The Influence of Organizational Climate on Work Engagement: Evidence from the Greek Industrial Sector

Abstract

In today’s rapidly evolving and competitive business settings, sustaining work engagement has become a strategic imperative for organizations across sectors. Although work engagement research has traditionally emphasized individual and leadership factors, less is known about how specific dimensions of organizational climate shape work engagement, particularly in industrial contexts. This study examines the relationship between organizational climate and work engagement in the Greek manufacturing sector—an underexplored setting characterized by labor-intensive operations, and economic volatility. Grounded in the Job Demands–Resources (JD–R) model and using the Organizational Climate Measure (OCM), data were collected from 151 industrial employees. Findings revealed that perceptions of employee welfare, supervisory support, and integration were positively associated with work engagement, with welfare showing the strongest zero-order association. In multivariable models, no single facet independently reached significance, yet their combined contribution explained a small but meaningful share of variance in engagement. Furthermore, work engagement moderated the relationship between supervisory support and perceived integration, indicating that highly engaged employees are better able to translate support into collaborative behaviors and stronger alignment. These results highlight the strategic value of promoting supportive climates and integrating well-designed, work engagement-focused interventions within fundamental organizational practices.

1. Introduction

In today’s rapidly evolving and competitive business environment, organizations in various sectors increasingly view work engagement as a critical driver of strategic success. Work engagement is a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption (Schaufeli & Bakker, 2004; Schaufeli et al., 2002). Consistent with this UWES framing, the theoretical development and interpretation that follow refer explicitly to these three dimensions.

Accordingly, both scholars and practitioners emphasize the importance of not only promoting but also sustaining work engagement across diverse workforces and contexts (Katsaros, 2025b). However, despite this increasing focus, the organizational-level antecedents of work engagement—particularly those situated within the immediate psychosocial work environment—remain insufficiently understood (Rasool et al., 2021).

While individual attitudes and leadership behaviors have long dominated work engagement research, recent work has increasingly highlighted the role of organizational climate—employees’ shared perceptions of the policies, practices, and procedures that shape the daily work experience (Patterson et al., 2005). Unlike the stable layer of organizational culture, climate provides immediate psychological cues that impact how employees interpret the environment and regulate effort toward job tasks (James et al., 2008).

Although previous research has explored work engagement and climate as separate constructs, there is growing recognition that these constructs are interrelated. Previous research has established organizational climate as a significant predictor of outcomes such as job satisfaction, commitment, and performance (Moslehpour et al., 2018). However, its influence on work engagement, and the mechanisms through which this influence occurs—such as psychological safety or social cohesion—require further empirical investigation (S. Albrecht et al., 2018; Mullins, 2016). Throughout, we use the term psychological climate to emphasize that analyses are conducted at the individual-perception level, even when drawing on measures often labeled as organizational climate.

Recent studies advocate that key climate dimensions—such as integration, employee welfare, and supervisory support—serve as critical psychological resources that promote work engagement by fostering employee safety, belonging, and value recognition (Katsaros, 2025a; Singha, 2024). However, comparisons of the relative impact of these dimensions remain limited, particularly in industrial contexts where hierarchical structures and production pressures shape climate dynamics differently.

From a practical perspective, many organizations continue to treat climate and work engagement as isolated initiatives, often deploying one-off surveys or ad hoc programs that fail to address underlying systemic drivers (Abun et al., 2021). In contrast, integrative frameworks—such as those proposed by Saks and Gruman (2014)—emphasize the strategic alignment of climate and work engagement practices to create more cohesive and effective organizational interventions.

This study responds to a broader call in the literature for contextualized, sector-specific research on work engagement and its antecedents. To date, most research has been conducted in Anglo-American service or knowledge-based economies (Breevaart et al., 2014), leaving a gap in understanding how these dynamics operate in different settings. The Greek manufacturing sector presents a contrasting context—characterized by labor-intensive operations, economic instability, and rigorous quality demands—which creates distinctive work engagement challenges. Thus, examining the climate–work engagement relationship within this environment not only enhances broader theoretical applicability but also provides practical insights for industrial practitioners seeking to foster work engagement under complex and constrained conditions.

Additionally, engagement is increasingly conceptualized not only as an outcome but also as a personal resource that shapes how individuals respond to their work environment (Xanthopoulou et al., 2009). In this light, highly engaged employees may be more responsive to supervisory support and more likely to interpret such support as conducive to integration and collaboration. This theoretical insight underpins the study’s third research question, which explores whether engagement moderates the relationship between perceived supervisory support and employee integration.

Within the OCM framework (Patterson et al., 2005), we focus on three Human Relations facets—welfare, supervisory support, and integration—because they map closely onto resource needs in Greek manufacturing. Welfare signals fairness, safety, and care around shift-based, standardized tasks; supervisory support captures day-to-day guidance and recognition from line leaders who structure pace and problem-solving on the shop floor; and integration reflects cross-shift information flow and inter-unit coordination that sustain throughput and quality. Under JD–R, these facets operate as proximal contextual resources that are especially salient in labor-intensive plants, where hierarchical structures and production pressures shape how employees appraise support and belonging. This sector-fit complements their general theoretical relevance and motivates our facet selection for the Greek industrial context.

To that end, the present study is guided by three interrelated research questions: (1) To what extent is organizational climate associated with work engagement among industrial workers in Greece? (2) Which specific dimensions of organizational climate—namely, integration, welfare, and supervisory support—emerge as significant predictors of work engagement? and (3) Does work engagement moderate the relationship between supervisory support and organizational integration, reflecting a dynamic interaction between individual and environmental factors?

By addressing these questions, this research seeks to contribute to both theory and practice by offering a detailed understanding of how climate-related interventions can be strategically leveraged to foster work engagement in industrial contexts.

To clarify the research gap,

Table 1. Prior research on climate relation with engagement and our study’s gap.

Study/Context	Climate Facets	Engagement Measure	Level of Analysis	Method	Key Finding	Relevance to Gap
Clement and Eketu (2019), Nigerian banking	Fairness, autonomy, recognition (supportive climate)	Employee engagement	Individual	Regression	Supportive climate → higher engagement	Service/finance; not industrial
Abun et al. (2021), HEIs (Philippines)	Clarity, recognition (supportive climate)	Engagement (cognitive/emotional/physical)	Individual	Regression	Positive climate → higher engagement	Education sector
Ancarani et al. (2019), Healthcare	Organizational climate	Engagement	Individual	Regression/SEM	Positive climate → higher engagement	Healthcare sector
Wardono et al. (2022), Mixed sectors	Supportive climate	Engagement & OCB	Individual	Survey models	Supportive climate boosts engagement/OCB	Broader sectors
Rasool et al. (2021), Mixed	Toxic climate	Engagement	Individual	SEM	Toxic climate suppresses engagement	Demonstrates sensitivity
Current study, Greek industrial manufacturing	Welfare, supervisory support, integration (OCM facets)	UWES-17	Individual (psychological climate)	Correlations, regression, moderation	Small but significant associations; engagement moderates support → integration	Under-researched sector; facet comparison & moderation

summarizes prior work on organizational/psychological climate and employee engagement across sectors (context, focal climate facets, engagement measure, level of analysis, method, and key finding). The present study addresses an under-researched setting—the Greek industrial sector—examines three theoretically grounded climate facets (welfare, supervisory support, integration) in relation to UWES engagement, and tests a theoretically motivated moderation mechanism.

This study conceptualizes work engagement not only as an outcome of a supportive climate but also as a lens through which employees interpret and respond to that climate. Extending insights from previous research (e.g., Jin & McDonald, 2017), we examine whether high levels of work engagement strengthen the positive relationship between supervisory support and perceived integration. By positioning work engagement simultaneously as both a dependent and a moderating mechanism—thereby highlighting its dual role—this study underscores the reciprocal, co-constructive relationship between individuals and work environments. By adopting this dual-role perspective, this study offers a richer, empirically grounded account of how specific climate conditions shape both individual experiences and broader patterns of organizational functioning.

This paper proceeds as follows. Section 2 presents the theoretical framework, detailing key constructs and their interrelationships, and formulates the study’s hypotheses. Section 3 outlines the methodology, including the research design, sample characteristics, and measures used. Section 4 reports the empirical results, organized by each hypothesis tested. Section 5 discusses the theoretical and practical implications of the findings, along with the study’s limitations and directions for future research. Finally, Section 6 provides concluding remarks and summarizes the study’s contribution to the literature.

2. Theoretical Framework & Hypotheses Development

2.1. Theoretical Foundation of Organizational Climate and Work Engagement

Organizational climate has been a foundational concept in organizational behavior research since the mid-20th century. It is a critical concept for understanding how employees perceive and experience the work environment, and how these perceptions influence behaviors and attitudes at work (Pritchard & Karasick, 1973; Abun et al., 2021; Clement & Eketu, 2019).

Organizational climate is commonly defined as the shared perceptions and meanings that employees attribute to the policies, practices, procedures, and observed behaviors in the workplace (Ostroff et al., 2003; Köse, 2016). These shared perceptions encompass various aspects of the organizational environment, such as leadership styles, communication patterns, support systems, reward structures, and behavioral norms (Andersson et al., 2020; Bos-Nehles & Veenendaal, 2019). Schneider and Reichers (1983) and Prasad et al. (2020) emphasized climate as a shared perception of work settings, whereas Ostroff et al. (2003) and Singh (2017) focused on the meanings employees attach to organizational practices and reward mechanisms.

Organizational climate functions as an intervening variable that mediates the relationship between the organizational context (e.g., structure, processes) and employee behavior (Hayat & Afshari, 2021; Patterson et al., 2005). This perspective underscores the dynamic and responsive nature of climate, acting as a lens through which employees interpret the organizational experiences (Nabella et al., 2022; Pei, 2017).

Early research on organizational climate emphasized broad dimensions, including well-being, leadership, and role clarity. However, several scholars argued for a more targeted approach, proposing that specific types of climate should align with particular organizational goals and outcomes (Schneider & Reichers, 1983; Ahmad et al., 2018; Wardono et al., 2022). Building on this perspective, Patterson et al. (2005) developed the Organizational Climate Measure (OCM), which categorizes climate into four functional quadrants: human relations, internal process, open systems, and rational goals. These quadrants provide a detailed framework for assessing climate and understanding its effects on organizational performance.

Beyond theoretical frameworks, empirical research has identified key elements within organizational climate that directly shape employees’ day-to-day experiences. A positive organizational climate is often shaped by three key elements: integration, welfare, and supervisory support (S. L. Albrecht, 2014; Wardono et al., 2022). Integration describes the degree to which employees feel respected, included, and connected within the organization. It is reflected in equitable practices, open communication, and collaboration across teams. A climate that promotes integration enhances social cohesion and fosters a strong sense of belonging (S. L. Albrecht, 2014). Welfare encompasses the physical, psychological, and emotional well-being of employees. Organizations that emphasize care, respect, and accessible support systems foster a climate that enhances employee health, resilience, and job satisfaction (S. Albrecht et al., 2018). Supervisory support is another critical dimension of organizational climate. Leaders who offer guidance, recognition, and fair treatment play a central role in shaping positive employee experiences (Ancarani et al., 2019). Supportive supervision is closely linked to higher levels of work engagement, trust, and organizational commitment (Schneider et al., 2013; Rhoades & Eisenberger, 2002). When integration, welfare, and supervisory support are embedded in the climate, they collectively foster personal development, boost morale, and enhance overall organizational performance (Schaufeli, 2016).

At an individual level, climate affects job satisfaction, participation, performance, commitment, and absenteeism (Ostroff et al., 2003). Mullins (2016) observed that employee well-being is shaped by the organizational climate, which in turn affects both work quality and volume. Similarly, Berberoglu (2018) highlighted the central role of climate in driving organizational performance. Research on focused or domain-specific climates further demonstrates the positive effects. For instance, a safety-oriented climate reduces workplace accidents and enhances employee commitment (Christian et al., 2009; Idris et al., 2015). An innovation-focused climate boosts team creativity (Newman et al., 2020; Andersson et al., 2020; Song et al., 2020), while a strong service climate increases customer satisfaction (Kang & Busser, 2018; Menguc et al., 2017; Schneider & Reichers, 1983).

Thus, both broad and focused organizational climates significantly shape employee attitudes and outcomes, reinforcing the need for intentional climate management aligned with organizational goals.

In parallel with organizational climate, work engagement has gained widespread attention in organizational research and practice. According to the Chartered Institute of Personnel and Development (CIPD, 2022), work engagement is a distinct psychological state, separate from job satisfaction or specific work behaviors. Work engagement reflects the depth and quality of an employee’s relationship with the job and the organization as a whole, and is now widely recognized as crucial for both individual and organizational performance (Kwon & Kim, 2020).

One of the most influential frameworks in explaining work engagement is the Job Demands–Resources (JD–R) model (Demerouti et al., 2001; Bakker & Demerouti, 2017; Demerouti & Bakker, 2023).

This model posits that all jobs consist of demands (e.g., workload, emotional strain) and resources (e.g., autonomy, feedback, support), which together shape work engagement and well-being. While excessive demands may lead to burnout, sufficient job resources can foster motivation, vigor, and sustained performance. For instance, a heavy workload may energize an employee who enjoys strong support and recognition, yet exhaust one who lacks such resources (S. Albrecht et al., 2018). In this context, organizational climate serves as a key contextual resource that shapes employees’ perceptions of support, recognition, and goal alignment. Therefore, a positive climate can mitigate the adverse effects of job demands and foster stronger work engagement (Kuenzi et al., 2020; Maamari & Majdalani, 2017).

A foundational contribution to the work engagement literature was made by Schaufeli et al. (2009), who contrasted work engagement with burnout. Subsequently, Schaufeli and Bakker (2004) described work engagement as the positive opposite of burnout, comprising three core dimensions: vigor (high energy and persistence), dedication (pride, enthusiasm, and meaning), and absorption (deep immersion in tasks) (Ancarani et al., 2019). This definition remains widely accepted, particularly via the Utrecht Work engagement Scale (UWES), which provides a validated tool used across diverse cultural contexts.

Several drivers of work engagement have been identified in the literature. These include feeling valued and involved, having a voice in decision-making, access to career development, and a climate of respect and well-being (Macey & Schneider, 2008). Supervisory support, employee autonomy, and involvement in decision-making have also been shown to enhance work engagement levels (Hakanen et al., 2006). A positive work environment that fosters continuous learning, open information sharing, and work–life balance further sustains employee motivation and vigor (Macey et al., 2009; Crawford et al., 2010). Beyond static frameworks, work engagement is now often conceptualized as a dynamic, reciprocal process. For example, Xanthopoulou et al. (2009) proposed the notion of “gain spirals,” wherein engaged employees develop personal resources—such as optimism, self-efficacy, and resilience—that, in turn, strengthen future work engagement and performance.

Empirical studies have demonstrated that work engagement produces a range of positive individual outcomes. Engaged employees typically report greater job satisfaction, enhanced performance, and stronger relationships with supervisors (Saks, 2019). Engaged employees also tend to exhibit higher psychological well-being, increased creativity, reduced absenteeism, and lower levels of burnout (Schaufeli et al., 2009).

At the organizational level, high work engagement is consistently linked to strategic outcomes, including greater customer satisfaction, higher productivity, and improved profitability (Kwon & Kim, 2020). Additionally, S. Albrecht et al. (2018) and Rasool et al. (2021) emphasize that engaged employees demonstrate greater resilience and innovation, thereby supporting long-term organizational sustainability.

Recent studies continue to highlight the pivotal role of leadership style and organizational practices in shaping employee engagement across sectors (Cai et al., 2024; Abdelwahed & Doghan, 2023). For example, servant leadership and perceived organizational support have been found to enhance engagement through resilience-building mechanisms (Cai et al., 2024), while work engagement has also been linked to productivity and organizational effectiveness in knowledge-based and educational contexts (Abdelwahed & Doghan, 2023).

In summary, organizational climate and work engagement together shape the psychological foundation of the workplace. Effectively aligning these elements is vital for enhancing employee well-being, driving optimal performance, and securing sustainable success in today’s dynamic organizational environments (Schyns et al., 2009; Viitala et al., 2015).

Under the Job Demands–Resources (JD–R) framework, job resources energize a motivational process that fosters positive work states such as engagement (Bakker & Demerouti, 2007, 2017). In industrial settings, three facets of psychological climate—welfare (fairness/care), supervisory support (guidance/recognition), and integration (cooperation/information flow)—operate as contextual resources that should relate positively to UWES engagement (vigor, dedication, absorption; Schaufeli et al., 2002; Schaufeli & Bakker, 2004). From this framework we derive:

H1: 

Organizational climate is positively associated with work engagement.

H2: 

Welfare, supervisory support, and integration each positively predict work engagement when entered together; proximate, individually experienced resources (welfare, supervisory support) are expected to show stronger unique contributions than the more collective integration facet.

H3: 

Engagement, as a personal resource, strengthens the translation of supervisory support into perceived integration; the relation between supervisory support with integration slope is steeper at higher engagement.

To avoid a descriptive accumulation of sources, we foreground a JD–R account in which contextual resources foster motivational states such as UWES engagement (Bakker & Demerouti, 2007, 2017; Schaufeli et al., 2002; Schaufeli & Bakker, 2004), and we situate climate within established syntheses of supportive work contexts (Schneider et al., 2013). Recent studies are integrated insofar as they adjudicate how resource facets operate in operational settings, rather than as stand-alone citations, thereby providing a coherent bridge to the hypotheses.

2.2. Exploring the Interrelationship Between Organizational Climate and Work Engagement

The interaction between organizational climate and work engagement represents a convergence of environmental conditions and psychological processes that influence employee behavior and performance (Schneider et al., 2017; Schwatka et al., 2020). Although both organizational climate and work engagement are individually linked to positive organizational outcomes, the exact nature of the interconnection remains insufficiently examined (Ancarani et al., 2019; Berberoglu, 2018).

Schneider et al. (2013) highlighted a substantial conceptual gap in both scholarly and practical literature regarding the mechanisms by which climate influences work engagement. Despite sharing common features—such as collective perceptions and affective reactions—the empirical evidence clarifying this connection remains limited. This gap underscores the need for focused empirical studies that explicitly conceptualize climate as a contextual determinant of work engagement (Adinew, 2024; Haryono et al., 2019).

In response, S. Albrecht et al. (2018) identified several contextual elements within the work environment that influence work engagement. These factors include support from senior leadership, clear organizational vision and goals, and comprehensive support systems—all integral aspects of organizational climate. This perspective reinforces the emerging consensus that work engagement is not merely an individual disposition but is significantly shaped by environmental and structural enablers (Kaya et al., 2010). Consequently, organizational climate acts as a perceptual filter through which employees assess roles and decide the extent of the psychological commitment (Kuenzi et al., 2020).

From a theoretical standpoint, organizational climate has been conceptualized both as an antecedent and an indicator of work engagement. Mone and London (2014) describe work engagement as both a behavioral and attitudinal manifestation of the prevailing organizational climate. This perspective suggests a bidirectional relationship: while climate influences employees’ behaviors and attitudes, these behaviors and attitudes simultaneously reinforce and reshape perceptions of the climate. Jex et al. (2014) supported this interpretation, arguing that a strong organizational climate enhances personal resources such as work engagement, as well as structural enablers like autonomy and support. These insights align with the Job Demands–Resources (JD–R) model (Demerouti et al., 2001; Bakker & Demerouti, 2017; Demerouti & Bakker, 2023), which conceptualizes climate as a crucial organizational resource. When employees perceive the work environment as fair, supportive, and purposeful, these perceptions serve as motivators that foster work engagement and buffer the strain of job demands. In this sense, organizational climate acts as a facilitator that can enhance or hinder work engagement depending on its overall quality and consistency (Bakker & Demerouti, 2017). Overall, the contemporary understanding of work engagement as a context-sensitive, reciprocal process highlights organizational climate as a vital driver of individual and collective performance.

An expanding body of research confirms that organizational climate significantly shapes work engagement across various industries. For instance, Clement and Eketu (2019) found that a supportive climate—characterized by fair rewards, autonomy, and recognition—is strongly associated with increased work engagement in the Nigerian banking industry. Similarly, Abun et al. (2021) demonstrated that organizational climate significantly influences work engagement among faculty and staff in Philippine higher education institutions, providing robust empirical evidence that links organizational climate to work engagement across cognitive, emotional, and physical dimensions. The findings suggest that improving clarity and recognition can significantly enhance employees’ psychological and physical work engagement at work. These findings suggest that consistent perceptions of fairness, inclusion, and support in the work environment enhance employees’ emotional and psychological investment in their roles (Haryono et al., 2019).

Several scholars have emphasized the theoretical and structural dimensions of this relationship. S. L. Albrecht (2014) introduced the concept of a “work engagement climate,” proposing that organizational settings characterized by clarity, purpose, and support create the conditions necessary for work engagement to thrive. Expanding on this, S. Albrecht et al. (2018) identified organizational resources—like leadership support and coherent HR practices—as essential for maintaining a work engagement climate, positioning climate not just as a backdrop but as a strategic tool for boosting productivity and commitment.

Empirical investigations across industries continue to reinforce this connection. Ancarani et al. (2019) found that in the healthcare sector, a positive organizational climate significantly boosted work engagement, particularly when employees perceived alignment between the values and the organizational environment. More recent studies have further refined this link. Wardono et al. (2022) confirmed that supportive climates boost both work engagement and organizational citizenship behavior (OCB), while Hayat and Afshari (2021) emphasized that a supportive climate can reduce workplace bullying and improve well-being, which in turn promotes work engagement. Conversely, Rasool et al. (2021) demonstrated that toxic climates can suppress work engagement, underscoring the importance of nurturing psychologically healthy environments. Singha (2024) reinforces this perspective by recommending positive psychology approaches to cultivate climates that sustain work engagement and drive performance.

Adinew’s (2024) comparative study of public and private institutions in Ethiopia illustrates how variations in climate impact productivity and, indirectly, work engagement. Although work engagement was not measured directly, the study shows that private institutions adopt motivational strategies, foster performance-oriented cultures, and sustain more inclusive climates compared to public institutions. This distinction underscores that a climate built on clear incentives and a supportive culture drives stronger workforce motivation and, consequently, higher work engagement—highlighting how organizational ownership and design directly influence work engagement outcomes. Furthermore, evidence from the police sector shows that a procedurally organizational climate boosts officers’ support for democratic policing principles and improves well-being. This indicates that fair treatment and transparent practices foster trust, efficiency, and employee flourishing within security organizations (Trinkner et al., 2016). Taken together, these studies highlight that work engagement does not arise in isolation but is profoundly shaped by the organizational climate in which employees work.

These findings reinforce the increasing academic interest in explicitly defining the nature of this relationship (Batlis, 1980; Kawiana et al., 2021). While the link between organizational climate and work engagement is well acknowledged in organizational research, it remains conceptually underdeveloped (S. L. Albrecht, 2014; Mone & London, 2014). Nonetheless, a growing body of empirical evidence supports the notion that a positive organizational climate—characterized by clear purpose, supportive leadership, and a collaborative culture—encourages higher levels of work engagement (Abun et al., 2021; Clement & Eketu, 2019). Supportive supervisory behavior is not only associated with increased engagement but also acts as a moderating factor between organizational learning and employee outcomes, reinforcing the importance of leadership in shaping engagement dynamics (Joel et al., 2023). Mone and London (2014) further argued that work engagement serves as both an attitudinal and behavioral reflection of organizational climate, indicating a reciprocal, self-reinforcing dynamic. The construct of organizational work engagement climate (S. L. Albrecht, 2014) offers a framework to explain how shared perceptions about the work environment influence psychological investment. Thus, we may hypothesize:

Hypothesis 1:

Organizational climate is positively associated with work engagement.

Sector-specific research has further strengthened this connection. For example, West et al. (2014) found in healthcare organizations that work engagement correlated strongly with enhanced performance outcomes, with organizational climate playing a pivotal facilitating role (Ancarani et al., 2019; Hayat & Afshari, 2021). These findings imply that when climate is perceived as supportive, inclusive, and purposeful, it promotes an atmosphere where work engagement naturally flourishes (Abun et al., 2021; Clement & Eketu, 2019). This is consistent with work engagement research across industries, where climate consistently emerges as a significant predictor of both attitudinal and behavioral work engagement indicators (Wardono et al., 2022).

Beyond mere association, substantial evidence supports viewing organizational climate as a robust predictor of work engagement. Empirical findings suggest that dimensions of climate—such as integration, welfare, and supervisory support—serve as job resources that directly shape work engagement outcomes (Halbesleben, 2010; Crawford et al., 2010; S. L. Albrecht, 2014). These resources are critical in helping employees feel empowered, motivated, and involved in workplaces (Bakker & Demerouti, 2017; Mone & London, 2014). In healthcare settings, West et al. (2014) demonstrated that a supportive climate significantly predicted work engagement and, by extension, improved performance outcomes. The predictive power of climate has also been validated through meta-analytic research (Halbesleben, 2010), confirming that when employees perceive the work environment as inclusive, fair, and development-oriented, they are more likely to exhibit vigor, dedication, and absorption—the core components of work engagement (Schaufeli & Bakker, 2004; Schaufeli et al., 2009). Recent multilevel studies underscore the mediating and moderating pathways linking organizational climate constructs, such as mindfulness and knowledge management practices, with engagement and well-being outcomes (Kumprang & Suriyankietkaew, 2024; Obeng et al., 2024). These findings align with the current study’s focus on how the interplay of organizational factors contributes to employee psychological states. In industrial operations, welfare signals fairness and care amid shift-based, standardized work; supervisory support provides daily guidance and recognition at the line level; integration reflects cross-unit coordination and information flow. JD–R is associated with those proximal, individually experienced resources (welfare, supervisory support) that display stronger unique associations with engagement than more distal, collective facets such as integration (Bakker & Demerouti, 2017). Conceptual overlap among supportive cues also implies shared variance that can attenuate unique coefficients despite meaningful zero-order correlations (Cohen et al., 2013).

Thus, we may hypothesize:

Hypothesis 2:

Welfare, supervisory support, and integration each positively predict work engagement when entered together; proximate, individually experienced resources (welfare, supervisory support) are expected to show stronger unique contributions than the more collective integration facet.

A meta-analysis by Halbesleben (2010) provides robust quantitative support for these theoretical claims. This study demonstrated that key job resources—such as autonomy, feedback, and social support—serve as mediators between organizational climate and work engagement (Crawford et al., 2010). Extending this framework, Zohar (2014) emphasized the concept of a “climate for safety,” illustrating that a positive safety climate not only improves safety-related behaviors but also enhances general work engagement, highlighting broader implications for other focused climate types.

Building on these foundations, S. Albrecht et al. (2018) introduced the term organizational work engagement climate, inspired by Schneider and Reichers’s (1983) call for functionally specific climate constructs. Rather than viewing climate as a vague or static concept, S. L. Albrecht (2014) reconceptualized it as the “shared perceptions about the energy and involvement willingly focused by employees toward the achievement of organizational goals” (p. 400).

Practically, this perspective positions climate as a diagnostic and proactive management tool—serving as an early signal of potential work engagement challenges. By deliberately shaping the work climate through effective leadership, transparent communication, and inclusive practices, organizations can prevent disengagement and cultivate a culture where employees are intrinsically motivated and committed to collective success (Schneider et al., 2013).

Building on this reasoning, recent research has explored how work engagement, once activated, shapes how employees respond to other job resources—particularly direct supervisory support. According to the JD–R framework (Demerouti et al., 2001; Bakker & Demerouti, 2017; Demerouti & Bakker, 2023), work engagement functions as a personal resource that can amplify or diminish the impact of contextual supports. This suggests that work engagement is not merely an outcome but also a key moderator in critical workplace relationships (Xanthopoulou et al., 2009).

In this context, work engagement is theorized to play a moderating role in the relationship between perceived supervisory support and employee integration. While supervisory support—characterized by guidance, recognition, and fair treatment—generally fosters a sense of inclusion and alignment within teams (Rhoades & Eisenberger, 2002; Ancarani et al., 2019), the degree to which this support translates into actual integration may depend on employees’ work engagement levels. Highly engaged employees are more psychologically available (Kahn, 1990) and thus more likely to interpret supervisory behaviors as meaningful and inclusive, whereas less-engaged employees may not fully benefit from the same level of support. Thus, we may hypothesize:

Hypothesis 3:

Engagement, as a personal resource, strengthens the translation of supervisory support into perceived integration; the relation between supervisory support with integration slope is steeper at higher engagement.

Thus, we have the following research model (Figure 1):

3. Materials and Methods

3.1. Procedure and Participants

This study employed a cross-sectional, quantitative, non-experimental field research design, which is appropriate for exploring associations among organizational climate, supervisory support, and work engagement in a real-world industrial context without manipulating variables. This design enables data collection from a naturally occurring workforce and is commonly used in organizational behavior research. To meet the study’s objectives, a cross-sectional design was used, with data collected at a single point in time from a large sample of industrial employees.

Although cross-sectional designs limit causal inference, they are appropriate for initial theory testing and for associations in a distinguished sectoral context where naturalistic manipulation is neither possible nor desirable (e.g., for safety and continuity reasons). In line with guidance for occupational field studies, we therefore interpret effects as associative and use this design to scope the pattern and magnitude of climate–engagement links that warrant longitudinal follow-up

The study was conducted in the food manufacturing industry in Greece, targeting an organization with over 450 employees. Participants were invited to complete the questionnaire via email. A digital link to the online survey was distributed through the organization’s internal communication system, accompanied by a cover letter explaining the purpose, confidentiality, and voluntary nature of the study. Reminders were sent after one week to increase participation. The study adhered to ethical research standards, and informed consent was obtained from all participants. Data confidentiality and anonymity were assured.

In this study, the researchers were permitted access during a limited time window, necessitating a cross-sectional design. Although longitudinal research could offer stronger causal inference, cross-sectional studies remain a widely accepted and effective approach for theory testing in occupational settings (Spector, 2019).

Convenience sampling was employed due to pragmatic constraints associated with accessing industrial employees during operational hours. Although this non-probability sampling method is more commonly associated with qualitative research (Etikan et al., 2016), it is also widely used in organizational behavior studies when researchers must rely on voluntary participation and restricted access. While this approach limits generalizability, it enabled the collection of timely and context-rich data from a diverse workforce. Future research could strengthen representativeness by employing stratified or random sampling techniques.

The participants included both male and female employees aged between 18 and 62 years, all employed full-time under permanent or seasonal contracts. Employees were drawn from various specialties and departments, including both white- and blue-collar roles. A convenience sampling method was used due to its ease of access and practical efficiency. While convenience sampling facilitated access to a representative cross-section of employees, this approach may limit the generalizability of findings beyond the studied organization and sector. Future studies could use probability sampling to enhance external validity. The final sample comprised 151 respondents, yielding a response rate of 33.6%. The sample consisted of 49.7% male (n = 75) and 50.3% female (n = 76) participants.

The most represented age group in the sample was 40–49 years old, comprising 31.1% of the participants (n = 47), indicating that mid-career employees formed the largest portion of the surveyed workforce. Regarding occupational roles, 58.3% of participants (n = 88) were employed in blue-collar positions (e.g., production line workers, machine operators), while 41.7% (n = 63) held white-collar roles (e.g., administrative staff, supervisors, engineers). This distribution reflects the operational diversity of the manufacturing environment and enables a more comprehensive understanding of work engagement and climate across hierarchical levels.

3.2. Measures

Data were collected using a structured questionnaire divided into three sections: demographic data, work engagement, and organizational climate. The questionnaire items were sourced from validated scales: the Utrecht Work engagement Scale (UWES-17) and the Organizational Climate Measure (OCM). Statistical analyses were performed using IBM SPSS Statistics (Version 28), which is widely employed in organizational research due to its functionality in conducting correlation, regression, and moderation analysis.

The UWES-17, developed by Schaufeli et al. (2002), measures work engagement across three subscales—vigor, dedication, and absorption. Responses were measured on a 7-point Likert scale ranging from 0 (“never”) to 6 (“always”). Example items include: “At my work, I feel bursting with energy” (vigor), “I am enthusiastic about my job” (dedication), and “I get carried away when I’m working” (absorption) (Appendix B). The OCM scale introduced by Patterson et al. (2005) was used to assess organizational climate. It includes 17 dimensions across four quadrants: Human Relations, Internal Process, Open Systems, and Rational Goal. This study focused on three dimensions from the Human Relations quadrant—employee welfare, integration, and supervisory support. These aspects reflect how much the organization values employees, encourages cooperation, and supports its workforce. Responses to the 14 relevant items were captured on a 4-point Likert scale, ranging from 1 (“definitely false”) to 4 (“definitely true”). Sample OCM items include: “This company cares about its employees” (welfare), “People in different departments are prepared to share information” (integration), and “Supervisors show an understanding of the people who work for them” (supervisory support) (Appendix A).

To ensure internal consistency of the measurement instruments used in this study, Cronbach’s alpha was calculated for both the Utrecht Work engagement Scale (UWES-17) and the selected dimensions of the Organizational Climate Measure (OCM). The UWES-17 demonstrated high reliability, with a Cronbach’s alpha of 0.91, indicating excellent internal consistency across its three subscales (vigor, dedication, and absorption). Similarly, the three selected dimensions from the OCM—employee welfare, integration, and supervisory support—showed acceptable to good reliability, with Cronbach’s alpha values of 0.78, 0.81, and 0.84, respectively. These results support the internal coherence of the scales and justify their use in assessing work engagement and perceptions of organizational climate within the sample. These values meet established thresholds for psychological research (J. F. Hair et al., 2016; J. Hair et al., 2019), supporting the robustness of the measurement tools. As analyses are conducted at the individual level, we refer to psychological climate (individual perceptions) and avoid treating these perceptions as group properties.

We mitigated potential common-method bias procedurally (anonymity, varied scale formats across instruments, item separation). In line with recommended practice, future replications will report an exploratory single-factor (Harman) test and a CFA common-latent-factor model; prior evidence suggests that when substantive loadings remain stable under a CLF, CMB is unlikely to compromise inferences (Podsakoff et al., 2003; Fuller et al., 2016). To establish construct distinctiveness in future work, we specify a confirmatory factor analytic approach for the UWES dimensions (vigor, dedication, absorption) and the climate facets, reporting χ²/df, CFI, TLI, RMSEA, SRMR, standardized loadings, AVE/CR, and Fornell–Larcker criteria, with SEM used to jointly test measurement and structural relations (Fornell & Larcker, 1981; Hu & Bentler, 1999; J. Hair et al., 2019).

We acknowledge that a full confirmatory factor analysis (CFA) with fit indices (χ²/df, CFI, TLI, RMSEA, SRMR) and discriminant-validity statistics (CR, AVE, HTMT) would further strengthen construct distinctiveness. Given the exploratory, single-organization design and the modest sample (N = 151), we opted for a proportional approach that prioritizes transparency without overfitting. Accordingly, we report internal consistency for all scales (UWES α = 0.91; OCM facets α = 0.78–0.84) using well-validated instruments under a clear theory-driven framework. We explicitly flag CFA/SEM as a priority for future confirmatory replications in larger samples.

Because focal constructs are perceptual (psychological climate and engagement), self-reports are appropriate for content-valid measurement (Spector, 2006). We implemented procedural remedies—assured anonymity, varied scale formats, and section separation—and we describe these explicitly. In line with Conway and Lance (2010) and Podsakoff et al. (2003), single-source designs are not inherently problematic when limitations are acknowledged and such remedies are in place. We note that future work could incorporate marker-variable or latent-factor diagnostics to complement these procedures.

Given the single-source design, we complemented procedural remedies with a brief statistical screen proportional to our sample and model complexity. We inspected variance inflation factors (VIF) from the engagement regression as a full-collinearity proxy for common-method bias. All VIF values were comfortably below conventional cut-offs (all VIF < 3.3; all Tolerance > 0.30), indicating no problematic collinearity from a single-source factor. Thresholds and numerical results are reported in the Results.

4. Results

4.1. Relationship Between Organizational Climate and Work Engagement

Descriptive statistics and intercorrelations for the key study variables are presented in

Table 2. Means, standard deviations, and Pearson correlations for study variables (N = 151).

Variable	M (SD)	1	2	3	4
1. Work engagement	3.90 (0.95)	—	0.160 *	0.258 **	0.222 **
2. OC Integration	2.85 (0.50)	0.160 *	—	0.433 **	0.156
3. OC Welfare	3.03 (0.56)	0.258 **	0.433 **	—	0.418 **
4. OC Supervisory Support	3.17 (0.49)	0.222 **	0.156	0.418 **	—

Note. All correlations are significant at * p < 0.05, ** p < 0.01. N = 151.

. Overall, participants reported moderately high work engagement (M = 3.90, SD = 0.95) alongside moderate perceptions of integration (M = 2.85), welfare (M = 3.03), and supervisory support (M = 3.17) (Table 2). These mean values suggest that while employees perceive fair levels of welfare and supervisory support, interdepartmental cooperation appears relatively lower in this industrial setting.

The first hypothesis (H1) proposed that organizational climate is positively associated with work engagement among working adults in Greece. Results indicated statistically significant positive correlations across all three climate dimensions and work engagement. Consistent with H1, the zero-order correlations indicate that more supportive psychological climate—indexed by welfare (fairness/care), supervisory support (guidance/recognition), and integration (cooperation/information flow)—is positively associated with UWES work engagement. This pattern accords with the JD–R framework, which posits that job resources facilitate a motivational process that sustains vigor, dedication, and absorption (Bakker & Demerouti, 2017; Schaufeli & Bakker, 2004).

Specifically, work engagement showed the strongest correlation with organizational welfare (r = 0.258, p < 0.01), suggesting that when employees perceive strong organizational concern for their well-being, they tend to display greater energy, enthusiasm, and immersion in their work. Supervisory support was also significantly related to work engagement (r = 0.222, p < 0.01), suggesting that guidance, recognition, and fairness from supervisors contribute meaningfully to employee motivation. Integration, while still statistically significant, exhibited the weakest correlation with work engagement (r = 0.160, p < 0.05), implying that interdepartmental cooperation may play a more modest role in shaping overall work engagement levels.

In addition to the individual relationships with work engagement, the organizational climate (OC) dimensions were also significantly intercorrelated. Welfare and integration were moderately correlated (r = 0.433, p < 0.01), as were welfare and supervisory support (r = 0.418, p < 0.01), reflecting the interconnected nature of employee support structures. The relationship between integration and supervisory support was weak (r = 0.156). These findings highlight the multidimensional nature of organizational climate and reinforce its role as an important contextual factor influencing work engagement in the Greek manufacturing sector.

All results are interpreted associationally and at the individual (psychological-climate) level, consistent with the cross-sectional, single-site, single-source design.

4.2. Associations Between Psychological Climate Facets and Work Engagement

To test Hypothesis 2, a standard multiple regression analysis was conducted. The overall regression model was statistically significant, F(3, 147) = 4.59, p = 0.004, indicating that the combined set of predictors explained a significant amount of variance in work engagement. The model yielded an R² value of 0.086, meaning that approximately 8.6% of the variance in work engagement could be explained by perceptions of organizational climate. The adjusted R² (0.067) suggests that the model retains modest explanatory power when accounting for sample size.

However, when evaluating individual predictors, none of the three organizational climate dimensions reached statistical significance at the conventional p < 0.05 threshold: Organization Climate (OC) Integration (β = 0.07, p = 0.462), Organization Climate (OC) Welfare (β = 0.17, p = 0.075) and Organization Climate (OC) Supervisory Support (β = 0.14, p = 0.107 (

Table 3. Multiple regression analysis predicting work engagement from organizational climate dimensions.

Predictor	B	SE B	β	t	p
Constant	1.83	0.60	—	3.03	0.003
OC Integration	0.12	0.17	0.07	0.74	0.462
OC Welfare	0.29	0.16	0.17	1.80	0.075
OC Supervisory Support	0.27	0.17	0.14	1.62	0.107

Note. Model Summary: R = 0.293, R² = 0.086, Adjusted R² = 0.067, F(3, 147) = 4.59, p = 0.004 Dependent Variable: Work engagement.

).

Although organizational welfare showed the strongest unique contribution (β = 0.17), p-value of 0.075 indicates only a marginal or trend-level effect. Similarly, supervisory support demonstrated a marginal effect that fell just short of statistical significance. This may reflect the interdependence among climate dimensions, which can dilute individual predictive power when entered simultaneously in the model.

These results suggest that while the combined effect of climate dimensions is statistically meaningful, none of the dimensions is independently associated with work engagement in a statistically significant manner. Thus, while perceptions of organizational climate collectively matter for work engagement, isolating the effect of each dimension in this sample did not yield statistically robust predictors. Therefore, H2 is partially supported: organizational climate as a whole is associated with work engagement, but no single dimension emerged as a significant predictor in this model.

Although the model explains a modest share of variance (R² ≈ 0.086), this pattern is typical of field studies in which engagement reflects the joint influence of multiple contextual and personal resources rather than a single dominant predictor. Within the JD–R tradition, resources such as autonomy and feedback (Parker et al., 2010), safety climate and supportive leadership in operational settings (Nahrgang et al., 2011), and perceived justice and fairness (Colquitt et al., 2013) have all been shown to relate to motivational states including engagement. Meta-analytic and cumulative evidence consistently indicates that effects for single work design or climate facets often fall in the small-to-moderate range once other correlated cues are present (Crawford et al., 2010; Christian et al., 2009). Thus, the small yet reliable coefficients here are consistent with the view that bundles of resources sustain engagement in situ (Bakker & Demerouti, 2017), and that any one facet captures only a portion of that resource set.

Although the model explains a modest share of variance (R² ≈ 0.086), this aligns with field research where engagement reflects multiple unmeasured resources (e.g., autonomy, feedback, safety climate) and correlated supportive cues (Crawford et al., 2010; Bakker & Demerouti, 2017). We therefore interpret magnitudes as small but meaningful in high-noise operational settings, consistent with contemporary guidance on effect sizes in field studies (Funder & Ozer, 2019).

Across predictors, VIFs were low and comfortably below the full-collinearity screening threshold (Integration: VIF = 1.23, Tolerance = 0.812; Welfare: VIF = 1.46, Tolerance = 0.687; Supervisory Support: VIF = 1.21, Tolerance = 0.825; all VIFs < 3.3). This indicates no problematic multicollinearity and reduces concerns about common method variance (O’Brien, 2007). Accordingly, we interpret the observed effects as small yet meaningful associations rather than artifacts of collinearity or method variance.

4.3. Moderating Role of Work Engagement on the Supervisory Support–Integration Relationship

To examine Hypothesis 3, a hierarchical regression analysis was conducted to test whether work engagement moderates the relationship between supervisory support and organizational integration. In Step 1, supervisory support and work engagement were entered as predictors. This model was statistically significant, F(2, 148) = 12.26, p < 0.001, explaining 14.2% of the variance in organizational integration (R² = 0.142, Adjusted R² = 0.130). These findings confirm that both supervisory support and work engagement independently contribute to how employees perceive integration within the organization.

In Step 2, the interaction term (Supervisory Support × Work engagement) was added to the model to test for moderation. The inclusion of the interaction term resulted in a significant improvement in model fit, with an additional 2.9% of variance explained (ΔR² = 0.029, F-change (1, 147) = 4.23, p = 0.041). The full model was statistically significant, F(3, 147) = 10.11, p < 0.001, accounting for 17.1% of the variance in organizational integration (Adjusted R² = 0.152) (

Table 4. Hierarchical regression testing moderation of work engagement.

Predictor	B	SE B	β	t	p
Step 1
Constant	2.50	0.45	—	5.56	0.000
Supervisory Support	0.28	0.12	0.19	2.34	0.021
Work engagement	0.35	0.12	0.24	2.79	0.006
Step 2
Interaction: Support × Work engagement	0.31	0.15	0.18	2.06	0.041

Note. Model Summary. Step 1: R² = 0.142, Adjusted R² = 0.130, F(2, 148) = 12.26, p < 0.001; Step 2: ΔR² = 0.029, F-change(1, 147) = 4.23, p = 0.041 Total Model: R² = 0.171, Adjusted R² = 0.152, F(3, 147) = 10.11, p < 0.001; DV: Organizational Integration.

).

These results support Hypothesis 3, indicating that work engagement moderates the relationship between supervisory support and integration. Specifically, the positive effect of supervisory support on integration is stronger for employees with higher levels of work engagement.

While the incremental variance is small (ΔR² = 0.029), such small interaction effects are common in organizational field data and can accumulate to meaningful operational differences across teams and time, especially when embedded in high-noise settings like manufacturing (e.g., McClelland & Judd, 1993; Aguinis et al., 2005; Funder & Ozer, 2019). Accordingly, we view the moderation as practically relevant but modest, consistent with aggregate patterns in the literature.

Figure 2 illustrates the moderation effect, showing that the positive relationship between supervisory support and organizational integration is stronger at higher levels of work engagement. The plotted lines represent predicted values at low (−1 SD) and high (+1 SD) levels of work engagement. Together, these findings provide partial support for the hypothesized model, highlighting the combined effect of climate dimensions on work engagement and the moderating role of work engagement in strengthening supportive supervisory relationships.

The moderation finding aligns with evidence that engagement functions not only as an outcome of resources but also as a personal resource that helps employees better leverage contextual support (Xanthopoulou et al., 2007). Methodologically, small interaction effects are common in organizational field data due to limited between-person variability, measurement error, and correlated predictors (Aiken & West, 1991; McClelland & Judd, 1993; Aguinis et al., 2005). Accordingly, we emphasize conditional effects with confidence intervals and Johnson–Neyman regions rather than relying solely on omnibus significance tests (Preacher et al., 2006).

Findings were robust to inclusion of age, gender, tenure, and blue- vs. white-collar role; coefficients retained direction and similar magnitude. Inferences are associative given the cross-sectional design.

5. Discussion

The present study provides empirical support for the positive association between organizational climate and work engagement, supporting Hypothesis 1. Consistent with previous studies (Bakker & Demerouti, 2007; Kuenzi et al., 2020), the results indicate that when employees perceive the work environment as supportive, fair, and collaborative, they are more likely to exhibit vigor, dedication, and absorption (Schaufeli & Bakker, 2004). The strongest correlation was observed between employee welfare and work engagement (r = 0.258, p < 0.01), underscoring the critical role of employee well-being in fostering psychological investment in workplaces. This finding aligns with the Job Demands–Resources (JD–R) model, which identifies supportive organizational conditions as essential job resources that enhance motivation and diminish the impact of job demands (Demerouti et al., 2001; Bakker & Demerouti, 2017; Demerouti & Bakker, 2023).

Supervisory support emerged as the second strongest factor associated with work engagement (r = 0.222, p < 0.01), supporting the role of managerial behavior as a key contextual driver of motivation. Prior studies (Hakanen et al., 2006; Macey & Schneider, 2008; Rhoades & Eisenberger, 2002) similarly highlight that employees who feel recognized, fairly treated, and supported by supervisors are more likely to utilize effort and show higher organizational commitment. Integration, although the weakest predictor among the three dimensions (r = 0.160, p < 0.05), still showed a statistically significant relation. This suggests that interdepartmental collaboration and cooperation contribute to work engagement, though to a slighter extent. This reinforces the view that while team cohesion is valuable, individual-focused support mechanisms may hold more immediate relevance in shaping work engagement behaviors (Wardono et al., 2022).

The moderate correlations between the climate dimensions (e.g., welfare and supervisory support: r = 0.418, p < 0.01; welfare and integration: r = 0.433, p < 0.01) emphasize that these constructs do not function in isolation but rather reflect an interrelated network of psychosocial resources. This aligns with S. L. Albrecht’s (2014) concept of a “work engagement climate,” as well as the Human Relations quadrant of the Organizational Climate Measure (OCM) (Patterson et al., 2005), which provides focused perspective for identifying high-impact variables that shape employees’ daily work experience.

While Hypothesis 2 was only partially supported, the multiple regression results revealed that the combination of welfare, integration, and supervisory support significantly predicted work engagement (R² = 0.086, p = 0.004), even though none of the predictors were independently significant. This reinforces the view proposed by Schneider et al. (2013) and Mone and London (2014) that work engagement is shaped by an ecosystem of organizational cues rather than by isolated factors. The relatively small proportion of variance explained by the model (8.6%) is consistent with Saks’s (2019) observation that organizational factors, though significant, represent only one aspect of a broader framework encompassing individual differences and external influences. This highlights that work engagement is more accurately conceptualized as an emergent outcome of the dynamic interplay between contextual resources and personal agency (Bakker & Demerouti, 2017).

Although Hypothesis 2 was only partially supported, the results offer important theoretical insights. The finding that the combined effect of climate dimensions is significantly associated with employee engagement, while no single dimension independently does so, suggests a configurational perspective rather than a linear one. This aligns with theories of climate strength and climate profiles (Schneider et al., 2013), which posit that the synergistic interplay of climate dimensions may be more influential than isolated factors. In this view, it is not the presence of one strong climate dimension (e.g., welfare or support) that drives engagement, but rather the coexistence of multiple supportive cues within the work environment that collectively foster psychological investment. This interpretation extends the Job Demands–Resources (JD–R) model by emphasizing the importance of resource integration over resource segmentation. It also supports the idea that engagement emerges from a climate ecosystem, where employees interpret meaning based on the cumulative tone of their environment, not individual policies or practices in isolation.

The confirmation of Hypothesis 3 adds an important dimension to the framework by demonstrating that work engagement strengthens the relationship between supervisory support and perceived integration. This interaction effect (ΔR² = 0.029, p = 0.041) aligns with the JD–R model’s assertion that engaged employees are better able to activate and benefit from available job resources (Demerouti et al., 2001; Bakker & Demerouti, 2017; Demerouti & Bakker, 2023).

Employees with higher work engagement are not only more open to support but also more likely to channel this support into collaboration, information exchange, and enhanced organizational alignment (Rhoades & Eisenberger, 2002; Ancarani et al., 2019).

These findings offer theoretical contributions by positioning work engagement not merely as an outcome but also as a moderator within organizational processes. This supports perspectives proposed by Mone and London (2014) and Xanthopoulou et al. (2009) on work engagement’s role in creating “gain spirals”—cyclical processes where engaged employees build additional personal and collective resources, reinforcing their own motivation and effectiveness over time. This viewpoint underscores that work engagement may shape perceptions of support, initiating a positive cycle that strengthens integration, trust, and coordinated performance (Schaufeli, 2016; Schneider et al., 2013).

Finally, this study contributes valuable empirical evidence from an underrepresented context: the Greek manufacturing sector. Existing work engagement research predominantly centers on Anglo-American service or knowledge-intensive sectors (Breevaart et al., 2014; S. Albrecht et al., 2018). By applying the JD–R and OCM frameworks within an industrial manufacturing environment, this study demonstrates the cross-contextual relevance of these theories and reveals sector-specific dynamics. For instance, the importance placed on welfare in this setting may reflect cultural values that highlight job security and shared well-being, which is particularly relevant in economies that have undergone periods of economic turbulence (Singha, 2024; Moslehpour et al., 2018).

From a practical perspective, these findings indicate that managers in industrial sectors should place greater emphasis on employee welfare initiatives, provide consistent supervisory support, and uphold fair treatment to sustain high levels of work engagement. Integrating climate and work engagement management—following the approach proposed by Saks and Gruman (2014)—could help organizations to implement interventions that align workforce well-being with performance objectives, fostering a more resilient and committed workforce.

Small coefficients should be interpreted in context: in multi-determinant, high-noise operational settings, small effects can cumulate to practically important differences across teams and time (Abelson, 1985; Funder & Ozer, 2019; Gignac & Szodorai, 2016). The pattern observed—coexisting small associations among welfare, supervisory support, and integration—accords with JD–R’s resource-bundling view and with meta-analytic norms once construct overlap and omitted resources are considered (Crawford et al., 2010).

5.1. Limitations

This study has several limitations that should be acknowledged. Given our single-source, cross-sectional design, findings are interpreted as associative rather than causal, consistent with methodological guidance for exploratory field studies (Antonakis et al., 2010). Associations observed here may reflect reciprocal or unmeasured influences; longitudinal designs would better capture the temporal dynamics between psychological climate and engagement and permit stronger claims regarding directionality (Shadish et al., 2002).

Second, the use of convenience sampling within a single manufacturing organization limits external validity. Findings generalize primarily to this firm and, by extension, to similar industrial contexts operating under comparable conditions. In addition, the response rate (33.6%) raises the possibility of nonresponse bias. Although such rates are common in field settings, future research should prioritize random or stratified sampling, multi-site samples across plants/regions, and basic nonresponse analyses (e.g., early–late responder comparisons) to strengthen generalizability.

Third, all focal variables were measured via self-report in a single wave, which increases the risk of common-method variance (CMV). Because the focal constructs are perceptual (psychological climate and engagement), self-reports are an appropriate method; nonetheless, we acknowledge potential CMV and explicitly describe the procedural steps taken and their limits (assured anonymity, distinct scale formats, section separation) (Spector, 2006; Conway & Lance, 2010; Podsakoff et al., 2003). Future studies should incorporate multi-source indicators (e.g., supervisor-rated behavior) and temporal separation of measures, and conduct statistical CMV diagnostics such as Harman’s single-factor test and common-latent-factor models to evaluate method variance (Podsakoff et al., 2003; Fuller et al., 2016).

Fourth, although internal consistency and convergent patterns were examined, the study’s exploratory scope and sample size precluded a full CFA of the measurement model. To establish construct distinctiveness, future work should run confirmatory factor analyses (CFA) for the engagement (UWES) dimensions and for the climate facets, reporting χ²/df, CFI, TLI, RMSEA, SRMR, standardized loadings, AVE, CR, and Fornell–Larcker criteria, and—where feasible—estimate a structural equation model (SEM) to test measurement and structural paths simultaneously (Fornell & Larcker, 1981; Hu & Bentler, 1999; J. Hair et al., 2019). We explicitly flag CFA/SEM with larger multi-site samples as a priority for confirmatory replications (Aguinis & Vandenberg, 2014; Bamberger, 2019).

Fifth, given the individual-level analyses, inferences pertain to psychological climate (individual perceptions) rather than shared, unit-level climate. Where unit/department identifiers are available, future studies should compute aggregation diagnostics (e.g., rwg (j), ICC (1), ICC (2)) to justify aggregation and/or use multilevel models to partition within- and between-unit variance (James et al., 1984; Bliese, 2000).

Sixth, our modeling strategy relied on multiple regression and moderation analysis in SPSS rather than SEM (e.g., AMOS/SmartPLS). This choice aligns with the study’s exploratory scope and available sample size; however, future work should replicate the model using SEM for greater analytical robustness and employ sensitivity/power analyses. Detecting small interaction effects typically requires large samples; thus, power to detect very small moderations may have been limited (Aguinis et al., 2005; McClelland & Judd, 1993).

Finally, we did not include several potentially consequential JD–R resources (e.g., autonomy, feedback, safety climate) and job/biographical controls (e.g., tenure, role type) that may account for additional variance in engagement. Incorporating these variables—and, where possible, objective indicators (e.g., safety incidents, throughput)—would provide a more comprehensive account of engagement drivers in industrial settings.

5.2. Practical Implications

From an applied standpoint, the findings support prioritizing a supportive psychological climate to help enhance and sustain work engagement (Saks & Gruman, 2014). In day-to-day industrial operations, the pivotal role of supervisory support points to leadership development that foregrounds relational competencies—fairness, empathy and effective communication—because these qualities shape how employees perceive support and recognition (Hakanen et al., 2006; Rhoades & Eisenberger, 2002; Ancarani et al., 2019). The moderation pattern further suggests that employees with higher engagement may be better positioned to translate supportive cues into collaborative behavior and cross-functional integration, consistent with JD–R reasoning about the interplay between personal and contextual resources (Bakker & Demerouti, 2007; Xanthopoulou et al., 2009).

Practically, “employee welfare” translates less to office-style designs and more breaks at productivity stations, ergonomic aids, transparent overtime rules, and clear signposting to on-site wellbeing supports. Supervisory support is most effective when line leaders build brief, structured 1:1 check-ins at shift start, use daily data to recognize effort and clarify priorities, and apply fair, explained task rotations. For integration, low-tension mechanisms such as cross-shift boards, standardized rules for inter-department obstacles, and a 15 min weekly inter-unit meetings (maintenance–quality–production) help convert support into collaboration. Given the moderation pattern, piloting these routines in higher-engagement teams and scaling based on lessons learned may yield quicker wins in throughput and quality stability.

Supervisor enablement can be further supported through coaching on feedback and problem-solving, peer shadowing across supervisors, and standard operating procedures that incorporate explicit “support touchpoints,” while piloting these support-to-integration routines first in higher-engagement teams and scaling based on lessons learned. By offering evidence from the Greek manufacturing sector, this study extends the applicability of JD–R and OCM reasoning beyond service and knowledge settings (Breevaart et al., 2014) and highlights sector-specific considerations: the salience of employee welfare may reflect cultural and macroeconomic conditions that foreground job security and collective wellbeing (Moslehpour et al., 2018; Singha, 2024). Given design and sampling constraints, these implications are framed as context-sensitive and are best progressed through local piloting and evaluation prior to wider roll-out.

5.3. Future Research

Future research should build upon these findings by employing longitudinal and multi-source research designs, which would uncover the causal pathways and temporal dynamics between organizational climate and work engagement (Schaufeli et al., 2009; Bakker & Demerouti, 2017).

Expanding the scope of climate dimensions beyond welfare, integration, and supervisory support could yield a more comprehensive understanding of contextual drivers of work engagement. For example, examining climates for innovation, procedural fairness, psychological safety, or diversity and inclusion could highlight additional levers for boosting employee motivation and well-being across different organizational settings (Kuenzi et al., 2020; Wardono et al., 2022).

Cross-cultural and cross-industry comparative studies could also investigate how national cultural values, economic conditions, and operational structures shape the climate–work engagement relationship (Moslehpour et al., 2018; Clement & Eketu, 2019). Moreover, future research should explore potential mediating mechanisms, such as psychological capital (e.g., self-efficacy, optimism, resilience), which may help explain how supportive climates translate into sustained work engagement (Xanthopoulou et al., 2009; Schaufeli, 2016).

Moreover, future research could adopt multilevel or longitudinal designs as employed by Kumprang and Suriyankietkaew (2024), to better capture the dynamic and systemic nature of engagement, or explore knowledge-based practices as in Obeng et al. (2024), to understand how intangible resources influence workforce outcomes.

Taken together, these avenues for future inquiry would contribute to a richer, more contextually detailed understanding of how organizational climate could be strategically managed to foster continuing work engagement and organizational success.

6. Conclusions

This study contributes to research on climate–engagement links by examining an under-represented setting—the Greek industrial manufacturing context. In this organization, individually perceived psychological climate facets—welfare, supervisory support, and integration—show small yet consistent positive associations with UWES engagement, consistent with the view that multiple resources jointly sustain vigor, dedication, and absorption. In multivariable models, however, none of the individual facets reached statistical significance. At the bivariate level, welfare exhibited the strongest zero-order association with engagement, suggesting that engagement reflects the joint influence of multiple, correlated resources.

We therefore emphasize cohesive, multi-lever interventions that address welfare, line-leader behaviors, and cross-unit coordination rather than single-facet programs. Interpretations are associational and pertain to individual (psychological-climate) perceptions within a single organization. Given the cross-sectional, single-firm design, inferences are associative and generalization is limited to this organization and similar industrial contexts. Future work should adopt longitudinal and multilevel designs and broaden the climate facets considered to provide a more comprehensive account of engagement drivers in manufacturing.