1 Introduction

In the dawn of the twenty-first century, we stand at the crossroads of technological evolution, witnessing the convergence of several groundbreaking innovations that promise to redefine the way we live, work, and interact. The rapid development of Artificial Intelligence (AI) technology has become the theme of the future era [76]. With a staggering contribution of $4.31 trillion to the market predicted by 2030, AI stands poised to shape our destiny, much like how the steam engine transformed society [39]. As we transition into the era of Industry 5.0, where human–machine collaboration is at its zenith, understanding the implications of these technologies becomes paramount. AI's capabilities, ranging from cognitive functions akin to human minds to solving complex business problems, place it at the heart of the industry 5.0 revolution [41]. He business landscape, in particular, has seen a transformative shift with the advent of Industry 5.0, digitalization, and a growing emphasis on sustainability [4, 27, 41]. Among the various facets of organizational management, Human Resource Management (HRM) has emerged as a critical area where AI’s potential is being harnessed and explored [51]. AI, characterized by its capability to process vast datasets and furnish invaluable insights, presents an intriguing potential to reshape traditional HR paradigms [74]. The convergence of AI with traditional HR paradigms has opened new avenues for innovation [48], enabling organizations to respond to the dynamic demands of the modern workforce and the global push towards environmental stewardship. These range from streamlining recruitment processes, where AI aids in screening potential candidates based on sophisticated predefined criteria [80], to the integration of AI-powered chatbots de-signed to tackle routine HR inquiries. This has notably enabled human HR professionals to channel their energies towards more strategic initiatives [14]. Sustainable Human Resource Management (HRM) is a set of HRM practices aimed at assisting businesses and their workforce in tackling global challenges. These challenges have been identified by the United Nations (UN) as Sustainable Development Goals (SDGs). These goals include eliminating poverty, safeguarding the planet, and ensuring prosperity for everyone [69]. The SDGs provide a universal framework comprising 17 objectives targeted at confronting global issues, which include, but are not limited to, inequality, poverty, prosperity, environmental degradation, climate change, and the establishment of justice and peace [75].The integration of AI into HRM is not merely a technological trend but a reflection of a broader transformation towards sustainable, human-centric approaches. This has led to the development of AI-driven sustainable HR practices, a subject that is both novel and essential in today’s rapidly evolving business environment.

Existing literature on AI's role in Sustainable HRM is extensive but fragmented. While some studies have explored AI technologies for talent analytics and organizational management [59], others have focused on streamlining recruitment processes and routine HR inquiries through AI algorithms [14, 80]. Additionally, among these relatively few studies, the mediating role of ‘employee resilience’ (a relatively stable and personality-based individual characteristic, and ‘affective commitment’ (the extent of an employee's attachment to the change management systems in their environment) have been explored in relation to sustainable human resource management and behaviors [44]. Although these studies are significant, they don’t rule out the potential to explore employee behavior through different psychological mechanisms. Furthermore, there is a lack of consensus on the impact of integrating artificial intelligence into sustainable human resource practices [52, 73]. Moreover, the rapid pace of technological advancements has outstripped the ability of academic research to keep up, leading to gaps in understanding and potential misconceptions about the real-world applications and implications of AI in HRM [53]. The complexity of human behavior, organizational culture, and environmental considerations adds further layers of controversy and challenge, making it imperative to approach the subject with a multidisciplinary perspective that encompasses technological, psychological, and environmental dimensions. The significance of addressing this gap is paramount. Therefore, it is crucial to empirically examine the potential impact of AI-driven Sustainable HRM on employee well-being and performance, as human resource management research should not be disconnected from practice [20]. This study offers a better theoretical understanding of the impact of AI-driven Sustainable HRM practices on employee outcomes, backed by empirical evidence. A nuanced understanding promises to equip organizations with strategies that dovetail seamlessly with global sustainability objectives.

While institutional factors like human resource management (HRM) demonstrably connect with employee performance, these subtle correlations boast a complex, interdisciplinary nature, potentially intertwined with other social and psychological variables [57]. Defined by a resolute commitment to organizational goals, a willingness to go the extra mile, and a profound sense of belonging and connection, employee engagement stands as a powerful construct [68]. The literature on employee engagement underscores its undeniable importance for organizations, highlighting its association with a plethora of positive outcomes, including enhanced productivity and performance, alongside reduced turnover and absenteeism [45]. Compared to constructs like job satisfaction, organizational commitment, and psychological climate, employee engagement boasts a broader scope, encompassing a wider range of individual cognitive, attitudinal, and behavioral outcomes [1, 2]. Empirically, studies reveal how specific personality traits act as crucial antecedents to engagement. Notably, a positive correlation has been established between employee engagement and traits like conscientiousness and positive affect [77]. Guided by the Person-Organization (P-O) Fit Theory, this study proposes that the alignment between individual and organizational factors (represented by AI-driven Sustainable HRM) significantly influences individual perceptions and attitudes, in turn impacting performance. This intricate process, the study posits, can be moderated by individual traits [1]. Following the logic of the AMO (Ability, Motivation, Opportunity) theory, we posit that a triumvirate of AI-driven Sustainable HRM practices collectively determines employee performance.

Taking all the above together, the primary goal of this study was to investigate the mediating role of employee engagement in the relationship between three bundles of AI-driven Sustainable HRM (Ability, Motivation, and Opportunity) and employee performance. The study also examined the moderating role of Conscientiousness personality traits. The study makes several contributions to existing knowledge. First, it expands the emerging and limited research on AI-enabled organizations and individual behavior from psychological and social perspectives. Specifically, the study invokes the classic AMO theory to propose and empirically test the relationship between AI-driven Sustainable HRM and employee performance behaviors. Second, in our quest to comprehend the intricate web of employee engagement, this inquiry melds the principles of Person-Organization (P-O) Fit Theory. It delves into how traits of Conscientiousness serve as a moderating force in the dynamic between HR practices and the engagement levels of employees. Further, the exploration uncovers a myriad of psychological and social exchanges encountered by individuals immersed in sustainability tasks, orchestrated via artificial intelligence. This amalgamation of personal attributes with organizational elements is posited to deepen the understanding of factors influencing employee behavior at an individual level. Such an approach is instrumental in nurturing a workplace ethos that actively supports participation in environmental endeavors. Third, the study is the first to examine AI-driven sustainable HR practices and individual behavior in a Chinese context, to the best of the researchers' knowledge. As a result, it is thought to broaden the perspective of regional businesses and global awareness of environmental sustainability.

Refer to Fig. 1 for the theoretical framework of this study, which delineates the hypothesized relationships among the constructs. AI-Driven Sustainable HRM, which comprises three dimensions, Ability, Motivation, and Opportunity, is posited as the independent variable. Employee Engagement is conceptualized as the mediating variable that links AI-Driven Sustainable HRM to Employee Performance, the dependent variable. Additionally, Conscientiousness Personality is introduced as a moderating variable that is hypothesized to influence the strength of the relationship between Employee Engagement and Employee Performance. Hypotheses H1, H2, and H3 represent the proposed direct and interactive effects within the model.

Fig. 1
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Theoretical Framework

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2 Literature review and hypotheses development

2.1 AI-driven sustainable HRM

Organizations across both emerging and developed economies have increasingly turned to modern technologies to refine their operations and processes. A prime example of this shift can be seen in the realm of human resource management. Sustainable human resource management (HRM) has emerged as a critical component of contemporary organizational strategy. Rooted deeply in its commitment to environmental stewardship, Sustainable HRM integrates HRM strategies that prioritize sustainable resource utilization [42]. Central to Sustainable HRM's philosophy is the commitment to the Triple Bottom Line (TBL), which encompasses economic viability, social responsibility, and environmental stewardship [26, 58],. These elements collectively define the movement and its aims, ensuring businesses not only focus on economic returns but also on their broader social and environmental footprints [24]. Within this landscape, human resource management's role becomes pivotal, driving both the sustainable growth of businesses and promoting responsible HR practices [8].

Moving from these foundations, amidst these evolutions in HR strategy, a novel concept has risen to prominence: AI-Driven Sustainable HRM. This approach embodies the seamless fusion of traditional human resource strategies with the transformative capabilities of AI. AI-Driven Sustainable HRM essentially leverages AI's prowess to accentuate the principles of Sustainable HRM, offering strategies, policies, and practices that aim for broader financial, social, and ecological impacts [24]. But how does AI integrate into this framework? For one, it significantly enhances the recruitment process. Through advanced algorithms, AI applications can effectively screen applicant resumes, identifying candidates whose profiles align closely with an organization's sustainability ethos [37]. Additionally, these tools offer cost savings by cutting down travel and related expenses for interviewers. Shifting beyond this initial phase, AI-driven platforms support the training and development of employees, promoting green skills and environmental consciousness. AI's role doesn't stop there; it extends into performance evaluations, guiding companies in their feedback processes. This involves not only assessing performance but also offering insights on sustainable practices, healthy living, and creative solutions to environmental challenges [31]. Taking a wider lens, the contribution of AI to Sustainable HRM is immense. It provides a renewed perspective, emphasizing evidence-based and adaptive strategies [75]. This transformation has been extensively analyzed by researchers. For instance, [1] highlights the pivotal role of Sustainable HRM in influencing employee attitudes.

Driven Sustainable HR Practices, it’s imperative to understand the foundational premise of the Ability-Motivation-Opportunity (AMO) framework [6]. The AMO model provides a lens through which we can comprehend the factors driving employee performance, underscoring the significance of an employee's capabilities, incentives, and opportunities on organizational outcomes. Specifically, "Ability" zooms in on the skills and competencies of employees and the ways they acquire and employ these to align with organizational objectives. This might be manifested in strategies that attract individuals aspiring to work environments with high ethical and sustainable standards and in recruitment endeavors to onboard those sharing the organization’s core values. “Motivation” pertains to the mechanisms that enhance employees' commitment and willingness to adopt sustainable action plans, potentially realized through contingent rewards and other incentives. Meanwhile, “Opportunity” offers employees the platform to engage in various organizational activities, from job design to labor relations [32]. Several studies have begun to explore the intersection of sustainable HR practices, AI, and employee outcomes, which can be related to the AMO framework. The AMO theory posits that for employees to perform well, they need the ability (skills and competencies), motivation (desire to engage in certain behaviors), and opportunity (a work environment that supports these behaviors) [6]. Sustainable HR practices, when supported by AI, can potentially enhance all three components of the AMO framework.

Ability-enhancing AI-Driven Sustainable HRM: AI-driven HR practices can facilitate the development of employee abilities by providing personalized learning experiences and career development opportunities. For instance, AI can analyze individual learning needs and recommend tailored training programs, thereby enhancing employees’ skills and competencies [15].

Motivation-enhancing AI-Driven Sustainable HRM: Sustainable HR practices that are supported by AI can also influence employee motivation. AI can help in recognizing and rewarding employee achievements in real-time, providing feedback, and creating a more engaging and fulfilling work environment. This can lead to increased job satisfaction and intrinsic motivation to perform well [43].

Opportunity-enhancing AI-Driven Sustainable HRM: AI can create opportunities for employees to excel by optimizing work processes, facilitating collaboration, and providing data-driven insights that enable better decision-making. For example, AI-driven tools can identify gaps in workflows, suggest improvements, and automate routine tasks, freeing employees to focus on more strategic and creative work [43].

In the context of sustainable HR practices, AI can support environmental and social governance (ESG) goals by tracking and analyzing data related to sustainability initiatives. This can empower employees to contribute to the organization's sustainability efforts, aligning personal values with corporate goals, which can further enhance motivation and performance [65].

2.2 Employee engagement

Over recent years, the importance of employee engagement has undergone a significant trans-formation. Once relegated to obscure corners of the Human Resources (HR) or Training & Development departments, it now occupies a central position in the dialogues of top-tier management [56]. This evolution underscores a growing recognition among organizations about their intrinsic role in nurturing employee engagement. The essence of employee engagement is anchored in three fundamental attributes: vigor, dedication, and absorption [66]. These attributes have positioned employee engagement as an indispensable element for modern organizational triumph. Vigor encapsulates the enthusiasm and mental tenacity individuals display, marked by a consistent dedication to their roles and resilience against challenges [66]. Dedication paints a picture of individuals' deep-rooted involvement in their work, reflecting sentiments of significance, inspiration, pride, and the exhilaration of facing challenges [66]. Absorption, in contrast, speaks to the intense concentration and pleasure employees derive from their tasks, often losing themselves in the work process [68]. However, building upon this, [2] introduced additional components in their five-factor operational definition of employee engagement, namely task performance and goal orientation. Specifically, [2] conceptualized employee engagement as a psychological construct en-compassing emotions and activation (comprising vigor, zeal, enthusiasm, pride, and emotional positivity), absorption (denoted by psychological presence, attention, and alertness), discretionary effort (aspiration for achievement and additional effort), task performance (task fulfillment and meeting role expectations), and goal alignment (goal-directedness and business acumen).

To delve deeper into the dynamics of employee engagement, peeling back the layers of employee engagement reveals its complex nature. It isn’t merely a one-sided equation but a bidirectional relationship. It emerges from the intricate dance between an individual’s inherent traits and the broader organizational milieu, encompassing HR practices and other structural nuances [2]. Several researchers have delved deep into this realm. Saks [64] viewed employee engagement through a multifaceted lens, pinpointing factors such as perceived supervisory support, rewards, recognition, and various forms of justice as vital engagement predictors. In a similar vein, [11] highlighted the role of both job-related resources, like autonomy and feedback, and personal reservoirs, such as self-efficacy and optimism, in steering job engagement towards optimal performance outcomes. Adding further depth, [83] distilled the literature to identify eight cardinal elements that positively influence employee engagement. These ranged from effective communication and trust to career development opportunities and a sense of pride in organizational achievements. Wollard and Shuck [79] broadened the horizons with their structured review, listing 42 precursors to engagement. Interestingly, while half of these were rooted in personal characteristics like optimism, the remaining were tied to organizational features, emphasizing the importance of feedback mechanisms and an encouraging organizational culture.

2.3 Theory and hypotheses

2.3.1 AI-driven sustainable HRM and employee performance

Researchers are increasingly adopting the so-called AMO framework, wherein the combination of an employee's Abilities (A), Motivation (M), and Opportunities (O) can offer predictive measures for individual or overall Performance (P). This framework has become one of the most practical theoretical perspectives for grasping the correlation between Human Resource Management and performance, particularly within the field of Human Resource Management journals [49]. According to the AMO theory, HR practices can enhance employee performance by improving their abilities, motivating them, and providing opportunities to contribute to organizational goals. These three dimensions, when combined to a certain extent, contribute to an individual’s good performance [16]. Recent research has delved into the intersection of sustainable HRM practices and the AMO framework. For instance, a study by [44] explored the role of employee resilience as a mediator between sustainable HRM practices and employee outcomes, emphasizing the importance of common good values. This research suggests that sustainable HRM practices not only directly impact employee performance but also foster resilience that can lead to positive employee outcomes. Another study by [25] examined the HR process lens to understand the link between sustainable HR practices and employee outcomes. This research highlights the significance of HR processes in translating sustainable practices into tangible employee performance improvements. The nexus between Green HRM and pro-environmental behavior has also been explored through the lens of AMO theory [38]. This research provides insights into how green HR practices can enhance pro-environmental behaviors among employees, contributing to overall organizational sustainability. In the year 2018, a comprehensive investigation scrutinized the intermediary facets of employee abilities, motivation, and opportunities. These elements played a pivotal role in shaping the intricate interconnection between HR bundles and the performance of employees [46]. Studies provided empirical evidence supporting the AMO framework, demonstrating that HR practices designed as bundles can effectively enhance employee abilities, motivation, and opportunities, which in turn positively affect performance. In light of the aforementioned arguments, the following hypothesis is postulated:

H1a: Ability-enhancing AI-Driven Sustainable HRM has a positive effect on Employee Performance.

H1b: Motivation-enhancing AI-Driven Sustainable HRM has a positive effect on Employee Performance.

H1c: Opportunity-enhancing AI-Driven Sustainable HRM has a positive effect on Employee Performance.

2.3.2 Mediating role of employee engagement

The concept of employee engagement emerges as a pivotal mediator, serving as a conduit through which HRM practices are translated into tangible performance outcomes. The integral role of employee voice, a facet of employee engagement, is particularly noteworthy in the realm of sustainable HRM. As posited by [54] in their exploration, fostering employee participation and feedback amplifies the impact of sustainable HRM on both employee engagement and performance. This mechanism of providing employees with a voice cultivates a sense of ownership and involvement, thereby bolstering engagement levels. in this context, employee engagement is not merely a byproduct of HRM practices but a critical mediator that harnesses the benefits of these practices to elevate employee performance.

This relationship is further substantiated by the research of [71], exploring the dynamics within federal public service organizations in Ethiopia involves elucidating the intricate connections among HRM, employee engagement, and organizational performance. Their findings corroborate the notion that employee engagement is a pivotal link between HRM practices and performance outcomes, thereby reinforcing the argument that engaged employees are intrinsically more productive and contribute significantly to organizational success. The study by [1] delves into the interplay between employee engagement, Green HRM practices, and green behaviors, highlighting the mediating role of employee engagement. This research suggests that engagement mediates the effect of green HRM practices on employees' environmentally friendly behaviors, aligning them with the organization's environmental sustainability objectives, which in turn enhances overall performance. Ahmad et al. [3] in their study underscore the synergy between these practices and knowledge sharing, which collectively foster environmental performance by augmenting employee commitment to environmental concerns. This underscores the significance of employee engagement in knowledge sharing as a mechanism through which sustainable HRM practices can positively impact environmental performance. The research conducted by [10] also provides empirical support for this hypothesis, confirming a robust positive correlation between dedication and performance. Drawing insights from both theoretical analyses and empirical discoveries, we posit that employee engagement is a conduit between AI-Driven Sustainable HRM and job performance.

H2a: Employee Engagement mediates the relationship between Ability-enhancing AI-Driven Sustainable HRM and Employee Performance.

H2b: Employee Engagement mediates the relationship between Motivation-enhancing AI-Driven Sustainable HRM and Employee Performance.

H2c: Employee Engagement mediates the relationship between Opportunity-enhancing AI-Driven Sustainable HRM and Employee Performance.

2.3.3 Moderating role of conscientiousness personality

Several impactful studies in the realm of human resource management behavior have indicated that personality traits can serve as a moderator, either enhancing or mitigating the connection between HRM practices and individual as well as organizational behavior and outcomes [22, 63]. In this context, an individual's personality traits, characterized by enduring patterns of thoughts, emotions, and behaviors rather than values that fluctuate with environmental changes, play a prominent role [47]. Conscientiousness, a key dimension of the Five-Factor Model of personality, is characterized by diligence, carefulness, thoroughness, and a tendency to strive for achievement [61]. Research has indicated that conscientious individuals are likely to engage more with their work [12].

The conscientiousness personality trait may moderate the relationship between ability-enhancing AI-driven sustainable HRM and employee engagement in several ways. Conscientious employees are more likely to embrace and effectively utilize the opportunities provided by AI-driven HRM practices due to their inherent desire for mastery and excellence [35]. They may also be more receptive to the sustainability goals of the organization and thus more engaged with initiatives that align with these goals. However, empirical research supporting the moderating effect of conscientiousness personality traits on AI-driven sustainable HRM is still limited.

Bakker et al. [12] delve into the moderating influence of conscientiousness on the correlation between work engagement and job performance, a phenomenon tested on a cohort of 144 employees. The findings of the study unveil a positive association between work engagement and both task performance and contextual performance. Notably, these correlations exhibit heightened strength, especially among individuals with elevated levels of conscientiousness. Thomason et al. [72] examine the roles of psychological ownership and conscientiousness as predictive factors in socially responsible workplace decision-making. They find that individuals with a higher sense of conscientiousness are more likely to recognize and choose options that are the most socially responsible. In their exploration, Pak and Chang [50] delve into the moderating impacts of conscientiousness on the correlation between high-commitment work systems (HCWS) and employee outcomes. Their focus centers on personal disposition, with conscientiousness emerging as a pivotal factor influencing variability in the relationship between HRM and performance.

The Person-Organization (P-O) fit theory offers a perspective for analyzing the congruence between individual characteristics and organizational culture. This theory articulates two distinct notions of fit: supplementary fit and complementary fit. Supplementary fit occurs when there is a congruence between individual attributes (such as personality, values, attitudes, and goals) and organizational characteristics (including values, norms, and goals) within the workplace. On the other hand, complementary fit is observed when there is a fulfillment of needs [33]. This transpires when the organization fulfills an individual's psychological, physical, and financial needs, along with providing opportunities for growth. Conversely, from the organizational perspective, a complementary fit is attained when an individual meets the organizational requirements for effort, time, knowledge, abilities, and commitment [60]. In the context of P-O fit theory, the alignment of employees' values, beliefs, and personalities with the culture and values of their organization is a significant predictor of positive work-related outcomes. This alignment leads to higher job satisfaction, increased engagement, and enhanced performance. Employees who perceive a high level of fit between their personal values (in this case, Conscientiousness) and the organizational culture promoted by AI-Driven Sustainable HRM are likely to exhibit greater engagement. This heightened engagement stems from the congruence in values and goals, fostering a sense of belonging and purpose. Therefore, rooted in these psychological viewpoints, the subsequent hypotheses were formulated: it can be hypothesized that the conscientiousness personality trait plays a moderating role in the relationship between AI-Driven Sustainable HRM and employee engagement.

H3a: Conscientiousness Personality moderates the relationship between Ability-enhancing AI-Driven Sustainable HRM and Employee Engagement.

H3b: Conscientiousness Personality moderates the relationship between Ability-enhancing AI-Driven Sustainable HRM and Employee Engagement.

H3b: Conscientiousness Personality moderates the relationship between Ability-enhancing AI-Driven Sustainable HRM and Employee Engagement.

3 Methodology

In our study, we used a quantitative descriptive approach, known for its efficient statistical data analysis, saving time and resources [19]. This method is effective because it handles both numerical and graphical data well [21]. We gathered cross-sectional data from employees involved in AI-driven sustainable HR practices in companies aiming for sustainability. Our sample consisted of top managers, CEOs, directors, and middle managers, whose strategic positions provide deep insights into AI-driven HR practices [13].

3.1 Sampling and data collection

We used a survey questionnaire for data collection, ensuring authenticity and ease for participants [78]. The questionnaire included proven scales, highlighting the survey’s cost-effectiveness and broad reach, both nationally and internationally, with quick responses [82]. Online questionnaires also offer advantages in data management and collaboration.

In China, recent government initiatives have tightened environmental and social responsibility regulations [85]. Chinese companies have therefore adjusted their HRM practices towards sustainability [84]. The choice of China for our survey was also due to the researcher's personal ties to the country, providing a deep understanding of the local context. This connection not only eased the survey process but also enriched the analysis.

Initially, we compiled a list of companies from industry reports and market analyses, then refined it by examining their public communications like websites and annual reports. This helped us focus on firms that clearly showcase their AI-driven HR efforts. We then contacted the HR departments and leadership of these companies with an invitation explaining our study's goals and ensuring data confidentiality. A significant number of the contacted companies, meeting our criteria, agreed to participate [5].

To avoid common method bias, we took several steps. We guaranteed anonymity to encourage honest feedback and used a multisource strategy by separately gathering data from managers and employees at two different times (T1 and T2), 2 weeks apart. This separation minimized bias and allowed for more accurate data matching [36]. For the surveys in English and Chinese, we followed a strict translation process recommended by [17], involving translation, back-translation, and resolution of discrepancies. Initially, the instruments, which were developed in English, were translated into Chinese by a bilingual expert. This was followed by a back-translation by another bilingual individual who was unaware of the original English version. Discrepancies between the original and back-translated versions were identified, discussed, and re-solved. In the analysis phase, we conducted tests to check for common method bias, ensuring our findings' validity and reliability.

The data collection for this study was conducted in December 2023. For the assessment of AI-Driven Sustainable HRM, 76 executives participated. They were sent an email with a survey on AI-Driven Sustainable HRM and a self-report questionnaire for their employees. After completing their respective questionnaires on aspects like work engagement and performance, both employees and executives sent their responses directly to the researchers, allowing for an analysis that included both organizational and individual levels. The survey was conducted in two stages, T1 and T2, starting with emails to senior management in December 2023. Following initial interest, an online survey for executives was distributed, and 2 weeks later, employee surveys were sent out. After removing mismatched responses, we ended up with paired questionnaires from 36 companies. Of these, 69.50% had fewer than 300 employees, 18.40% had between 300 and 1000, and 12.10% had over 1000, mainly in the manufacturing (46%) and consulting (30.20%) sectors.

Out of the 747 questionnaires distributed, we initially received a response rate of 48.9%. A second reminder email was sent to non-respondents, which increased the final response rate to 64.9%. Following the exclusion of 23 questionnaires with more than 25% missing values [23, 29], resulting in 470 valid responses. According to the simple rule of thumb proposed by American sociologist Babbie, “a response rate of at least 50% is adequate for analysis and reporting, a response rate of 60% is good.” and thus, we can confidently assert that the data obtained from this sample can be generalized to the relevant population [9].

The majority of participants were Millennials/Gen Z (ages 18–25) and Millennials/Gen Y (ages 26–43), accounting for over 64% of the sample. The gender ratio was relatively balanced, with males comprising 47.87% and females 50%. In terms of job positions/roles, there was a uniform distribution among senior management, middle management, team leaders/supervisors, professional/technical experts, and administrative/support staff. Regarding work experience, the participants’ years of service were primarily concentrated in the 1–6-year range. On average, 2 executives and 11 employees from each company participated in the survey.

3.2 Measures

In accordance with the standards set by Cronbach's alpha coefficient for reliability assessment, a Cronbach's alpha coefficient above 0.8 indicates excellent reliability for the test or scale [70]. For the dimensions of AI-Driven Sustainable HRM, Employee Engagement (EE), Employee Performance (EP), and Conscientiousness Personality (CP), the Cronbach's Alpha values are respectively 0.927, 0.891, 0.929, and 0.855. Given that all four research variables exceed 0.8, this indicates a high level of internal consistency among the items within each variable, reflecting the latent meanings of the underlying constructs. The measurements of these variables are discussed below. To rigorously examine the outlined constructs, this study leveraged and adapted various scales from the extant literature, ensuring their relevance and applicability.

3.2.1 AI-driven sustainable HRM

We employed a 15-item instrument adapted from a scale developed by [30], which measures AI-Driven Sustainable HRM, based on the AMO framework, using a 5-point Likert-type scale (1 = “never” and 5 = “always”). The dimensions of this instrument align with the following United Nations Sustainable Development Goals: SDG 3 (Good Health and Wellbeing), SDG 4 (Quality Education), SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), SDG 10 (Reduced Inequalities), and SDG 17 (Partnerships for the Goals). These dimensions are key aspects of these specific SDGs. The Cronbach's alpha coefficient was 0.93.

3.2.2 Employee engagement

Employee engagement was measured using a modified version of the Utrecht Work Engagement Scale (UWES-9) by [66, 67]. All items were scored on a seven-point rating scale ranging from 1 (‘never’) to 5 (‘always’). This section aims to gauge how connected employees feel to their job roles and their level of engagement, and examined three aspects of engagement, namely vigor, dedication, and absorption. The Cronbach’s alpha coefficient was 0.89.

3.2.3 Employee performance

We utilized five items from the nine-item scale developed by [28] to assess task performance and five items from their situational performance scale, employing a 5-point Likert-type scale (1 = “never” and 5 = “always”). The Cronbach’s alpha coefficient was 0.93.

3.2.4 Conscientiousness personality

We utilized a scale extracted from the seven major personality questionnaires formulated by [62], encompassing six factors: industriousness, order, self-control, responsibility, traditionalism, and virtue. Participants rated the applicability of each characteristic to themselves using a scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The Cronbach’s alpha coefficient was 0.86.

4 Results

4.1 Measurement and model tests

This study intends to use confirmatory factor analysis to test the level of relationship between each factor and its corresponding measurement items, verifying whether the relationship between factors and measurement items corresponds to the hypothesized design relationship of this study and demonstrating the independence of variables [62]. Through confirmatory factor analysis, according to reference standards for fit indices, the χ2/df should be between 1 and 3, the RMSEA should meet a threshold requirement of less than 0.1, and while RMR is influenced by sample size and may not be a focal reference, indices like CFI, NNFI, TLI, and IFI are generally considered satisfactory if greater than 0.9, indicating that the correspondence between factors and measurement items can well explain the hypothesized design relationships of this study and also demonstrate the independence among variables. In the four-factor model constructed in this study, the χ2/df level is 1.357, RMSEA level is 0.028, RMR level is 0.035, and indices such as CFI, NNFI, TLI, IFI are all greater than 0.9, meeting the reference standards for fit indices. The four-factor model exhibits a better fit than three-factor, two-factor, and single-factor models, indicating a high level of independence among research variables and meeting the requirements for testing structural validity [7]. Table 1 presents the validity analysis for our study, with common indicators meeting the established criteria, confirming the robustness of our model.

Table 1 Validity analysis
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In mitigating potential concerns related to common method bias in the survey data, this study uses the adoption of principal component analysis (PCA) as a statistical verification method. Based on the analysis, there are four factors with eigenvalues greater than 1, and the cumulative variance percentage of the first factor is at 38.077%, which is below the critical threshold of 40%. The cumulative variance explained by the four factors exceeds 50%, indicating that there is no issue of common method bias.

4.2 Hypotheses testing

4.2.1 The direct effect

The analysis of path coefficients in the structural equation model reveals significant predictive effects of various AI-Driven Sustainable HRM components on Employee Performance. Specifically, the Ability-enhancing AI-Driven Sustainable HRM demonstrates a positive predictive effect on Employee Performance, with a regression coefficient of 0.292 (p < 0.01). Similarly, the Motivation-enhancing AI-Driven Sustainable HRM exhibits a positive impact on Employee Performance, indicated by a regression coefficient of 0.140 (p < 0.05). Furthermore, the Opportunity-enhancing AI-Driven Sustainable HRM also positively predicts Employee Performance, as evidenced by a regression coefficient of 0.216 (p < 0.01). Additionally, the predictive effects of these AI-Driven Sustainable HRM dimensions on Employee Engagement are noteworthy. The Ability-enhancing, Motivation-enhancing, and Opportunity-enhancing components of AI-Driven Sustainable HRM predict Employee Engagement with coefficients of 0.269 (p < 0.01), 0.196 (p < 0.01), and 0.212 (p < 0.01), respectively. Moreover, there is a significant positive predictive relationship between Employee Engagement and Employee Performance, with a regression coefficient of 0.290 (p < 0.05). Therefore, these findings validate the hypotheses H1a, H1b, and H1c. This following Fig. 2 provide a visual representation of the relationships and their respective strengths.

Fig. 2
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Structural Equation Path Coefficient Graph

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4.2.2 The mediation effect of employee engagement

This study employs the Bootstrap method to test for mediating effects. The investigation of the mediating path “Ability-enhancing AI-Driven Sustainable HRM—Employee Engagement—Employee Performance” revealed that under the Bias-corrected percentile test, the Bootstrap 95% confidence interval for this mediating effect is (0.025, 0.134), excluding zero. This indicates that the positive impact of Ability-enhancing AI-Driven Sustainable HRM on employee performance through employee engagement is significant. Furthermore, the mediating effect is partial, accounting for 16.58% of the total effect, thus lending support to H2a.

Similarly, examining the mediating path “Motivation-enhancing AI-Driven Sustainable HRM—Employee Engagement—Employee Performance” showed that under the Bias-corrected percentile test, the Bootstrap 95% confidence interval for this mediating effect is (0.011, 0.097), excluding zero. This demonstrates that the positive impact of Motivation-enhancing AI-Driven Sustainable HRM on employee performance through employee engagement is significant, with a partial mediating effect accounting for 18.75% of the total effect, thereby lending support to H2b.

Finally, the analysis of the mediating path "Opportunity-enhancing AI-Driven Sustainable HRM—Employee Engagement—Employee Performance" found that under the Bias-corrected percentile test, the Bootstrap 95% confidence interval for this mediating effect is (0.008, 0.113), excluding zero. This confirms that the positive impact of Opportunity-enhancing AI-Driven Sustainable HRM on employee performance through employee engagement is significant. The mediating effect is partial, contributing 15.77% to the total effect, which lends support to H2c.

Table 2 outlines the mediation effect test analysis, detailing the direct, indirect, and total effects along with their respective confidence intervals and effect proportions for the study paths.

Table 2 Mediation effect test analysis
Full size table

4.2.3 The moderation effect of conscientiousness personality

In this research, hierarchical regression model was constructed to investigate the moderating effects. Demographic variables were used as control variables in the model, acknowledging the potential influence of these individual characteristics on participants' perceptions of HRM practices, as suggested by [40]. As for moderating effect of Conscientiousness Personality between Ability-enhancing AI-Driven Sustainable HRM and employee engagement, with employee engagement as the outcome variable, the Years of Experience in the Current Organization (YEO) factor demonstrated a significant positive effect. Ability-enhancing AI-Driven Sustainable HRM significantly predicts employee engagement (β = 0.436***, p < 0.001), as does the individual's Conscientiousness Personality (β = 0.198***, p < 0.001). Moreover, incorporated the interaction term "Ability-enhancing AISHRM-A * Conscientiousness Personality", and the interaction coefficient displayed a significant positive predictive effect (β = 0.130**, p < 0.05). This indicates that Conscientiousness Personality positively moderates the relationship between Ability-enhancing AI-Driven Sustainable HRM and employee engagement. When Conscientiousness Personality levels are high, the positive effect between Ability-enhancing AISHRM and employee engagement is enhanced, and vice versa when Conscientiousness levels are low. Thus, the hypothesis (H3a) that individual Conscientiousness Personality moderates the relationship between Ability-enhancing AI-Driven Sustainable HRM and Employee Engagement is supported.

The moderating effect of Conscientiousness Personality between Motivation-enhancing AI-Driven Sustainable HRM and employee engagement was also analyzed. With employee engagement as the outcome variable, the Position/Role in the Organization (PRO) factor had a significant positive effect. Motivation-enhancing AI-Driven Sustainable HRM significantly predicted employee engagement (β = 0.444***, p < 0.001), as did Conscientiousness Personality (β = 0.204***, p < 0.001). The inclusion of the interaction term "AISHRM-M * Conscientiousness Personality", with the interaction coefficient showing a significant positive predictive effect (β = 0.315***, p < 0.05). This suggests that Conscientiousness Personality positively moderates the relationship between Motivation-enhancing AI-Driven Sustainable HRM and employee engagement, enhancing the positive effect when Conscientiousness Personality levels are high and diminishing it when they are low. Hence, the hypothesis (H3b) that individual Conscientiousness Personality moderates the relationship between Motivation-enhancing AI-Driven Sustainable HRM and Employee Engagement is validated.

Lastly, the analysis of the moderating effect of Conscientiousness Personality between Opportunity-enhancing AI-Driven Sustainable HRM and employee engagement, with employee engagement as the outcome variable, the Years of Experience in the Current Organization (YEO) factor had a significant positive impact. Opportunity-enhancing AI-Driven Sustainable HRM significantly predicted employee engagement (β = 0.461***, p < 0.001), as did Conscientiousness Personality (β = 0.196***, p < 0.001). The inclusion of the interaction term "AISHRM-O * Conscientiousness Personality", with the interaction coefficient showing a significant positive predictive effect (β = 0.360***, p < 0.05). This indicates that Conscientiousness Personality positively moderates the relationship between Opportunity-enhancing AI-Driven Sustainable HRM and employee engagement, enhancing the positive effect when Conscientiousness Personality levels are high and reducing it when they are low. Therefore, the hypothesis (H3c) that Conscientiousness Personality moderates the relationship between Opportunity-enhancing AI-Driven Sustainable HRM and Employee Engagement is confirmed.

5 Discussion

The study endeavors to bridge a crucial knowledge void within the extant literature regarding how organizational systems and practices can harness artificial intelligence and sustainability principles to foster employee involvement in sustainable development behaviors via latent psychological mechanisms. This research rigorously interrogates the impact of AI-driven sustainable Human Resource Management (HRM) strategies on employee performance and engagement. It asserts that AI-driven sustainable HRM practices bolster employee performance by enhancing work capabilities, stimulating motivation, and engendering developmental opportunities, which in turn, influence performance through the mediating role of employee engagement. Moreover, the study integrates the Person-Organization (P-O) fit theory, examining how the congruence between AI-driven sustainable HRM practices and employees' personal values fortifies this engagement.

The constructs of Ability, Motivation, and Opportunity, when augmented with AI and sustainability considerations, positively impact both engagement and performance, affirming hypotheses H1a, H1b, and H1c. This implies that AI's role in HRM transcends mere operational efficiencies, deeply influencing the behavioral dynamics of the workforce in a manner that resonates with the AMO framework and sustainability objectives. The synergy of AI with sustainable HRM practices has been empirically evidenced to sharpen employee abilities through personalized learning experiences, motivate through immediate feedback, and create opportunities by streamlining work processes [18, 52, 73].

The observed partial mediation effects of employee engagement indicate that AI-driven sustainable HRM not only directly influences performance but also imparts a significant indirect effect through engagement. This nuanced pathway accentuates the intricacies inherent in the antecedents of employee performance, advocating for a comprehensive approach to HRM practices that exploit the full potential of AI [74]. The role of engagement in this context is particularly salient, embodying psychological states of vigor, dedication, and absorption, which are critical for optimal employee performance [12, 66]. The mediation effects highlight the substantial role that engagement occupies within the performance landscape.

The moderation analysis underscores that Conscientiousness Personality strengthens the nexus between the AI-driven sustainable HRM facets and employee engagement. This corroborates hypotheses H3a, H3b, and H3c, lending empirical support to the proposition that individual variances can modulate the impact of HR practices. The conscientiousness trait, indicative of thoroughness and a strive for accomplishment, synergizes with AI-driven sustainable HRM to enhance engagement, acting as a lever that magnifies the positive influences of AI-driven sustainable HRM [53]. This interplay suggests that the integration of AI into HRM, attentive to the dispositional elements of the workforce, is essential to fully capitalize on its advantages and to promote a sustainable organizational ethos [72]. This phenomenon may be ascribed to the complex interplay between the cultural norms, beliefs, and values of participants and their approach to interpreting role expectations, adapting to dynamic business environments, and discerning the nuances of HRM practices and organizational strategies, as postulated by [34]. Put differently, the way individuals perceive and make sense of an organization's initiatives toward the environment is likely to be influenced by their unique cultural frameworks, which encompass diverse norms, standards, and traditions. Collectively, the empirical evidence from this study corroborates the initial hypotheses, thereby enriching both the theoretical foundations and practical applications concerning the role of AI in HRM behavioral research.

5.1 Theoretical implications

This study marks a significant contribution to the field of Human Resource Management (HRM), especially in the realm of AI-driven Sustainable HRM. It delves into the relationship between AI-driven Sustainable HRM and employee performance, thereby extending the research landscape previously highlighted by [14, 18, 59],. Grounded in the Ability-Motivation-Opportunity (AMO) framework [6, 16], this research moves beyond the existing literature’s predominant focus on AI in talent analytics and routine HR processes to explore the wider implications of AI in HRM, particularly regarding employee well-being and performance.

In investigating the AMO framework within the AI-driven Sustainable HRM context, the study not only corroborates but also extends upon the findings of previous research such as that by [25, 46]. It offers a comprehensive understanding of how AI-driven SHRM practices can collectively enhance employee abilities, motivation, and opportunities, thereby positively affecting performance. Furthermore, the study addresses the evolving understanding of employee engagement as a critical mediator in the AI-driven Sustainable HRM and employee performance relationship. It situates employee engagement within this new context, enriching the existing discourse on this construct as illustrated by [2, 56, 66],. This research highlights that employee engagement, influenced by both organizational and individual factors and considered as a multidimensional construct, is vital in enhancing employee performance. This approach builds upon and adds to the work of [79].

Additionally, the study aligns with the Person-Organization (P-O) Fit Theory, exploring the role of individual personality traits, especially conscientiousness, in moderating the relationship between HR practices and employee engagement [1, 61]. This perspective offers a nuanced understanding of the psychological and social dynamics within AI-driven sustainable HR environments. A pioneering aspect of this research is its exploration of AI-driven sustainable HR practices and individual behavior in the Chinese context, addressing a research gap identified by [52, 73], and expanding the understanding of environmental sustainability within organizational settings both regionally and globally.

Overall, this research not only fills identified gaps in the literature but also presents a multidisciplinary perspective on AI-driven Sustainable HRM. It significantly contributes to the theoretical understanding of the interplay between technology, psychology, and environmental sustainability in HRM. This study provides valuable insights for academic research, emphasizing the importance of integrating AI into sustainable HR practices to effectively enhance employee well-being and performance.

5.2 Practical implications

Integrating the practical implications of this study on AI-driven Sustainable Human Resource Management (HRM) with the global trend towards sustainability and environmentally-conscious strategies, it's clear that the exploration of AI's role in sustainable HRM is both timely and significant [81]. As organizations increasingly strive to align their operations with global sustainability objectives, AI presents a unique opportunity to modernize HR practices while fostering an eco-aware workspace. This resonates with the principles of Industry 5.0, where the collaboration between humans and machines is emphasized, and technology is harnessed to enhance human potential rather than replace it [41]. This alignment with broader societal trends underscores the relevance and criticality of this study for future organizational success.

The positive effects of AI-driven SHRM on employee abilities, motivation, and opportunities necessitate that organizations strategically implement AI technologies in their HR processes. By utilizing AI to create personalized employee development programs and real-time recognition and feedback systems, companies can foster a culture of continuous learning and increase job satisfaction. The critical role of employee engagement as a mediator between AI-driven Sustainable HRM and employee performance points towards the importance of using AI to analyze employee feedback and engagement levels. This will enable organizations to tailor their HR initiatives to better align with employee needs and expectations. Moreover, the study highlights the role of conscientiousness in moderating the relationship between HR practices and employee engagement. Organizations should therefore consider personality traits when designing AI-driven HR practices, ensuring that these initiatives resonate effectively with employees, enhancing their engagement and performance. The exploration of AI-driven sustainable HR practices within the Chinese context provides specific implications for businesses in emerging markets. Understanding regional differences in employee expectations and sustainability concerns is crucial when implementing AI-driven HR practices in these regions.

The practical implications of this study advocate for a strategic, personalized, and culturally sensitive approach to implementing AI-driven Sustainable HRM. This approach is not only in line with current global sustainability trends but also crucial for the future success of organizations in a rapidly evolving world.

5.3 Limitation and directions to future research

The current study, while providing valuable insights into AI-driven Sustainable Human Resource Management (HRM), presents certain limitations that pave the way for future research directions.

Firstly, the study's reliance on cross-sectional data limits its ability to establish causality or to capture the dynamic nature of AI integration in HRM practices over time. An important avenue for future research would be to undertake longitudinal studies. Such studies would enable researchers to track changes and developments in HRM practices and employee responses over extended periods, providing a more nuanced understanding of the long-term effects and sustainability of AI-driven HRM interventions.

Secondly, the research’s focus on a single country—China context, while beneficial for in-depth analysis, raises questions about the generalizability of the findings. Future research should aim to replicate and extend this study in diverse geographical and cultural settings. Comparative studies across multiple countries or regions would be particularly valuable, as they could reveal how different cultural, economic, and regulatory environments influence the effectiveness and acceptance of AI-driven Sustainable HRM practices.

Thirdly, the sustainability dimensions of AI-Driven HRM in this study were explored primarily from the AMO perspective. While this approach offers a robust framework for examining HR practices, it may not fully encapsulate the multidimensional nature of sustainability. Future research could adopt a more comprehensive approach by incorporating the three pillars of sustainability—economic, environmental, and social [69]. This entails the categorization of HRM practices into economic, environmental, and social domains. The investigation revolves around assessing the impact of each category on both employee and organizational outcomes.

Fourth, we acknowledge potential improvements in our measurement and research design. Firstly, although we collected data from managers and employees at different time points to reduce common method bias [55], the interval of 2 weeks may have been too short. The complexity of managing multi-wave data collection and the reluctance of many participating organizations in this study to respond over longer time lags, such as 3 months, posed challenges. Additionally, as we measured both employee conscientiousness personality and employee engagement, as well as employee performance simultaneously, we cannot make causal inferences. Future research could employ a longitudinal design to test these relationships, gathering mediator and employee performance data at different time points.