Does Digitalization Benefit Employees? A Systematic Meta-Analysis of the Digital Technology–Employee Nexus in the Workplace

Abstract

The adoption of digital technologies (DTs) in the workplace has emerged as a core driver of organizational effectiveness, and many studies have explored the intrinsic connection between the two. However, due to the wide range of subdivisions of employee performance, existing studies present inconsistent research conclusions on the implementation effects of DTs and lack a systematic review of their impact on employee psychology and behavior for large sample data. To address this issue, employing a random-effects model and a psychometric meta-analysis approach based on subgroup and meta-regression analyses, this study examines 106 empirical studies, comprising 119 effect sizes. The findings reveal that DTs exhibit a “double-edged sword” effect. On the bright side, DTs significantly enhance task performance, innovation performance, employee engagement, job satisfaction, and job efficacy. On the dark side, DTs aggravate service sabotage, withdrawal behavior, job burnout, and work anxiety and have a suppressive effect on job well-being, while their influence on turnover intention is non-significant. Furthermore, this study identifies the moderating effects of industry characteristics, technology usage types, and demographic factors on the relationships between DTs and behavioral and psychological outcomes. The research conclusions help clarify the logical relationship between DTs and employee psychology and behavior and provide explanations for the differentiated research conclusions of previous studies. This study provides information for scientific management decisions regarding DTs in the workplace.

1. Introduction

In the Fourth Industrial Revolution (IR.4.0), the adoption of digital technologies (DTs) was extensively embedded in the workplace [1,2]. Virtual assistants, intelligent robots, big data analytics (BDA), and other DTs are gradually replacing human-performed tasks, becoming a powerful alternative and playing a supporting role [3]. DTs reduce employee engagement in repetitive and mechanical tasks, allowing a focus on professional skills and high-value, creative work [4]. For example, virtual assistants can streamline workload and optimize business performance [5], while automation with intelligent robots not only boosts productivity but also provides insights into customer preferences and market dynamics, prompting more targeted and superior customer services [6]. Flexible work designs and remote work modes have become more familiar with the impetus of DTs, which have substantially promoted job satisfaction [7].

However, scholars have increasingly examined the “dark side” of DTs. While DTs enhance efficacy, their pervasive adoption in the workplace may lead to structural unemployment [8] and exacerbate concerns regarding career instability [9]. With their broader adoption in traditional industries, work content, and business processes, DTs undergo a substantial transformation, demanding more rigorous professional proficiency and job competency [10]. Concurrently, employees face insecurity and marginalization of their values at work owing to the transformational pressures of technologies, which pose an unavoidable psychological burden [11]. Job satisfaction and organizational commitment are eroded by imbalance, which also raises the crisis of turnover and the risk of psychological disorders like anxiety [12].

Research on DTs has shown a clear upward trajectory over the past five years, particularly in relation to employee outcomes (psychology and behavior). Empirical studies on the impact of DTs have also been emerging. However, the impact of DTs on employee outcomes remains a topic of ongoing debate in research. Some studies suggest that DTs can improve engagement, task performance, and job satisfaction by enhancing task autonomy and reducing repetitive work [13,14,15]. Conversely, other studies indicate that electronic monitoring, technology overload, and digital contexts may lead to negative outcomes such as turnover intention, emotional exhaustion, and anxiety [16,17,18]. The effects of DTs on employee behavior can vary, with some studies highlighting how they may negatively impact well-being through information overload and constant connectivity [19], while others suggest that they can enhance well-being by increasing autonomy and access to resources [13]. Similarly, DTs may reduce turnover intention in certain circumstances [20] but could also exacerbate it through technology anxiety and a diminished sense of control [18]. These conflicting findings underscore a critical issue: the existing literature often focuses on the direction of the impact without delving into the underlying mechanisms that explain why different psychological reactions occur. Particularly concerning contextual factors such as organizational characteristics, technology usage patterns, and individual differences among employees, current research lacks a comprehensive explanatory framework. This gap hinders the precise understanding of how DTs affect employees under varying usage conditions. Despite attempts to reconcile divergent findings through meta-analytic approaches, the limitation that persists is as follows: one study concentrates mainly on the relationship between DTs and employee or organizational performance [21] but fails to provide in-depth analyses of outcome variables and moderating mechanisms due to sample constraints. Therefore, an urgent need exists for a study that systematically synthesizes existing research to quantify the overall impact of DTs and identify key moderating factors influencing their effects. Such a study would elucidate the conditional differences in the impact of DTs on employee outcomes and clarify inconsistencies in previous findings. This study’s core research question is as follows: for which organizational and individual employee characteristics do DTs yield positive or negative outcomes?

In response to the above research gaps, this study employs a meta-analytic approach to systematically synthesize and reanalyze effect sizes from existing empirical studies to explore the relationship between DTs and employee outcomes. The focus is on outcomes such as job satisfaction, efficacy, well-being, burnout, turnover intention, anxiety, task performance, innovation performance, engagement, withdrawal behavior, and service sabotage. This study also aims to identify potential moderating variables to elucidate mechanisms and boundary conditions and reconcile divergent findings.

This study makes dual contributions. Theoretically, it assesses existing studies to resolve the persistent issues of conflicting conclusions and unclear mechanisms in DTs’ research. Practically, it provides a systematic framework for enterprises to effectively leverage technology, promoting positive behavior while mitigating negative reactions during digital transformation. This paper is structured as follows: Section 2 covers the theoretical background and research hypotheses; Section 3 details the methodology, including the retrieval process, criteria for inclusion and exclusion, coding, and integration schemes; Section 4 presents the results; and Section 5 discusses the findings and implications.

2. Theoretical Background and Research Hypothesis

2.1. DTs

DTs are recognized in IR.4.0 as a pivotal technology cluster that reshapes the value creation paradigm through intelligent and connected systems [22]. Scholars conceptualize this technology consolidation as a “digital toolbox” to characterize its systemic and combinable nature [23]. The digital technology framework is structured around four core pillars, including artificial intelligence (AI), BDA, the Internet of Things/Cyber-Physical System (IoT/CPS), and 3D printing, formed through the deep integration of physical entities and digital realms [21]. Researchers note that DTs cover a broad range of technologies, including additive manufacturing (3D printing), the industrial internet, blockchain, simulation technology, and cloud computing, as well as BDA and AI [24]. The digital technology architecture exhibits multi-dimensional structural characteristics. It incorporates several mainstream frameworks and standards, including (1) the collaborative network structure of the SMACIT (Social, Mobile, Analytics, Cloud, IoT) model, comprising social, mobile, analytics, cloud computing, and IoT [25]; (2) the intelligent technologies’ convergence outlined in the BRAICQ (Blockchain, Robotics, Artificial Intelligence, Cognitive and Quantum Computing) framework, which encompasses blockchain, robotics, AI, cognitive computing, and quantum computing [26]; and (3) the core components defined by global institutions such as the United Nations Industrial Development Organization (UNIDO) and the Organization for Economic Cooperation and Development (OECD), namely, CPS, the Industrial IoT, AI, BDA, augmented reality (AR), and cloud computing [27]. According to Oduro et al. [21], the components of DTs are AI, BDA, IoT/CPS, blockchain, and 3DP, based on the principles of technological maturity, performance salience, and functional heterogeneity. The conceptual diagram is illustrated in Figure 1 as the key technological domains adopted in this study.

2.2. DTs and Employee Behavior

Previous research into the impact of DTs on employee behavior and psychology categorizes effects into four dimensions: positive behavior, positive psychology, negative behavior, and negative psychology (

Table 1. Classification of the effects of DTs on employee behavior and psychology.

Variable	Definition of the Concept	Reference
Positive Behavior	Employees engage in behavior that positively impacts their careers or the organization.	[28,29]
Negative Behavior	Employees engage in negative work behavior that hinders their personal career development or the organization progress.
Positive Psychology	Employee-perceived enduring, stable positive emotional experiences and subjective tendencies.	[30,31]
Negative Psychology	Negative emotional states experienced by employees that negatively affect their personal health and development.

). This study deploys a meta-analysis to explore the associations between DTs and these four effect types.

The impact of DTs on employee behavior and psychology is analyzed through the lens of the job demands–resources (JD-R) model. This framework posits that job resources and demands shape individual resource balance, subsequently influencing work outcomes [32,33]. Work resources include material, psychological, and organizational support, which mitigate employee strains, stimulate positive attitudes, and encourage positive behavior [34]. Conversely, job demands are the physical and psychological costs associated with completing tasks, often acting as “attrition factors” that lead to negative perceptions that can harm employee behavior and psychology [35]. However, when perceived as challenging, these demands can motivate performance. DTs significantly influence employee behavioral patterns by establishing a multi-dimensional work resource system [36,37]. Drawing on the JD-R model, DTs enable employees to efficiently achieve established work goals and create a resource gain spiral by fostering knowledge iteration and skill development [38]. Specifically, DTs are thought to enhance communication transparency, promote a flatter collaboration structure, improve resource integration efficacy, and ultimately support superior individual performance [20]. For instance, AI virtual assistants fulfill employees’ instrumental needs by providing real-time decision support and personalized guidance, fostering a collaborative human–machine environment, and alleviating workplace loneliness through parasocial interaction, thereby enhancing engagement [39]. DTs facilitate the formation and enhancement of employee structural, relational, and cognitive social capital, enhance knowledge sharing and exchange, and improve task performance, as explained within the framework of social capital theory (SCT) [40]. Furthermore, DTs promote the articulation of tacit knowledge and institutionalize informal learning processes [41]. Employees leverage knowledge-sharing platforms to accelerate innovation and boost productivity, translating these improvements into a sustainable innovation advantage [42,43]. According to the conservation of resources (COR) theory, DTs gradually allow employees to accumulate psychological resources. Coworking platforms, in particular, give individuals greater control over their work rhythm, easing emotional stress linked to role overload and enhancing engagement [44].

However, the deployment of DTs has increased workloads, work intensity, and skill development demands [45,46]. Digital technology overloads arise during digital transformation, where intense monitoring and standardization demands contribute to job burnout [47]. Burnout impairs cognitive resource integration and hinders innovation, accelerating turnover intention [48]. Studies indicate that non-contextual interactions with intelligent machines heighten employee loneliness and alienation [49]. Furthermore, attrition diminishes motivation, triggers withdrawal, and exacerbates family-related withdrawal, resulting in personal distress [50,51]. Moreover, employees working with service robots face competitive pressure, undermining their ability to provide high-quality service [18]. This pressure may cause disruptive behavior, such as superficial customer care, improper operation, data privacy misuse, and service sabotage [52].

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 1 (H1). 

DTs positively affect employee positive behavior, which includes (1a) task performance, (1b) innovation performance, and (1c) employee engagement.

Hypothesis 2 (H2). 

DTs positively affect employee negative behavior, which includes (2a) withdrawal behavior and (2b) service sabotage behavior.

2.3. DTs and Employee Psychology

Employee psychological state affects behavioral performance. DTs equip employees with essential resources, boosting efficacy [53]. Social exchange theory suggests that companies introduce digital technology resources and authorize employees to participate in operational and innovative activities through learning digital technological knowledge, which fosters trust and psychological reliance on digital culture and improves job satisfaction [54,55]. Evidence shows that intelligent performance management systems use employees to clarify the value of work and enhance efficacy through real-time feedback and data visualization [56]. Practical DTs aid in completing complex tasks and offer instant feedback, promoting accomplishment and satisfaction [57]. With the support of AI, organizations can minimize repetitive tasks, relieve work stress, and offer flexible schedules, thus improving well-being [58]. This shift mitigates the long-term adverse effects of high-pressure work on physical health and reduces burnout. Furthermore, DTs enhance communication efficacy and information flow within management. Digital HR practices clarify career paths by strengthening evaluation and monitoring, thereby enhancing employees’ sense of identity and belonging [59].

However, DTs are usually accompanied by job demands, leading to work-related stress. Employees are compelled to develop advanced digital skills, thereby increasing workload, work intensity, and skill demand [45]. This necessitates greater psychological effort to maintain equilibrium. Studies indicate that when employees struggle with the complexities of DTs, stress levels rise, resulting in burnout [60,61]. Additionally, DTs may jeopardize career advancement, leaving employees feeling undervalued or replaceable, which diminishes job satisfaction and fulfillment [47] and triggers job insecurity [62], identity threats [63], and increased future anxiety [64]. These negative psychological states deplete the resources needed to utilize challenges, leading to emotional exhaustion [65]. Without adequate coping strategies, psychological well-being worsens and may cause depression [66]. Employees who adopt negative coping mechanisms like avoidance are more likely to experience increased turnover intention [67].

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 3 (H3). 

DTs positively affect employee positive psychology, which includes (3a) job satisfaction, (3b) job efficacy, and (3c) job well-being.

Hypothesis 4 (H4). 

DTs positively affect employee negative psychology, which includes (4a) job burnout, (4b) turnover intention, and (4c) work anxiety.

2.4. The Moderators of Potential Factors

2.4.1. Contextual Factors

(1) Industry type

High-tech industries rely on advanced technologies, substantial research and development (R&D) investments, high-value products, and favorable prospects in global markets. Companies in these sectors typically possess advanced innovation capabilities, strategic insight, and efficient resource utilization [68]. High-tech firms provide an optimal workplace setting for DTs due to extremely structured workflows and well-defined efficacy goals. These industries often prioritize leveraging technology intelligence as a central aspect of digital transformation [69], fostering the feedback loop of “technology empowerment–performance enhancement–efficacy growth” through automated systems with operational proficiency. In smart factories, the seamless integration of real-time data feedback systems with production processes allows employees to optimize operations based on precise data, reducing physical burden and significantly enhancing work control [70,71]. High-tech companies emphasize establishing collaborative mechanisms among technologies, organizational structures, and employees. Systematic training in digital skills and intelligent support systems enhances employees’ acceptance of and proficiency in DTs [72]. Employees perceive technological tools as an effective means to expand their career competencies, thus enhancing positive psychological outcomes.

In contrast, non-high-tech industries face challenges in integrating DTs, leading to conflicts in human–machine adoption. These sectors primarily encompass traditional services, retail, hospitality, catering, and tourism, among other labor-intensive industries. In fields, digital transformation initiatives focus on efficacy monitoring, and algorithm-driven service evaluation systems compel employees to modify emotional responses to align with data-driven metrics, exacerbating emotional exhaustion and alienation [73]. Employees in non-high-tech industries experience dual pressure from digital surveillance. Standardized processes restrict autonomous decisions, fostering anxiety related to “de-skilling”. On the other hand, the excessive collection and analysis of user data evoke perceptions of privacy violations, prompting passive resistance behavior like data manipulation [63]. Moreover, the deployment of DTs follows efficacy over vocational skill development, leaving employees vulnerable to technology-induced performance pressure. In the absence of a transparent technology adoption model, the cost of engaging in negative behavior is lower than in high-tech industries, ultimately fostering a detrimental cycle of “digital alienation”.

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 5 (H5). 

There is a moderating effect of industry type on the relationship between DTs and positive outcomes. Compared to non-high-tech industries, DTs show a stronger positive relationship with positive employee behavior (H5a) and psychology (H5b) in high-tech industries.

Hypothesis 6 (H6). 

There is a moderating effect of industry type on the relationship between DTs and negative outcomes. Compared to high-tech industries, DTs show a stronger positive relationship with negative employee behavior (H6a) and psychology (H6b) in non-high-tech industries.

(2) Usage type

As a form of “tool box” resource, DTs have been extensively embedded in the production and operation of firms, fundamentally transforming traditional employee workflows. A typology of DTs can be delineated based on functional positioning and role characteristics within workplace settings, drawing from the existing literature and the practical implementation context [67]. The detailed typology is presented in

Table 2. Types of DTs, underlying technological foundations, practical application contexts, and functional attributes.

Type of DT	Practical Application Example	Definition	Reference
Assisted	1. Automatic entry and verification of invoices in the finance department. 2. Customer service work orders keyword extraction and prioritization. 3. Meeting minutes speech to text and key points extraction. 4. IoT and data analysis to generate replenishment warning. 5. Natural Language automatic document review to detect compliance issues. etc.	Support employees in efficiently completing routine tasks, minimizing time spent on repetitive labor.	[21] [67] [74] [75]
Enhanced	1. Engineers view equipment through AR glasses for 3D repair guidelines. 2. Designers use the AI Generative Adversarial Network to quickly output multiple product concept sketches. 3. Management optimizes decision-making through big data and machine learning. 4. Sales teams use predictive analytics and machine learning to forecast market trends. 5. Employees are trained to operate equipment through AR technology to improve operational efficiency. etc.	Expand the boundaries of employee capability and improve the accuracy, scientific rigor, and efficiency of their decision-making and action.
Autonomous	1. Intelligent customer service robots handle 80% of standardized inquiries. 2. Unmanned warehouse AGV carts automatically sort goods. 3. Blockchain audit system verifies transaction authenticity in real time. 4. AI system automatically screens suitable candidates based on resumes. 5. Drones automatically complete equipment inspection tasks to reduce human intervention. etc.	Restructure the workflow to eliminate low-efficiency, repetitive work, aiming for fully automated management throughout the entire process chain.

Note: AR: augmented reality; AGV: automated guided vehicle.

.

The different types of DTs distinctly influence employee behavior and psychological outcomes through varied mechanisms, differing in influence pathway and effect magnitude. Assisted digital technology, which is human-led, alleviates the burden of low-value tasks, allowing employees to focus on higher-level work. This shift promotes learning, competency enhancement, and engagement in creative, strategic, and high-value tasks, fostering positive behavior and psychology. Enhanced technology, through human–machine synergy, propels employees into a “competence gain cycle”, continuously strengthening learning and cognitive depth in handling complex tasks. This process encourages individuals to surpass the ability boundary, creating a positive spiral mechanism of “technology empowerment–achievement awareness–innovative behavior” in the workplace. Both assisted and enhanced technologies effectively promote positive behavior and psychology by reducing work costs and improving goal attainment and accuracy [76], thus enhancing efficacy and job satisfaction. While autonomous technologies can improve task-matching efficacy [77], their capacity to elicit similarly positive effects may be constrained by diminished perceptions of control and suppressed autonomy need fulfillment.

Autonomous technology, while markedly boosting operational efficacy and decision-making accuracy, poses the risk of resource depletion [78]. The opacity of algorithmic surveillance systems commonly evokes perceptions of procedural unfairness, leading to cognitive resource drain, emotional exhaustion, and reduced motivation for innovation [79]. The autonomy deprivation theory suggests that when technology supplants manual decision making, employees may experience decreased efficacy and alienation [80]. Neuroscientific evidence shows that perceived autonomy deprivation suppresses prefrontal cortex activity and increases amygdala activation. This neural response is associated with negative emotional states such as anxiety and depression, potentially manifesting as resistance to DTs or deliberate reductions in work productivity [81]. While enhanced technologies can autonomously perform certain tasks, their functions remain limited and subject to human oversight. For example, industrial robots require engineers to configure parameters and monitor performance, ensuring control through active management [82]. Although enhanced technologies expand capabilities, they can also exacerbate emotional exhaustion due to the paradox of “increasing capabilities–increasing stress”, inducing burnout and decreased efficacy. In contrast, assisted technology as a decision support tool retains employee control over work processes, with reduced technological intervention, posing a minor threat to psychological autonomy. Anxiety and negative reactions, such as increased dependency and passive delay, may only temporarily occur when automated processes malfunction.

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 7 (H7). 

There is a moderating effect of usage type on the relationship between DTs and positive outcomes. Compared to autonomous technology, DTs show a stronger positive relationship with positive employee behavior (H7a) and psychology (H7b) in assisted and enhanced technologies.

Hypothesis 8 (H8). 

There is a moderating effect of usage type on the relationship between DTs and negative outcomes. Compared to assisted and enhanced technologies, DTs show a stronger positive relationship with negative employee behavior (H8a) and psychology (H8b) in autonomous technology.

2.4.2. Demographic Factors

(1) Age

The Unified Theory of Acceptance and Use of Technology (UTAUT) posits that age significantly influences individuals’ willingness to adopt technology [83]. Younger individuals generally exhibit a more positive attitude toward emerging technologies, whereas older individuals approach them cautiously, resulting in variations in technology acceptance [84]. Non-senior employees are characterized by stronger technological adaptability and digital natives, adeptly leveraging DTs for innovation, thus improving positive psychology and innovation behavior through fulfilling autonomy and competence needs. Conversely, senior employees often occupy experience-dependent roles and may encounter challenges such as elevated technology adaptation costs and occupational inertia. Despite this, senior employees can also utilize DTs to improve productivity, and their work patterns are vulnerable to being subverted or replaced by autonomous DTs, leading to a devaluation of their accumulated experience and a diminished sense of control. Consequently, the influence of DTs on the behavior and psychology of senior employees may be less pronounced than that of their younger counterparts. Nevertheless, owing to their extensive experience and established work routines, senior employees tend to experience lower levels of work-related anxiety and role ambiguity amid the ongoing evolution of DTs. Non-senior employees at lower organizational levels often face limited decision-making authority and reduced job control [85]. When companies widely adopt automated systems, AI decision support tools, and digital performance monitoring, these employees may experience increased control pressures, leading to dissatisfaction, resistance, and negative behavior. The social comparison theory suggests that in a highly digitalized environment, non-senior employees may feel tremendous competitive pressure due to higher expectations of their technical skills, especially in rapid technological development. They are more likely to develop a sense of occupational insecurity, adversely affecting their job satisfaction and well-being.

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 9 (H9). 

There is a moderating effect of employee age on the relationship between DTs and positive outcomes. Compared to senior employees, DTs show a stronger positive relationship with positive behavior (H9a) and psychology (H9b) of non-senior employees.

Hypothesis 10 (H10). 

There is a moderating effect of employee age on the relationship between DTs and negative outcomes. Compared to senior employees, DTs show a stronger positive relationship with negative behavior (H10a) and psychology (H10b) of non-senior employees.

(2) Education

The technology acceptance model (TAM) notes that highly educated people are more inclined to embrace DTs [86]. Such employees possess strong learning and adaptation skills for new technologies [87]. This adaptability enables them to efficiently enhance work skills, forming a positive cycle of “skill enhancement–career advancement” that boosts workplace recognition and efficacy, thereby encouraging positive work behavior [88]. While highly educated employees can learn digital skills, the widespread adoption of digital technology may also impose more significant pressure to adapt to these advancements. Research indicates that highly educated employees usually undertake more complex digital management tasks in a digital work environment, such as AI model tuning, data analysis, and intelligent decision support [89]. These tasks require continuous investment in digital management efforts during technological updates and iterations, or they may face digital knowledge obsolescence and intense digital competition pressure. High demands for learning and intense competitive pressures may lead employees to experience technology-related anxiety, burnout, and cognitive overload, ultimately undermining well-being [90]. In addition, roles for highly educated employees often require making more complex technical decisions, which carry a higher risk of failure [91]. Increased job responsibilities can cause psychological burdens and anxiety. In contrast, while non-highly educated employees face a risk of job displacement due to DTs, the standardized and procedural nature of their tasks results in comparatively lower psychological pressure and reduced learning demands. Non-highly educated employees primarily engage in execution and operational tasks, where technology serves more as a tool for task completion rather than driving continuous complex innovation or decision making. Therefore, although introducing DTs may cause discomfort, negative psychological responses and behavior tend to be less pronounced in non-highly educated employees than in highly educated employees.

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 11 (H11). 

There is a moderating effect of employee education level on the relationship between DTs and positive outcomes. Compared to non-highly educated employees, DTs show a stronger positive relationship with positive behavior (H11a) and psychology (H11b) of highly educated employees.

Hypothesis 12 (H12). 

There is a moderating effect of employee educational level on the relationship between DTs and negative outcomes. Compared to non-highly educated employees, DTs show a stronger positive relationship with negative behavior (H12a) and psychology (H12b) of highly educated employees.

(3) Position

An employee’s position within an organization is a key factor influencing behavior and attitudes [92]. Employees in managerial roles must possess more complex managerial skills and face more uncertainty than those in non-managerial positions [43]. Management-level employees possess more strategic information within the enterprise and are more inclined to use digital platforms to drive business innovation and organizational change. Therefore, managerial employees’ effective use of DTs further enhances their efficacy and professional identity. Evidence suggests that enterprise social media in the workplace enable managerial employees to communicate with superiors, peers, and subordinates conveniently and access relevant resources [93]. R&D management teams have significantly increased innovators’ sense of accomplishment by using digital virtual tools to shorten experimentation cycles [43]. However, some digital tools, while facilitating managers’ quick decisions, may weaken managers’ authority due to the transparency of information, which may lead to power anxiety [73]. In addition, over-reliance on technical data may overshadow employees’ demands, fostering a phenomenon of “data bureaucratization”. Non-managerial employees primarily focus on task execution, with technology as a support tool rather than decision making. Consequently, there are limited opportunities for increased autonomy and innovation, resulting in a diminished impact of DTs on positive outcomes. Non-managerial employees’ tasks are highly standardized and process-driven, adhering to fixed operational norms. As a result, DTs aim to optimize work processes rather than fundamentally alter core responsibilities. Moreover, non-managerial employees experience lower decision complexity and cognitive load during digital transformation, reducing the stress associated with adapting to technological changes. Hence, the negative behavior and psychological stress of non-managerial employees due to DTs are milder compared to managerial employees.

Building on the above analysis, this study proposes the following hypotheses:

Hypothesis 13 (H13). 

There is a moderating effect of employee position on the relationship between DTs and positive outcomes. Compared to non-managerial employees, DTs show a stronger positive relationship with the positive behavior (H13a) and psychology (H13b) of managerial employees.

Hypothesis 14 (H14). 

There is a moderating effect of employee position on the relationship between DTs and negative outcomes. Compared to non-managerial employees, DTs show a stronger positive relationship with the negative behavior (H14a) and psychology (H14b) of managerial employees.

To sum up, the research framework of this study is shown in Figure 2 below.

3. Methodology

This study uses a meta-analytic approach to conduct empirical testing, originally applied in psychology and medicine to extract generalized conclusions by systematically integrating the existing research findings. Owing to its objectivity, methodological rigor, and inductive capability, this approach has been widely utilized in management research, including strategy, human resources, and entrepreneurship. Based on the meta-analytic approach, this study synthesizes findings on the differences between DTs and behavioral and psychological responses in the workplace, aiming to enhance the understanding of their interrelationship. This study adheres strictly to the meta-analytic procedures proposed by Lipsey and Wilson [94].

3.1. Search Process and Article Identification

This study conducted a comprehensive literature review focusing on DTs, employee behavior, and psychology. The literature was sourced from academic databases such as Web of Science, Science Direct, Scopus, EBSCOhost, Emerald, Taylor & Francis, CNKI, and Springer. Keywords related to employee behavior and psychology (e.g., “employee behavior”, “employee psychology”, “task performance”, “innovation performance”, “job satisfaction”, “employee engagement”, “well-being”, “job burnout”, “turnover intention”, “emotional exhaustion”, “withdrawal behavior”, “service sabotage”, “anxiety”, “employee efficacy”, and “employee creativity”) and digital technology (e.g., “digitalization”, “digital transformation”, “artificial intelligence”, “big data analysis”, “social media”, “ICT”, “algorithm”, “robot”, “IT”, and “quantum information”) refined the search. Since McAfee proposed the concept of “Enterprise 2.0”, digital technology has gradually become an important topic in organizational management research [95], so this study limits the literature search to the period from January 2006 to February 2025. Initially, 4253 articles were identified through title screening, comparison, deduplication, and synthesis.

The literature screening for this study followed these criteria: (1) The research must explore the relationship between at least one form of digital technology and employee outcomes, focusing on their correlation. (2) Only quantitative studies were included, excluding those lacking statistical indicators of the relationship between enterprise-level digital technology use and outcomes. Specifically, non-experimental studies should report correlation coefficients (r) or convertible path and regression coefficients (β), while experimental studies should provide convertible statistics like F, t, and d values. (3) Each study must be independent; if multiple studies used the same sample, only one was included. Articles not meeting these criteria were excluded. Following these principles, 106 studies were included in this meta-analysis. The screening process is detailed in Figure 3.

3.2. Literature Coding

This study establishes a coding manual to ensure accuracy, with two researchers independently coding variables, achieving a consistency coefficient of 0.961. Discrepancies are resolved by a senior researcher. Coding is divided into research characteristics and effect sizes. Research characteristics include publication details and design elements such as author, publication year, journal source, sample size, demographic information (country, gender, position, age, education level), and measurement methods. Effect sizes pertain to statistical data like bivariate correlation coefficients, including pairwise effect sizes and the reliability coefficient α for each variable. For variables with multiple sub-constructs, combined effect sizes are calculated to prevent bias from inflated sample sizes [96]. If a study lacks a reported reliability coefficient α, the average reliability from other studies is used, and for objectively measured variables, the coefficient is set to 1. Following Peterson and Brown, Equations (1) and (2) are employed to convert path or regression coefficients (β) from the literature [97].

$$r=\beta \times 0.98+0.05 \left(\beta \ge 0\right)$$

(1)

$$r=\beta \times 0.98-0.05 \left(\beta <0\right)$$

(2)

Table 3. Description of variables.

Variable	Description
Independent variable
DTs	The components of digital technologies are AI, BDA, IoT/CPS, Blockchain, and 3DP, based on the principles of technological maturity, performance salience, and functional heterogeneity.
Dependent variable
2.1 Positive behavior	Employees exhibit behaviors that foster positive changes in their careers or the organization.
(a) Task performance	A set of behaviors directed toward task completion, encompassing the quantity and quality of work-related outputs.
(b) Innovation performance	Activities exceeding routine expectations to deliver novel outcomes through idea generation and innovative problem-solving.
(c) Employee engagement	Employees consciously exert their subjective initiative and actively carry out various tasks.
2.2 Negative behavior	Negative workplace behaviors that hinder individual career development or organizational progress.
(a) Withdrawal behavior	Avoidant workplace behaviors aimed at evading work situations or task responsibilities.
(b) Service sabotage behavior	Deliberate violations of organizational rules intended to disrupt service delivery and undermine customer interests.
2.3 Positive psychology	Enduring and stable positive emotional experiences and subjective orientations as perceived by employees.
(a) Job satisfaction	A pleasant or positive emotional state resulting from employees’ evaluations of their work or work experiences.
(b) Job efficacy	Employees’ self-assessed confidence in their ability to successfully accomplish job-related tasks.
(c) Job well-being	Employees’ pleasurable judgments or positive affective experiences related to their work.
2.4 Negative psychology	Negative emotional states experienced by employees that are detrimental to personal health and development.
(a) Job burnout	A prolonged state of physical and mental exhaustion resulting from an individual’s inability to effectively cope with work-related stress.
(b) Turnover intention	The intention or inclination of employees to leave their current organization.
(c) Work anxiety	A negative emotional response characterized by worry, fear, and anxiety arising from work practices or job-related thoughts.
3.Moderating variables
3.1 Industry technology intensity
(a) High-tech	Dummy variable equal to 1 if belonging to high-tech industries as defined by OECD standards.
(b) Non-high-tech	Dummy variable equal to 0 if not belonging to high-tech industries as defined by OECD standards.
3.2 Usage type
(a) Assisted	Dummy variable equal to 0 if belonging to the auxiliary technology.
(b) Enhanced	Dummy variable equal to 1 if belonging to the enhanced technology.
(c) Autonomous	Dummy variable equal to 2 if belonging to the autonomous technology.
3.3 Age
(a) Senior	Dummy variable equal to l if over 50% of the respondents are aged 35 or above.
(b) Non-senior	Dummy variable equal to 0 if over 50% of the respondents are under 35 years old.
3.4 Education
(a) Highly educated	Dummy variable equal to l if over 50% of the respondents are bachelor’s degree or above.
(b)Non-highly educated	Dummy variable equal to 0 if over 50% of the respondents are less than bachelor’s degree.
3.5 Position
(a) Management	Dummy variable equal to l if the respondents are management.
(b) Non-management	Dummy variable equal to 0 if the respondents are non-management.

provides definitions of the core variables used for coding.

3.3. Procedure

Firstly, the correlation coefficients of each study are first Fisher’s Z transformed as shown in Equation (3), and after calculating the weighted mean of Fisher’s Z, the mean is inverted to the overall correlation coefficient as shown in Equation (4) [98].

$$Z_i=0.5\times ln\left[\left(l+r_i\right)/\left(l-r_i\right)\right]$$

(3)

$$\overline{r}=\left(e^{2\overline{Z}}-1\right)/\left(e^{2\overline{Z}}+1\right)$$

(4)

where $Z_i$ is the value of study(i)’s $r_i$ after Fisher’s Z transformation, $\overline{Z}$ is the weighted mean value of each effect value, and $\overline{r}$ is the overall coefficient after inverse Fisher’s Z transformation.

Secondly, in terms of publication bias test, this paper examines the severity of publication bias of the papers covered in the study using two methods: fail-safe N and funnel plot. The fail-safe N test requires that the fail-safe N should be greater than the “5k + 10” criterion, while the funnel plot method requires that the corrected effect values of each group basically show a normal distribution around the mean [99].

Finally, this paper uses Q and I² to perform heterogeneity tests for meta-analysis. In this case, Q responds to the degree of heterogeneity of each effect value, and I² represents the proportion of the between-group variance in the effect values. If Q is significant and I² exceeds 75%, it indicates a high degree of between-study heterogeneity. When the test results indicate a high degree of between-study heterogeneity, the random-effects model provides a more accurate estimate of the true effect than the fixed-effects model [100].

4. Results

4.1. Publication Bias Test

Table 4. Results of the publication bias test.

Dependent Variable	p	Fail-Safe N	5k + 10
Positive psychology	<0.001	1708	110
Job satisfaction	<0.001	1191	50
Job efficacy	<0.001	217	35
Job well-being	<0.001	56	45
Positive behavior	<0.001	32,212	280
Task performance	<0.001	3589	75
Innovation performance	<0.001	8104	190
Employee engagement	<0.001	853	85
Negative psychology	<0.001	2949	150
Job burnout	<0.001	349	45
Turnover intention	<0.001	77	40
Work anxiety	<0.001	837	85
Negative behavior	<0.001	4490	95
Withdrawal behavior	<0.001	1142	55
Service sabotage behavior	<0.001	1096	50

presents the fail-safe numbers for DTs and employee behavior and psychology, and each dimension exceeds the safety threshold of 5k + 10, indicating minimal publication bias. Additionally, the funnel plot in Figure 4 illustrates a symmetrical distribution of effect values at the funnel’s apex, indicating that the meta-analysis effect values are mainly unaffected by publication bias.

4.2. Heterogeneity Test and Main Effect Analysis

The study employed CMA 3.0 to analyze coded data, with results for the heterogeneity and main effect test on the impact of DTs on various sub-dimensions of employee behavior and psychology detailed in

Table 5. Heterogeneity test and main effects analysis results.

Dependent Variable	Sample		Heterogeneity Test			Main Effects Test			Hypothesis	Result
	K	N	Q	Df	I2	r	95%CI	Z
Positive behavior	54	25,721	1992.121 ***	53	97.340	0.348 ***	[0.293–0.402]	11.519	H1	SUPPORT
Task performance	13	6178	122.975 ***	12	90.242	0.417 ***	[0.346–0.484]	10.368	H1a	SUPPORT
Innovation performance	26	9644	940.455 ***	25	97.342	0.332 ***	[0.219–0.437]	5.491	H1b	SUPPORT
Employee engagement	15	9899	565.912 ***	14	97.526	0.171 **	[0.037–0.300]	2.489	H1c	SUPPORT
Negative behavior	17	5524	653.652 ***	16	97.552	0.444 ***	[0.317–0.555]	6.309	H2	SUPPORT
Withdrawal behavior	9	3435	522.860 ***	8	98.470	0.357 ***	[0.099–0.569]	2.670	H2a	SUPPORT
Service sabotage behavior	8	2089	113.221 ***	7	93.817	0.478 ***	[0.331–0.602]	5.784	H2b	SUPPORT
Positive psychology	20	7647	1049.219 ***	19	98.189	0.203 ***	[0.037–0.359]	2.388	H3	SUPPORT
Job satisfaction	8	2364	167.870 ***	7	95.830	0.447 ***	[0.274–0.591]	4.722	H3a	SUPPORT
Job efficacy	5	1800	195.220 ***	4	97.951	0.272 ***	[−0.049–0.541]	1.669	H3b	SUPPORT
Job well-being	7	3483	205.264 ***	6	98.189	−0.153 *	[−0.354–0.051]	−0.141	H3c	NOT SUPPORT
Negative psychology	28	10,484	618.185 ***	27	95.632	0.198 ***	[0.112–0.281]	4.451	H4	SUPPORT
Job burnout	7	2137	197.820 ***	6	96.967	0.294 **	[0.057–0.500]	2.413	H4a	SUPPORT
Turnover intention	6	2758	212.363 ***	5	97.646	0.026	[−0.249–0.296]	0.180	H4b	NOT SUPPORT
Work anxiety	15	5589	192.811 ***	14	92.739	0.203 ***	[0.105–0.298]	4.017	H4c	SUPPORT

Note: k = number of studies; N = total number of participants for all the studies combined; Q is the test statistic for between-group heterogeneity; Df = degrees of freedom; I² represents the proportion of true effect size variance to total variation; r = average effect; 95%CI pertains to the corrected effect size; Z = Z-value; *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1, same as below.

. The heterogeneity test yielded a p-value below 0.01, with all I-squared values exceeding 90%, indicating significant heterogeneity among the variables. This points to substantial inter-group differences within the independent samples. Consequently, a random-effects model was used for the meta-analysis. To evaluate the strength of relationships between variables, the study referenced Lipsey and Wilson (2001) [94]: r < 0.1 indicates a weak correlation, 0.1 < r < 0.4 indicates a moderate positive correlation, and r > 0.4 shows a strong positive correlation.

Empirical findings reveal that DTs significantly influence employee behavior and psychology, both positively and negatively, supporting H1 through H4. Notably, DTs most strongly affect negative behavior (r = 0.444, p < 0.01), followed by positive behavior (r = 0.348, p < 0.01) and positive psychology (r = 0.203, p < 0.01). The weakest association is with negative psychology (r = 0.198, p < 0.01). These results suggest that while DTs primarily elicit negative behavioral responses, psychological reactions tend to be positive.

Research demonstrates a strong correlation between DTs and task performance (r = 0.417, p < 0.01) and service sabotage behavior (r = 0.478, p < 0.01). Additionally, there is a moderate correlation with innovation performance (r = 0.332, p < 0.01), employee engagement (r = 0.171, p < 0.05), and withdrawal behavior (r = 0.357, p < 0.01), thus supporting H1a, H1b, H1c, H2a, and H2b.

For dimensions of employee psychology, the findings indicate a strong correlation between DTs and job satisfaction (r = 0.447, p < 0.01). A moderate correlation is observed with job efficacy (r = 0.272, p < 0.01), job burnout (r = 0.294, p < 0.05), and work anxiety (r = 0.203, p < 0.01). A moderate negative correlation is noted with job well-being (r = −0.153, p < 0.1), while no significant correlation is found with turnover intention (r = 0.026, p > 0.1). Consequently, H3a, H3b, H4a, and H4c are supported, whereas H3c and H4b are not.

4.3. Moderating Effect Analysis

4.3.1. The Moderating Effect of Industry Technology Intensity

Table 6. The moderating effect of industry technological intensity.

Dependent Variable	Type	Sample		Main Effects Test			QB	Hypothesis	Result
		K	N	r	95%CI	Z
Positive behavior	High-tech	19	7557	0.412 ***	[0.296–0.516]	6.481	4.956 *	H5b	SUPPORT
	Non-high-tech	13	4668	0.230 ***	[0.077–0.372]	2.917
Positive psychology	High-tech	5	1840	0.478 ***	[0.173–0.699]	2.951	5.117 *	H5a	SUPPORT
	Non-high-tech	10	4349	0.175 *	[−0.007–0.345]	1.885
Negative behavior	High-tech	3	725	0.432 **	[0.092–0.682]	2.447	5.362 *	H6a	SUPPORT
	Non-high-tech	9	2973	0.517 ***	[0.322–0.670]	4.702
Negative psychology	High-tech	7	2034	0.022	[−0.112–0.155]	0.321	18.244 ***	H6b	SUPPORT
	Non-high-tech	13	5211	0.341 ***	[0.259–0.417]	7.764

Note: QB = test statistic for heterogeneity between groups; *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1, same as below.

displays the findings on the moderating effect of industry technology intensity. Industry technology level significantly moderates the relationship between DTs and both positive behavior (QB = 4.956, p < 0.1) and positive psychology (QB = 5.117, p < 0.1), confirming H5a and H5b. These effects are more significant in high-tech industries. Likewise, technology intensity moderates the link between DTs and negative behavior (QB = 5.362, p < 0.1) and negative psychology (QB = 18.244, p < 0.01), supporting H6a and H6b. These effects are more significant in non-high-tech industries.

4.3.2. The Moderating Effects of Digital Technology Types

The analysis in

Table 7. The moderating effects of digital technology types.

Dependent Variable	Type	Sample		Main Effects Test			QB	Hypothesis	Result
		K	N	r	95%CI	Z
Positive behavior	Assisted	32	13,358	0.327 ***	[0.238–0.412]	6.817	5.166 *	H7a	SUPPORT
	Enhanced	9	3807	0.412 ***	[0.311–0.503]	7.415
	Autonomous	13	8556	0.192 **	[0.017–0.356]	2.143
Positive psychology	Assisted	12	4978	0.181 **	[0.003–0.348]	1.995	15.051 ***	H7b	SUPPORT
	Enhanced	3	1066	0.572 ***	[0.426–0.689]	6.507
	Autonomous	5	1603	−0.003	[−0.399–0.394]	−0.013
Negative behavior	Assisted	8	1971	0.343 ***	[0.125–0.529]	3.029	9.712 **	H8a	SUPPORT
	Enhanced	2	627	0.449 ***	[0.384–0.509]	12.044
	Autonomous	6	2652	0.513 ***	[0.238–0.712]	3.426
Negative psychology	Assisted	14	4503	0.005	[−0.107–0.116]	0.082	36.981 ***	H8b	SUPPORT
	Enhanced	4	726	0.267 *	[−0.047–0.533]	1.673
	Autonomous	10	5255	0.400 ***	[0.335–0.461]	11.021

Note: *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1.

presents the moderating effects of digital technology types. Significant moderating effects are observed in the relationships between DTs and positive behavior (QB = 5.166, p < 0.1), positive psychology (QB = 15.051, p < 0.01), negative behavior (QB = 9.712, p < 0.05), and negative psychology (QB = 36.981, p < 0.01). This supports H7a, H7b, H8a, and H8b. The impact of DTs on positive psychology and behavior is strongest with enhanced technology, followed by assisted technology and autonomous technology. Conversely, its effect on negative psychology and behavior is most significant with autonomous technology, followed by enhanced technology and assisted technology.

4.3.3. The Moderating Effect of Employee Age

Table 8. The moderating effect of employee age.

Dependent Variable	Type	Sample		Main Effects Test			QB	Hypothesis	Result
		K	N	r	95%CI	Z
Positive behavior	Non-senior	38	15,646	0.331 ***	[0.237–0.420]	6.542	12.606 ***	H9a	SUPPORT
	Senior	3	952	0.144 ***	[0.081–0.206]	4.452
Positive psychology	Non-senior	16	6593	0.290 ***	[0.131–0.435]	3.509	26.745 ***	H9b	SUPPORT
	Senior	3	749	−0.358 **	[−0.586–0.078]	−2.480
Negative behavior	Non-senior	13	4085	0.456 ***	[0.307–0.583]	5.512	15.942 ***	H10a	SUPPORT
	Senior	2	983	0.004	[−0.174–0.182]	0.043
Negative psychology	Non-senior	16	5370	0.300 ***	[0.199–0.395]	5.609	8.708 **	H10b	SUPPORT
	Senior	9	3980	0.064	[−0.112–0.237]	0.711

Note: *** denotes p < 0.01, ** denotes p < 0.05.

presents the research findings on the moderating effect of employee age. Employee age significantly moderates the relationships between DTs and positive behavior (QB = 12.606, p < 0.01), positive psychology (QB = 26.745, p < 0.01), negative behavior (QB = 15.942, p < 0.01), and negative psychology (QB = 8.708, p < 0.05). H9a, H9b, H10a, and H10b are validated. The influence of DTs on psychology and behavior is significant among non-senior employees.

4.3.4. The Moderating Effect of Employee Education

The findings presented in

Table 9. The moderating effect of employee education.

Dependent Variable	Type	Sample		Main Effects Test			QB	Hypothesis	Result
		K	N	r	95%CI	Z
Positive behavior	Non-highly educated	9	6199	0.166 *	[−0.001–0.325]	1.949	3.710 *	H11a	SUPPORT
	Highly educated	36	16,546	0.340 ***	[0.250–0.425]	6.991
Positive psychology	Non-highly educated	3	1957	0.037	[−0.163–0.234]	0.361	11.334 ***	H11b	SUPPORT
	Highly educated	14	4793	0.307 ***	[0.090–0.496]	2.744
Negative behavior	Non-highly educated	3	1409	0.171	[−0.168–0.475]	0.989	3.537 *	H12a	SUPPORT
	Highly educated	8	2607	0.524 ***	[0.311–0.687]	4.382
Negative psychology	Non-highly educated	5	1265	0.013	[−0.186–0.210]	0.125	3.552 *	H12b	SUPPORT
	Highly educated	19	6710	0.227 ***	[0.113–0.335]	3.862

Note: *** denotes p < 0.01 and * denotes p < 0.1.

detail the moderating role of employee education. Employee education significantly influences the relationships between DTs and positive behavior (QB = 3.710, p < 0.1), positive psychology (QB = 11.334, p < 0.01), negative behavior (QB = 3.537, p < 0.1), and negative psychology (QB = 3.552, p < 0.1). These results support H11a, H11b, H12a, and H12b. Highly educated employees exhibit a more significant effect of DTs on employee psychology and behavior.

4.3.5. The Moderating Effect of Employee Position

Table 10. The moderating effect of employee position.

Dependent Variable	Type	Sample		Main Effects Test			QB	Hypothesis	Result
		K	N	r	95%CI	Z
Positive behavior	Non-management	5	1277	0.331	[0.214–0.439]	5.330	2.021	H13a	NOT SUPPORT
	Management	15	9446	0.219	[0.048–0.376]	2.508
Positive psychology	Non-management	5	2684	0.235	[−0.123–0.540]	1.292	0.335	H13b	NOT SUPPORT
	Management	2	711	−0.093	[−0.836–0.770]	−0.164
Negative behavior	Non-management	6	1903	0.419 ***	[0.244–0.568]	4.419	15.956 ***	H14a	SUPPORT
	Management	2	627	0.730 ***	[0.602–0.822]	7.805
Negative psychology	Non-management	12	3289	0.085	[−0.081–0.247]	1.003	27.937 ***	H14b	SUPPORT
	Management	2	479	0.482 ***	[0.410–0.548]	11.439

Note: *** denotes p < 0.01.

displays the results concerning the moderating impact of employee position. The impact of employee position on the association between DTs and positive behavior (QB = 2.021, p > 0.1) and positive psychology (QB = 0.335, p > 0.1) is not statistically significant, thus failing to validate H13a and H13b. Nevertheless, the moderating effect is significant for the link between DTs and negative behavior (QB = 15.956, p < 0.01) and negative psychology (QB = 27.937, p < 0.01), confirming H14a and H14b. These associations are more evident among managers.

5. Discussion and Implications

5.1. Discussion

This study uses a meta-analytic approach to quantitatively synthesize and analyze empirical research on the relationship between DTs and individual employee behavioral and psychological outcomes. DTs in the workplace present a “double-edged sword” effect, simultaneously fostering positive behavior and psychological resources while also inducing negative behavior and depleting psychological resources. Specifically, DTs are positively linked to task performance (H1a), innovation performance (H1b), and engagement (H1c) at the behavioral level, yet they also significantly contribute to withdrawal (H2a) and service sabotage (H2b). Psychologically, DTs enhance job satisfaction (H3a) and efficacy (H3b) but negatively affect well-being (H3c). Furthermore, DTs are associated with increased job burnout (H4a) and anxiety (H4c), though their effect on turnover intention (H4b) remains statistically insignificant. The mechanisms by which DTs positively affect employee psychology and behavior include reducing the burden of repetitive labor, thereby enabling employees to focus on skill enhancement and efficient value creation, and creating new work opportunities with immediate, personalized feedback that empowers positive psychological resources. Conversely, the negative impacts of DTs may arise from increased workload, task intensity, skill demands, and perceived career threats, which collectively deplete the psychological resources necessary for maintaining equilibrium. Moreover, frequent interaction with intelligent systems lacking contextual awareness and personalized engagement can heighten feelings of loneliness and alienation, reducing work motivation and prompting withdrawal.

Among the positive behavioral and psychological dimensions, DTs most strongly influence job satisfaction (H3a), followed by task performance (H1a), innovation performance (H1b), employee efficacy (H3b), and engagement (H1c). However, DTs notably inhibit job well-being (H3c) within positive psychological dimensions. This may be attributed to DTs’ emphasis on performance orientation, optimizing processes, and efficacy, thereby increasing job satisfaction and efficacy but neglecting employees’ deeper emotional needs and well-being. This aligns with the resource-based view of technology empowerment for resource reconfiguration and the computer self-efficacy theory [101]. However, DTs may impose additional learning demands, system malfunctions, and workflow disruptions, intensifying stress, and diminishing well-being. Low-skilled workers face anxiety over AI replacement and limited career advancement, highlighting the growing social inequalities exacerbated by the skill gap [65].

Within the negative behavioral and psychological dimensions, DTs have the most significant contributory effect on service sabotage (H2b), followed by withdrawal behavior (H2a), job burnout (H4a), and anxiety (H4c). However, their impact on turnover intention (H4b) is statistically insignificant. While DTs offer efficacy and information processing, they can elevate cognitive load and emotional strain, leading to service-disruptive behavior as resistance to the surveillance and automation inherent to digital management systems [81]. This finding aligns with the resource conservation theory and the technology stress model, which suggest that negative behavioral responses are more likely when perceived technology-induced stress surpasses coping capacity. Conversely, despite the impact of DTs on withdrawal, burnout, and anxiety, these reactions may not directly lead to turnover intention if moderated by organizational support mechanisms. This view is consistent with the JD-R model, which posits that adequate organizational resources and support can mitigate turnover intention under high job demands [102]. Job stress and technostress from DTs can cause emotional fluctuations and negative behavior, but organizational mitigation strategies may support job retention [103].

The impact of DTs varies markedly across contexts and industries. In high-tech sectors, DTs positively influence behavior (H5a) and psychology (H5b), while in non-high-tech industries, DTs tend to induce negative outcomes (H6a–H6b). This disparity likely stems from the greater digital literacy and adaptability of high-tech employees, who effectively utilize digital tools to improve efficacy and innovation performance, fostering job satisfaction and organizational commitment. In comparison, employees in non-high-tech fields may be less familiar with or resistant to DTs, which can elevate job stress and contribute to psychological strain such as burnout and anxiety, as well as negative behavior like withdrawal and service sabotage. Digital transformation raises competency demands, particularly in applying DTs to solve complex business issues, posing challenges for organizational management and human resources [104]. Therefore, when promoting digital transformation, enterprises should consider industry characteristics and employee digital literacy, providing appropriate training and support to maximize the positive effects of DTs and effectively mitigate the negative psychological and behavioral impacts.

The relationship reveals notable differences among the various types of DTs. Assisted (H7a) and enhanced (H7b) technologies facilitate goal achievement while reducing resource expenditure, fostering a resource-enriched work environment, and enhancing well-being and job satisfaction. This aligns with the COR theory, which posits that abundant resources enhance employee engagement, creativity, and productivity. In contrast, although autonomous technologies (H8a–H8b) improve efficiency and provide some autonomy, they often incorporate digital surveillance and control mechanisms that heighten psychological strain, perceived threats, and insecurity, thereby diminishing positive affective experiences and leading to negative psychology. According to the self-determination theory, restricting autonomy undermines intrinsic motivation and degrades work quality [105]. Algorithmic management intensifies performance pressure and exacerbates uncertainty and anxiety [63].

This study found significant variations in the impact of DTs across demographic groups. (a) DTs exert a stronger influence on both the behavioral and psychological outcomes of non-senior employees than their senior counterparts (H9–H10). This may be attributed to the digital-native advantage, as non-senior employees demonstrate higher adaptability and faster assimilation of emerging technologies. However, increased exposure to digital surveillance and information overload can heighten work-related stress and psychological strain. (b) A more significant impact of DTs is observed on the behavior and psychology of highly educated employees than those of non-highly educated employees (H11–H12). This is likely because the highly educated group is more adept at acquiring and applying new knowledge to improve efficacy with technology, yet they are also prone to negative behavior and emotion due to information overload and regulatory pressure. (c) For positive behavior and psychology, DTs have a negligible effect on employees across different positions, possibly due to the comprehensive digital support system that mitigates perception differences between management and non-management staff. In contrast, DTs have a more significant impact on negative outcomes among management employees and non-management employees (H13–H14). This phenomenon may result from increased responsibility and pressure on management to make decisions. Management roles depend on digital tools for strategic planning, data analysis, and organizational coordination. The transparent and real-time monitoring nature of digital technology may amplify workload, potentially leading to increased psychological stress. According to the JD-R model, burnout, and negative behavioral responses are more probable when job demands surpass available resources [106]. In addition, the technology stress theory suggests that the constant evolution of digital technology causes managers to adapt continually, escalating cognitive load and role conflict, thus increasing susceptibility to negative emotions and behavior [107].

5.2. Theoretical Contributions

Firstly, the meta-analysis synthesizes the recent empirical literature on the workplace impact of DTs, revealing a strong positive correlation with behavior and a moderate positive correlation with psychological outcomes. The analysis uncovers heterogeneous effects across specific behavioral and psychological dimensions, underscoring the contextual variability and complexity of DTs of underlying mechanisms. This addresses the scholarly call for deeper exploration into how DTs affect individual employees [7,81], enriches theoretical explanations of the “double-edged sword” of human–computer collaboration, and partially bridges the theoretical divide between resource and stress perspective. Previous research has highlighted inconsistency regarding the effects of DTs on employee behavior and psychological outcomes, with relational mechanisms being insufficiently defined. The systematic integration and empirical assessment of fragmented findings elucidate the overarching impact on employees. Grounded in the JD-R model, it identifies a dual mechanism effect on employees in the workplace, encompassing both resource-gaining and resource-depleting processes.

Secondly, the analysis reveals the moderating role of contextual factors, with industry type identified as a critical determinant of DTs. While the existing literature often discusses the “double-edged sword” of technology, it overlooks the important influence of industry characteristics on employees’ acceptance of and adaptability to technology. Our findings confirm the negative impact of technological inequality in the digital divide theory and revise the prevailing assumption of a “homogeneity” effect in prior technology impact studies. These results underscore the importance of industry context as a key boundary condition, offering solid theoretical support for organizations to devise differentiated digital transformation strategies.

Thirdly, this study explores the interaction between technological features and individual psychological needs by examining the application of various technologies. Assisted and enhanced technologies promote positive behavior and psychological experiences by reducing task completion costs and offering additional resources, aligning with the COR that “resourcefulness drives positive response”. However, although autonomous technology boosts job efficacy, it diminishes autonomy and decision-making participation, resulting in the motivational depletion effect emphasized by self-determination theory, which exacerbates anxiety and alienation. These findings challenge the assumption of technological neutrality, illustrating how technological features affect behavior by interfering with psychological needs and refining the “technology–human” interaction in the socio-technical systems theory.

Lastly, the analysis uncovers demographic heterogeneity in the behavioral and psychological impacts of DTs, challenging the universal assumptions common in traditional technology impact studies. By examining age, education, and position, this study clarifies variations in employee responses to digital technology in the workplace. This approach addresses the gap between individual differences and organizational context in the current research on technology’s dual effects, promoting a theoretical shift from technological determinism to a “human–technology–environment” co-evolution paradigm.

5.3. Managerial Implications

First, the “double-edged sword” effect of DTs necessitates that enterprises thoroughly assess their multifaceted impacts during digital transformation [108]. Enterprises should implement a strategic planning process to define the objectives, pace, and scope of digital technology integration, thereby mitigating adverse effects from indiscriminate digital expansion [109]. Managers might employ a strategy that pairs gradual DTs with an employee feedback system to gauge adaptability and psychological responses, thus preventing negative outcomes from excessive oversight and workload [110]. Furthermore, companies should focus on equipping employees with the skills to effectively use digital tools through targeted training and ongoing digital literacy programs, thereby enhancing their adaptability to new technologies. A phased, job-specific training model with tailored content for various technologies can be implemented to alleviate anxiety and resistance stemming from inadequate understanding and application [110].

Secondly, enterprises must craft digital technology empowerment and psychological support programs tailored to specific industry needs. In high-tech sectors, the focus should be on training and counseling for technological stress and emotion management. Conversely, non-high-tech industries should prioritize basic digital skills training and practical guidance to ease the digital learning curve and psychological burden. When choosing DTs, their impact on employee outcomes should be considered. Assisted and enhanced technologies can lower task completion costs, streamline workflows, and provide ample resources, boosting job satisfaction and productivity. These technologies should be prioritized in departments with lower skill levels or higher stress to ensure performance and psychological safety. While autonomous technologies offer more autonomy, they may cause insecurity and anxiety due to digital supervision features. To mitigate this, enterprises should establish clear privacy protection measures, transparent data usage policies, and effective incentive systems, complemented by robust communication and feedback mechanisms, to reduce resistance and enhance technology acceptance and efficiency.

Thirdly, enterprises should tailor training and support strategies based on age, education, and position. Research indicates that highly educated and non-senior employees typically exhibit strong digital technology learning capabilities and innovation potential. However, their propensity for information overload and continuous digital monitoring may lead to negative psychological outcomes. To address this issue, companies can develop information management training programs tailored to young, highly educated employees. Implementing information-filtering tools and intelligent reminder systems can assist in efficiently managing their workload. Creating a flexible, autonomous work environment, minimizing unnecessary technical oversight, and effectively reducing work-related stress and anxiety are also recommended. Furthermore, establishing a clear digital support system that considers job hierarchy variances is crucial. While top-down digital support can bridge cognitive gaps between management and non-management employees in fostering positive behavior and attitudes, management personnel may be susceptible to negative behavior and psychological challenges due to heightened responsibilities and decision-making pressures. Therefore, enterprises should provide management with intelligent decision support systems, data visualization tools, and decision-sharing mechanisms to alleviate the psychological burdens associated with digital decision making and oversight. Psychological support and stress management training for managers are essential to ensure both decision-making efficacy and mental well-being during digital transformations [111]. Conversely, non-management staff may encounter significant skill gaps or difficulties adapting to digital technology applications. In response, companies should offer foundational software training, basic digital tool usage skills, and access to digital tutors and support channels to promptly address specific issues employees encounter with digital tools. This approach aims to enhance employees’ acceptance of digital transformation and improve their practical application skills.

5.4. Limitations and Future Research

First, the literature included in this study was limited to Chinese and English literature, potentially omitting relevant findings from other linguistic domains and introducing accessibility bias. Subsequent studies should expand on the multilingual literature to enhance the diversity and representativeness of the sample.

Second, the analysis did not investigate the distinct impacts of individual DTs on employee outcomes. Instead, it grouped applications into three types to assess moderating effects. Future research could concentrate on specific technologies, like generative AI, to elucidate the mechanisms of their impact on employee outcomes.

Thirdly, this study highlights the dual impact of DTs on employees, capturing both the “bright side” and the “dark side”. While integrating data from a large sample, most empirical studies have concentrated on the relationship between DTs and positive behavior, positive psychology, and negative psychology. However, the influence on negative behavior remains underexplored. Future research should delve deeper into this area by investigating the specific mechanisms and contextual boundaries of the impact of DTs on negative behavior.

Lastly, the current literature often omits information on individual employee traits and organizational contextual characteristics, hindering the exploration of moderating variables in this study. Future research can explore the potential moderating mechanisms influencing the relationship between DTs and employee outcomes, supported by richer empirical evidence. Additionally, future studies should prioritize the standardized presentation of descriptive statistical information from survey samples to provide a more robust and reusable data foundation for meta-analysis in this field.