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Abstract

The rise of remote and hybrid work following the COVID-19 pandemic has intensified the need to understand how organizational incentives influence employee satisfaction. This study develops and tests an integrated model linking extrinsic rewards, socio-technical systems acceptance, and employee satisfaction. Grounded in expectancy theory, reinforcement theory, and socio-technical systems theory, the model also examines the moderating effects of work arrangements, job functions, and tenure. Data collected from 362 knowledge workers were analyzed using PLS-SEM, regression, and artificial neural networks. Results show that extrinsic rewards enhance satisfaction indirectly by promoting acceptance of work-related technologies. This indirect effect is stronger for remote workers, core functional roles, and longer-tenured employees. The study extends motivation theory by conceptualizing technology use as a motivational mechanism and offers practical insights into reward system design for diverse work settings in digitally mediated environments.

Keywords

employee satisfaction extrinsic rewards hybrid work socio-technical systems technology acceptance

Introduction

The COVID-19 pandemic has fundamentally reshaped how organizations operate. Global leaders such as Google, Facebook, and Microsoft swiftly transitioned from traditional office environments to remote and hybrid models. What began as a temporary response has evolved into a structural norm, as digital tools and collaborative technologies have become embedded in everyday work. This shift has been associated with improvements in both employee satisfaction and organizational performance (Alipour et al., 2021; Chatterjee et al., 2022; Gajendran et al., 2024; Kausto et al., 2024; Kumar et al., 2024).

Remote and hybrid work have now become integral elements of organizational culture. Their continued adoption is not merely a function of technical feasibility (Chan et al., 2023); rather, these arrangements promote autonomy, reduce stress, and support work–life balance (Jamaludin & Kamal, 2023). These benefits lead to higher satisfaction and engagement, which in turn enhance individual and organizational performance (Judge et al., 2001). Consequently, flexible digital environments are maintained not only for convenience but also for their capacity to generate sustained motivational and functional value.

However, the use of digital systems alone does not guarantee organizational effectiveness. In technology-mediated settings, the alignment between technical infrastructure and human systems has emerged as a critical success factor (Rasool et al., 2022). Socio-Technical Systems Theory (STST) posits that optimal performance depends on the integration of technological tools with employees’ roles, motivations, and perceptions (Trist & Bamforth, 1951). From this perspective, technology acceptance extends beyond usability; it involves psychological alignment with work identities and reward structures (Mumford, 2006; Orlikowski, 1992).

Although substantial research has been conducted on motivation and technology acceptance as separate domains, their intersection remains underexplored. Specifically, it is unclear how extrinsic rewards psychologically stimulate engagement with internal systems and how such engagement, in turn, influences employee satisfaction (Ajmal et al., 2015). Empirical studies have rarely examined how this mechanism differs across work models, job functions, or tenure (Gajendran & Harrison, 2007; Malik et al., 2015). Furthermore, the mediating role of system alignment in the relationship between rewards and satisfaction has received limited attention (Baxter & Sommerville, 2011).

To address these gaps, this study investigates how extrinsic rewards influence employee satisfaction both directly and indirectly through socio-technical system acceptance. We argue that rewards act not only as external motivators but also as psychological triggers that foster alignment between employees and internal systems (Anthonysamy et al., 2025). Such alignment enhances participation and emotional attachment, ultimately improving satisfaction (Gerhart & Fang, 2015). In addition, we explore how these relationships vary across contextual conditions, including work arrangement, job function, and length of tenure (Kooij et al., 2011; Ng & Feldman, 2010).

By integrating motivational theories with the alignment logic of STST, this study proposes a novel framework that reconceptualizes technology acceptance as a psychologically driven process. The findings contribute to both theory and practice by informing differentiated HR strategies tailored to digitally evolving work environments and diverse employee profiles.

Accordingly, this study seeks to answer the following research question: How do extrinsic rewards shape employee satisfaction, and to what extent is this relationship influenced by employees’ acceptance of socio-technical systems and contextual factors such as work model, job function, and tenure?

This paper proceeds as follows: Section “Literature Review and Hypothesis Development” reviews relevant theories and hypotheses. Section “Methodology” outlines the methodology. Section “Empirical Analysis” presents the empirical results, followed by moderation analysis in Section “Empirical Results,” robustness checks in Section “Robustness Check,” and theoretical and practical implications in Section “Conclusion.”

Literature Review and Hypothesis Development

Literature Review

Extrinsic Rewards and Employee Motivation

Extrinsic rewards refer to tangible and externally provided benefits such as salaries, bonuses, and other financial incentives that are intended to shape work behavior (Eisenberger & Cameron, 1996; Venkatesh & Bala, 2008). Expectancy theory suggests that individuals are motivated when they believe their efforts will lead to desirable outcomes (Van Eerde & Thierry, 1996; Vroom, 1964). Similarly, reinforcement theory argues that behaviors followed by positive consequences are more likely to be repeated (Skinner, 1965; Stajkovic & Luthans, 2003). In organizational settings, extrinsic incentives function as signals of recognition, fairness, and performance expectations (Gerhart & Fang, 2015). A large body of empirical research has shown that extrinsic rewards are positively related to job satisfaction, organizational commitment, and performance, particularly in task-driven or competitive environments (Jiang et al., 2012; Kuvaas, 2006; Skaggs et al., 1991).

Beyond these classical motivational theories, alternative perspectives such as Self-Determination Theory (Deci & Ryan, 1985), Social Exchange Theory (Blau, 1964), and Goal-Setting Theory (Locke & Latham, 1990) have also been used to explain how rewards influence employee behavior. However, these approaches mainly emphasize intrinsic or relational drivers, giving limited attention to how external incentives interact with organizational structures or technological systems.

Previous studies have indirectly explored the relationships among extrinsic rewards, socio-technical systems, and employee satisfaction. For example, Chen and Qi (2015) and Choi et al. (2008) found that extrinsic motivation and socio-technical factors jointly enhance system participation and satisfaction. Bednar and Welch (2020) as well as Vidgen and Madsen (2003) emphasized that technological engagement within socio-technical systems strengthens job satisfaction. Similarly, Thomas (2024) and Land (2000) demonstrated that socio-technical alignment and digital transformation positively affect employee satisfaction and organizational efficiency. While these studies provide indirect empirical evidence linking rewards, systems, and satisfaction, few have explicitly tested a mediating mechanism in which socio-technical systems acceptance connects extrinsic rewards to satisfaction. In particular, little is known about how this process varies across contextual factors such as work model, job function, and tenure.

To address these gaps, this study proposes a unified theoretical model that integrates motivation theory and socio-technical systems theory. The model empirically examines both the direct and indirect effects of extrinsic rewards on employee satisfaction, taking into account contextual moderators (work model, job function, and tenure). This integrative perspective helps explain how extrinsic rewards influence both behavioral and cognitive responses. It also shows that rewards operate not merely as transactional benefits but as motivational signals that enhance engagement, perceived support, and satisfaction through socio-technical alignment.

Employee Satisfaction

Research has extensively examined the link between extrinsic rewards and job-related attitudes, with job satisfaction as a central focus. Job satisfaction refers to employees’ affective responses to their tasks, which influence organizational attitudes, performance, and turnover intentions (Harter et al., 2002; Judge et al., 2001). Studies show that extrinsic rewards improve job satisfaction, especially in performance-based or structured task contexts (Jiang et al., 2012; Kuvaas, 2006). Both expectancy and reinforcement theories explain that when rewards are perceived as fair, they foster satisfaction and positive attitudes (Stajkovic & Luthans, 2003).

However, the ongoing digital transformation and evolving work environments require a broader conceptualization: employee satisfaction. While job satisfaction remains a key component, this study extends the concept to include affective responses to technology and system interactions across the organization. This expanded view incorporates psychological engagement and experiences of digital system integration.

The Technology Acceptance Model (TAM) identifies perceived usefulness and ease of use as drivers of positive attitudes and user satisfaction (Davis, 1989; Venkatesh & Bala, 2008). Ajmal et al. (2015) further demonstrate that extrinsic rewards can enhance technology acceptance, which in turn contributes to employee satisfaction (Anthonysamy et al., 2025).

In summary, this study seeks to explain employee satisfaction in socio-technical contexts, where extrinsic rewards and technology acceptance jointly operate. This approach contributes to both performance research and HR strategy by highlighting the role of alignment between digital systems and employee perceptions (Gerhart & Fang, 2015; Judge et al., 2001). This alignment mechanism is further conceptualized through socio-technical systems theory (Mawhinney, 2011).

Socio-Technical Systems Theory

Socio-technical systems theory, first introduced by Trist and Bamforth (1951), explains how interactions between the social environment of an organization and its technical infrastructure influence outcomes. Social components include employee attitudes, team dynamics, and organizational culture, while technical components refer to information systems and technology platforms (Bostrom & Heinen, 1977). His theory suggests that technology cannot be understood in isolation, but must be interpreted in the context of how it fits with the people who use it (Mumford, 2006).

In contemporary organizational settings, digital tools such as artificial intelligence systems and cloud-based platforms have become central. The key issue is no longer whether technologies are adopted, but whether they are meaningfully integrated into work practices and employee experiences (Orlikowski, 1992). As a result, socio-technical theory has re-emerged as a useful lens for understanding how organizations adapt to digital change. Core concepts from the Technology Acceptance Model, such as perceived usefulness and ease of use, are consistent with this framework because they emphasize the importance of socially supported adoption (Davis, 1989; Venkatesh & Bala, 2008).

Most prior applications of this theory have focused on large-scale implementations, user-centered design, and the general challenges of digital transformation (Baptista et al., 2020; Barrett et al., 2012). In contrast, the present study emphasizes the motivational dimension of the theory. It suggests that extrinsic rewards may play a role in encouraging acceptance of technology and, as a result, improving satisfaction (Gerhart & Fang, 2015). Compensation is viewed not only as a practical incentive but also as a way to align technical tools with employee expectations and behavior (Rasool et al., 2022).

This approach allows us to examine how the relationship between systems and people might vary depending on organizational conditions. Specifically, the study considers how this alignment process may differ according to work model, job role, and length of employment (Gajendran et al., 2024).

Work Model: Onsite, Remote, and Hybrid

Recent developments in digital infrastructure have allowed organizations to move beyond traditional office-based work and adopt remote or blended formats (Choudhury et al., 2021; Wang et al., 2021). These models offer very different experiences in terms of structure, autonomy, and interaction. Employees who work on location tend to benefit from face-to-face communication and clear procedures. Those who work remotely often enjoy greater flexibility but may also encounter ambiguity and reduced social contact. Hybrid work attempts to combine both formats, but it can lead to mental fatigue and coordination difficulties (Gajendran et al., 2024).

These structural distinctions influence the psychological and social conditions in which employees carry out their work. Theories such as Conservation of Resources and the Job Demands and Resources Model suggest that different working conditions affect stress and engagement by shaping the balance between demands and available resources (Bakker & Demerouti, 2007; Hobfoll, 2011). Remote work may reduce physical stress but increase the sense of being digitally overwhelmed (Golden, 2012). Onsite work can offer clarity and support but may limit autonomy and increase pressure (Gerhart & Fang, 2015).

From the perspective of socio-technical theory, work arrangements influence how people interpret and respond to both technological systems and reward structures. In environments where communication and coordination depend on digital tools, external incentives such as rewards can have a stronger impact on employee motivation and satisfaction (Bostrom & Heinen, 1977; Deci et al., 1999). The same reward may be seen as empowering in one setting and as restrictive in another, depending on how it relates to the structure of the work.

For this reason, the model of work that employees follow is likely to shape how rewards affect their relationship with technology and their overall satisfaction. This study therefore examines whether the effect of extrinsic rewards on system engagement and satisfaction depends on whether employees work remotely, on site, or in a combined format.

Job Function and Reward Sensitivity

Job function significantly influences how employees perceive and respond to extrinsic incentives. Role theory explains that different positions are shaped by specific expectations, reward structures, and motivational norms (Katz & Kahn, 1978). Employees in sales or performance-focused roles tend to be more responsive to financial incentives because their output is directly measurable (Gerhart & Fang, 2015). In contrast, individuals in administrative or managerial positions often value autonomy, task meaning, and interpersonal recognition over monetary rewards (Hackman & Oldham, 1976). These differences suggest that the impact of extrinsic rewards on satisfaction or system use may vary depending on functional responsibilities.

As such, the effectiveness of extrinsic rewards in enhancing job satisfaction or system acceptance may vary significantly across functional domains (Alhmoud & Rjoub, 2020).

Work Position Tenure and Motivation Dynamics

Job tenure refers to how long an employee has remained in a specific role, influencing how they interpret and react to extrinsic rewards (Feldman & Weitz, 1988; Ng & Feldman, 2010). Career stage theory suggests that motivation and reward expectations shift across different stages of employment (Kosine & Lewis, 2008; Super, 1957). Early in their careers, employees often seek recognition and validation, making them more sensitive to external incentives (Wright & Bonett, 2002). During this period, extrinsic rewards can encourage technology adoption and help establish positive attitudes toward the organization (Gerhart & Rynes, 2003).

As employees enter the middle stages of their careers, they may experience reduced motivation from standard incentives. Adjustments to their roles and the organizational environment can lead to value misalignment or a lower marginal response to rewards (Ng & Feldman, 2010; Sturman, 2003). At this point, the link between rewards, technology acceptance, and satisfaction may weaken (Tenhiälä et al., 2023). However, in later stages of tenure, as employees consider long-term career goals and organizational legacy, rewards may once again regain motivational significance. These factors can reignite interest in both technological engagement and structured compensation systems (Gajendran et al., 2024; Ng & Feldman, 2010).

In sum, job tenure illustrates that the effect of extrinsic rewards is dynamic, not fixed. Motivation evolves over time and is shaped by an employee's stage within the organization. This study incorporates these time-related moderating effects in its empirical framework.

Hypothesis Development

Extrinsic Rewards, Socio-Technical Systems, and Employee Satisfaction

Extrinsic rewards have long functioned as a central motivational tool in performance-focused organizations. Expectancy theory proposes that employees are more motivated and satisfied when they believe their efforts lead to valuable outcomes (Vroom, 1964), while reinforcement theory suggests that rewards encourage desired behavior by strengthening positive outcomes (Skinner, 1965). Empirical evidence supports the positive influence of performance-related rewards on employee satisfaction (Deci et al., 1999; Kuvaas, 2006).

In contemporary digital workplaces, the adoption and use of internal systems are essential for both productivity and employee experience. Socio-Technical Systems Theory (STST) emphasizes the joint optimization of the social and technical dimensions of work (Trist & Bamforth, 1951). From this perspective, employee engagement with systems becomes a critical driver of successful implementation. The Technology Acceptance Model (TAM) complements this view by identifying perceived usefulness and ease of use as key determinants of system acceptance (Davis, 1989).

However, TAM focuses mainly on individual cognitive evaluations of technology and does not fully capture the broader organizational and psychological contexts in which employees operate. In this regard, Socio-Technical Systems Theory (STS) provides a crucial bridge between technology adoption and employee satisfaction. It argues that satisfaction emerges when the social and technical subsystems of an organization are jointly aligned. Technology thus functions not merely as an operational tool but as a medium that shapes collaboration, communication, and role clarity. When employees perceive that digital systems effectively support their work processes and social interactions, they experience higher efficacy, psychological comfort, and belonging—core antecedents of satisfaction (Clegg, 2000; Mumford, 2006). Conversely, misalignment between social structures and technical tools can create frustration, inefficiency, and lower morale.

Extrinsic rewards can reinforce this alignment by promoting participation and engagement with internal systems. Within the STS framework, such system engagement serves as a mediating mechanism through which rewards enhance satisfaction—by improving employees’ efficacy, perceived support, and clarity of expectations. This reasoning aligns with motivational mediation theory, which posits that extrinsic factors influence attitudinal outcomes through psychological processes (Pinder, 2008).

H1: Extrinsic rewards have a positive and significant effect on employee satisfaction.

H2: Extrinsic rewards have a positive and significant effect on socio-technical systems acceptance.

H3: Socio-technical systems acceptance has a positive and significant effect on employee satisfaction.

H4: Socio-technical systems acceptance mediates the relationship between extrinsic rewards and employee satisfaction.

Work Model as a Contextual Moderator

Work arrangements influence how employees access organizational resources and systems (Bakker & Demerouti, 2007). According to Conservation of Resources Theory and the Job Demands and Resources Model, perceived access to autonomy, feedback, and support shapes both stress and motivation (Bakker & Demerouti, 2007; Hobfoll, 2011). Remote work tends to limit these elements, increasing the importance of extrinsic incentives as psychological compensation (Choudhury et al., 2021). Onsite work offers more direct feedback and supervision, which may reduce the need for such rewards (Wang et al., 2021). Hybrid arrangements offer a mixed experience and may result in moderate responsiveness (Datta et al., 2025).

STS theory emphasizes that technological tools must fit the social structure in which they operate. This suggests that work models shape how employees perceive the value of reward structures in relation to technology use (Trist & Bamforth, 1951). In digital-first settings such as remote and hybrid work, extrinsic rewards may play a stronger role in driving engagement and acceptance (Davis, 1989; Gajendran et al., 2024).

This view aligns with Contingency Theory, which holds that organizational mechanisms are most effective when they match structural conditions (Donaldson, 2001). As such, the strength of the relationship between rewards and system acceptance is expected to vary depending on the work model, with stronger effects observed in remote settings and weaker effects in onsite settings (Gajendran et al., 2024; Golden et al., 2008).

Hypothesis 5 (a–c): The effect of extrinsic rewards on socio-technical systems acceptance is expected to be strongest in remote work models (a), moderate in hybrid models (b), and weakest in onsite models (c).

Job Function as a Role-Based Moderator

Job function determines not only the tasks employees perform but also how they respond to incentives. Role theory explains that individuals internalize the expectations tied to their roles, which in turn shape motivational preferences (Katz & Kahn, 1978). Sales roles often involve performance metrics and measurable outputs, leading to higher sensitivity to rewards (Jiang et al., 2012). In contrast, administrative and managerial roles are more stable and tend to rely on intrinsic motivation such as autonomy, competence, and relational feedback (Hackman & Oldham, 1976).

Theories of reward sensitivity suggest that the effect of extrinsic incentives on satisfaction is stronger in outcome-driven roles such as sales or engineering, and weaker in support or leadership roles where other motivational factors dominate (Gerhart & Rynes, 2003).

Hypothesis 6 (a–d): The effect of extrinsic rewards on employee satisfaction is expected to be greater in sales roles (a), lower in administrative support (b) and management roles (d), and moderate in R&D positions (c).

Time in Position as a Temporal Moderator (H7a–c)

Job tenure reflects how long an individual has held a specific role and influences both motivation and reward responsiveness (Ng & Feldman, 2010; Wright & Bonett, 2002). Career Stage Theory argues that motivational needs change over time (Feldman & Weitz, 1988; Super, 1957). In early stages, employees seek external recognition and show high sensitivity to rewards due to limited social capital and organizational embeddedness (Rousseau, 1995).

As tenure increases, the marginal utility of rewards may decline. Employees may experience role plateauing or shifts in personal values, reducing their responsiveness to standard incentives (Bal et al., 2012; Sturman, 2003). However, during later stages, particularly near retirement or promotion thresholds, extrinsic incentives such as bonuses and advancement opportunities may regain motivational significance (Kooij et al., 2011; Shore & Barksdale, 1998). These patterns suggest that the influence of rewards on satisfaction is shaped by the psychological contract over time (Ones & Viswesvaran, 2003; Rousseau, 1995).

Hypothesis 7 (a–c): The effect of extrinsic rewards on employee satisfaction is expected to be strongest during the first year of employment (a), weakest during the second to third year (b), and moderate during the fourth year and beyond (c).

Research Framework

The proposed research framework (Figure 1) examines both the direct and indirect effects of extrinsic rewards on employee satisfaction. Specifically, socio-technical systems acceptance is modeled not only as a mediator between rewards and satisfaction but also as an independent predictor of satisfaction. The model further integrates three contextual moderators: work model moderates the relationship between extrinsic rewards and system acceptance, while job function and time in position moderate the link between rewards and satisfaction. This structure captures how motivational factors, system engagement, and contextual conditions interact within technology-integrated work environments. Accordingly, this study presents an integrated theoretical model that combines motivational, technological, and contextual perspectives, providing a comprehensive theoretical foundation for the hypothesized relationships illustrated in Figure 1.

Figure 1.

Research framework.

The integration of these theories is not arbitrary but conceptually necessary. Motivation theory explains at the individual level why employees respond to external incentives, while socio-technical systems theory explains at the organizational level how such motivational effects are transformed into satisfaction through engagement with technological systems. The inclusion of contextual moderators such as work model, job function, and tenure reflects that these processes are not fixed but vary depending on organizational context and career stage. Therefore, the integrated model is both theoretically coherent and empirically valid, offering a multi-level explanation of how extrinsic rewards lead to satisfaction in technology-based organizations.

Methodology

Data Collection

This study investigates both the direct and indirect effects of extrinsic rewards on employee satisfaction, with socio-technical systems acceptance serving as a mediating variable. It also examines how these effects vary across work arrangements, job functions, and tenure. To test the proposed model, a structured online survey was conducted among employees of major technology companies and their affiliated firms in China, including logistics providers, software integrators, and distribution networks.

The survey was distributed via QR code through internal messaging platforms and emails. A screening question, “Are you currently employed in the technology industry?” was used to ensure eligibility. Respondents who answered “No” were automatically screened out. IP address filtering was applied to prevent duplicate submissions.

A total of 372 responses were collected. After removing five incomplete cases, 367 valid responses remained for analysis. The study received IRB approval and adhered to ethical standards regarding informed consent and data protection.

Survey Instrument

The questionnaire included items measuring three primary constructs: extrinsic rewards (ER), socio-technical systems acceptance (STST), and employee satisfaction (ES). All items were adapted from established sources and measured on a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Employee satisfaction was assessed using validated items derived from the Job Satisfaction Survey developed by the Minnesota Satisfaction Questionnaire by Weiss et al. (1967), and the affective model proposed by Judge and Watanabe (1993). Measures of extrinsic rewards were based on the multidimensional pay satisfaction scale by Heneman and Schwab (1985), complemented by meta-analytic findings on compensation and employee attitudes (Judge et al., 2010; Williams et al., 2006). Socio-technical systems acceptance was measured using the core components of the Technology Acceptance Model (Davis, 1989), as extended by Venkatesh and Bala (2008), and conceptually grounded in Bandura’s (1986) theory of self-efficacy, particularly as it relates to digital interaction and system engagement.

The survey was originally developed in English and subsequently translated into Chinese using a rigorous translation and back-translation procedure to ensure conceptual equivalence across languages. Expert reviews and pilot testing were conducted to refine item clarity and establish content validity. In addition to the core constructs, the survey captured categorical data on work arrangement (onsite, remote, or hybrid), job function (sales, administration, R&D, or management), and tenure (years in current role). These contextual variables were used in subgroup analyses and defined according to organizational differentiation models proposed in prior research (Marjerison & Kim, 2022).

Sampling

Demographic characteristics of the final sample (N = 367) are summarized in Table 1. The participants in this study are classified as “knowledge workers.” This term refers to employees who perform cognitive, analytical, and problem-solving tasks using digital tools rather than engaging in routine manual labor (Drucker, 2007). Accordingly, the respondents were primarily employed in technology-oriented organizations such as software, logistics, and digital-service firms. This definition aligns with the characteristics of technology-intensive work environments, where productivity and value creation largely depend on knowledge application and interaction with socio-technical systems.

Table 1.

Demographic Characteristics of the Respondents (N = 367).

	Subcategory	Frequency	Percent
Gender	Male	172	46.9
	Female	195	53.1
	Other	0	0
Education	High school	22	6.0
	Tech/vocational degree	64	17.4
	Undergraduate	166	45.3
	Master	87	23.7
	Doctoral/professional degree	28	7.6
Birth year	1965∼1980	98	26.7
	1981∼1995	150	40.9
	1996∼2006	113	30.8
	Others	6	1.6
Work model	Onsite work	132	36
	Remote work	121	33
	Hybrid work	114	31
Model duration	1 year∼2 years	119	32.4
	3 years∼5 years	137	37.3
	6 years and above	111	30.2
Job function	Sales	89	24.3
	Administrative support	91	24.7
	Research and development	89	24.3
	Management	98	26.7
Time in position	1 year	125	34.1
	2 years∼3 years	101	27.5
	4 years and above	141	38.4
Work environment	Intercultural work environment	183	49.9
	Monocultural work environment	184	50.1

The gender distribution was relatively balanced (53.1% female). Approximately 45.3% of respondents held a bachelor’s degree, and 40.9% were born between 1981 and 1995. In terms of work arrangement, 36% reported working onsite, 33% remotely, and 31% in hybrid settings. Tenure ranged from less than 2 years (32.4%) to over 6 years (30.2%). Respondents were distributed across job functions as follows: sales (24.3%), administration (24.7%), R&D (24.3%), and management (26.7%). Roughly half of the participants (49.9%) indicated that they worked in intercultural environments.

Empirical Analysis

Variability of the Model

Table 2 presents the descriptive statistics for the four items measuring Extrinsic Rewards (ER1 to ER4) and the composite ER score. All items were measured on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The mean values span from 4.332 to 4.610, indicating respondents’ overall perceptions were generally neutral to slightly positive. Standard deviations (ranging from 1.214 to 1.463) suggest moderate variability in responses. Skewness values are consistently negative (from −0.250 to −0.526), indicating a slight left-skew, with more frequent responses at the higher end of the scale. Similarly, the negative kurtosis values (from −0.142 to −0.642) imply platykurtic distributions, suggesting the responses were more dispersed than a normal distribution. These patterns collectively indicate that while perceptions of extrinsic rewards lean somewhat positive, there is meaningful variation in how employees experience these rewards.

Table 2.

Descriptive Statistics for Extrinsic Rewards (ER).

Scale	Min	Max	Mean	SD	Kurtosis	Skewness
ER1	1	7	4.61	1.30	−.14	−.53
ER2	1	7	4.40	1.32	−.40	−.31
ER3	1	7	4.39	1.46	−.57	−.39
ER4	1	7	4.33	1.30	−.31	−.25
ER	1.25	6.50	4.43	1.21	−.64	−.49

Table 3 presents the descriptive statistics for the socio-technical systems acceptance (STST) scale, comprising 18 items and a composite score, measured using a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). The item means ranged from 3.621 to 4.381, with the composite mean at 4.059, suggesting neutral to moderately positive attitudes toward STST. Standard deviations fell between 0.992 and 1.262, indicating modest variability across items. The skewness values (ranging from −0.343 to 0.241) suggest that the data are approximately symmetric, while kurtosis values (from −0.264 to 0.638) show mild leptokurtic tendencies in several items. The composite scale exhibits a kurtosis of 0.476 and near-zero skewness (−0.041), indicating the distribution is suitable for parametric statistical analyses. These descriptive features collectively support the psychometric appropriateness of the STST scale for subsequent structural modeling and validation.

Table 3.

Descriptive Statistics of Socio-Technical Systems Acceptance (STST).

Scale	Min	Max	Mean	SD	Kurtosis	Skewness
STST1	1	7	4.16	1.24	−.26	−.02
STST2	1	7	4.16	1.11	.24	−.02
STST3	1	7	3.93	1.18	−.15	−.09
STST4	1	7	4.11	1.26	−.21	−.18
STST5	1	7	3.62	1.24	−.04	.19
STST6	1	7	4.03	1.13	−.01	−.08
STST7	1	7	4.27	1.07	−.08	.18
STST8	1	7	3.90	1.06	.32	.18
STST9	1	7	4.13	1.09	.13	−.20
STST10	1	7	4.22	1.10	.16	−.15
STST11	1	7	4.35	1.08	.62	−.33
STST12	1	7	3.82	1.06	.40	−.13
STST13	1	7	4.01	.99	.35	−.01
STST14	1	7	4.17	1.07	−.06	−.05
STST15	1	7	4.21	1.09	.07	−.20
STST16	1	7	3.82	1.03	.41	.24
STST17	1	7	4.38	1.09	.64	−.34
STST18	1	7	3.80	1.16	.30	.24
STST	1.6	6.9	4.06	.822	.48	−.04

Table 4 summarizes the descriptive statistics of the Employee Satisfaction (ES) construct, which consists of seven items and a composite score measured on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). Item-level means range from 4.076 to 4.575, with the composite mean at 4.298, indicating neutral-to-slightly positive perceptions of job satisfaction. Standard deviations between 0.934 and 1.257 suggest moderate dispersion around the mean. Skewness values fall within the range of −0.266 to 0.033, pointing to a distribution that is slightly left-skewed but still approximately symmetric. Kurtosis values span from −0.252 to 0.255, with a composite value of 0.002, indicating near-normal or platykurtic distributions. These characteristics collectively validate the suitability of the ES scale for parametric analysis and provide a stable foundation for subsequent structural modeling and hypothesis testing.

Table 4.

Descriptive Statistics of Employee Satisfaction (ES).

Scale	Min	Max	Mean	SD	Kurtosis	Skewness
ES1	1	7	4.40	1.21	−.20	−.18
ES2	1	7	4.37	1.11	.26	−.21
ES3	1	7	4.08	1.05	−.12	.03
ES4	1	7	4.43	1.08	.05	−.08
ES5	1	7	4.11	1.22	−.14	−.08
ES6	1	7	4.13	1.26	−.25	−.16
ES7	1	7	4.58	1.19	−.17	−.17
ES	1	6.71	4.30	.93	.00	−27

Reliability Analysis

Table 5 presents the results of the reliability analysis for the key constructs measured in this study. Cronbach’s Alpha values were calculated for each scale to assess internal consistency. All constructs—Extrinsic Rewards (ER), Socio-Technical Systems Acceptance (STST), and Employee Satisfaction (ES)—demonstrated high reliability, with alpha values exceeding the commonly accepted threshold of .90. Specifically, ER showed an alpha of .920, STST an alpha of .950, and ES an alpha of .909. The overall reliability for all 29 items combined was .954. These results indicate excellent internal consistency across all scales, supporting the robustness of the measurement model for subsequent structural equation modeling and hypothesis testing.

Table 5.

Reliability Analysis for the Scales Used in the Study.

Scale	Cronbach’s alpha	N of items
ER	.92	4
STST	.95	18
ES	.91	7
Total dimension	.95	29

Confirmatory Factor Analysis

To evaluate the construct validity of the three latent variables—Extrinsic Rewards (ER), Employee Satisfaction (ES), and Socio-Technical Systems Acceptance (STST)—a confirmatory factor analysis (CFA) was conducted. Each observed item was assigned to its corresponding latent construct based on theoretical rationale and prior scale development. The results demonstrate that all factor loadings are statistically significant and exceed commonly accepted thresholds for convergent validity (i.e., standardized loadings > 0.70).

The average loading for each construct exceeds 0.90, indicating strong internal coherence among indicators and reinforcing the reliability of the measurement model in Table 6. These results provide robust evidence of convergent validity and suggest that the items effectively capture their intended latent dimensions, thereby supporting the adequacy of the measurement model for subsequent structural analyses.

Table 6.

CFA Results Summary: Factor Loadings and Convergent Validity.

Construct	Items	Factor loadings	Average loading
ER	Q17–Q20	1.0, 1.02, 1.12, 1.01	1.04
ES	Q10–Q16	1.0, 1.01, 1.04, 1.02, 1.05, 1.06, 1.08	1.04
STST	Q33–Q50	1.00, .98, 1.01, 1.02, 1.05, 1.02, 1.02, 1.03, 1.04, 1.05, 1.05, 1.03, 1.04, 1.05, 1.03, 1.03, 1.02, 1.04	1.03

Note. All measurement items were adapted from established scales. ER items were derived from Heneman and Schwab (1985), supported by Judge et al. (2010) and Williams et al. (2006). ES items were adapted from Weiss et al. (1967) and Judge and Watanabe (1993). STST items were drawn from Davis (1989), Venkatesh and Bala (2008), and conceptually grounded in Bandura (1986). The complete questionnaire is provided in the Appendix.

Empirical Results

To comprehensively examine the hypothesized relationships within the proposed research framework, a multi-method analytical strategy was adopted. The primary analysis was conducted using partial least squares structural equation modeling (PLS-SEM), a variance-based approach well-suited for complex models involving both direct and indirect effects, latent constructs, and non-normal data distributions. PLS-SEM enabled the estimation of path coefficients for all proposed relationships simultaneously, including mediating and moderating effects, while accounting for measurement error.

To further assess the stability and predictive validity of the core relationships—particularly those proposed in H1 through H3—two additional predictive models were employed as robustness checks. First, PLS regression (PLS-R) was used to test the linear predictive power of extrinsic rewards and socio-technical systems acceptance on employee satisfaction. PLS-R retains conceptual alignment with PLS-SEM but focuses solely on prediction using observed variables. Second, an artificial neural network (ANN) was applied to explore nonlinear associations and potential interactions between predictors without imposing a predefined functional form. ANN models are particularly valuable for uncovering complex patterns that linear models may overlook.

By triangulating the results across these three modeling approaches—PLS-SEM, PLS-R, and ANN—this study ensures both theoretical consistency and empirical robustness in the evaluation of the proposed hypotheses. The convergent results across methods reinforce confidence in the stability of the findings and the generalizability of the theoretical model. The following sections detail the empirical results for each hypothesis, beginning with the core direct and indirect effects (H1–H4), followed by the contextual moderation hypotheses (H5–H7).

To test the hypothesized relationships among extrinsic rewards (ER), socio-technical systems acceptance (STST), and employee satisfaction (ES), a partial least squares structural equation modeling (PLS-SEM) approach was employed. This approach was chosen due to its suitability for complex models with both direct and indirect effects, as well as its ability to handle measurement error and non-normality.

The structural model results provide empirical support for the hypothesized relationships (Table 7). Extrinsic rewards had a significant positive effect on employee satisfaction (β = .474, p < .001), confirming H1. This indicates that when employees perceive higher levels of material or external rewards, their overall satisfaction with the organization increases.

Table 7.

Hypothesis Testing Results (H1–H3).

Hypothesis	Path	β	p-value	Supported
H1	ER_mean → ES_mean	.47	<.001	Yes
H2	ER_mean → STST_mean	.34	<.001	Yes
H3	STST_mean → ES_mean	.20	<.001	Yes

Extrinsic rewards also exerted a significant influence on socio-technical systems acceptance (β = .337, p < .001), supporting H2. This suggests that tangible rewards can enhance employees’ willingness to accept and utilize digital platforms and internal systems, likely by increasing the perceived value or fairness of adopting such technologies.

Socio-technical systems acceptance had a positive and statistically significant effect on employee satisfaction (β = .197, p < .001), providing evidence in support of H3. This implies that employees who are more comfortable with or positively oriented toward organizational systems also tend to report greater satisfaction with their work experience.

These results collectively validate the proposed theoretical framework, demonstrating that extrinsic rewards influence employee satisfaction both directly and indirectly via socio-technical systems acceptance, both through motivational pathways and through the facilitation of system-level engagement.

In addition to examining path coefficients, the model’s overall fit was assessed using multiple fit indices. The results indicated excellent model fit, with CFI = 1.003, TLI = 1.011, GFI = 1.000, AGFI = 1.000, and NFI = 1.000, all well above conventional thresholds of 0.90. Furthermore, the RMSEA value was 0, suggesting minimal discrepancy between the model and observed data. The non-significant chi-square statistic (χ² = 0.000, p = .998) further supports the conclusion that the model fits the data well. These indices affirm that the proposed structural model is both theoretically sound and empirically robust.

To examine whether socio-technical systems acceptance (STST) mediates the relationship between extrinsic rewards (ER) and employee satisfaction (ES), a mediation analysis was conducted using a component-based structural equation modeling approach. This method is particularly suitable when assessing indirect effects in models with latent variable composites, as it does not rely on distributional assumptions and allows for flexible path specification.

The mediation model was specified such that ER predicts both STST and ES directly, while STST also predicts ES in Table 8. Based on the estimated path coefficients, the indirect effect of ER on ES via STST was calculated by multiplying the ER → STST path (β = .337) and the STST → ES path (β = .197), resulting in an indirect effect estimate of 0.066. Both of these component paths were statistically significant (p < .001), meeting the criteria for mediation as defined by the product-of-coefficients approach, which establishes significance when both component paths are individually significant.

Table 8.

Mediation Effect Summary (H4).

Effect type	Pathway	Estimate(β)	Supported
Direct effect	ER_mean → ES_mean	.47	Yes
Indirect effect	ER_mean → STST → ES_mean	.07	Yes
Total effect	ER_mean → ES_mean (Total)	.54	Yes
Mediation type	–	Partial	–

In addition, the direct effect of ER on ES remained strong and significant (β = .474, p < .001), indicating the presence of partial mediation. These findings suggest that while extrinsic rewards directly influence employee satisfaction, a portion of this influence is also transmitted through employees’ acceptance of socio-technical systems. The mediation pathway underscores the role of system-level engagement in enhancing the impact of organizational incentives.

To investigate the potential moderating effects of work model (WM), job function (JF), and time in position (HP) on the relationship between extrinsic rewards (ER) and key outcome variables, a multi-group analysis (MGA) was conducted using a PLS-based approach. MGA was chosen due to its strength in comparing structural path coefficients across categorical subgroups without requiring distributional assumptions or measurement invariance. For each moderating variable, the dataset was split into mutually exclusive subgroups, and the ER-related path coefficients were estimated separately.

The empirical results are summarized in Table 9. To test H5a–H5c, a multi-group analysis was conducted to examine whether the effect of extrinsic rewards (ER) on socio-technical systems acceptance (STST) differs across work models. The findings reveal that the standardized path coefficient from ER to STST was highest among employees in remote work settings (β = .458), moderate among those in hybrid settings (β = .364), and lowest in onsite environments (β = .186). These results support Hypotheses H5a, H5b, and H5c, respectively, and indicate that the influence of extrinsic incentives on employees' engagement with organizational systems is contingent on their work model. Specifically, remote workers may depend more on extrinsic motivators due to reduced face-to-face oversight and increased reliance on digital tools.

Table 9.

Moderation Effect Summary (H5–H7).

Hypothesis	Moderator	Path	Groups (β)	Supported
H5a	WM	ER→STST	RM: .46 > HM: .36 > OM: .19	Yes
H5b	WM	ER→STST	HM coefficient moderate	Yes
H5c	WM	ER→STST	OM coefficient weakest	Yes
H6a	JF	ER→ES	SA highest	Yes
H6b	JF	ER→ES	AD lowest	Yes
H6c	JF	ER→ES	RD moderate	Yes
H6d	JF	ER→ES	MG low	Yes
H7a	HP	ER→ES	1st year highest	Yes
H7b	HP	ER→ES	2nd–3rd year lowest	Yes
H7c	HP	ER→ES	4+years moderate	Yes

To assess H6a–d, which proposed differences in the ER → ES relationship across job functions, the sample was divided into four groups: sales (SA), administrative support (AD), research and development (RD), and management (MG). The results showed that the ER → ES path coefficient was highest for SA employees, moderate for RD, and lowest for AD and MG roles, aligning with the expectations in H6. These patterns suggest that extrinsic incentives may be more salient for employees in performance-oriented or output-driven roles, whereas managerial and support staff may prioritize other motivational factors.

Finally, H7a–c examined whether the effect of ER on ES varies across tenure groups. As hypothesized, the ER → ES relationship was strongest among employees in their first year, followed by those with four or more years of tenure, and weakest among those in the second to third year. This pattern provides empirical support for H7a, H7b, and H7c, and implies that the motivational impact of extrinsic rewards may diminish over time before partially recovering among more experienced employees who have integrated the organization’s reward structure into their long-term expectations.

Robustness Check

Robustness Check: Predictive Model Validation of H1–H3

To assess the robustness of the structural model tested through PLS-SEM (H1–H3), two alternative predictive models were employed: partial least squares regression (PLS-R) and artificial neural network (ANN). These models were selected to evaluate the stability of the observed relationships under different analytical assumptions and estimation strategies (Table 10).

Table 10.

Robustness Check for H1–H3: Predictive Model Comparison.

Model	R ²	RMSE	Coefficients (β)	Interpretation
PLS Regression	.54	.63	ER → ES: .63STST → ES: .15	Slinear support for H1–H3
ANN	.47	.68	N/A	Nonlinear confirmation of trend

PLS-R, a linear projection method, was adopted due to its conceptual alignment with PLS-SEM while offering direct estimation using observed variables. In contrast, ANN is a non-parametric, nonlinear modeling technique capable of capturing complex, non-additive relationships and latent interactions without requiring a predefined functional form. This triangulated modeling strategy enables an assessment of whether the hypothesized effects—specifically, the positive influence of extrinsic rewards (ER) and socio-technical systems acceptance (STST) on employee satisfaction (ES)—hold consistently across linear and nonlinear frameworks.

Both models were trained using standardized training and test datasets, and predictive performance was evaluated using the coefficient of determination (R²) and root mean squared error (RMSE). As shown in Table 10, the PLS-R model yielded an R² of .540, indicating that ER and STST together explained more than half of the variance in employee satisfaction. The regression coefficients further revealed that ER had a strong positive effect (β = .625), while STST showed a weaker but still positive influence (β = .148), consistent with the original PLS-SEM findings.

The ANN model, while not providing interpretable coefficients due to its black-box architecture, produced a comparable R² of .472 and an RMSE of .679. This similarity in predictive performance suggests that the directional pattern and relative strength of the predictors are stable across modeling approaches.

Together, these results confirm that the core theoretical relationships proposed in H1 through H3 are not artifacts of a single modeling technique. Rather, they demonstrate empirical robustness across both parametric and non-parametric estimation methods, thereby strengthening confidence in the validity and generalizability of the findings.

Robustness Check: Mediation via Two-Step Regression and Sobel Test

To further validate the hypothesized mediation effect proposed in H4—that socio-technical systems acceptance (STST) mediates the relationship between extrinsic rewards (ER) and employee satisfaction (ES)—a robustness check was performed using a two-step regression method followed by the Sobel test (Table 11). This approach offers a linear, regression-based alternative to the component-based estimation used in the PLS-SEM analysis, and is well-established for evaluating indirect effects when working with observed composite indicators.

Table 11.

Robustness Check for H4: Mediation via Sobel Test.

Method	Indirect effect (β)	Z-value	p-value	Supported
Two-step regression +Sobel test	.07	3.86	<.001	Yes

The two-step procedure involved first regressing STST on ER to estimate the first-stage path coefficient, followed by regressing ES on both ER and STST to estimate the second-stage path. The indirect effect was then calculated by multiplying the two coefficients (β₁ × β₂). The resulting estimate was 0.0663, closely matching the indirect effect observed in the original PLS-SEM model.

To assess the statistical significance of this indirect effect, the Sobel test was conducted. The test yielded a Z-value of 3.861 and a p-value of .00011, indicating that the indirect effect was highly significant. These results confirm that the mediating role of STST is statistically robust and not dependent on the choice of modeling framework. Hence, the evidence continues to support H4, demonstrating that extrinsic rewards contribute to employee satisfaction both directly and indirectly by enhancing employees’ acceptance of socio-technical systems.

In summary, the findings consistently demonstrate that extrinsic rewards influence employee satisfaction both directly and indirectly through system-level acceptance. Furthermore, contextual factors such as work model, job function, and tenure significantly moderate the strength of these relationships. The robustness of the findings across multiple modeling strategies further enhances the validity of the proposed theoretical framework.

Conclusion

This study moves beyond the assumption that extrinsic rewards directly increase employee satisfaction. Instead, it reveals how rewards facilitate engagement with socio-technical systems, which in turn enhance psychological alignment, perceived role clarity, and affective attachment to the organization. Extrinsic rewards function not merely as transactional incentives but as psychological mechanisms that synchronize employee perceptions with increasingly complex digital infrastructures.

System engagement operates as a mediating path where interaction with technology reshapes self-efficacy and fosters a deeper organizational bond. Satisfaction emerges not as a terminal outcome but as the result of this alignment process between the individual and the system.

The findings also highlight that identical rewards produce different effects depending on how they are interpreted. The motivational value lies not in the amount of reward but in the meaning attributed to it by employees. This offers a refined view of reward design under digital conditions, shifting the focus from magnitude to perception.

Theoretical Implications

First, this study extends classical motivation theories by integrating them with socio-technical systems theory. While expectancy and reinforcement theories describe how rewards influence behavior, this study shows that such effects are mediated by system engagement. Technology adoption is not simply compliance, but part of an activated motivational process.

Second, socio-technical alignment is shown to be context-dependent. The effects of extrinsic rewards vary significantly across work arrangements, job functions, and tenure stages. These findings align with role theory and career stage theory, which explain how motivational responses are shaped by position-specific expectations and temporal factors.

Third, this study proposes an integrative framework that links motivation theory, technology acceptance (TAM), socio-technical systems theory, and contextual perspectives such as contingency, role, and career stage models. This approach provides a foundation for understanding how structural, psychological, and contextual factors jointly shape employee attitudes and behaviors in digitally mediated organizations.

Ultimately, extrinsic rewards serve not only as motivators but also as cues that support psychological and organizational alignment. Their impact depends less on their absolute value and more on how they are interpreted and embedded within the organizational system. This insight contributes to more adaptive and context-sensitive approaches to reward design in the era of digital transformation.

Practical Implications

This study offers a strategic perspective for organizational leaders and human resource professionals seeking to enhance employee motivation and digital system engagement in increasingly complex work environments.

The findings show that extrinsic rewards do not have a uniform effect across all employee groups. Their influence varies significantly depending on role type, tenure, and work setting. For instance, monetary incentives may be effective in sales or early-career roles but less so for mid-level managers in administrative functions. This challenges conventional incentive schemes that assume uniform reward sensitivity. Effective compensation policies must recognize these differences and allocate resources accordingly.

In addition, the results indicate that digital system adoption is not simply a matter of usability or training, but is strongly tied to the presence of meaningful incentives. Employees engage more with digital platforms when their usage is linked to valued outcomes. Particularly in remote or hybrid contexts, where spontaneous feedback is limited, incentives play a critical role in reinforcing participation. Organizations should therefore treat digital engagement not as a compliance issue, but as a behavior that can be guided and supported through targeted reward signals.

The findings also suggest the need for adaptive reward systems that evolve with an employee’s career stage and exposure to digital tools. Static models fail to account for shifting motivational needs. By incorporating ongoing feedback mechanisms, behavioral analytics, and differentiated support strategies, organizations can build dynamic incentive structures that remain relevant over time.

Overall, this study provides actionable insight into the complex relationship between extrinsic rewards and system engagement. It highlights how thoughtful incentive design—sensitive to individual context and digital realities—can serve not just as a motivational tool, but as a foundation for organizational alignment. In this sense, the study offers a timely and practically valuable framework for decision-makers navigating the challenges of digital transformation.

Limitations and Future Research

While this study provides both conceptual and empirical contributions, several limitations remain that should inform future research directions.

First, the use of a cross-sectional design limits the ability to establish causality. Although the observed relationships between extrinsic rewards, system acceptance, and employee satisfaction are statistically significant, the temporal ordering of these effects remains uncertain. Longitudinal approaches are needed to explore how motivation and digital engagement change over time.

Second, the study relies on self-reported survey data, which may introduce bias through common method variance or socially desirable responding. Although procedural safeguards were applied, future research would benefit from integrating alternative data sources such as behavioral system logs, supervisor assessments, or administrative records to enhance validity and reduce bias.

Third, the sample is drawn entirely from China's technology sector. This setting reflects specific institutional and cultural characteristics, including centralized organizational structures, rapid technological adoption, and culturally embedded attitudes toward authority and incentive structures. Future studies should examine whether the proposed framework applies to other national and organizational contexts.

Fourth, the analysis focuses exclusively on extrinsic rewards. While this approach supports theoretical clarity, it does not consider the influence of intrinsic motivation, including factors such as autonomy, purpose, and personal identity. Future work should investigate how these motivational sources interact, and whether they reinforce or interfere with one another in different organizational environments.

Finally, the socio-technical systems construct was treated as a unified latent factor. Disaggregating it into dimensions such as perceived usefulness, system usability, and task alignment may yield more refined insights into the mechanisms through which digital systems shape employee motivation and satisfaction.

Addressing these limitations will support the development of more comprehensive and contextually grounded models of motivation, digital engagement, and organizational alignment.

Footnotes

Appendix

ORCID iDs

Rob Kim Marjerison

Jin Young Jun

Jong Min Kim

Ethical Considerations

This study was reviewed and approved by the Wenzhou-Kean University Institutional Review Board (IRB Approval No. WKUIRB2025-071, approved on March 10, 2025). Participation was voluntary, and informed consent was obtained from all respondents prior to data collection.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

The data can be made available by the corresponding author upon reasonable request.

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