92	.06
Five-factor model
PO + PE, EDT, CA, HA, EDTS	492.17***	142	218.38***	5	.14	.84	.80	.12
CA + HA, EDT, PO, PE, EDTS	667.33***	142	393.54***	5	.18	.75	.70	.13
Four-factor model
PO + PE, CA + HA, EDT, EDTS	871.12***	146	597.33***	9	.20	.66	.60	.16
Three-factor model
PO + PE, CA + HA + EDTS, EDT	960.85***	149	687.06***	12	.21	.62	.56	.17
Two-factor model
PO + PE + CA + HA + EDTS, EDT	1424.97***	151	1151.18***	14	.26	.40	.32	.21
Single-factor model
EDT + PO + PE + CA + HA + EDTS	1794.29***	152	1520.50***	15	.30	.23	.13	.24

Note. EDT = enterprise digital transformation; PO = promotion focus; PE = prevention focus; CA = challenge appraisal; HA = hindrance appraisal; EDTS = employee digital transformation support. N = 121. Δχ² is compared with the six-factor model. ***p < .001.

Hypotheses Testing

We used Stata 13.10 software to conduct the path analysis for testing the proposed hypotheses, and the results are discussed below.

Moderation Effects

We proposed in Hypothesis 1 that employees’ promotion focus moderates the relationship between enterprise digital transformation and challenge appraisal, such that this relationship is more positive for employees with higher (vs. lower) promotion focus. Table 3 shows a significant moderating effect (B = 0.27, SE = 0.10, p = 0.006). Moreover, the interaction plot (Figure 2) and simple slope test demonstrate a significant positive effect (B = 0.46, SE = 0.12, p < 0.001) of enterprise digital transformation on challenge appraisal for employees with higher promotion focus (1 SD above the mean). By contrast, for those with lower promotion focus (1 SD below the mean), the effect of enterprise digital transformation on challenge appraisal was not significant (B = −0.02, SE = 0.13, p = 0.86). Thus, Hypothesis 1 is supported.

Table 3.

Results of Path Analysis.

Variables	Challenge appraisal		Hindrance appraisal		Employee digital transformation support
Variables	B (SE)	p	B (SE)	p	B (SE)	p
Intercept	5.53*** (.72)	<.001	4.08** (1.44)	.005	2.21^* (.89)	.01
Control variables
Gender	.37 (.21)	.09	−.58 (.43)	.18	−.27 (.22)	.22
Education	−.12 (.26)	.66	−.26 (.53)	.62	.58^* (.26)	.03
Rank	.03 (.15)	.83	−.22 (.29)	.46	−.13 (.15)	.39
Tenure	.01 (.02)	.83	.05 (.05)	.29	−.002 (.02)	.94
Independent variable
Enterprise digital transformation	.22^* (.09)	.01	.36^* (.18)	.04	−.04 (.09)	.67
Moderators and interactive terms
Promotion focus	.23^* (.10)	.02	−.37 (.21)	.07	.21 (.11)	.054
Prevention focus	−.13 (.10)	.16	.27 (.19)	.15	−.01 (.10)	.95
Enterprise digital transformation × promotion focus	.27** (.10)	.006	.06 (.20)	.77	−.10 (.10)	.30
Enterprise digital transformation × prevention focus	.07 (.09)	.42	.36^* (.18)	.04	.08 (.09)	.41
Mediators
Challenge appraisal					.48*** (.09)	<.001
Hindrance appraisal					−.12** (.05)	.009

Note. N = 121. Unstandardized coefficients are reported and standard errors (SEs) are presented in parentheses. *p < .05, **p < .01, ***p < .001.

Figure 2.

Interactive Effect of Enterprise Digital Transformation and Employee Promotion Focus on Challenge Appraisal. Note. Higher promotion focus (1 SD above the mean): B = 0.46, SE = 0.12, p < 0.001; lower promotion focus (1 SD below the mean): B = −0.02, SE = 0.13, p = 0.86.

Meanwhile, we proposed in Hypothesis 2 that employees’ prevention focus moderates the relationship between enterprise digital transformation and hindrance appraisal, such that this relationship is more positive for employees with higher (vs. lower) prevention focus. The results in Table 3 demonstrate a significant moderating effect (B = 0.36, SE = 0.18, p = 0.04). The interaction plot (Figure 3) and simple slope test show a significant positive effect (B = 0.77, SE = 0.27, p = 0.005) of enterprise digital transformation on hindrance appraisal for employees with higher prevention focus (1 SD above the mean). By contrast, for those with lower prevention focus (1 SD below the mean), the effect of enterprise digital transformation on hindrance appraisal was not significant (B = −0.04, SE = 0.26, p = 0.88). Thus, Hypothesis 2 is supported.

Figure 3.

Interactive Effect of Enterprise Digital Transformation and Employee Prevention Focus on Hindrance Appraisal. Note. Higher prevention focus (1 SD above the mean): B = 0.77, SE = 0.27, p = 0.005; lower prevention focus (1 SD below the mean): B = −0.04, SE = 0.26, p = 0.88.

Main Effects

We proposed in Hypothesis 3 that employees’ challenge appraisal is positively associated with employee digital transformation support. Table 3 shows a significant positive effect of challenge appraisal on employee digital transformation support (B = 0.48, SE = 0.09, p < 0.001). Hence, Hypothesis 3 is supported.

We also proposed in Hypothesis 4 that employees’ hindrance appraisal is negatively related to employee digital transformation support. The results in Table 3 show a significant negative effect of hindrance appraisal on employee digital transformation support (B = −0.12, SE = 0.05, p = 0.009). Therefore, Hypothesis 4 is supported.

Moderated Mediation Effects

We proposed in Hypothesis 5 that employees’ promotion focus moderates the mediating effect of enterprise digital transformation on employee digital transformation support through challenge appraisal, such that this mediation effect is more positive when promotion focus is higher rather than lower. Using the bootstrapping method (1000 samples) recommended by Preacher et al. (2007) and Hayes (2013), we calculated the 95% confidence interval (CI) of the indirect effect under higher and lower promotion focus conditions. Table 4 reports a significant positive indirect effect (B = 0.22, SE = 0.07, 95% CI [0.10, 0.39]) of enterprise digital transformation on employee digital transformation support through challenge appraisal for employees with higher promotion focus, whereas for employees with lower promotion focus, the indirect effect was non-significant (B = −0.01, SE = 0.07, 95% CI [–0.14, 0.14]). Moreover, the differences in the indirect effect for each condition were significant (B = 0.23, SE = 0.10, 95% CI [0.05, 0.45]). Hence, Hypothesis 5 is supported.

Table 4.

Results of Moderated Mediation Effects.

Conditions	Enterprise digital transformation → Challenge appraisal → Employee digital transformation support
Conditions	Indirect effect (SE)	95% CI
Lower promotion focus (−1 SD)	−.01 (.07)	[–.14, .14]
Higher promotion focus (+1 SD)	.22 (.07)	[.10, .39]
Difference	.23 (.10)	[.05, .45]

Conditions	Enterprise digital transformation → Hindrance appraisal → Employee digital transformation support
Conditions	Indirect effect (SE)	95% CI
Lower prevention focus (−1 SD)	.00 (.04)	[–.06, .10]
Higher prevention focus (+1 SD)	−.09 (.04)	[–.19, −.03]
Difference	−.09 (.06)	[–.25, −.01]

In Hypothesis 6, we predicted that employees’ prevention focus moderates the mediating effect of enterprise digital transformation on employee digital transformation support through hindrance appraisal, such that this mediation effect is more negative when prevention focus is higher rather than lower. Table 4 shows a significant negative indirect effect (B = −0.09, SE = 0.04, 95% CI [–0.19, −0.03]) of enterprise digital transformation on employee digital transformation support through hindrance appraisal for employees with higher prevention focus, whereas for employees with lower prevention focus, this indirect effect was non-significant (B = 0.00, SE = 0.04, 95% CI [–0.06, 0.10]). Moreover, the difference between the indirect effect for each condition was also significant (B = −0.09, SE = 0.06, 95% CI [–0.25, −0.01]). Thus, Hypothesis 6 is supported.

Discussion

Our research explored why employees (not) support enterprise digital transformation. Drawing upon the transactional theory of stress, we developed and tested a moderated dual-path model to examine the mediating roles of challenge and hindrance appraisals and the moderating roles of promotion and prevention focus in the relationship between enterprise digital transformation and employee digital transformation support. We summarize the theoretical and managerial implications of our work below.

Theoretical Implications

Our research contributes to the enterprise digital transformation literature in three ways. First, by integrating enterprise digital transformation and employee digital transformation support in one theoretical framework, this study provides theoretical and empirical evidence for enterprise digital transformation’s effect on employees at the individual level. The existing research on employee management in the digital age has focused on human–technology interaction, discussing the positive or negative effects of digital technology on individuals in a relatively fragmented manner (Glikson & Woolley, 2020; Kellogg et al., 2020; Wang et al., 2020). However, digital transformation includes not only the adoption of new technologies, but also the changes in intra-organizational relationships and employees’ overall work experience (Hanelt et al., 2021; Meske & Junglas, 2021; Van Der Schaft et al., 2022; Vom Brocke et al., 2018). Nevertheless, despite the considerable achievement of previous studies on enterprise digital transformation, there is a lack of a theoretical basis and empirical evidence directly demonstrating whether and why employees support (do not support) enterprise digital transformation (Gajendran & Harrison, 2007; Meske & Junglas, 2021). Our research complements this aspect by directly linking enterprise digital transformation, as a key strategic choice, with employee digital transformation support, as an employee reaction toward digital transformation. In doing so, we demonstrate the effect of enterprise digital transformation on employees and provide one of the preliminary bases for research on enterprise digital transformation at the individual level.

Second, our research shows that challenge and hindrance appraisal are important mediators that explain whether and how employees support (do not support) enterprise digital transformation. Previous studies explaining the different effects of enterprise digital transformation focused on macro mechanisms, such as environmental protection cognition (Xie et al., 2022), ambidextrous innovation (Ma et al., 2022), and R&D capability (Liang & Li, 2022). Among the few studies focusing on the micro-level effects of enterprise digital transformation on employees, Van Der Schaft et al. (2022) drew on construal-level theory and dynamic change perspectives to explore employees’ attitudes toward digital transformation. However, employees’ appraisal of enterprise digital transformation has been largely overlooked. Drawing upon the transactional theory of stress, we consider enterprise digital transformation a stressor relevant to employees and propose that employees may make challenge or hindrance appraisals of enterprise digital transformation, which then determines their supportive behavior toward digital transformation. In summary, by revealing the mediating role of different types of employee stress appraisal, we provide a novel perspective to explain the relationship between enterprise digital transformation and employee digital transformation support.

Third, our work expands the digital transformation literature by highlighting regulatory focus as a vital contingency. Previous studies on digital transformation focused on macro moderators such as equity concentration (Li et al., 2022) and cross-border search (Xie et al., 2022). Among the few studies on employee attitude and behavior in the context of digital technology application, scholars have documented individual demographics, such as age (Wang et al., 2020), gender (Wang et al., 2020), and tenure (Tong et al., 2021), as moderators, without paying attention to employees’ self-regulatory dispositions, which comprise a key factor determining employees’ interpretations of and reactions to the stressor. By contrast, our study draws on the transactional theory of stress to probe into the moderating roles of regulatory foci, thereby enriching the contingencies for the impacts of enterprise digital transformation.

Managerial Implications

Our research has two important managerial implications. First, it is helpful for enterprises to understand the impact of enterprise digital transformation on their employees so that they can take targeted measures to strengthen the positive effects of digital transformation while reducing its negative effects. The findings show that employee digital transformation support can be enhanced, while employees’ active participation and contribution can be encouraged. Specifically, in the process of carrying out digital transformation, enterprises should not only pay attention to the improvement of a “rigid index” (assessing the enterprise’s processes and efficiency), but also consider employees’ feelings and attitudes. If employees have negative perceptions of enterprise digital transformation (hindrance appraisal), or even engage in negative behaviors (digital transformation resistance), this will impair the implementation of digital transformation and hamper the enterprise’s progress (LePine et al., 2016). Based on our findings, we recommend that managers and enterprises take measures to promote employees’ challenge appraisal, rather than hindrance appraisal. For example, enterprises and managers can invite employees to share their attitudes and concerns regarding digital transformation and communicate with them openly. For employees who are concerned about changes and risks of unemployment, enterprises can assure them of the stability of employment and implement digital transformation gradually and steadily to reduce employees’ sense of uncertainty (Fedor et al., 2006). Regarding employees who do not believe in the benefits of digital transformation, managers can preach and personally demonstrate the benefits of digital technology in facilitating work efficiency (Bautista et al., 2018). If possible, enterprises can also commit to providing higher compensation for employees who are proficient in digital technologies, in which case employees can see the benefits and prospects of digital transformation for them (Yan et al., 2022), thus making challenge (rather than hindrance) appraisals (LePine et al., 2016).

Second, based on the moderating role of the regulatory foci, enterprises aiming to launch digital transformation can pay more attention to employees’ level of regulatory foci during the recruitment and promotion process (Liu et al., 2022). For instance, if it is hard to directly measure employees’ regulatory foci, enterprises can launch a simulation to present employees with a stressful situation in the workplace and ask them to come up with solutions (LePine et al., 2016). Further, managers can observe their reactions and analyze their answers to evaluate whether employees focus more on hopes and aspirations or difficulties and obstacles (Webster et al., 2011). Moreover, enterprises can provide guidance and care to employees with a prevention focus to satisfy their need for security during enterprise digital transformation (Miao et al., 2022). For example, they can provide job training to help these employees master the necessary skills for digital transformation (Zhao & Thompson, 2019).

Limitations and Directions for Future Research

Despite having several theoretical and managerial implications, this study also has its limitations. First, the data were collected from one enterprise. Although the studied enterprise has made some progress in digital transformation, employees in other enterprises or industries may have different reactions to digital transformation. Therefore, our field research based on one enterprise may limit the external validity of our conclusions. Future research can collect data from other industries to verify and expand these conclusions. In addition, given that digital transformation often takes a long time and its level remains relatively stable over limited periods (Wimelius et al., 2021), it is feasible to measure employees’ evaluations of digital transformation at one time point and examine its impacts on employees. However, the level of digital transformation may change over a long period (Van Der Schaft et al., 2022), thus impacting employees differently. Therefore, we suggest that future studies trace the whole process of enterprise digital transformation and examine its influences at all stages.

Second, as with other survey studies, we cannot draw causal inferences. Challenge appraisal, hindrance appraisal, and employee digital transformation support were reported by employees. Indeed, self-reported data have been used to measure these variables in many previous studies. For example, LePine et al. (2016) and Kronenwett and Rigotti (2020) adopted a self-report method to collect data on the type of employee stress appraisal, while Kim et al. (2010) invited employees to self-evaluate their change support behavior. However, common method bias may also be a concern. We provide evidence to alleviate this problem. For example, theoretically, the core view of the transactional theory of stress holds that an individual’s appraisal of stressors affects their coping style; additionally, empirically, the interactive term between enterprise digital transformation and regulatory foci showed no significant effect of employee digital transformation support (Table 3). However, a longitudinal or quasi-experimental study design is recommended for future studies to strengthen the causal inference.

Third, drawing upon the transactional theory of stress, our study focused on the mediating role of challenge and hindrance appraisals in the relationship between enterprise digital transformation and employee digital transformation support. However, there may be other explanatory mechanisms. For example, existing studies have shown that digital transformation affects employees’ job autonomy (Dettmers et al., 2016), self-identity recognition (Petriglieri et al., 2018), social support (Siampou et al., 2014), and sense of control (Curchod et al., 2019). Thus, future research may explore other mechanisms that explain enterprise digital transformation’s positive and negative impacts on employees.

Fourth, we mainly examined the moderating role of regulatory foci as individual characteristics in influencing the effect of enterprise digital transformation. However, employees’ reactions toward enterprise digital transformation may also be affected by contextual factors such as leaders’ managerial skills (Furst & Cable, 2008), leader–member exchanges (Furst & Cable, 2008), the duration of change (Kim et al., 2010), and environmental uncertainty (Venus et al., 2019). Thus, future studies can discuss other important boundary conditions from different theoretical perspectives to gain a deeper understanding of how employees cope with a new digital working environment, and help employees transform “pressure” into “motivation.”

Fifth, we framed digital transformation support as a coping strategy but did not assess different types of coping. Indeed, prior research has identified different coping types. For example, Lazarus (1991) argued that problem-focused and emotion-focused coping are two distinct coping styles used when individuals face stressors. In addition, Mukerjee et al. (2021) proposed proactive and preventive coping in the context of organizational change. Hence, we urge future studies to assess these different coping styles, which can further contribute to our knowledge on digital transformation’s impacts on employees.

Finally, although this study enriches the nomological network of enterprise digital transformation by uncovering its impacts on employees (Verhoef et al., 2021), future studies can advance this stream by exploring its meso-level impacts (e.g., teams). Digital transformation is indeed known to influence social dynamics (Van Der Schaft et al., 2022). Future research can also adopt a multilevel approach to examine the impacts of enterprise digital transformation at the organizational, team, and individual levels simultaneously (Liao et al., 2009).

Conclusion

While digital transformation is increasingly recognized as a key trend shaping organizational development, existing literature has predominantly concentrated on its macro-level impacts. The micro-level perspective, particularly employees’ reactions to digital transformation, has been relatively neglected. To complement this aspect, our study grounds in the transactional theory of stress to identify challenge and hindrance appraisals as dual mediating mechanisms and reveal promotion and prevention focus as key contingencies that explain why employees (not) support enterprise digital transformation. We expect our research to spur more future work to contribute to the burgeoning literature on enterprise digital transformation.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Natural Science Foundation of China (Grant 72202012, 72372151, and 72002214), the Funding of Taishan Young Scholars Project of Shandong Province (tsqn202312098), Shandong Provincial Natural Science Foundation (Grant ZR2024QG088), and the Ministry of Education of China Humanity and Social Science Research (Grant 21YJC630178).

Ethical Statement

ORCID iDs

Xin Liu

Bo Lv

Note

Author Biographies

Xin Liu is a professor at Renmin Business School, Renmin University of China. She received her Ph.D. from School of Economics and Management at Tsinghua University. Her current research interests include creativity and leadership. Her work has appeared in distinguished journals, such as Academy of Management Journal, Strategic Management Journal, Organization Science, Journal of Applied Psychology, Journal of Management, among others.

Bo Lv is a master student at Renmin Business School, Renmin University of China. He received his bachelor’s degree from Shandong University. His research focuses on leadership, ethics, and emotion. His work has been accepted by Group & Organization Management, Journal of Business Ethics, and Asia Pacific Journal of Human Resources.

Qiwei Zhou received her PhD from Tsinghua University, China, in 2019. She is now an associate professor in College of Management, Ocean University of China. Her main research interests focus on organizational behavior and human resource management, employee learning, and enterprise digital transformation. Her work has appeared in distinguished journals, such as Journal of Management, Psychological Science, Technological Forecasting & Social Change, Journal of Business Research, among others.

References

Bartsch

Weber

Büttgen

Huber

(2020). Leadership matters in crisis-induced digital transformation: How to lead service employees effectively during the COVID-19 pandemic. Journal of Service Management, 32(1), 71–85. https://doi.org/10.1108/JOSM-05-2020-0160

Bautista

J. R.

Rosenthal

Lin

T. T. C.

Theng

Y. L.

(2018). Predictors and outcomes of nurses’ use of smartphones for work purposes. Computers in Human Behavior, 84, 360–374. https://doi.org/10.1016/j.chb.2018.03.008

Biggs

Brough

Drummond

(2017). Lazarus and Folkman’s psychological stress and coping theory. In Cooper

C. L.

Quick

J. C.

(Eds.), The handbook of stress and health: A guide to research and practice (pp. 351–364). Wiley Blackwell. https://doi.org/10.1002/9781118993811.ch21

Brislin

R. W.

(1986). The wording and translation of research instruments. In Lonner

Berry

(Eds.), Field methods in cross-cultural research (pp. 137–164). Sage.

Brockner

Higgins

E. T.

(2001). Regulatory focus theory: Implications for the study of emotions at work. Organizational Behavior and Human Decision Processes, 86(1), 35–66. https://doi.org/10.1006/obhd.2001.2972

Byron

Peterson

S. J.

Zhang

LePine

J. A.

(2018). Realizing challenges and guarding against threats: Interactive effects of regulatory focus and stress on performance. Journal of Management, 44(8), 3011–3037. https://doi.org/10.1177/0149206316658349

Cetindamar Kozanoglu

Abedin

(2021). Understanding the role of employees in digital transformation: Conceptualization of digital literacy of employees as a multi-dimensional organizational affordance. Journal of Enterprise Information Management, 34(6), 1649–1672. https://doi.org/10.1108/JEIM-01-2020-0010

Crum

A. J.

Salovey

Achor

(2013). Rethinking stress: The role of mindsets in determining the stress response. Journal of Personality and Social Psychology, 104(4), 716–733. https://doi.org/10.1037/a0031201

Curchod

Patriotta

Cohen

Neysen

(2019). Working for an Algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024

10.

Dettmers

Bamberg

Seffzek

(2016). Characteristics of extended availability for work: The role of demands and resources. International Journal of Stress Management, 23(3), 276–297. https://doi.org/10.1037/str0000014

11.

Eller

Alford

Kallmünzer

Peters

(2020). Antecedents, consequences, and challenges of small and medium-sized enterprise digitalization. Journal of Business Research, 112, 119–127. https://doi.org/10.1016/j.jbusres.2020.03.004

12.

Fedor

D. B.

Caldwell

Herold

D. M.

(2006). The effects of organizational changes on employee commitment: A multilevel investigation. Personnel Psychology, 59(1), 1–29. https://doi.org/10.1111/j.1744-6570.2006.00852.x

13.

Fischer

Brettel

Mauer

(2018). The three dimensions of sustainability: A delicate balancing act for entrepreneurs made more complex by stakeholder expectations. Journal of Business Ethics, 163(1), 87–106. https://doi.org/10.1007/s10551-018-4012-1

14.

Fugate

Soenen

(2018). Predictors and processes related to employees’ change-related compliance and championing. Personnel Psychology, 71(1), 109–132. https://doi.org/10.1111/peps.12235

15.

Furst

S. A.

Cable

D. M.

(2008). Employee resistance to organizational change: Managerial influence tactics and leader-member exchange. Journal of Applied Psychology, 93(2), 453–462. https://doi.org/10.1037/0021-9010.93.2.453

16.

Gajendran

R. S.

Harrison

D. A.

(2007). The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology, 92(6), 1524–1541. https://doi.org/10.1037/0021-9010.92.6.1524

17.

Glikson

Woolley

A. W.

(2020). Human trust in artificial intelligence: Review of empirical research. The Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057

18.

Guo

(2021). The effects of digital transformation on firm performance: Evidence from China’s manufacturing sector. Sustainability, 13(22), Article 12844. https://doi.org/10.3390/su132212844

19.

Hanelt

Bohnsack

Marz

Antunes Marante

(2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159–1197. https://doi.org/10.1111/joms.12639

20.

Hayes

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. The Guilford Press. https://doi.org/10.1111/jedm.12050

21.

Herold

D. M.

Fedor

D. B.

Caldwell

Liu

(2008). The effects of transformational and change leadership on employees’ commitment to a change: A multilevel study. Journal of Applied Psychology, 93(2), 346–357. https://doi.org/10.1037/0021-9010.93.2.346

22.

Higgins

E. T.

Spiegel

(2004). Promotion and prevention strategies for self-regulation: A motivated cognition perspective. In Baumeister

R. F.

Vohs

K. D.

(Eds.), Handbook of self-regulation: Research, theory, and applications (pp. 171–187). Guilford.

23.

Kaltiainen

Lipponen

Fugate

Vakola

(2020). Spiraling work engagement and change appraisals: A three-wave longitudinal study during organizational change. Journal of Occupational Health Psychology, 25(4), 244–258. https://doi.org/10.1037/ocp0000163

24.

Kark

Van Dijk

(2007). Motivation to lead, motivation to follow: The role of the self-regulatory focus in leadership processes. Academy of Management Review, 32(2), 500–528. https://doi.org/10.5465/amr.2007.24351846

25.

Kellogg

K. C.

Valentine

Christin

(2020). Algorithms at work: The new contested terrain of control. The Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

26.

Kim

T. G.

Hornung

Rousseau

D. M.

(2010). Change-supportive employee behavior: Antecedents and the moderating role of time. Journal of Management, 37(6), 1664–1693. https://doi.org/10.1177/0149206310364243

27.

Kim

Y. H.

Sting

F. J.

Loch

C. H.

(2014). Top-down, bottom-up, or both? Toward an integrative perspective on operations strategy formation. Journal of Operations Management, 32(7–8), 462–474. https://doi.org/10.1016/j.jom.2014.09.005

28.

Kolade

Owoseni

(2022). Employment 5.0: The work of the future and the future of work. Technology in Society, 71, Article 102086. https://doi.org/10.1016/j.techsoc.2022.102086

29.

Kronenwett

Rigotti

(2020). All’s well that ends well!? Moderating effects of goal progress on the relation between challenge and hindrance appraisal and well-being. Journal of Managerial Psychology, 37(5), 444–466. https://doi.org/10.1108/JMP-11-2019-0618

30.

Lazarus

(1991). Emotions and adaptations. Plenum Press.

31.

Lazarus

R. S.

Folkman

(1984). Stress, appraisal, and coping. Springer Publishing Company.

32.

LePine

J. A.

Podsakoff

N. P.

LePine

M. A.

(2005). A meta-analytic test of the challenge stressor–hindrance stressor framework: An explanation for inconsistent relationships among stressors and performance. Academy of Management Journal, 48(5), 764–775. https://doi.org/10.5465/AMJ.2005.18803921

33.

LePine

M. A.

Zhang

Crawford

E. R.

Rich

B. L.

(2016). Turning their pain to gain: Charismatic leader influence on follower stress appraisal and job performance. Academy of Management Journal, 59(3), 1036–1059. https://doi.org/10.5465/amj.2013.0778

34.

Liu

(2016). Why do employees resist knowledge management systems? An empirical study from the status quo bias and inertia perspectives. Computers in Human Behavior, 65, 189–200. https://doi.org/10.1016/j.chb.2016.08.028

35.

Zhang

Mao

J.-Y.

(2018). Digital transformation by sme entrepreneurs: A capability perspective. Information Systems Journal, 28(6), 1129–1157. https://doi.org/10.1111/isj.12153

36.

Wen

Zeng

Liu

(2022). Has enterprise digital transformation improved the efficiency of enterprise technological innovation? A case study on Chinese listed companies. Mathematical Biosciences and Engineering, 19(12), 12632–12654. https://doi.org/10.3934/mbe.2022590

37.

Liang

(2022). Can digital transformation promote innovation performance in manufacturing enterprises? The mediating role of R&D capability. Sustainability, 14(17), Article 10939. https://doi.org/10.3390/su141710939

38.

Liao

Toya

Lepak

D. P.

Hong

(2009). Do they see eye to eye? Management and employee perspectives of high-performance work systems and influence processes on service quality. Journal of Applied Psychology, 94(2), 371–391. https://doi.org/10.1037/a0013504

39.

Little

T. D.

Rhemtulla

Gibson

Schoemann

A. M.

(2013). Why the items versus parcels controversy needn’t be one. Psychology Methods, 18(3), 285–300. https://doi.org/10.1037/a0033266

40.

Liu

J. J.

Zhou

X. H.

Wang

(2022). The influence of entrepreneurial leadership on employee improvisation in new ventures: Based on cognitive-affective processing system framework. Kybernetes. ahead of print. https://doi.org/10.1108/K-10-2021-0933

41.

Lockwood

Jordan

C. H.

Kunda

(2002). Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83(4), 854–864. https://doi.org/10.1037/0022-3514.83.4.854

42.

Jia

Wang

(2022). Digital transformation, ambidextrous innovation and enterprise value: Empirical analysis based on listed Chinese manufacturing companies. Sustainability, 14(15), 9482. https://doi.org/10.3390/su14159482

43.

Madsen

S. R.

Miller

John

C. R.

(2005). Readiness for organizational change: Do organizational commitment and social relationships in the workplace make a difference? Human Resource Development Quarterly, 16(2), 213–234. https://doi.org/10.1002/hrdq.1134

44.

Mahmood

M. A.

Hall

Swanberg

D. L.

(2001). Factors affecting information technology usage: A meta-analysis of the empirical literature. Journal of Organizational Computing and Electronic Commerce, 11(2), 107–130. https://doi.org/10.1207/S15327744JOCE1102_02

45.

Meske

Junglas

(2021). Investigating the elicitation of employees’ support towards digital workplace transformation. Behaviour & Information Technology, 40(11), 1120–1136. https://doi.org/10.1080/0144929X.2020.1742382

46.

Miao

Zheng

Zang

Grisaffe

D. B.

Evans

(2022). Managing differential effects of salespersons' regulatory foci–a dual process model of dominant and supplemental pathways. Journal of the Academy of Marketing Science, 50(3), 563–585. https://doi.org/10.1007/s11747-021-00821-y

47.

Mukerjee

Montani

Vandenberghe

(2021). A dual model of coping with and commitment to organizational change: The role of appraisals and resources. Journal of Organizational Change Management, 34(5), 1144–1161. https://doi.org/10.1108/JOCM-01-2021-0027

48.

Muylaert

Decramer

Audenaert

(2022). How leader’s red tape interacts with employees’ red tape from the lens of the job demands–resources model. Review of Public Personnel Administration, 43(3), 430–455. https://doi.org/10.1177/0734371X221087420

49.

Oreg

(2003). Resistance to change: Developing an individual differences measure. Journal of Applied Psychology, 88(4), 680–693. https://doi.org/10.1037/0021-9010.88.4.680

50.

Petriglieri

Ashford

S. J.

Wrzesniewski

(2018). Agony and ecstasy in the gig economy: Cultivating holding environments for precarious and personalized work identities. Administrative Science Quarterly, 64(1), 124–170. https://doi.org/10.1177/0001839218759646

51.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

52.

Preacher

K. J.

Rucker

D. D.

Hayes

A. F.

(2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227. https://doi.org/10.1080/00273170701341316

53.

Prem

Ohly

Kubicek

Korunka

(2017). Thriving on challenge stressors? Exploring time pressure and learning demands as antecedents of thriving at work. Journal of Organizational Behavior, 38(1), 108–123. https://doi.org/10.1002/job.2115

54.

Sacramento

C. A.

Fay

West

M. A.

(2013). Workplace duties or opportunities? Challenge stressors, regulatory focus, and creativity. Organizational Behavior and Human Decision Processes, 121(2), 141–157. https://doi.org/10.1016/j.obhdp.2013.01.008

55.

Schneider

Sting

F. J.

(2020). Employees perspectives on digitalization-induced change: Exploring frames of industry 4.0. Academy of Management Discoveries, 6(3), 406–435. https://doi.org/10.5465/amd.2019.0012

56.

Siampou

Komis

Tselios

(2014). Online versus face-to-face collaboration in the context of a computer-supported modeling task. Computers in Human Behavior, 37, 369–376. https://doi.org/10.1016/j.chb.2014.04.032

57.

Tong

Jia

Luo

Fang

(2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600–1631. https://doi.org/10.1002/smj.3322

58.

Tseng

H. C.

Kang

L. M.

(2008). How does regulatory focus affect uncertainty towards organizational change? The Leadership & Organization Development Journal, 29(8), 713–731. https://doi.org/10.1108/01437730810916659

59.

Van Der Schaft

A. H. T.

Lub

X. D.

Van Der Heijden

Solinger

O. N.

(2022). How employees experience digital transformation: A dynamic and multi-layered sensemaking perspective. Journal of Hospitality and Tourism Research, 48(5), 803–820. https://doi.org/10.1177/10963480221123098

60.

Venus

Stam

Van Knippenberg

(2019). Visions of change as visions of continuity. Academy of Management Journal, 62(3), 667–690. https://doi.org/10.5465/amj.2015.1196

61.

Verhoef

P. C.

Broekhuizen

Bart

Bhattacharya

Qi Dong

Fabian

Haenlein

(2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022

62.

Vial

(2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

63.

Vom Brocke

Maaß

Buxmann

Maedche

Leimeister

J. M.

Pecht

(2018). Future work and enterprise systems. Business and Information Systems Engineering, 60(4), 357–366. https://doi.org/10.1007/s12599-018-0544-2

64.

Wang

Liu

Parker

S. K.

(2020). How does the use of information communication technology affect individuals. The Academy of Management Annals, 14(2), 695–725. https://doi.org/10.5465/annals.2018.0127

65.

Warner

K. S. R.

Wäger

(2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001

66.

Webster

J. R.

Beehr

T. A.

Love

(2011). Extending the challenge–hindrance model of occupational stress: The role of appraisal. Journal of Vocational Behavior, 79(2), 505–516. https://doi.org/10.1016/j.jvb.2011.02.001

67.

Wen

Zhong

Lee

C.-C.

(2022). Digitalization, competition strategy and corporate innovation: Evidence from Chinese manufacturing listed companies. International Review of Financial Analysis, 82, Article 102166. https://doi.org/10.1016/j.irfa.2022.102166

68.

Wessel

Baiyere

Ologeanu-Taddei

Cha

Blegind Jensen

(2021). Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems, 22(1), 102–129. https://doi.org/10.17705/1jais.00655

69.

Williams

L. J.

Vandenberg

R. J.

Edwards

J. R.

(2009). 12 structural equation modeling in management research: A guide for improved analysis. The Academy of Management Annals, 3(1), 543–604. https://doi.org/10.1080/19416520903065683

70.

Wimelius

Mathiassen

Holmström

Keil

(2021). A paradoxical perspective on technology renewal in digital transformation. Information Systems Journal, 31(1), 198–225. https://doi.org/10.1111/isj.12307

71.

Xie

Chen

Boadu

Tang

(2022). How does digital transformation affect agricultural enterprises’ pro-land behavior: The role of environmental protection cognition and cross-border search. Technology in Society, 70, Article 101991. https://doi.org/10.1016/j.techsoc.2022.101991

72.

Yan

Wang

Shen

Guo

(2022). The effect of transformational change on performance: An employee’s stress appraisals perspective. Frontiers in Psychology, 13, Article 897769. https://doi.org/10.3389/fpsyg.2022.897769

73.

Zeike

Choi

K. E.

Lindert

Pfaff

(2019). Managers’ well-being in the digital era: Is it associated with perceived choice overload and pressure from digitalization? An exploratory study. International Journal of Environmental Research and Public Health, 16(10), 1746. https://doi.org/10.3390/ijerph16101746

74.

Zhao

Thompson

(2019). Investments in managerial human capital: Explanations from prospect and regulatory focus theories. International Small Business Journal: Researching Entrepreneurship, 37(4), 365–394. https://doi.org/10.1177/0266242619828264