This study explores how high-performance work practices (HPWPs) can do more than enhance performance–they can help faculty members find meaning, purpose, and psychological strength in their professional lives. By focusing on the academic workplace, it investigates how HPWPs support the development of spiritual intelligence (SI), through the energizing effects of employee engagement and psychological capital (PsyCap), while considering how personal traits like job crafting and emotional intelligence (EI) shape this process.
Survey data were collected from 456 faculty members across diverse Indian higher education institutions. After validating the measures, the study used moderated mediation analyses to test the relationships between the core constructs.
The results reveal that HPWPs boost both engagement and PsyCap, with engagement acting as a key stepping-stone. Faculty who feel supported and energized are more likely to develop the psychological resources needed to approach work with meaning, coherence, and purpose. Importantly, the effects of HPWPs are even stronger when individuals actively shape their roles (job crafting) and demonstrate emotional sensitivity (EI). The study confirms that institutional support and individual agency work hand-in-hand to nurture spiritual intelligence at work.
This research offers a fresh perspective on how organizational practices can foster not only better work outcomes, but better people. By connecting workplace systems with deeper human needs–engagement, resilience, purpose–it reveals new ways to design academic environments that are both high-performing and deeply human. The study opens new conversations about how institutions can cultivate personal growth, not just professional output.
1. Introduction
Higher education institutions (HEIs) are facing a quiet but pressing crisis: faculty members are increasingly overwhelmed by role overload, emotional exhaustion, and a loss of meaningful connection to their work (Ahumada-Tello and Ramos, 2024; Ramesh and Sinnu, 2024). The simultaneous demands of research productivity, teaching quality, service responsibilities, and institutional performance metrics have created environments marked by role conflict, value fragmentation, and psychological strain (Perdomo-Ortiz et al., 2021; Ahumada-Tello and Ramos, 2024). While organizational responses have often focused on enhancing efficiency and output through structural reforms and performance incentives, they have largely overlooked the deeper psychological and existential resources faculty members need to navigate complexity, ambiguity, and emotional labor.
One such neglected resource is spiritual intelligence (SI)—the human capacity to find meaning, purpose, and interconnectedness in work and life (Wolman, 2001; Singh and Singh, 2022). Despite growing evidence that SI supports resilience, ethical conduct, and well-being in professional contexts (Pant and Srivastava, 2019; Petchsawang and Duchon, 2012), it remains marginal in management and HRM research, particularly in relation to human resource practices. This gap is striking, given mounting evidence that academic workplaces are not only cognitively demanding but also emotionally and morally charged. Faculty members often operate under conditions of autonomy without adequate guidance, creativity without recognition, and collaboration without shared values. SI offers a critical yet underutilized resource for coping with such challenges by fostering internal coherence, moral clarity, and meaningful engagement (Ramesh and Sinnu, 2024).
At the same time, high-performance work practices (HPWPs)—such as job autonomy, teamwork, job rotation, and participatory decision-making—have gained recognition for improving employee performance, commitment, and well-being (Boxall and Macky, 2009; Ronda et al., 2016; Salin et al., 2023). HPWPs empower employees to take ownership of their roles, foster adaptive behavior, and enhance organizational resilience (Huselid, 1995; Tawk, 2021; Peccei and Van De Voorde, 2019). Yet, the potential of HPWPs to cultivate spiritual resources—particularly SI—has been largely ignored. Existing research overwhelmingly focuses on the instrumental outcomes of HPWPs, such as productivity and engagement (Combs et al., 2006; Jiang et al., 2012; Rhee et al., 2020), overlooking their capacity to support deeper human development.
Moreover, how HPWPs translate into SI is not self-evident. Psychological capital (PsyCap)—the composite of hope, efficacy, resilience, and optimism—has been proposed as a key mediator linking HR practices to employee well-being and flourishing (Luthans et al., 2007). Likewise, individual and contextual factors such as emotional intelligence (EI) and job crafting are likely to moderate this relationship by shaping how employees interpret and act upon their work environments (Mayer and Salovey, 1997; Wrzesniewski and Dutton, 2001). Yet, these dynamics remain theoretically underdeveloped and empirically underexplored—especially in academic institutions grappling with profound challenges to purpose, identity, and psychological sustainability. This study addresses these critical gaps by asking: How do high-performance work practices help faculty members develop spiritual intelligence?
We explore the mediating role of PsyCap in this relationship and examine the moderating effects of job crafting and emotional intelligence. In doing so, the study contributes to a more holistic understanding of how human resource systems can support not only employee performance, but also the spiritual and psychological resources essential for academic flourishing in turbulent times. Data were collected from 456 faculty members across various HEIs in India. First, the psychometric properties of the survey instrument were checked to ensure reliability and validity. Hypotheses were then tested using Hayes's PROCESS macros, which allowed for an in-depth examination of mediation and moderation effects. The results demonstrate that HPWPs significantly enhance PsyCap and engagement, which in turn positively impact SI. Additionally, the findings highlight how job crafting and EI interact with HPWPs to amplify their effects on engagement, offering new insights into the interplay between HR strategies and individual-level psychological resources.
This study offers a timely and original contribution to the literature on human resource management (HRM), organizational behavior, and academic work by advancing a more humanistic and developmentally oriented understanding of employee engagement and well-being in higher education institutions (HEIs). Rather than focusing narrowly on performance outcomes, it highlights how high-performance work practices (HPWPs) contribute to the psychological and spiritual enrichment of employees—an area that remains underexplored in HRM research. One core contribution lies in demonstrating that psychological capital (PsyCap) is not merely a byproduct of good HR practices but a vital psychological mechanism through which employees come to experience their work as meaningful and coherent. By linking PsyCap to spiritual intelligence (SI)—a construct often overlooked in organizational research—this study brings new conceptual depth to discussions of purpose and human flourishing at work, especially in mission-driven, knowledge-intensive settings like HEIs.
The study also introduces a more nuanced view of context by showing that the effects of HPWPs are not universally experienced, but shaped by individual factors such as emotional intelligence (EI) and job crafting. These findings challenge one-size-fits-all assumptions in HR design and underscore the importance of supporting adaptive, emotionally intelligent, and proactive behaviors among employees. In particular, the finding that emotionally intelligent faculty are better able to translate enabling conditions into meaningful engagement opens new conversations about the emotional foundations of HRM effectiveness. Finally, by situating these insights within the academic profession—often characterized by autonomy, role ambiguity, and shifting institutional demands—the study responds to urgent calls to rethink HRM for the post-pandemic university. The relationships uncovered here suggest that enabling purpose-driven engagement is not a luxury but a strategic imperative. In doing so, the study lays the groundwork for HRM systems that not only build capability and motivation but also nurture the deeper human capacities needed to navigate uncertainty with purpose, connection, and resilience.
2. Theoretical background and hypotheses development
This study draws upon three well-established theoretical frameworks—Ability-Motivation-Opportunity (AMO) theory (Appelbaum et al., 2000; Bailey et al., 2001), Job Crafting Theory (JCT) (Wrzesniewski and Dutton, 2001), and Conservation of Resources (COR) theory (Hobfoll, 1988, 1989)—to examine how high-performance work practices (HPWPs) influence psychological capital (PsyCap) and spiritual intelligence (SI) among faculty members in higher education institutions (HEIs), with employee engagement acting as a key mediating mechanism and job crafting and emotional intelligence (EI) serving as moderators.
AMO theory offers a foundational lens for understanding how organizational practices influence employee behavior and outcomes. It posits that employees perform well when they possess the necessary Abilities (A), are adequately Motivated (M), and are provided with the Opportunities (O) to contribute effectively (Bos-Nehles et al., 2023; Lepak et al., 2006; van Iddekinge et al., 2018). In this framework, HPWPs can be categorized into three aligned bundles—ability-enhancing (e.g. training), motivation-enhancing (e.g. performance-based rewards), and opportunity-enhancing (e.g. participative decision-making)—which collectively support the development of human capital and organizational performance (Boxall and Purcell, 2011; Kehoe and Wright, 2013). While prior studies highlight the synergistic interplay among the AMO components (Marin-Garcia and Martinez Tomas, 2016; Kellner et al., 2016), this study focuses on how HPWPs, as AMO-informed mechanisms, foster higher employee engagement and build psychological resources, particularly PsyCap. For faculty in HEIs, who often work with high autonomy, HPWPs may serve not only to structure performance expectations but also to signal institutional support, thereby enhancing motivational and emotional commitment.
Job Crafting Theory (JCT) offers a more agentic perspective, suggesting that employees do not passively accept their job roles but actively reshape them to better align with their skills, interests, and identities (Berg et al., 2010; Wrzesniewski and Dutton, 2001). JCT identifies three domains of crafting: task, cognitive, and relational. In the context of HEIs, faculty members often engage in task crafting by reconfiguring teaching, research, or service activities, either by altering their scope, sequencing, or delivery. Cognitive crafting—changing the way one perceives job tasks—and relational crafting—modifying interpersonal interactions—may also help academic staff find deeper meaning and satisfaction in their work (Slemp and Vella-Brodrick, 2013). These self-initiated adaptations can reinforce emotional and cognitive engagement, potentially amplifying the positive effects of HPWPs on psychological outcomes.
COR theory complements the AMO and JCT perspectives by focusing on how individuals acquire, protect, and invest personal resources to cope with work-related demands (Hobfoll, 2002, 2011). PsyCap—comprising self-efficacy, hope, optimism, and resilience—is one such resource. COR theory suggests that when faculty members accumulate and preserve psychological resources, they are more likely to perform effectively, adapt to stressors, and maintain well-being (Halbesleben, 2006; Leung et al., 2020). Importantly, the COVID-19 pandemic heightened resource strain across HEIs globally (Antony et al., 2023), reinforcing the urgency of understanding how institutions can replenish depleted resources via supportive practices. HPWPs, in this regard, may act as resource-enabling mechanisms that not only mitigate perceived losses but also generate new psychological capital. Moreover, COR theory emphasizes that the accumulation of resources tends to beget further resource gains—a gain spiral that is central to this study's proposed mediation and moderation pathways.
Bringing these perspectives together, this study develops a theoretical framework that explains how HPWPs promote faculty members' engagement, which in turn enhances their PsyCap and ultimately contributes to their sense of meaning at work (SI). The model also considers individual-level moderators—job crafting and EI—that may condition the strength of these relationships. While AMO theory provides a macro-organizational rationale for the deployment of HPWPs, JCT and COR offer micro-level explanations for how employees respond to these practices cognitively and emotionally.
Accordingly, this study contributes to the literature in several ways. First, it advances the application of AMO theory by examining not only performance outcomes but also psychological and spiritual dimensions of employee development in HEIs. Second, it offers a novel integration of JCT and COR theory to model both agency (through job crafting) and resource dynamics (through PsyCap). Third, it incorporates spiritual intelligence—an underexplored construct in HRM research—as a downstream outcome of organizational practices and psychological capital. Finally, the proposed moderated mediation model, presented in Figure 1, provides a comprehensive view of the multilevel dynamics at play in fostering sustainable engagement and well-being among academic professionals.
The diagram shows a text box on the bottom left labeled “High-performance work practices.” A right-pointing arrow labeled “H 1” points to a text box on the bottom center labeled “Psychological Capital.” An upward-pointing arrow labeled “H 2” points from “High-performance work practices” to a text box at the top center labeled “Employee Engagement.” A text box on the upper left is labeled “Job Crafting.” From “Job Crafting,” a diagonal right-pointing arrow labeled “H 2 a” points to the arrow “H 2.” Below “Job Crafting,” there is another text box labeled “Emotional Intelligence.” An upward diagonal arrow labeled “H 2 b” points to the arrow “H 2 a.” From “Employee Engagement,” a downward diagonal arrow labeled “H 3” points to “Psychological Capital.” An arrow labeled “H 4” connects “High-performance work practices” to “Psychological Capital” via “Employee Engagement.” A right-pointing arrow labeled “H 5” connects “Psychological Capital” to a text box on the far right labeled “Spiritual Intelligence (Meaning at work).” Another right-pointing arrow labeled “H 6” connects “High-performance work practices” to “Spiritual Intelligence (Meaning at work)” via “Psychological Capital.”Conceptual model. Source: Own elaboration
The diagram shows a text box on the bottom left labeled “High-performance work practices.” A right-pointing arrow labeled “H 1” points to a text box on the bottom center labeled “Psychological Capital.” An upward-pointing arrow labeled “H 2” points from “High-performance work practices” to a text box at the top center labeled “Employee Engagement.” A text box on the upper left is labeled “Job Crafting.” From “Job Crafting,” a diagonal right-pointing arrow labeled “H 2 a” points to the arrow “H 2.” Below “Job Crafting,” there is another text box labeled “Emotional Intelligence.” An upward diagonal arrow labeled “H 2 b” points to the arrow “H 2 a.” From “Employee Engagement,” a downward diagonal arrow labeled “H 3” points to “Psychological Capital.” An arrow labeled “H 4” connects “High-performance work practices” to “Psychological Capital” via “Employee Engagement.” A right-pointing arrow labeled “H 5” connects “Psychological Capital” to a text box on the far right labeled “Spiritual Intelligence (Meaning at work).” Another right-pointing arrow labeled “H 6” connects “High-performance work practices” to “Spiritual Intelligence (Meaning at work)” via “Psychological Capital.”Conceptual model. Source: Own elaboration
2.1 High-performance work practices as enablers of psychological capital
High-performance work practices (HPWPs) are widely recognized as a strategically aligned bundle of human resource management activities that, when implemented cohesively, generate synergistic effects on individual and organizational outcomes (Gkorezis et al., 2018; Huselid, 1995; Kehoe and Wright, 2013; Luthans and Youssef-Morgan, 2017; Zatzick and Iverson, 2011). In contexts such as higher education institutions (HEIs), where performance is closely tied to individual autonomy, psychological well-being, and engagement, the relevance of HPWPs becomes even more salient. When organizations adopt refined staffing practices—such as rigorous recruitment and selection, person–job fit assessment, and continuous professional development—they enhance employees' knowledge, skills, and abilities (Appelbaum et al., 2000; Bamberger and Meshoulam, 2000; Jiang et al., 2012; Lepak et al., 2006; Subramony, 2009). Moreover, by fostering participatory decision-making processes and transparent communication channels, HPWPs create enabling conditions for employees to experience psychological growth and resource development (Lepak et al., 2006; Prieto and Santana, 2012; Subramony, 2009). These practices align with the Ability–Motivation–Opportunity (AMO) framework, wherein well-designed HR systems not only enhance functional competencies but also nurture positive psychological states. Among these states, Psychological Capital (PsyCap)—encompassing hope, efficacy, resilience, and optimism—emerges as a critical personal resource that thrives under supportive HR conditions (Luthans and Youssef-Morgan, 2017). Thus, we propose:
HPWPs are positively and significantly related to PsyCap.
2.2 HPWPs as drivers of employee engagement
The relationship between high-performance work practices (HPWPs) and employee engagement is particularly salient in knowledge-intensive, autonomy-driven settings like higher education institutions (HEIs), where faculty members must navigate complex and evolving demands across teaching, research, and service. HPWPs, defined as coherent clusters of human resource practices aimed at enhancing employees' skills, motivation, and opportunities (Takeuchi et al., 2007), are theorized to activate core psychological resources that foster deep, sustained work involvement. According to AMO theory, such practices not only build individual capabilities but also create enabling conditions for engagement by enhancing perceived value and autonomy in one's role. This is especially important in academic contexts, where the intrinsic value of work and freedom to craft one's tasks critically shape engagement outcomes.
Through mechanisms such as professional development, participatory decision-making, and performance recognition, HPWPs stimulate employees' cognitive, emotional, and behavioral connection to their work—key elements of engagement as conceptualized by Schaufeli et al. (2006). Extant empirical work confirms this effect across sectors, showing that HPWPs enhance not only employee involvement and performance (Alfes et al., 2021; Bal et al., 2013; Chuang and Liao, 2010; Huertas-Valdivia et al., 2018; Huselid, 1995; Messersmith et al., 2011; Salin et al., 2023), but also psychological strengths such as hope, resilience, and efficacy (Sweetman and Luthans, 2010)—the core dimensions of PsyCap.
However, there remains some debate regarding the directionality of the relationship. Echoing discussions on the job satisfaction–performance link (Judge et al., 2001), one might argue that employee engagement may itself influence how HPWPs are perceived or enacted. Yet this study, grounded in AMO and Conservation of Resources (COR) theory, adopts the position that HPWPs are antecedent to engagement. We argue that when HR systems are structured to provide consistent support, academic professionals are more likely to feel empowered and intrinsically motivated to invest their full selves into work. As demonstrated by Cooke et al. (2019) in a related professional context, HPWPs can serve as resource-enriching mechanisms that drive engagement, especially in environments where job autonomy and cognitive load are high.
Accordingly, and in line with the paper's broader aim to explore how HR practices influence psychological capital and spiritual intelligence in academic professionals, we offer the following hypothesis:
HPWPs are positively and significantly related to employee engagement.
2.3 Employee engagement as a precursor to PsyCap
Drawing on COR Theory (Hobfoll, 2002, 2011), we argue that employee engagement serves as a critical pathway through which individuals acquire and accumulate valued psychological resources, notably PsyCap. Engagement facilitates proactive coping, strengthens task focus, and reinforces a sense of control in dynamic academic settings (Bakker and Demerouti, 2008). When employees are engaged—cognitively, emotionally, and behaviorally—they are better positioned to develop self-efficacy, hope, optimism, and resilience (Luthans et al., 2007; Sweetman and Luthans, 2010), the key components of PsyCap.
Although earlier research often posits PsyCap as an antecedent to engagement (Avey et al., 2010; Halbesleben, 2010; Schaufeli and Salanova, 2007; Xanthopoulou et al., 2009), we argue for reverse causality in the context of higher education institutions. Faculty who are deeply engaged in their work—through teaching, research, and service—are more likely to explore innovative practices, accumulate competencies, and cultivate adaptive psychological states. This ongoing engagement nurtures PsyCap by reinforcing confidence, goal pursuit, resilience, and a positive explanatory style. Recent empirical evidence supports this directionality (Farrukh et al., 2024; Zhao et al., 2025), suggesting that engagement can be a fertile ground for PsyCap development.
Employee engagement is positively and significantly related to PsyCap.
2.4 The mediating role of employee engagement between HPWPs and PsyCap
Although the direct relationship between HPWPs and PsyCap is well-established, it is plausible that this connection operates indirectly through employee engagement. When HEIs implement HPWPs—such as rigorous selection, continuous training, and participatory decision-making—faculty members are likely to invest greater cognitive, emotional, and physical energy into their work (Sun et al., 2007). This engagement reflects a positive reciprocal exchange, whereby faculty respond to institutional support with heightened involvement and commitment, consistent with social exchange theory and COR principles (Hauff et al., 2022; Jiang et al., 2012; Ogbonnaya and Messersmith, 2019; Zhang et al., 2018).
In the context of HEIs, employee engagement can thus be seen not merely as a direct outcome of HPWPs, but as an active psychological process that facilitates the development of PsyCap. Through engaged work behaviors, faculty enhance their confidence (efficacy), persistence (hope), adaptability (resilience), and positive outlook (optimism)—the core dimensions of PsyCap. While prior research has not directly tested this mediating pathway, we propose it as a key mechanism in understanding how HPWPs shape psychological capabilities in academic settings.
Employee engagement mediates between HPWPs and PsyCap.
2.5 The moderating role of job crafting in the relationship between HPWPs and employee engagement
Drawing on Job Crafting Theory (JCT), employees proactively shape their work by modifying tasks, relationships, and perceptions to align with personal strengths, values, and preferences (Berg et al., 2010; Wrzesniewski and Dutton, 2001). This self-initiated change process enables individuals to find deeper meaning and satisfaction in their work (Harju et al., 2024; Tims and Bakker, 2010). Job crafting comprises three interrelated dimensions: task crafting, cognitive crafting, and relational crafting (Slemp and Vella-Brodrick, 2013).
Within HEIs, faculty members typically possess substantial autonomy across teaching, research, and service domains (Siluvai et al., 2023), enabling them to engage in various forms of job crafting. These include task crafting (e.g. altering pedagogical methods), cognitive crafting (e.g. reframing the purpose of academic duties), and relational crafting (e.g. strengthening collaborations with peers) (Slemp and Vella-Brodrick, 2013; Wrzesniewski and Dutton, 2001). Such crafting fosters increased meaningfulness and psychological ownership over work (Demerouti, 2014). When high-performance work practices (HPWPs)—such as supportive leadership, regular professional development, and participative decision-making—are implemented, they signal trust and investment from the institution, which in turn encourages faculty to proactively shape their roles (Sun et al., 2007; Jiang et al., 2012). This alignment between organizational support and individual agency can significantly enhance employee engagement (Alfes et al., 2021; Ogbonnaya and Messersmith, 2019). Although job crafting has been linked to engagement (Harju et al., 2024), the extent to which it strengthens the relationship between HPWPs and engagement in academic contexts remains underexplored. Accordingly, we offer the following hypothesis:
Job crafting moderates the relationship between HPWPs and employee engagement such that higher (lower) levels of job crafting will be associated with a stronger (weaker) relationship between HPWPs and employee engagement.
2.6 Emotional intelligence as a moderator of the HPWPs–Job Crafting–Engagement relationship
Extant research has consistently demonstrated the positive impact of emotional intelligence (EI) on individual and organizational performance (Barbuto and Burbach, 2006; Cuéllar-Molina et al., 2019; D'Souza et al., 2023; Narayanasami et al., 2024; Saeed and Naser, 2025; Salovey and Mayer, 1990). Defined as “a set of emotional and social skills that influence the way we perceive and express ourselves, develop and maintain social relationships, cope with challenges, and use emotional information in an effective and meaningful way” (Mayer et al., 2004, p. 197), EI enables individuals to recognize and regulate their own emotions while responding effectively to others' emotional cues (Boyatzis et al., 2012). These abilities are especially important in work settings where collaboration, interpersonal understanding, and conflict resolution are essential to group cohesion and performance (Presbitero et al., 2025). For example, a recent study in the hospitality sector found that EI significantly enhances employee engagement (Al-Romeedy et al., 2025), affirming its relevance across contexts.
In the present study, we propose that EI functions as a critical amplifying factor in the relationship between HPWPs and employee engagement. While job crafting strengthens the alignment between institutional HR practices and individual proactivity, EI equips individuals to interpret, adapt to, and capitalize on these workplace dynamics. This dual mechanism is particularly salient in HEIs, where faculty members must navigate emotionally complex roles in teaching, research, and service. Through job crafting, they tailor these roles to match personal strengths and preferences; through EI, they manage emotional demands and social relationships that shape engagement.
We argue that when both job crafting and EI are high, the impact of HPWPs on employee engagement is maximized. Although each moderator (job crafting and EI) independently enhances engagement, their combined (multiplicative) effect has not been systematically examined. This study fills that gap by exploring how EI and job crafting jointly moderate the relationship between HPWPs and employee engagement. Based on this rationale, we propose the following exploratory hypothesis.
EI (second moderator) moderates the relationship between HPWPs and job crafting (first moderator) in influencing employee engagement. At higher (lower) levels of EI, higher (lower) levels of job crafting will strengthen (weaken) the relationship between HPWPs and employee engagement.
2.7 The relationship between psychological capital and spiritual intelligence
Spiritual Intelligence (SI) is defined as an individual's capacity to receive, understand, and apply spiritual information to navigate daily life meaningfully (Amram, 2007; Singh and Singh, 2022). It represents a synthesis of spirituality—comprising sacred or transcendent experiences—and intelligence, understood as the ability to learn, abstract, and adapt knowledge effectively to one's environment (Wiseman and Watts, 2022). As individuals increasingly seek purpose and meaning in their professional lives, researchers have devoted growing attention to the role of spirituality in the workplace. SI has been conceptualized as a distinct form of intelligence that empowers individuals to develop clarity of values, a future-oriented vision, and a strong sense of meaning (Emmons, 2000; Koenig, 2008; Pargament, 2013; Visser et al., 2017; Wiseman and Watts, 2022).
Empirical studies have linked SI to various positive work-related outcomes, including job satisfaction (Gupta et al., 2014), psychological well-being (Gupta et al., 2014), and transformational leadership behaviors (Ramachandaran et al., 2017). Individuals high in SI are more likely to find deeper meaning in their work, remain motivated to fulfill responsibilities, and persist through challenges (James et al., 2011; Singh et al., 2016). This resonates with the tenets of Psychological Capital (PsyCap), especially the dimension of self-efficacy, which supplies the internal energy and discipline needed to pursue goals (Bandura, 1997; Luthans et al., 2007). More recently, resilience—a core component of PsyCap—has also been positively associated with SI (Sullivan and Lindsay, 2023), suggesting that individuals with strong spiritual intelligence may be better equipped to adapt in the face of adversity.
Given the conceptual and empirical overlap between PsyCap and SI, and in light of the increasing relevance of meaningful work in academic settings, we explore whether PsyCap contributes to the development of SI. We therefore offer the following exploratory hypothesis:
PsyCap capital is significantly and positively related to SI (meaning at work)
2.8 Psychological capital as a mediator between high-performance work practices and spiritual intelligence
The implementation of High-Performance Work Practices (HPWPs) by HR departments is intended to cultivate an environment where employees find meaning in their work and remain committed to their roles. While the direct influence of HPWPs on Spiritual Intelligence (SI) may appear intuitive, it is equally plausible that HPWPs enhance Psychological Capital (PsyCap)—as established in Hypothesis 1—which, in turn, contributes to the development of SI. Drawing on AMO theory, HPWPs offer structured opportunities for employees to build their capabilities, increase motivation, and exert greater control over their work, thereby enhancing PsyCap (Bello-Pintado, 2015; Boxall, 2012). This improved PsyCap may then serve as a foundational psychological resource that enables individuals to derive deeper meaning and purpose from their professional experiences.
Empirical studies have shown that HPWPs enhance various positive employee attitudes and behaviors, such as job satisfaction and performance (Wood and de Menezes, 2011). These outcomes are conceptually aligned with SI, which reflects individuals' capacity to find meaning in work and life. In academic contexts, where faculty members often seek purpose in research, teaching, and service, the presence of HPWPs may indirectly foster SI by first strengthening internal psychological resources such as efficacy, hope, resilience, and optimism. Despite the logical connections among these constructs, there is a lack of empirical research examining the mediating role of PsyCap in the relationship between HPWPs and SI. Based on the theoretical rationale and preliminary evidence, we propose the following exploratory mediation hypothesis:
PsyCap mediates between HPWPs and SI.
3. Method
3.1 Research design
This study adopts a quantitative, cross-sectional survey design to examine how high-performance work practices (HPWPs) influence employee engagement, psychological capital (PsyCap), and spiritual intelligence (SI) among faculty members in Indian higher education institutions (HEIs). To test the conceptual framework developed in Section 2, we collected self-reported data using a structured questionnaire. A cross-sectional design was deemed appropriate to capture perceptual data on HRM practices and psychological experiences, while enabling statistical modeling of relationships through moderated mediation analysis.
To collect data, we used an online questionnaire administered via Google Forms, a method adopted widely in post-pandemic organizational research due to its accessibility, efficiency, and compliance with health restrictions (Antony et al., 2023; Jayaraman et al., 2023; Shaik et al., 2023). A snowball sampling strategy was employed to reach a dispersed population of faculty members across diverse HEIs. We began by contacting faculty known to the research team and asked them to refer peers in other institutions. Participants were informed that the study was strictly academic, their participation was voluntary, and that their responses would remain anonymous.
A total of 650 survey invitations were distributed via email over a four-week period (March–April 2023). One reminder email was sent after two weeks to improve response rates. No incentives were offered. The survey remained open throughout the data collection window. We received 456 fully completed responses, representing a response rate of 70.1%. Google Forms was configured to ensure mandatory responses for all items, eliminating missing data. The geographic breakdown of the sample is as follows: 231 respondents (51%) were from Andhra Pradesh, 144 (31%) from Telangana, 37 (8%) from Tamil Nadu, 26 (6%) from Karnataka, 11 (2.5%) from Maharashtra, 4 (0.8%) from Uttar Pradesh, and 3 (0.7%) from Rajasthan. This distribution reflects broad regional representation within the Indian HEI landscape.
To assess potential non-response bias, we compared early (first 50) and late (last 50) respondents across all study variables, following Armstrong and Overton's (1977) approach. No statistically significant differences were observed, suggesting that non-response bias was not a concern. All participants provided informed consent before beginning the survey.
3.2 Sample description and measures
The respondents are 273 (59.9%) males and 183 (40.1%) females. The demographic profile of respondents is mentioned in Table 1.
Demographic profile of respondents
| Category | Profile | Total number | Percentage |
|---|---|---|---|
| Gender | Male | 273 | 59.9 |
| Female | 183 | 40.1 | |
| Age (in years) | 20–29 years | 101 | 22.1 |
| 30–39 years | 175 | 38.4 | |
| 40–49 Years | 100 | 22.0 | |
| 50–59 years | 80 | 17.5 | |
| Educational qualification | Undergraduate Bachelor | 39 | 8.55 |
| Master's Degree | 243 | 53.3 | |
| Professional degrees | 174 | 38.2 | |
| Annual income (INR/Us $) | Less than Rs. 500,000 ($6,250) | 203 | 44.5 |
| Rs. 500,000–900,000 ($6,250-$11,250) | 109 | 23.90 | |
| Rs. 900,000–1,400,000 ($11,250- $17,500) | 97 | 21.27 | |
| 1,400,000–1,900,000 ($17, 750-$23750) | 41 | 8.99 | |
| 1,900,000–2,400,000 ($23,750-$30,000) | 6 | 1.32 | |
| Employee designation | Assistant Professor | 270 | 59.21 |
| Associate Professor | 120 | 26.32 | |
| Professor | 66 | 14.47 | |
| Work experience | 0–4 years | 201 | 44.08 |
| 5–9 year | 79 | 17.32 | |
| 10–14 years | 101 | 22.15 | |
| Above 15 years | 75 | 16.45 |
| Category | Profile | Total number | Percentage |
|---|---|---|---|
| Gender | Male | 273 | 59.9 |
| Female | 183 | 40.1 | |
| Age (in years) | 20–29 years | 101 | 22.1 |
| 30–39 years | 175 | 38.4 | |
| 40–49 Years | 100 | 22.0 | |
| 50–59 years | 80 | 17.5 | |
| Educational qualification | Undergraduate Bachelor | 39 | 8.55 |
| Master's Degree | 243 | 53.3 | |
| Professional degrees | 174 | 38.2 | |
| Annual income (INR/Us $) | Less than Rs. 500,000 ($6,250) | 203 | 44.5 |
| Rs. 500,000–900,000 ($6,250-$11,250) | 109 | 23.90 | |
| Rs. 900,000–1,400,000 ($11,250- $17,500) | 97 | 21.27 | |
| 1,400,000–1,900,000 ($17, 750-$23750) | 41 | 8.99 | |
| 1,900,000–2,400,000 ($23,750-$30,000) | 6 | 1.32 | |
| Employee designation | Assistant Professor | 270 | 59.21 |
| Associate Professor | 120 | 26.32 | |
| Professor | 66 | 14.47 | |
| Work experience | 0–4 years | 201 | 44.08 |
| 5–9 year | 79 | 17.32 | |
| 10–14 years | 101 | 22.15 | |
| Above 15 years | 75 | 16.45 |
To ensure both conceptual validity and replicability, this study employed well-established and widely validated measurement scales for all constructs. The survey instrument was designed to measure each latent variable through multiple items, using a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Prior to distribution, the questionnaire underwent pre-testing with a small sample of faculty members to confirm item clarity, content relevance, and face validity. This helped to ensure cultural appropriateness and alignment with the professional context of higher education in India.
High-performance work practices (HPWPs) were conceptualized as a second-order construct composed of three core dimensions: ability-enhancing, motivation-enhancing, and opportunity-enhancing practices. All items were adapted from Chuang et al. (2013). The ability-enhancing dimension included four items (α = 0.75) that assessed HR practices focused on skill acquisition and training opportunities. The motivation-enhancing dimension comprised four items (α = 0.81) capturing practices that incentivize effort and align employee values with organizational goals. The opportunity-enhancing dimension consisted of three items (α = 0.76), measuring access to participatory and developmental opportunities within the organization. The overall HPWPs construct showed strong reliability (α = 0.82).
Employee engagement was assessed using a three-dimensional structure adapted from Kahn (1990), encompassing emotional, physical, and cognitive aspects of engagement. The emotional engagement dimension was measured using four items (α = 0.76), focused on affective responses to work. The physical engagement dimension included four items (α = 0.72), assessing energy and resilience at work. The cognitive engagement dimension used four items (α = 0.78), reflecting mental focus and absorption in tasks. Together, these three subdimensions formed a second-order construct with a composite reliability of 0.80.
Psychological capital (PsyCap) was measured using seven items adapted from Luthans et al. (2007), with a reliability coefficient of 0.77. The items captured four key dimensions of PsyCap: hope, efficacy, resilience, and optimism. Respondents indicated their level of agreement with statements such as “I feel confident in analyzing a long-term problem to find a solution.”
Job crafting was measured using ten items adapted from Sora et al. (2018), with a Cronbach's alpha of 0.79. These items assessed the extent to which faculty members proactively modified their work roles, relationships, and perceptions. A representative item read, “If there are new developments, I am one of the first to learn about them and try them out.”
Emotional intelligence (EI) was measured as a second-order construct based on the framework developed by Salovey and Mayer (1990), incorporating four dimensions: self-emotion appraisal (SEA), others' emotional appraisal (OEA), use of emotions (UOE), and regulation of emotions (ROE). Each subdimension was measured using three items, with internal consistency ranging from α = 0.76 to α = 0.79. The composite EI construct had an overall reliability of 0.82. Example items included “I have a good understanding of my own emotions” for SEA and “I can control my temper and handle difficulties rationally” for ROE.
Spiritual intelligence (SI) was assessed using four items adapted from Ashmos and Duchon (2000), which focus on the extent to which individuals find purpose, meaning, and spiritual coherence in their professional roles. This construct demonstrated acceptable reliability (α = 0.75). A sample item stated, “I believe that my work energizes my spirit.”
4. Findings
4.1 Measurement model and confirmatory factor analysis
We first checked the measurement model by conducting confirmatory factor analysis (CFA) as suggested by Anderson and Gerbing (1988). We used the LISREL package of covariance-based structural equation modeling (SEM) and presented the results in Supplementary Materials (SM) 1 and 2. In this study, we used second-order latent variables as HPWPs have three dimensions, EI has four, and employee engagement has three dimensions. SM2 captures the second-order latent variables. As shown in SM1 and SM2, the factor loadings of all the constructs were well above the acceptable levels of 0.70 (Hair et al., 2014). The average variance extracted (AVE) estimates were well above the minimum acceptable level of 0.50, and the composite reliability (CR) for all the constructs was above the acceptable levels (Montgomery et al., 2021). The factor loadings, CR, Cronbach's alpha reliability coefficients, and AVE values reveal that the indicators measure the intended constructs and vouch for convergent validity.
4.2 Descriptive statistics and preliminary analysis
We presented the descriptive statistics (means, standard deviations, and zero-order correlations) in Table 2.
Correlations, reliability, and validity
| Variable | Mean | Standard deviation | 1 | 2 | 3 | 4 | 5 | 6 | Cronbach alpha | Composite reliability | Average variance extracted |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. HPWP | 3.74 | 0.64 | 0.84 | 0.82 | 0.86 | 0.71 | |||||
| 2. EENG | 3.74 | 0.60 | 0.66** | 0.83 | 0.80 | 0.85 | 0.70 | ||||
| 3. PsyCap | 3.84 | 0.72 | 0.63** | 0.64** | 0.74 | 0.77 | 0.90 | 0.56 | |||
| 4. JCRAFT | 3.47 | 0.80 | 0.29** | 0.30** | 0.32** | 0.73 | 0.87 | 0.92 | 0.53 | ||
| 5. EI | 3.87 | 0.59 | 0.54** | 0.60** | 0.74** | 0.24** | 0.78 | 0.79 | 0.86 | 0.61 | |
| 6. SPINT | 3.74 | 0.59 | 0.67** | 0.69** | 0.68** | 0.24** | 0.71** | 0.76 | 0.75 | 0.85 | 0.58 |
| Variable | Mean | Standard deviation | 1 | 2 | 3 | 4 | 5 | 6 | Cronbach alpha | Composite reliability | Average variance extracted |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. HPWP | 3.74 | 0.64 | 0.84 | 0.82 | 0.86 | 0.71 | |||||
| 2. EENG | 3.74 | 0.60 | 0.66** | 0.83 | 0.80 | 0.85 | 0.70 | ||||
| 3. PsyCap | 3.84 | 0.72 | 0.63** | 0.64** | 0.74 | 0.77 | 0.90 | 0.56 | |||
| 4. JCRAFT | 3.47 | 0.80 | 0.29** | 0.30** | 0.32** | 0.73 | 0.87 | 0.92 | 0.53 | ||
| 5. EI | 3.87 | 0.59 | 0.54** | 0.60** | 0.74** | 0.24** | 0.78 | 0.79 | 0.86 | 0.61 | |
| 6. SPINT | 3.74 | 0.59 | 0.67** | 0.69** | 0.68** | 0.24** | 0.71** | 0.76 | 0.75 | 0.85 | 0.58 |
Note(s): **p < 0.01; Abbreviations: HPWP = High performance work practices; EENG = Employee engagement; PsyCap = Psychological Capital; JCRAFT = Job crafting; EI = Emotional intelligence; SPINT = Spiritual intelligence. Elements in diagonal are the square root of average variance extracted (AVE)
A preliminary analysis of correlations reveals that the correlations between the variables were less than 0.75, suggesting multicollinearity was not a problem with the data (Tsui et al., 1995). The highest correlation was between PsyCap and EI (r = 0.74; p < 0.01), and the lowest correlation was between EI and SI (r = 0.24; p < 0.01). We also found the variance inflation factor (VIF) values for all the variables were less than 5, indicating that multicollinearity is not a problem in this study (Montgomery et al., 2021).
As reported in SM1 and SM2, the AVE for each construct is higher than 0.5, confirming the convergent validity and internal consistency of the indicators (Hair et al., 2014). Further, the correlations between the variables were less than the square root of AVEs of the variables, vouching for the discriminant validity of the constructs (Henseler et al., 2015). For example, the AVEs for HPWP and employee engagement were 0.71 and 0.70, respectively, and both exceeded the squared correlation between these variables (Φ21 = 0 0.66, Φ221 = 0.44; p < 0.01). Further, the squared correlation between PsyCap and Ei was lower than the AVEs 0.56 and 0.61respectively (Φ21 = 0.74, Φ221 = 0.55; p < 0.05). These statistics, together with the CFA results, offer support for discriminant validity between these six variables.
CMB may affect the data since we collected data on endogenous and exogenous variables using the survey method (Kraus et al., 2020; Podsakoff and Organ, 1986). To reduce CMB we used straightforward language in the survey and randomized the order of questions (Podsakoff et al., 2012). In addition to the procedural remedies, we conducted statistical techniques to reduce CMB. First, we did Harman's single-factor analysis and found that single factors accounted for less than 35% of the variance, suggesting that CMB is not a problem in this study (Podsakoff et al., 2003). Second, we compared the thirteen-first-order factor model with twelve alternative measurement models (see SM3) and found that the thirteen-factor model was the best fit of the data [χ2 = 2549.46; df = 956; χ2/df = 2.67; Root Mean Square Error of Approximation (RMSEA) = 0.060; Root Mean-square Error (RMR) = 0.048; Standardized RMR = 0.051; Comparative Fit Index (CFI) = 0.93; Non-normal fit index (NNFI) = 0.01; Goodness of Fit Index (GFI) = 0.87]. Since the RMSEA (<0.08) and CFI (>0.90) indicate a good fit for the model. On the contrary, the single-factor yielded poor fit to the data one-factor model was poor fit [χ2 = 3794.62; df = 1034; χ2/df = 3.67; RMSEA = 0.077; RMR = 0.071; Standardized RMR = 0.63; CFI = 0.61; GFI = 0.70]. The third check of CMB is the latent variable method, according to which we loaded all the indicators into one factor at a time and found that the inner VIF values were less than 3.3, indicating that the data was not infected by CMB (Kock, 2015).
4.3 Empirical results for the relationships between HPWPs, employee engagement, and PsyCap (H1–H4)
We used Hayes' (2018) PROCESS macros to check H1–H4 and presented the results in Table 3.
| Hypotheses | Relationship | Coeff | se | t | p | Boot LLCI | Boot ULCI | R2 and F values | Result |
|---|---|---|---|---|---|---|---|---|---|
| H1 | HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 | 0.253 F (1,454) = 153.52 | Supported |
| H2 | HPWP → Employee engagement | 0.4773 | 0.0279 | 17.1190 | 0.0000 | 0.4225 | 0.5321 | 0.392 F (1,454) = 293.08 | Supported |
| H3 | Employee engagement → PsyCap | 0.6483 | 0.0537 | 12.0673 | 0.0000 | 0.5427 | 0.7539 | 0.434 F (2,453) = 174.02 | Supported |
| Hypotheses | Relationship | Coeff | se | t | p | Boot LLCI | Boot ULCI | R2 and F values | Result |
|---|---|---|---|---|---|---|---|---|---|
| HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 | 0.253 | Supported | |
| HPWP → Employee engagement | 0.4773 | 0.0279 | 17.1190 | 0.0000 | 0.4225 | 0.5321 | 0.392 | Supported | |
| Employee engagement → PsyCap | 0.6483 | 0.0537 | 12.0673 | 0.0000 | 0.5427 | 0.7539 | 0.434 | Supported |
The regression coefficient of HPWPs on PsyCap was positive and significant (β = 45, t = 12.39; p < 0.001). The results based on 20,000 bootstrap samples show that the 95% bias-corrected confidence interval (BCCI) was 0.3821 (LLCI) and 0.5262 (ULCI). The model was significant and explains 25.3% variance in the PsyCap, and the magnitude is very substantial (f2 = 0.33), [the effect size f2 between 0.02 and 0.15 represents “small”; f2 between 0.15 and 0.35 represent medium effect size, and f2 > 0.35 represents “large effect size” (Cohen, 1988)] and is statistically significant [R2 = 0.253; F (1,454) = 153.52; p < 0.001]. These results support H1, which shows that HPWPs were significant predictors of PsyCap.
Hypothesis 2 proposes that HPWPs are positively related to employee engagement. The regression coefficient of HPWPs on employee engagement was positive and significant (β = 0.48; t = 17.11; p < 0.001). The 95% (BCCI) LLCI and ULCI were 0.4225 and 0.5321 respectively. The model was significant and explains 39.2% variance in employee engagement because of HPWPs [R2 = 0.39.2; F (1, 454) = 293.08; p < 0.001], thus supporting H2.
As predicted in Hypothesis 3, the regression coefficient of employee engagement on PsyCap was positive and significant (β = 0.65; t = 12.06; p < 0.001). The model is significant and explains 43.4% of the variance in PsyCap [R2 = 0.434; F (2,453) = 174.02; p < 0.001]. These results render support for H3.
Employee engagement as a mediator between HPWPs and PsyCap (Hypothesis 4) was checked with an indirect effect (Hayes, 2018). Table 4 shows the mediation, and Table 5 shows the indirect effect.
Results of mediation analysis [HPWP → Employee engagement → PsyCap]
| Coeff | se | t | p | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|---|---|
| HPWP → Employee engagement | 0.4773 | 0.0279 | 17.1190 | 0.0000 | 0.4225 | 0.5321 |
| HPWP → PsyCap | 0.1447 | 0.0409 | 3.5334 | 0.0005 | 0.0642 | 0.2251 |
| Employee engagement → PsyCap | 0.6483 | 0.0537 | 12.0673 | 0.0000 | 0.5427 | 0.7539 |
| Total effect of HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 |
| Coeff | se | t | p | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|---|---|
| HPWP → Employee engagement | 0.4773 | 0.0279 | 17.1190 | 0.0000 | 0.4225 | 0.5321 |
| HPWP → PsyCap | 0.1447 | 0.0409 | 3.5334 | 0.0005 | 0.0642 | 0.2251 |
| Employee engagement → PsyCap | 0.6483 | 0.0537 | 12.0673 | 0.0000 | 0.5427 | 0.7539 |
| Total effect of HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 |
Indirect effect (H4)
| Effect | se | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|
| HPWP → Employee engagement → PsyCap | 0.3095 | 0.0365 | 0.2419 | 0.3845 |
| Effect | se | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|
| HPWP → Employee engagement → PsyCap | 0.3095 | 0.0365 | 0.2419 | 0.3845 |
Note(s): Total Effect of HPWP → PsyCap = Direct effect (0.1447) + Indirect effect (0.3094) = 0.4541. Indirect effect = (0.4773) (0.6483) = 0.3094. N = 456; Boot LLCI refers to the lower bound bootstrapping confidence intervals. Boot ULCL refers to the upper bound bootstrapping confidence intervals. Number of bootstrapping samples for this bias corrected bootstrapping confidence intervals are 20,000. The level of confidence for all confidence intervals in output was 0.95. We have four decimal digits for bootstrap results because some values may be very close to zero
The total effect of HPWPs on PsyCap consisted of direct effect (0.1447) and indirect effect (0.3094), which comes to 0.4541. Further, the indirect effect (0.3094) was a product of the effect of HPWP on employee engagement (0.4773) and the effect of employee engagement on PsyCap (0.6483). As can be seen from SM4, the result based on 20,000 bootstrap samples shows that the indirect effect is significant [Boot LLCI = 0.2419; and Boot ULCI = 0.3845], and since zero was not contained in the confidence intervals, the mediation hypothesis is supported.
4.4 Moderation and conditional interaction effects of job crafting and emotional intelligence on the HPWPs–Engagement relationship (H2a–H2b)
The two-way interaction hypothesis (H2a) and three-way interaction hypothesis (H2b) were tested by using model number 11 of Hayes's (2018) PROCESS Macros, and the results are presented in Table 6.
Results of moderation analysis
| Hypotheses | Relationship | Coeff. | se | t | p | Boot LLCI | Boot ULCI | R2 and F values | Result |
|---|---|---|---|---|---|---|---|---|---|
| H2a | HPWP x Job crafting → Employee engagement | 0.3780 | 0.0577 | 6.5520 | 0.0000 | 0.2646 | 0.4915 | 0.441 F (3,452) = 118.94 | Supported |
| H2b | HPWP x Job crafting x EI → Employee engagement | 0.4717 | 0.0940 | 5.0154 | 0.0000 | 0.2869 | 0.6565 | 0.563 F (7,448) = 82.63 | Supported |
| Hypotheses | Relationship | Coeff. | se | t | p | Boot LLCI | Boot ULCI | R2 and F values | Result |
|---|---|---|---|---|---|---|---|---|---|
| HPWP x Job crafting → Employee engagement | 0.3780 | 0.0577 | 6.5520 | 0.0000 | 0.2646 | 0.4915 | 0.441 | Supported | |
| HPWP x Job crafting x EI → Employee engagement | 0.4717 | 0.0940 | 5.0154 | 0.0000 | 0.2869 | 0.6565 | 0.563 | Supported |
As shown in Table 6, the regression coefficient of the interaction term (HPWPs x job crafting) was significant (βHPWPs x job crafting = 0.38; t = 6.55; p < 0.001; Boot LLCI (0.2646); Boot ULCI (0.4915). These results support H2a, which states that job crafting moderates the relationship between WPWPs and employee engagement. The visualization of two-way interaction was presented in Figure 2.
The horizontal axis is labeled “High-performance work practices” and is marked with three categories: “Mean minus 1 s d” on the left, “Mean” in the middle, and “Mean plus 1 s d” on the right. The vertical axis is labeled “Employee engagement” and ranges from 3.40 to 4.00 in increments of 0.20 units. The graph shows three diagonal lines extending upward from left to right. A legend on the top right labeled “Job crafting” indicates that the lines represent three level of job crafting. The first line represents “Low job crafting” and begins at (Mean minus 1 s d, 3.35) and increases to end at (Mean plus 1 s d, 3.97). The second line represents “Medium job crafting” and begins at (Mean minus 1 s d, 3.50) and increases to end at (Mean plus 1 s d, 4). The third line represents “High job crafting” and begins at (Mean minus 1 s d, 3.68) and increases to end at (Mean plus 1 s d, 4.03). Note: All numerical data values are approximated.Job crafting as a moderator between HPWP and employee engagement. Source: Own elaboration
The horizontal axis is labeled “High-performance work practices” and is marked with three categories: “Mean minus 1 s d” on the left, “Mean” in the middle, and “Mean plus 1 s d” on the right. The vertical axis is labeled “Employee engagement” and ranges from 3.40 to 4.00 in increments of 0.20 units. The graph shows three diagonal lines extending upward from left to right. A legend on the top right labeled “Job crafting” indicates that the lines represent three level of job crafting. The first line represents “Low job crafting” and begins at (Mean minus 1 s d, 3.35) and increases to end at (Mean plus 1 s d, 3.97). The second line represents “Medium job crafting” and begins at (Mean minus 1 s d, 3.50) and increases to end at (Mean plus 1 s d, 4). The third line represents “High job crafting” and begins at (Mean minus 1 s d, 3.68) and increases to end at (Mean plus 1 s d, 4.03). Note: All numerical data values are approximated.Job crafting as a moderator between HPWP and employee engagement. Source: Own elaboration
As can be seen in Figure 2, HPWPs result in higher employee engagement when job crafting is high, compared to a lower level of job crafting. Further, when HPWPs increase from low to high levels, employee engagement is higher when job crafting is high compared to lower levels of job crafting. The difference in the slopes of curves representing “low,” “medium,” and “high” levels of job crafting is visible and supports the moderation hypothesis 2a. Hypothesis 2b posits that EI moderates the relationship between HPWPs and job crafting to influence employee engagement. The regression coefficient of the three-way interaction term (HPWPs x job crafting x EI) was significant (βHPWPs x job crafting x EI=0.47; t = 5.01; p < 0.001; Boot LLCI (0.2869); Boot ULCI (0.6565), thus supporting Hb2. The visual presentation of three-way interaction is shown in Figure 3.
The graphs are arranged in a vertical series. The horizontal axis is labeled “High-performance work practices” and ranges from 2.50 to 4.50 in increments of 0.50. The vertical axis on the left is labeled “E N G” and ranges from 3.00 to 4.20 in increments of 0.20 units. The three graphs represent different levels of “Emotional intelligence,” and the values “4.46,” “3.87,” and “3.29” on the right correspond to the top, middle, and bottom graphs, respectively. All three graphs show three lines extending upward from left to right. A legend on the right labeled “Job crafting” indicates the lines represent different levels. In the top graph, the first line represents “Low, 4.46 R squared Linear equals 1” and begins at (2.42, 3.38) and rises linearly to end at (4.58, 4.17). The second line represents “Medium, 4.46 R squared Linear equals 1” and begins at (2.42, 3.61) and rises linearly to end at (4.58, 4.17). The third line represents “High, 4.46 R squared Linear equals 1” and begins at (2.42, 3.83) and rises linearly to end at (4.58, 4.17). In the middle graph, the first line represents “Low, 3.87 R squared Linear equals 1” and begins at (2.42, 3.20) and rises linearly to end at (4.58, 3.96). The second line represents “Medium, 3.87 R squared Linear equals 1” and begins at (2.42, 3.37) and rises linearly to end at (4.58, 3.96). The third line represents “High, 3.87 R squared Linear equals 1” and begins at (2.42, 3.54) and rises linearly to end at (4.58, 3.98). In the bottom graph, the first line represents “Low, 3.29 R squared Linear equals 1” and begins at (2.42, 3.02) and rises linearly to end at (4.58, 3.74). The second line represents “Medium, 3.29 R squared Linear equals 1” and begins at (2.42, 3.15) and rises linearly to end at (4.58, 3.77). The third line represents “High, 3.29 R squared Linear equals 1” and begins at (2.42, 3.28) and rises linearly to end at (4.58, 3.79). Note: All numerical data values are approximated.Interaction of HPWP with job crafting on EI at low, medium, and high levels of EI. Abbreviation: ENG = Employee engagement. Source: Own elaboration
The graphs are arranged in a vertical series. The horizontal axis is labeled “High-performance work practices” and ranges from 2.50 to 4.50 in increments of 0.50. The vertical axis on the left is labeled “E N G” and ranges from 3.00 to 4.20 in increments of 0.20 units. The three graphs represent different levels of “Emotional intelligence,” and the values “4.46,” “3.87,” and “3.29” on the right correspond to the top, middle, and bottom graphs, respectively. All three graphs show three lines extending upward from left to right. A legend on the right labeled “Job crafting” indicates the lines represent different levels. In the top graph, the first line represents “Low, 4.46 R squared Linear equals 1” and begins at (2.42, 3.38) and rises linearly to end at (4.58, 4.17). The second line represents “Medium, 4.46 R squared Linear equals 1” and begins at (2.42, 3.61) and rises linearly to end at (4.58, 4.17). The third line represents “High, 4.46 R squared Linear equals 1” and begins at (2.42, 3.83) and rises linearly to end at (4.58, 4.17). In the middle graph, the first line represents “Low, 3.87 R squared Linear equals 1” and begins at (2.42, 3.20) and rises linearly to end at (4.58, 3.96). The second line represents “Medium, 3.87 R squared Linear equals 1” and begins at (2.42, 3.37) and rises linearly to end at (4.58, 3.96). The third line represents “High, 3.87 R squared Linear equals 1” and begins at (2.42, 3.54) and rises linearly to end at (4.58, 3.98). In the bottom graph, the first line represents “Low, 3.29 R squared Linear equals 1” and begins at (2.42, 3.02) and rises linearly to end at (4.58, 3.74). The second line represents “Medium, 3.29 R squared Linear equals 1” and begins at (2.42, 3.15) and rises linearly to end at (4.58, 3.77). The third line represents “High, 3.29 R squared Linear equals 1” and begins at (2.42, 3.28) and rises linearly to end at (4.58, 3.79). Note: All numerical data values are approximated.Interaction of HPWP with job crafting on EI at low, medium, and high levels of EI. Abbreviation: ENG = Employee engagement. Source: Own elaboration
In Figure 3, we can see three panels: the op panel representing the interaction effect on higher levels of EI, the middle panel at medium levels of EI, and the lower panel at lower levels of EI, on employee engagement. The panel at the bottom shows the moderating effect of job crafting in the relationship between HPWPs and employee engagement at lower levels of EI. As shown in Figure 3, employee engagement is higher when job crafting is higher when compared to a lower level of job crafting. When we move to the upper panel, employee engagement is higher at higher levels of job crafting than at lower levels. However, the difference is more significant at lower levels of HPWPs. Gradually, higher levels of EI increase employee engagement at higher and lower levels of job crafting. The difference in the slope of curves renders support to the three-way interaction Hypothesis 2b.
The conditional effects of the focal predictor (Employee engagement) at values of moderators (Job crafting x EI) and the Conditional X*W interaction (HPWPs x job crafting) at values of the moderator Z (EI) were presented at the bottom of SM5. The indirect effects are presented in SM6.
4.5 The effect of psychological capital on spiritual intelligence and its mediating role between HPWPs and SI (H5–H6)
Hypothesis 5 proposes that PsyCap is positively related to SI. The regression coefficient of PsyCap on SI was positive and significant (β = 0.52; t = 12.45; p < 0.001). The 95% (BCCI) LLCI and ULCI were 0.4350 and 0.5980, respectively. The model was significant and explained 45.1% variance in SI because of PsyCap [R2 = 0.45; F (2, 453) = 186.34; p < 0.001], thus supporting H5. Hypothesis 6 posits that PsyCap is a mediator between HPWPs and SI. The mediation and indirect effect results are shown in Tables 7 and 8.
Results of mediation analysis [HPWP → PsyCap → SI]
| Coeff | se | t | p | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|---|---|
| HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 |
| HPWP → SI | 0.2434 | 0.0375 | 6.4948 | 0.0000 | 0.1698 | 0.3171 |
| PsyCap → SI | 0.5165 | 0.0415 | 12.4511 | 0.0000 | 0.4350 | 0.5980 |
| Total Effect of HPWP → SI | 0.4780 | 0.0375 | 12.7487 | 0.0000 | 0.4043 | 0.5517 |
| Coeff | se | t | p | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|---|---|
| HPWP → PsyCap | 0.4541 | 0.0367 | 12.3906 | 0.0000 | 0.3821 | 0.5262 |
| HPWP → SI | 0.2434 | 0.0375 | 6.4948 | 0.0000 | 0.1698 | 0.3171 |
| PsyCap → SI | 0.5165 | 0.0415 | 12.4511 | 0.0000 | 0.4350 | 0.5980 |
| Total Effect of HPWP → SI | 0.4780 | 0.0375 | 12.7487 | 0.0000 | 0.4043 | 0.5517 |
Indirect effect (H6)
| Effect | se | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|
| HPWP → PsyCap → SI | 0.2346 | 0.0306 | 0.1764 | 0.2960 |
| Effect | se | Boot LLCI | Boot ULCI | |
|---|---|---|---|---|
| HPWP → PsyCap → SI | 0.2346 | 0.0306 | 0.1764 | 0.2960 |
Note(s): Total Effect of HPWP → SI = Direct effect (0.2434) + Indirect effect (0.2346) = 0.4780
Indirect effect = (0.4541) (0.5165) = 0.2346
The total effect of HPWPs on SI consisted of direct effect (0.2434) and indirect effect (0.2346), which comes to 0.4780. Further, the indirect effect (0.3094) was a product of the effect of HPWPs on PsyCap (0.4541) and the effect of PsyCap on SI (0.5165). The result based on 20,000 bootstrap samples shows that the indirect effect is significant [Boot LLCI = 0.1764; and Boot ULCI = 0.2960], and since zero was not contained in the confidence intervals, the mediation hypothesis (H6) is supported. The empirical model was presented in Figure 4.
The diagram shows a text box on the bottom left labeled “High-performance work practices.” A right-pointing arrow labeled “0.45 triple asterisks” points to a text box on the bottom center labeled “Psychological Capital.” An upward-pointing arrow labeled “0.48 triple asterisks” points from “High-performance work practices” to a text box at the top center labeled “Employee Engagement.” A text box on the upper left is labeled “Job Crafting.” From “Job Crafting,” a diagonal right-pointing arrow labeled “0.38 triple asterisks” points to the arrow “0.48 triple asterisks.” Below “Job Crafting,” there is another text box labeled “Emotional Intelligence.” An upward diagonal arrow labeled “0.47 triple asterisks” points from “Emotional Intelligence” to the arrow “0.38 triple asterisks.” From “Employee Engagement,” a downward diagonal arrow labeled “0.65 triple asterisks” points to “Psychological Capital.” Above “Employee Engagement,” text shows values: “0.3095, Boot L L C I equals 0.2419, Boot U L C I equals 0.3845.” Below “Psychological Capital,” text shows values: “0.2346, Boot L L C I equals 0.1764, Boot U L C I equals 0.2960.” A right-pointing arrow labeled “0.52 triple asterisks” connects “Psychological Capital” to a text box on the far right labeled “Spiritual Intelligence (Meaning at work).”Empirical model. Source: Own elaboration
The diagram shows a text box on the bottom left labeled “High-performance work practices.” A right-pointing arrow labeled “0.45 triple asterisks” points to a text box on the bottom center labeled “Psychological Capital.” An upward-pointing arrow labeled “0.48 triple asterisks” points from “High-performance work practices” to a text box at the top center labeled “Employee Engagement.” A text box on the upper left is labeled “Job Crafting.” From “Job Crafting,” a diagonal right-pointing arrow labeled “0.38 triple asterisks” points to the arrow “0.48 triple asterisks.” Below “Job Crafting,” there is another text box labeled “Emotional Intelligence.” An upward diagonal arrow labeled “0.47 triple asterisks” points from “Emotional Intelligence” to the arrow “0.38 triple asterisks.” From “Employee Engagement,” a downward diagonal arrow labeled “0.65 triple asterisks” points to “Psychological Capital.” Above “Employee Engagement,” text shows values: “0.3095, Boot L L C I equals 0.2419, Boot U L C I equals 0.3845.” Below “Psychological Capital,” text shows values: “0.2346, Boot L L C I equals 0.1764, Boot U L C I equals 0.2960.” A right-pointing arrow labeled “0.52 triple asterisks” connects “Psychological Capital” to a text box on the far right labeled “Spiritual Intelligence (Meaning at work).”Empirical model. Source: Own elaboration
From the correlation matrix, we can see that the correlation coefficient between EI and SI is positive and significant (r = 0.71; p < 0.01). The post-hoc analysis revealed that the regression coefficient of EI on SI is positive and significant (β = 0.62; p < 0.001). Since we did not include EI as an antecedent to SI in the model, we did not hypothesize the relationship between EI and SI.
5. Discussion
This study explored how high-performance work practices (HPWPs) affect faculty members' psychological capital (PsyCap), employee engagement, and spiritual intelligence (SI), while accounting for the moderating roles of job crafting and emotional intelligence (EI). Framed by Ability-Motivation-Opportunity (AMO) theory (Appelbaum et al., 2000), Conservation of Resources (COR) theory (Hobfoll, 1989, 2002), and Job Crafting Theory (Wrzesniewski and Dutton, 2001), the study produced full empirical support for all proposed hypotheses. Below, we explicitly connect each finding to theoretical elements discussed in the introduction and explain how our work advances extant management theory and HRM practice.
First, the positive relationship between HPWPs and PsyCap (H1) confirms the theoretical proposition—outlined in both AMO and COR theories—that supportive work environments function as resource-enabling contexts. As argued in the introduction, HR systems designed to enhance ability (training, support), motivation (incentives, recognition), and opportunity (participation, autonomy) serve as structural inputs into the development of personal resources (Jiang et al., 2012; Lepak et al., 2006). By empirically linking HPWPs to higher PsyCap among academic professionals, this study contributes to theory by demonstrating that AMO-enhancing practices generate deep psychological outcomes, not only task performance (Kehoe and Wright, 2013; Bos-Nehles et al., 2023). This reframes HPWPs as psychological infrastructure—a view underrepresented in mainstream HRM discourse—and provides specific empirical grounding in the underexplored HEI sector (Gkorezis et al., 2018).
Second, the study confirms that HPWPs significantly predict employee engagement (H2), reinforcing AMO theory's assertion that employees are more engaged when they perceive the organization as investing in their capabilities and well-being (Bal et al., 2013; Chuang and Liao, 2010). However, unlike most studies focusing on corporate employees, our findings situate engagement in a high-autonomy, high-ambiguity academic context, showing that institutional HR architecture plays a critical motivational role even where self-direction is dominant. This insight advances HRM theory by illustrating that engagement can be cultivated institutionally, even in professional settings often assumed to be self-managed (Salin et al., 2023; Messersmith et al., 2011). It also contributes to emerging discussions of how HRM intersects with intrinsic motivation and autonomy in knowledge-based work.
Third, the significant relationship between employee engagement and PsyCap (H3) challenges the dominant causal assumption in the literature—that PsyCap precedes engagement (Avey et al., 2010; Xanthopoulou et al., 2009). Our data, consistent with Rich et al. (2010) and Sweetman and Luthans (2010), suggests the reverse may also hold: engaged employees may develop greater psychological resources over time. This finding advances COR theory by introducing a reciprocal dynamic between affective states and resource accumulation. Specifically, the experience of engagement—defined by absorption, vigor, and dedication—may generate or reinforce efficacy, optimism, and resilience. We thus contribute to the literature by proposing a bidirectional resource-building loop and encouraging future longitudinal models that account for reverse causality.
Fourth, we found that employee engagement mediates the relationship between HPWPs and PsyCap (H4), a connection suggested but rarely tested in prior studies (Jiang et al., 2012; Zhang et al., 2018). This result offers a novel theoretical bridge between AMO theory and COR theory: while AMO practices create structural conditions, engagement acts as the motivational engine that translates those conditions into durable psychological capacities. For HEIs, this insight underscores that engagement is not simply a behavioral output, but a developmental mechanism through which HRM systems foster internal psychological growth. This enriches the theoretical understanding of HRM's indirect effects, especially in purpose-oriented contexts where motivation and resource development are intertwined (Hauff et al., 2022; Ogbonnaya and Messersmith, 2019).
Fifth, the positive link between PsyCap and spiritual intelligence (H5) represents a significant theoretical contribution. As discussed in the literature review, prior research has often treated SI as a dispositional or spiritual trait (Emmons, 2000; Pargament, 2013), largely unconnected to HRM or psychological resources. By showing that higher PsyCap is associated with higher SI, our study reconceptualizes SI as a context-sensitive, developable capacity—a function of psychological resilience, hope, and perceived purpose. This bridges the HRM and spirituality literature and highlights that resource-enriched employees are better able to find meaning, transcend ego, and align their work with higher-order values (Ashmos and Duchon, 2000; Cuéllar-Molina et al., 2019). This opens new theoretical ground by suggesting that SI is not static, but organizationally cultivable.
Sixth, the mediating role of PsyCap in the HPWPs–SI link (H6) expands current HRM theory by illustrating how strategically designed HR practices can influence deep-seated outcomes such as existential meaning and ethical engagement. This directly responds to recent calls to explore how HRM systems affect the whole person, not just performance metrics (D’Souza et al., 2023; Narayanasami et al., 2024). It adds to the literature by specifying PsyCap as the psychological mechanism through which HR practices influence faculty members' ability to perceive their work as spiritually coherent. This advances theory by demonstrating that HRM systems can be vectors of purpose, not merely productivity.
Seventh, job crafting significantly moderated the HPWPs–engagement relationship (H2a), showing that proactive employees benefit more from supportive HR environments. This confirms the assumptions of Job Crafting Theory (Wrzesniewski and Dutton, 2001) and complements studies highlighting the interactive effects of individual agency and institutional design (Slemp and Vella-Brodrick, 2013; Sullivan and Lindsay, 2023). Our contribution lies in empirically testing this interaction in academia, a setting where crafting is both culturally accepted and structurally feasible. Theoretically, we demonstrate that HPWPs are contingent enablers—they gain traction when filtered through individual action. For HEIs, this suggests HR practices should not only offer structure, but also encourage autonomous redesign of work.
Finally, the three-way interaction among HPWPs, job crafting, and EI (H2b) presents a unique contribution. Faculty members high in EI were more capable of leveraging institutional support through crafting behaviors. This aligns with emerging literature on EI as an adaptive amplifier of HR systems (Boyatzis et al., 2012; Presbitero et al., 2025). Theoretically, this introduces emotional regulation as a boundary condition for the success of engagement-enhancing practices. It suggests that the effectiveness of HPWPs depends not only on organizational intent or personal initiative, but also on the emotional intelligence to navigate complexity, stress, and social interaction. This finding refines AMO theory by adding emotional modulation as a complementary lens for understanding employee responses to HR inputs.
In sum, this study advances management theory by weaving together AMO, COR, and Job Crafting perspectives to explain how HR systems affect not only behavior and attitudes, but also psychological strength and existential purpose. From a practical standpoint, it offers HEIs a multidimensional roadmap: to design HR systems that empower, engage, and uplift; to encourage job crafting; and to support emotional development. Together, these interventions can help transform faculty work from routine to meaningful, aligning personal purpose with institutional goals in an era of post-pandemic transformation.
5.1 Theoretical implications
This study makes a timely and multifaceted contribution to scholarship on human resource management (HRM), organizational behavior, and academic work in higher education institutions (HEIs). By integrating high-performance work practices (HPWPs), psychological capital (PsyCap), emotional intelligence (EI), job crafting, and spiritual intelligence (SI) into a unified model, the study offers both theoretical advancement and empirical validation of novel relationships. The findings challenge narrow, performance-centric views of HRM by foregrounding the deeper psychological and spiritual consequences of institutional practices—especially in knowledge-intensive, autonomy-driven settings such as HEIs.
First, this study extends theory on academic engagement by empirically demonstrating that HPWPs exert a direct and positive influence on both faculty engagement and PsyCap. The results show that HPWPs—particularly those that foster autonomy, recognition, participation, and development opportunities—activate engagement not merely as a motivational outcome but as a psychologically enriching experience. This finding speaks directly to and expands upon prior works in the HEI context that emphasize stress and burnout (Kinman and Jones, 2008) by instead highlighting the conditions under which faculty can flourish. In positioning engagement as both an outcome of institutional design and a developmental mechanism that enhances PsyCap, the study redefines academic engagement as a generative state, not just a reactive one.
Second, our results support and extend Ability-Motivation-Opportunity (AMO) theory (Appelbaum et al., 2000) by showing how HPWPs operationalize each AMO component in ways that build internal psychological resources. Ability-enhancing practices such as faculty development programs build efficacy; motivational systems such as performance incentives fuel optimism; and opportunity-rich environments—such as those supporting participation in decision-making—nurture resilience and hope. These resource gains are consistent with Conservation of Resources (COR) theory (Hobfoll, 2002), which our findings confirm: individuals who gain resources via HPWPs exhibit higher levels of PsyCap, which in turn predict greater spiritual intelligence. This layered pathway reinforces the idea that HR systems, when aligned with individual growth, can initiate upward spirals of psychological and spiritual enrichment.
Third, our moderation results reveal that job crafting significantly amplifies the relationship between HPWPs and engagement. This provides new evidence for Job Crafting Theory (Wrzesniewski and Dutton, 2001) by highlighting the interactive role of individual proactivity in unlocking the latent potential of institutional practices. Faculty who actively shape their tasks, relationships, and cognitive framing of work are more responsive to HPWPs—suggesting that personal agency and contextual affordances operate synergistically. Notably, these findings advance previous work (e.g. Nagarajan et al., 2023; Sethi et al., 2023) by embedding job crafting within a larger institutional framework and demonstrating its moderating rather than merely mediating role.
Fourth, the study introduces a novel three-way interaction between HPWPs, job crafting, and emotional intelligence (EI), showing that the positive effects of HR practices are maximized when faculty are both proactive and emotionally attuned. This offers a new theoretical bridge between HRM and the emotional capabilities literature (Mayer et al., 2004; Boyatzis et al., 2012), revealing that emotionally intelligent faculty—those who perceive, manage, and use emotions effectively—are better able to engage with their work when supported by enabling HR structures. The strength of this interaction in our findings indicates that EI is not merely an individual trait but a context-sensitive amplifier of HRM effectiveness. This extends emerging research (D’Souza et al., 2023; Presbitero et al., 2025) and introduces a new direction for understanding person–practice fit.
Fifth, the study advances a rarely explored but theoretically meaningful relationship between PsyCap and spiritual intelligence (SI). Our findings show that PsyCap mediates the relationship between HPWPs and SI, highlighting that employees who feel hopeful, efficacious, resilient, and optimistic are more likely to experience meaningful, coherent, and purpose-driven work. This empirically anchors SI (Emmons, 2000; Gupta et al., 2014; Wiseman and Watts, 2022) within mainstream HRM and OB theory, offering a new model of how organizations can influence deeper psychological states without invoking religiosity or abstract values. The novelty lies in moving SI from a peripheral construct to a theoretically embedded and empirically supported outcome of institutional design.
In sum, this study contributes original insights by theorizing HRM systems not simply as mechanisms for enhancing performance, but as architectures of human development. It advances multi-level theorizing by showing how structural practices (HPWPs), psychological capacities (PsyCap), emotional dispositions (EI), and behavioral agency (job crafting) converge to influence both engagement and spiritual well-being. This integrated model not only responds to contemporary calls for more humanistic approaches in HRM (Boxall et al., 2016; Pargament, 2013) but also demonstrates empirically how institutions can cultivate skilled and engaged employees and resilient, emotionally aware, and spiritually grounded individuals.
5.2 Practical implications
This study advances management practice by demonstrating that high-performance work practices (HPWPs) are not merely instruments of performance optimization but can be reimagined as strategic levers for cultivating psychological capital (PsyCap) and spiritual intelligence (SI)—two capacities central to resilience, engagement, and ethical commitment in complex, post-pandemic organizational life.
First, the results call for a paradigmatic shift in HRM practice from transactional to developmental systems thinking. Traditional performance-focused HRM often neglects employees' internal resource development. Our findings show that HPWPs can serve as infrastructures for psychological empowerment when designed to support autonomy, inclusion, and growth. Management practice is thus advanced by repositioning HR practices as enablers of faculty flourishing. For HEIs, this suggests the need to institutionalize PsyCap-oriented audits of existing HR systems and link them to career advancement, mentoring, and performance reviews. Such alignment transforms HRM from an administrative function into a strategic engine of psychological capability development.
Second, employee engagement should be operationalized as an endpoint and a developmental mechanism. Our study shows that engagement mediates the effect of HR practices on PsyCap, revealing it to be a psychological process rather than a static metric. This insight advances practice by encouraging leaders to invest in experiential engagement initiatives—such as interdisciplinary teaching projects, research incubators, or participatory governance—that stimulate deep work, reflection, and ownership. HEIs should treat engagement as a dynamic feedback channel through which HRM interventions are internalized and converted into personal growth.
Third, identifying job crafting and emotional intelligence (EI) as moderators provides a concrete basis for personalizing HR systems. These findings suggest that the same HPWP intervention can produce uneven outcomes depending on employees' behavioral agency and emotional competence. To integrate this nuance into practice, HEIs and other knowledge-intensive organizations should encourage faculty-led job crafting laboratories, embed EI development in 360-degree feedback systems, and create reflective practices that normalize adaptive behavior. Leadership development programs should explicitly include EI as both a selection criterion and a trainable capacity, thereby anchoring HRM in a more emotionally intelligent organizational culture.
Fourth, the mediating role of PsyCap in driving SI shifts the focus of HR practice from behavior management to meaning cultivation. Our findings position SI as an emergent outcome of a supportive organizational climate, signaling that HR systems can shape how individuals connect their work to transcendent values, moral coherence, and societal contribution. For HEIs, this means designing institutional spaces—storytelling circles, purpose alignment workshops, or values-based reflection sessions—where faculty can articulate how their professional identities align with the university's mission. This advances practice by linking HRM to ethical and existential dimensions of organizational life, particularly vital in institutions where public trust and social purpose are central.
Fifth, the study offers a roadmap for phased HRM implementation. Rather than attempting wholesale transformation, HEIs can begin by optimizing HPWPs and monitoring their effect on engagement, then layering on interventions around job crafting and EI as internal capacity develops. This phased logic helps bridge theoretical complexity with practical feasibility, making the model actionable across various institutional maturities and resource environments. In particular, this approach supports HR professionals in balancing standardization with adaptability, personalization with scalability.
Finally, although developed in the context of Indian HEIs, the insights generalize to other mission-driven, knowledge-intensive sectors such as healthcare, non-profit work, R&D, and social enterprise. In these environments, where emotional resilience and ethical orientation are as critical as technical expertise, management practice can be transformed by embedding the HPWP–PsyCap–SI pathway into leadership development, professional learning, and team design. Doing so reframes talent development not only as skill acquisition, but as a process of building people's inner architectures of meaning, strength, and contribution—a vital capability in uncertain, high-demand contexts.
In sum, this study invites HR professionals and institutional leaders to adopt an expanded view of performance that recognizes human work's relational, emotional, and spiritual dimensions. By integrating this broader perspective into HRM practice, organizations can foster environments where individuals thrive not just as performers, but as whole persons contributing with resilience, integrity, and purpose.
5.3 Limitations and future research
While offering a novel contribution to understanding the psychological and spiritual dimensions of high-performance work systems (HPWPs) in higher education institutions (HEIs), this study has limitations.
First, the cross-sectional research design constrains our ability to make strong causal claims, particularly in light of potential feedback loops among variables such as team member engagement, psychological capital (PsyCap), and spiritual intelligence (SI). Although our theoretical model specifies unidirectional effects grounded in the literature (e.g. COR theory; Luthans et al., 2007), it is equally plausible that engagement or SI recursively influence HPWPs through employee expectations and feedback mechanisms. Future research should adopt longitudinal or panel designs that capture the temporal dynamics and test for reverse or reciprocal causality, particularly in the engagement–PsyCap and PsyCap–SI relationships.
Second, while we address emotional intelligence (EI) as a moderator, we stop short of modeling its full explanatory potential. Given the significant post-hoc correlation and regression findings between EI and SI, future studies should conceptualize EI as a moderator and potentially as a complementary or sequential antecedent to spiritual intelligence. A moderated mediation or dual-path model that captures emotional and spiritual intelligences as components of higher-order personal resources would enhance theoretical precision and practical relevance.
Third, the study's focus on faculty members in Indian HEIs raises questions of contextual embeddedness. Rather than viewing this as a narrow limitation, future research could explicitly explore how institutional logics (e.g. bureaucratic vs collegial governance), cultural orientations (e.g. collectivism vs individualism), and religio-spiritual traditions shape the enactment of job crafting, PsyCap, and SI. Comparative studies across countries—and even across sectors within the same national context—would illuminate how HPWPs interact with broader socio-institutional settings to produce differentiated outcomes.
Fourth, we do not disaggregate HPWPs by intensity, type, or coherence. Future research should move beyond treating HPWPs as a unitary construct and investigate the differential effects of specific HR bundles (e.g. developmental vs evaluative practices), their sequencing, or mutual reinforcement. This could clarify whether some configurations are more conducive to building PsyCap and SI than others—and under what organizational climates or leadership styles.
Fifth, the construct of spiritual intelligence, although theoretically grounded, remains under-theorized in empirical HRM and OB research. Future inquiry should critically engage with the ontological and epistemological assumptions behind SI and explore its potential overlap or distinction from constructs like calling, meaning-making, and existential motivation. Additionally, mixed-method designs—such as qualitative interviews or narrative analysis—could help capture the experiential dimensions of spirituality at work that quantitative scales may overlook.
Lastly, individual-level differences such as identity orientation, intrinsic religiosity, and epistemic motivation may moderate how faculty members experience and respond to HPWPs and psychological stimuli. Integrating personality traits (e.g. Big Five), values orientation (e.g. Schwartz's value theory), or moral foundations could further enrich understanding of why HPWPs affect people differently, even under similar structural conditions.
In sum, rather than treating limitations as deficits, we see them as openings for deepening the theoretical sophistication and contextual sensitivity of future scholarship at the intersection of HRM, psychology, and spirituality in organizations.
6. Conclusion
This study advances our understanding of how high-performance work practices (HPWPs) can shape not only the behavior but the inner capacities of employees—namely, their psychological capital (PsyCap), emotional intelligence (EI), and spiritual intelligence (SI). Drawing on the integrated insights of AMO theory, Conservation of Resources (COR) theory, and Job Crafting Theory (JCT), we show that when institutions create enabling environments—through developmental HR systems and supportive leadership—faculty members respond not merely with greater effort, but with deeper engagement, emotional attunement, and a heightened sense of purpose.
Our findings suggest that human resource practices are not neutral levers of control but powerful architectures of meaning-making. Faculty members who are encouraged to craft their jobs, supported in developing emotional intelligence, and equipped with psychological resources are more engaged and more likely to experience their work as meaningful and coherent. In this way, the study reframes engagement not simply as a performance outcome, but as a bridge to well-being, growth, and transcendence.
At a time when institutions—especially in higher education—are grappling with profound transformations in work expectations, identity, and purpose, this study offers a timely reminder: organizational success depends not only on technical systems and policy design but on cultivating human potential. Investing in HPWPs, aligning with human needs for autonomy, development, and purpose, can foster institutional resilience and individual flourishing.
Ultimately, this study invites scholars and practitioners alike to rethink the goals of HRM—not just as a tool for managing performance, but as a means for developing emotionally intelligent, psychologically resilient, and spiritually aware individuals capable of navigating complexity with clarity, connection, and meaning. In doing so, we call for a more humanistic and future-oriented approach to organizational life that recognizes the full spectrum of what it means to thrive at work.
The supplementary material for this article can be found online.

