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Article

The Influence of Sustainable Human Resource Management Practices on Logistics Agility: The Mediating Role of Artificial Intelligence

by
Sayeda Jahangir
1,
Ruhe Xie
1,*,
Amir Iqbal
2,* and
Muttahir Hussain
1
1
School of Management, Guangzhou University, Guangzhou 510006, China
2
School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(7), 3099; https://doi.org/10.3390/su17073099
Submission received: 10 March 2025 / Revised: 27 March 2025 / Accepted: 28 March 2025 / Published: 31 March 2025
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Abstract

:
Adopting innovative, systematically structured, and sustainable human resource management (SHRM) practices is essential for enhancing logistics agility and deriving sustainable development in logistics operations. This study examines the influence of sustainable human resources management practices on logistics agility with a mediating role of artificial intelligence (AI) in China’s logistics industry. Given the rapid growth and technological advancements in China’s logistics sector, this study employed quantitative research and convenience sampling techniques to collect data from 341 employees working in the industry. Smart PLS was used to test the proposed hypotheses through structural equation modeling (SEM). The study’s findings reveal that reward management, training and development, job appraisal, and teamwork significantly enhance logistics agility, while recruitment and selection show an insignificant impact. Similarly, the results reveal that sustainable HRM practices and artificial intelligence positively and significantly influence logistics agility. In addition, artificial intelligence substantially mediates the relationship between sustainable HRM practices and logistics agility. These findings offer valuable insights for HRM and logistics management, highlighting how AI can strengthen sustainable HRM practices to foster agility and improve logistics performance. The findings are particularly relevant for practitioners and policymakers aiming to enhance sustainability and efficiency in the logistics sector.

1. Introduction

Early in the 21st century, globalization and outsourcing reshaped the logistics industry, emphasizing the critical role of supply chain management (SCM) in achieving competitive advantages [1]. As a fundamental pillar of global trade, logistics ensures the smooth flow of goods and services, enabling businesses to meet rising demands for faster, more efficient, and responsive operations [2]. The industry has endured substantial changes in the past few decades driven by globalization, technological advancements, and evolving customer expectations [3]. As logistics becomes increasingly vital to global trade, its market value has surged, with the Asia–Pacific region, particularly China, emerging as a dominant player.
According to Allied Market Research [4], the global logistics market was valued at USD 9.833 trillion in 2022 and is expected to reach USD 16.794 trillion by 2032, with a compound annual growth rate of 5.6%. The Asia–Pacific region dominates the market, with China leading due to rapid growth in cross-border e-commerce, industrial activities, and trade. In 2023, China’s logistics market grew to RMB 353 trillion, with total industry revenue reaching RMB 13.2 trillion, a 3.9% yearly upsurge [5]. Despite this growth, inefficiencies persist in China’s logistics sector, as evidenced by China’s logistics expenditure accounting for 14.4% of GDP, underscoring the need for optimization to achieve the target of reducing this figure to 12% by 2025 [6]. This disparity highlights the urgent need for optimization, requiring not only technological innovations but also sustainable human resource management to build a more agile workforce capable of addressing these gaps.
Human resource management is a strategic approach or process aimed at optimizing the use of human resources to achieve organizational objectives [7]. In the logistics sector, HRM practices such as recruitment, training, reward management, job appraisal, and teamwork are crucial in improving logistics agility, defined as an organization’s ability to respond swiftly and efficaciously to supply chain disruptions and changing market demands [8,9]. However, China’s logistics industry faces persistent challenges from a critical skills shortage that hampers both efficiency and innovation.
These challenges highlight the growing importance of sustainable HRM practices, referring to the implementation of human resource management strategies and methods that balance financial, social, and ecological objectives both within and outside an organization over a long-term horizon [10,11]. These practices aim to minimize negative feedback and unintended side effects while ensuring the sustained well-being of employees, society, and the environment [12,13]. Research consistently identifies the shortage of trained logistics professionals and insufficient expertise as major barriers to logistics efficiency in China [14,15], emphasizing how sustainable HRM practices can address these workforce gaps while fostering the adaptable, responsive teams needed in modern logistics [16,17]. Against this backdrop, the current study investigates the impact of sustainable HRM practices on logistics agility with a mediating role of artificial intelligence.
Artificial intelligence has emerged as a transformative solution to these dual challenges. AI has transformed the logistics industry, offering firms the capability to enhance productivity, optimize operational costs, and ensure long-term sustainability [18,19]. AI-driven technologies, such as route optimization, predictive analytics, and autonomous systems, have streamlined logistics operations and significantly improved decision-making [20,21,22]. AI enhances sustainable HRM through improved recruitment, skills development, and workforce adaptability [23]. By automating complex processes and providing real-time data insights, AI empowers firms to respond swiftly to fluctuations in demand and mitigate supply chain disruptions [24]. Despite these demonstrated benefits, the synergistic potential between AI and sustainable HRM in logistics remains significantly underexplored, presenting a critical research opportunity.
Previous studies have examined HRM practices, such as recruitment and selection, reward management, training and development, job appraisal, and teamwork in isolation, overlooking their synergetic impact on logistics agility. While AI’s logistical applications have been widely recognized in recent years, its mediating role between sustainable HRM practices and logistics agility lacks empirical scrutiny. Studies by Ding, Kam [25], Yu and Guo [26], and Haq, Gu [27] have shown the importance of HRM practices in enhancing operational efficiency in China’s logistics and supply chain sector. However, these studies lack a rigorous investigation of the intersection between HRM practices and AI. Therefore, in the present study, the authors aim to address this gap by answering the following key research questions:
RQ1: 
How do HRM practices, such as recruitment and selection, reward management, training and development, job appraisal, and teamwork, influence logistics agility within China’s logistics industry?
RQ2: 
Does artificial intelligence contribute to enhancing logistics agility in China?
RQ3: 
How does artificial intelligence mediate between HRM practices and logistics agility?
This research addresses a significant gap in the existing literature and makes several key contributions. First, in this study, we examine the influence of sustainable HRM practices such as recruitment and selection, reward management, training and development, job appraisal, and teamwork on logistics agility in China’s logistics industry. While prior studies have explored these HRM practices in other contexts, their collective impact on logistics agility in China’s unique logistics industry remains underexplored. Second, this study introduces artificial intelligence (AI) as a mediator variable, providing a novel and deeper understanding of the mechanisms through which sustainable HRM practices enhance logistics agility. By integrating AI into the framework, this research offers an innovative perspective on the interplay between HRM practices and logistics agility. Third, the study evaluates these factors’ individual and combined effects, shedding new light on the intersection of sustainable HRM practices and logistics agility, particularly through the lens of AI. Finally, the findings advance theoretical knowledge while providing practical insights for optimizing workforce strategies and technological investments to improve logistics agility and competitive advantage. This research also enriches academic discourse and establishes groundwork for future studies exploring the nuanced relationships between sustainable HRM practices, AI, and logistics agility.
This article is structured systematically to ensure clarity and ease of navigation for readers. The second part provides a comprehensive review of the existing empirical literature. The third part delineates the research methodology, detailing the approach and techniques used to collect and analyze data. The findings are presented, analyzed, and interpreted in the fourth part, offering insights into the relationships explored in the study. Finally, the fifth part concludes the article, presents actionable recommendations for policymakers, and highlights implications for future research.

2. Literature Review

This research examines the role of sustainable human resource management (SHRM) practices in influencing logistics agility, which is defined as an organization’s capacity to respond promptly and effectively to shifts in supply chain demands, disruptions, or market conditions. Key sustainable HRM practices, such as recruitment and selection, reward management, training and development, job appraisal, and teamwork, are recognized as critical factors that directly impact logistics agility. While prior research has explored these variables, their specific roles and mechanisms in enhancing logistics agility remain inadequately understood. Through this literature review, we synthesize the existing studies to clarify the associations between these sustainable HRM practices and logistics agility. Accordingly, we aim to build a better understanding of how they collectively contribute to an organization’s adaptive capabilities in dynamic supply chain environments.

2.1. Recruitment, Selection, and Reward Management

Recruitment and selection involve identifying, attracting, and hiring qualified individuals to fill organizational roles, ensuring the right talent aligns with business needs [28]. In the logistics sector, effective recruitment and selection are critical for building a skilled workforce capable of managing complex supply chains and adapting to dynamic operational demands [9]. Logistics companies can enhance their agility by hiring employees with relevant expertise and problem-solving abilities, enabling faster responses to disruptions, improved operational efficiency, and better customer service. A well-structured recruitment process ensures the availability of talent proficient in advanced technologies and innovative practices, further strengthening logistics agility [29]. Ultimately, strategic recruitment and selection are pivotal in enabling a company to sustain its competitive edge and resilience in a fast-changing industry. Understanding the role of recruitment and selection in shaping logistics agility is crucial. A significant body of research highlights that effective recruitment and selection practices, designed to attract and onboard employees with relevant expertise and strong problem-solving skills, enable logistics companies to significantly enhance their operational agility [30,31]. A study by Ding, Kam [25] investigated the effect of HRM practices on logistics and supply chain competencies in 76 logistics service providers in China. Through hierarchical multiple regression analysis, the authors found that recruitment and selection practices significantly enhanced L&SC competencies. However, earlier research highlights that many Chinese companies do not have structured evaluation systems for recruiting and retaining skilled and professional staff [32,33]. In contrast, informal practices, such as leveraging (personal connections) and word-of-mouth referrals, are frequently employed in recruitment processes [34]. This suggests a gap between formal HRM practices and the informal methods commonly utilized in the Chinese context.
Reward management involves creating and executing systems to recognize and compensate employees for their contributions, aligning their efforts with organizational goals [35,36]. In the logistics sector, effective reward management motivates employees to deliver their best performance, fostering higher productivity and commitment [37]. Reward systems encourage employees to embrace agile practices and respond proactively to challenges by incentivizing innovation, efficiency, and adaptability. This enhances operational flexibility and resilience, key components of logistics agility. Ultimately, a well-structured reward management system drives employee engagement and retention, ensuring a skilled workforce capable of sustaining a competitive advantage in a rapidly evolving industry [38]. A study by Ding, Kam [25] investigated the effect of HRM practices on logistics and supply chain competencies in 76 logistics service providers in China. Their hierarchical multiple regression analysis showed that reward management insignificantly contributed to the L&SC competencies. Menon [39] investigated the impact of human resource practices on supply chain performance, taking data from 228 supply chain professionals. His study revealed that rewards significantly boost supply chain performance. According to prior research, effective recruitment and selection processes and appropriate reward management systems can substantially enhance employee performance, contributing to increased logistics agility. Therefore, the following hypotheses are proposed:
H1. 
Recruitment and selection positively impact logistics agility.
H2. 
Reward management positively impacts logistics agility.

2.2. Training and Development, Job Appraisal, and Teamwork

Logistic firms should prioritize training as a core strategy to enhance employees’ learning capabilities, enabling them to acquire, internalize, and apply knowledge effectively [40]. Training equips employees with essential communication, IT, and interpersonal skills, fostering collaboration and seamless information sharing within the organization and across supply chain networks [41]. Training in advanced technologies, inventory management, and process optimization enables employees to respond swiftly to disruptions, improving logistics agility [42]. By addressing supplier- and customer-specific needs, training strengthens network relationships and facilitates the exchange of critical market- and logistics-related information. Skilled employees can then disseminate this knowledge across the organization, improving responsiveness and decision-making [27]. Ultimately, training programs cultivate versatile, adaptable, and well-connected employees, which are vital for achieving logistics agility and sustaining a competitive position. Within the logistics industry, training and development are indispensable for enhancing workforce capabilities in areas such as technology adoption, process optimization, and problem solving [43]. Continuous investment in training enables logistics firms to boost operational efficiency, reduce errors, and foster innovation. A well-trained workforce is better equipped to handle disruptions, implement agile strategies, respond swiftly to market changes, streamline operations, and implement innovative solutions, all of which contribute to enhanced agility in logistics. A study by Menon [39] involving data from 228 supply chain professionals highlighted that training significantly improves supply chain performance, underscoring its importance in driving operational excellence and competitiveness. Numerous studies have demonstrated that training in areas such as problem-solving, team building, and job-specific skills significantly enhances the effectiveness of supply chain management (SCM) practices [44,45].
Job appraisal involves the evaluation of employee performance against predefined standards or objectives [46]. Regular and constructive appraisals provide valuable insights into employees’ strengths and development areas, enabling them to align their contributions with organizational goals [47]. In logistics, performance appraisals can focus on delivery accuracy, turnaround time, and cost efficiency [48]. Providing feedback and setting clear expectations during job appraisals can motivate employees to improve their performance, directly impacting logistics operations’ efficiency and adaptability. This, in turn, fosters greater logistics agility by ensuring that employees are consistently working toward optimizing supply chain processes [49]. Job appraisal aligns employee performance with organizational goals, driving continuous improvement in logistics processes.
Teamwork emphasizes employee collaboration and coordination to achieve common objectives [50]. Effective teamwork is critical in logistics, where multiple functions such as procurement, transportation, warehousing, and distribution must work in harmony. Collaborative teams can share knowledge, resolve issues quickly, and adapt to changes more efficiently. For example, cross-functional teams can address supply chain disruptions more effectively by pooling their expertise and resources. Strong teamwork enhances communication, reduces delays, and improves decision-making, all of which are essential for achieving logistics agility. Teamwork fosters cross-functional collaboration and communication, reducing barriers to information and knowledge sharing across organizational units [51]. Teamwork creates a collaborative culture that enhances learning from suppliers and customers, enabling the identification of supply and demand information critical for innovation [52]. By working together, team members can jointly acquire and utilize knowledge from supply chain partners, improving their ability to identify new opportunities [53]. A study by Menon [39] investigated the impact of human resource practices on supply chain performance, taking data from 228 supply chain professionals. His study revealed that teamwork significantly boosts supply chain performance. Thus, teamwork is vital in enhancing knowledge acquisition and fostering agility in supply chain operations. Collectively, these practices cultivate a skilled, motivated, and collaborative workforce, all of which are critical for achieving and sustaining logistics agility. By prioritizing investments in these areas, organizations can strengthen their capacity to adapt to dynamic environments, maintain operational efficiency, and secure a competitive edge in the marketplace. Based on this rationale, the following hypotheses are proposed:
H3. 
Training and development positively impacts logistics agility.
H4. 
Job appraisal positively impacts logistics agility.
H5. 
Teamwork positively impacts logistics agility.

2.3. Sustainable HRM Practices

Sustainable HRM represents an evolution in human resource management, defined as “the strategic adaptation of HRM practices to achieve simultaneous financial, ecological, and social objectives across extended time horizons while mitigating negative externalities and unintended consequences” [10,11]. The theoretical foundation for this approach is strengthened by the Paradox Theory Framework developed by Ehnert and Harry [54]. This theoretical perspective explains how sustainable HRM practices must navigate competing demands between immediate operational needs and long-term sustainability goals, with these tensions serving as potential catalysts for strategic innovation in human resource practices. HRM practices are crucial in improving logistics agility, defined as an organization’s ability to respond swiftly and efficaciously to supply chain disruptions and changing market demands [8]. As human resources are universally accepted as a core element of organizational success, contemporary firms prioritize attracting and retaining top talent to meet strategic objectives [55]. However, having human resources is not enough to ensure success; ineffective HRM practices can diminish employee satisfaction and commitment [25], ultimately hindering a firm’s capacity to meet its performance objectives [56]. Research by Fong and Ooi [57] highlights that specific HRM practices, such as recruitment and selection, teamwork, training and development, and performance appraisal, foster knowledge sharing and indirectly enhance the performance of manufacturing and service organizations in Malaysia. Similarly, Haq and Asadullah [58] explored the interplay between HRM, IT, supply chain learning, and operational performance in 213 Chinese manufacturing firms. Their findings revealed that HRM enhances all three dimensions of SCL. This study highlights five vital HRM practices: recruitment and selection, reward management, training and development, performance appraisal, and teamwork. These five are highlighted due to their direct relevance to attracting and retaining a capable logistics workforce. The existing literature suggests that a workforce equipped with the right talent and skills is a key driver of service competencies and logistics performance. By addressing these HRM practices, this study tackles one of the most pressing challenges faced by the Chinese logistics industry. HRM practices, particularly those focused on soft skill development, planning, communication, employee exchange programs with partners, and performance evaluations tied to system outcomes, serve as critical organizational mechanisms. These practices foster the development of specialized human capital, which in turn drives improved organizational performance, agility, and profitability [39,59]. Together, these practices foster a skilled, motivated, and collaborative workforce, which is essential for achieving and sustaining logistics agility in highly competitive markets. Based on this discussion, the following hypotheses are proposed:
H6. 
Sustainable HRM practices positively impact logistics agility.
H7. 
Sustainable HRM practices positively impact artificial intelligence.

2.4. Nexus Between Artificial Intelligence and Logistics Agility

In the 21st century, artificial intelligence has gained prominence as a vital research topic, applying to many industries, including logistics. AI enhances productivity, optimizes costs, and supports firms’ enduring sustainability and expansion [18,19]. AI has revolutionized logistics operations by enabling machines to learn from experience, adjust to new inputs, and carry out human-like tasks, allowing businesses to make data-driven decisions, automate processes, and improve overall efficiency [20,21]. One of AI’s most impactful logistics applications is data management and analysis. Purchasing departments produce enormous amounts of data, yet these valuable resources often remain underutilized due to resource constraints or a lack of analytical capabilities [60]. Organizations are increasingly investing in AI technologies to address this challenge to extract meaningful insights, optimize procurement decisions, and enhance supply chain visibility [61]. Warehouse and inventory management represent another critical area where AI contributes to logistics agility [62,63]. Efficient inventory flow from warehouses is essential for seamless supply chain operations. To enhance accuracy and responsiveness, AI-driven models such as artificial neural networks (ANNs) can predict demand patterns and optimize stock levels. These AI techniques help to identify and analyze inventory trends throughout the supply chain, improving forecasting precision and reducing operational inefficiencies [62].
AI also plays a significant role in sustainable transport management. By leveraging AI-driven solutions, firms can mitigate congestion, enhance travel reliability, and improve overall economic and operational productivity [64,65]. Beyond operational efficiency, AI-powered information technology (IT) facilitates digital transformation, enabling organizations to navigate hypercompetitive environments [66]. IT-driven digital capabilities allow firms to adapt quickly to market shifts, enhance internal and external communication, and foster knowledge sharing among stakeholders, all contributing to logistics agility. Prior research has demonstrated that IT-enabled agility enhances decision-making by improving the speed and quality of responses to market fluctuations. AI is a strategic enabler of logistics agility by fostering an organization’s ability to sense and seize business opportunities, respond to threats, and adapt to external and internal changes. Therefore, this study hypothesizes the following relationship:
H8. 
Artificial intelligence positively impacts logistics agility.

2.5. Mediating Role of Artificial Intelligence in Sustainable HRM Practices and Logistics Agility

Embracing digital technologies has become essential for logistics companies seeking to optimize their operations. Among these technologies, incorporating artificial intelligence into human resource management is reshaping recruitment, workforce management, and strategic decision-making processes. AI-powered HRM practices streamline hiring processes and cultivate a more adaptive and skilled workforce, enhancing an organization’s logistics agility [23]. HRM practices have transitioned from traditional administrative functions such as recruitment, training, and performance management to a more strategic role that leverages AI-driven analytics and automation. Incorporating AI into HRM enables organizations to make data-driven decisions, automate repetitive tasks, and personalize employee experiences. This shift results in greater workforce management efficiency, accuracy, and agility [67]. AI-powered recruitment tools can precisely match candidates’ skills with job requirements, improving hiring outcomes and reducing employee turnover [68]. Similarly, AI-driven performance analytics enhance employee engagement and productivity, bolstering organizational resilience and adaptability. Ultimately, the integration of AI into HRM generates significant cost savings and operational efficiencies.
AI is considered a revolutionary technology in logistics, capable of performing cognitive functions traditionally associated with human intelligence, like learning, problem-solving, and real-time decision-making. AI enhances logistics agility by enabling real-time monitoring, predictive analytics, and the automation of supply chain processes [24]. Facilitating seamless communication between machines and systems, AI ensures uninterrupted logistics operations and enhances service quality, including on-time product delivery and efficient resource allocation [8,69]. Notably, AI acts as a bridge between HRM practices and logistics agility. By augmenting HRM strategies with AI-driven insights and automation, organizations can develop a workforce more responsive to dynamic logistics demands. AI-powered HRM fosters agility by empowering employees with essential expertise, predictive capabilities, and adaptive decision-making tools to effectively address logistical challenges. AI not only enhances HRM efficiency but also acts as a catalyst for achieving agility in logistics. Thus, this study hypothesizes the following relationships:
H9. 
Artificial intelligence mediates the relationship between sustainable HRM practices and logistics agility.

3. Research Methodology

3.1. Research Setting

The study was conducted in China’s logistics industry, which plays a crucial role in the country’s economic development. As a rapidly expanding industry, logistics drives economic growth through capital investment, fostering technological innovation, and creating employment opportunities that benefit millions of people both directly and indirectly. However, the sector faces significant challenges, including excessive energy use, resource exhaustion, environmental degradation, and waste production, which must be addressed to optimize logistics processes and ensure sustainable growth. Logistics agility is essential for China’s economic stability and global trade dominance, particularly as the world’s largest exporter and a key manufacturing hub. Efficient logistics enable supply chain resilience, empowering businesses to adapt promptly to health crises or political instability. Advanced infrastructure, digitalization, and AI-driven systems enhance operational efficiency, reduce costs, and shorten delivery times, supporting domestic consumption e-commerce growth.
Sustainable HRM practices are critical in enhancing logistics agility by developing a skilled, adaptable, and motivated workforce. Through effective recruitment, training, reward, performance management, and teamwork, HRM ensures employees can navigate complex supply chains and leverage advanced technologies. HRM strengthens China’s competitive edge in global trade and supply chain resilience by fostering continuous improvement and aligning workforce capabilities with logistical demands. Therefore, the current study tests the impact of sustainable HRM practices on logistics agility through a mediating role of artificial intelligence in China’s logistics industry.

3.2. Population and Sample

The study targeted employees across various management levels (lower, middle, and senior management) within logistics companies. The study employed a non-probability convenience sampling strategy to identify companies for data collection, offering greater accessibility and adaptability in reaching participants from diverse regions and demographic backgrounds. While convenience sampling may pose limitations in terms of generalizability, its use in this study was justified due to practical constraints, including resource limitations and time restrictions within the participating organizations [70,71]. As a comprehensive list of logistics companies and their employee details was inaccessible, an initial target sample size of 400 was chosen to align with the analytical needs of partial least squares structural equation modeling (PLS-SEM). To confirm the least possible necessary sample size, the G*Power tool was utilized, taking into account key parameters such as effect size (f2 = 0.15), error probability (α = 0.05), number of predictors (3), and power (0.80). The analysis revealed that at least 98 respondents were necessary to satisfy these criteria [72,73]. A total of 341 valid questionnaires were obtained from the target group, enabling comprehensive data analysis and allowing us to address any uncertainties about sample size adequacy.

3.3. Data Collection

As illustrated in Figure 1, this research investigates the influence of sustainable HRM practices on logistics agility with the mediating role of artificial intelligence. Quantitative data were gathered via self-administered structured questionnaires targeting employees across China’s top 60 logistics companies. After excluding companies with invalid contact details, 50 companies were retained for the final survey. Respondents were selected from three hierarchical levels (lower, middle, and upper management) within each participating company to capture diverse organizational perspectives. Consistent with methodologies employed in prior studies [74,75], the current research adopted a convenience sampling approach for respondent selection. While convenience sampling is often criticized for potential selection bias and limited generalizability due to its non-probabilistic nature, it was deemed appropriate for this exploratory research for several reasons. First, the method provided a cost- and time-effective means of data collection, particularly given the resource constraints typical of large-scale organizational studies. Second, as the study prioritizes hypothesis generation and relationship identification over population-level inferences, the trade-off between practicality and representativeness was justified. Third, by stratifying responses across management levels, the approach mitigated single-respondent bias and enabled us to conduct a more nuanced analysis of how sustainable HRM practices are perceived and implemented at different organizational echelons. Most importantly, acknowledging the limitations of convenience sampling, particularly its potential to skew results due to unrepresentative participation, this methodological choice aligned with the study’s exploratory objectives.
The study involved distributing 400 questionnaires to employees in Chinese logistics firms. Respondents were contacted in person or through e-mail, as per their preference, and asked to contribute to the research. The data-gathering phase spanned from September to December 2024, with necessary permissions secured from the participating firms. At the end of the four months, 352 completed questionnaires were received, achieving an 88% response rate. After a rigorous quality check, 341 valid responses were included in the final analysis. A pilot test was carried out using a Smart-PLS 4 statistical software before the primary survey to evaluate the questionnaire’s clarity and comprehensibility. Insights from the pilot study played a key role in refining the survey instrument, enhancing the precision and reliability of the final data.
Building on this robust dataset, we employed a research model presented in Figure 1, which incorporates five core components of sustainable HRM practices, such as recruitment and selection, reward management, training and development, job appraisal, and teamwork. Our analytical process utilized structural equation modeling in three sequential phases: First, H1–H5 were used to examine the individual effects of each HRM component on logistics agility. Second, H7 was used to evaluate the combined impact of all HRM components. Finally, H9 was used to investigate AI’s mediating role between sustainable HRM practices and logistics agility, completing our comprehensive assessment of these relationships.

3.4. Common Method Bias

The study utilized cross-sectional data, which carry the risk of common method bias (CMB), potentially threatening the validity of the findings. Two established methods were employed to address this issue: the full collinearity assessment test [76] and Harman’s single-factor test [77]. The full collinearity test demonstrated that all the variance inflation factor values were below the threshold of 5, confirming the absence of multicollinearity issues. Harman’s single-factor test showed that the first factor explained 46.3% of the total variance, below the 50% benchmark [75]. These findings demonstrate that common method bias is not a significant concern in this research.

3.5. Variable Items

The variables employed in this research were primarily drawn from established and validated studies in the field, with necessary modifications to suit the specific research context. A structured questionnaire survey was designed to assess these variables using a five-point Likert scale ranging from ’strongly agree’ (5) to ’strongly disagree’ (1). The study aimed to examine the influence of sustainable HRM practices on logistics agility with the mediating role of artificial intelligence. The questionnaire was structured to gather statistical data and respondents’ views on sustainable HRM practices and their influence on logistics agility. It consisted of two sections: Section 1 focused on gathered demographic information about the participants, and Section 2 included closed-ended questions designed to explore insights, perspectives, and understanding of sustainable HRM practices, including recruitment and selection, reward management, training and development, job appraisal, and teamwork, along with artificial intelligence and logistics agility.
The sustainable HRM practices were operationalized across five dimensions: recruitment and selection, reward management, training and development, job appraisal, and teamwork. Specifically, six items for recruitment and selection were adapted from [25,57,78,79]; four items for reward management were adapted from [16,25,80]; three items for training and development were adapted from [16,25,39,78]; three items for job appraisal were adapted from [16,25,78]; and four items for teamwork were adapted from [27,81]. Additionally, three items for artificial intelligence were adapted from [8,82], and three items for logistics agility were adapted from [8,83]. The questionnaire survey was administered to gather responses on the items detailed in Table 1, ensuring a comprehensive assessment of the constructs under investigation.

3.6. Demographics

The demographic characteristics of the respondents are summarized in Table 2, presenting a detailed snapshot of the workforce in the logistics companies surveyed. The findings revealed that most respondents are male, representing 63.34% of the sample, while female respondents account for 36.65%. This gender distribution highlights a notable male predominance in logistics, which may reflect broader industry trends. Regarding age distribution, most respondents fall within the 31–40 age group, representing 48.68% of the sample. Younger employees aged 20–30 constitute 29.03% of respondents, and those aged 41 and above represent 22.28% of the sample. This age distribution suggests that the logistics workforce is relatively young–middle-aged, with a significant portion of employees in their prime working years, which may contribute to higher levels of energy, adaptability, and productivity. Regarding educational qualifications, most respondents hold a BA/BS degree, accounting for 43.40% of the sample. This is followed by 26.97% of respondents with a master’s degree, 20.52% with an MS/MPhil degree, and 9.09% with an intermediate-level qualification. The relatively high percentage of respondents with advanced degrees highlights the importance of higher education in the logistics industry, particularly for roles that require strategic thinking, problem-solving, and technical expertise.
The study also captures the distribution of respondents across different management levels. Lower management constitutes the largest group, representing 45.45% of the sample, followed by middle management at 39.29% and upper management at 15.24%. This distribution reflects the hierarchical structure typical of logistics organizations, with a more extensive base of operational and supervisory staff supporting middle and upper management. Additionally, the study examines respondents’ job tenure, providing insights into their experience levels within their current organizations. The findings indicate that 40.76% of respondents possess 1–4 years of experience, 33.72% have 5–8 years, and 25.51% have over 9 years of experience. This distribution reflects a well-balanced combination of early-career, mid-level, and highly experienced professionals, which may contribute to a dynamic, experienced workforce capable of addressing diverse logistical challenges. These demographic characteristics provide meaningful insights for researchers and practitioners seeking to understand the composition of the logistics workforce.

4. Results and Discussion

The study examined the proposed relationships using Smart-PLS 4 software, which allows researchers to analyze structural equation models (SEM). Smart-PLS also facilitates the assessment of full collinearity, testing for reliability and validity, performing bootstrapping, and evaluating model fit indices.

4.1. Measurement Model Evaluation

The assessment of the measurement model is based on four key criteria to evaluate data reliability and validity: Cronbach’s alpha, composite reliability, variance inflation factor, and average variance extracted. The acceptable thresholds for these metrics are 0.70 for Cronbach’s alpha, 0.70 for CR, 5 for VIF, and 0.50 for AVE. These benchmarks ensure the model meets reliability and validity standards. The data in Table 3 demonstrate strong reliability and validity, with all factor loadings exceeding 0.70, demonstrating a strong correlation between each variable and its intended construct [75,84]; VIF assesses multicollinearity, with values above 5 indicating potential issues. This study’s VIF values range between 1.359 and 2.922, confirming no significant multicollinearity [85,86,87]. All constructs (AG, AI, JA, RM, RS, TD, and TW) exhibit Cronbach’s alpha values above 0.70, demonstrating strong internal consistency reliability [88,89]. Composite reliability values, which are more robust for smaller sample sizes, exceed 0.80 for all constructs, further confirming reliability [90]. For all AVE values above 0.50, it is evident that over half of the variance in each component is explained by its associated construct [91].

4.2. Assessment of Structural Model

Once the measurement model yielded satisfactory outcomes, the structural model was evaluated to assess its predictive accuracy and overall fit. With 58.1% of the variation in logistics agility and 61% of the variation in artificial intelligence explained by SHRM practices, the model indicated strong explanatory power for these constructs. Barroso, Carrión [92] emphasized the importance of predictive relevance, which was evaluated using the Stone–Geisser Q2 value calculated through the PLS blindfolding technique. Q2 values greater than zero indicate predictive relevance; the results (0.552 and 0.606) significantly exceed this threshold, confirming the model’s strong predictive capability. Using nonparametric bootstrapping with 5000 replications, the structural paths were tested for significance, revealing significant relationships that support the study’s hypotheses. Additionally, the effect size of the exogenous variables was evaluated using f-Square, with values grouped as small (≥0.02), medium (≥0.15), or large (≥0.35) following the Cohen [93] guidelines. The f-Square values in Table 4 fall within these ranges, further validating the model’s robustness and explanatory power. Finally, the Standardized Root Mean Square Residual was employed to evaluate the model’s overall fit. The SRMR value of 0.07 (Table 4) falls below the accepted threshold of 0.08, indicating a good fit [94].

4.3. Discriminant Validity

This study employed the heterotrait–monotrait (HTMT) ratio of correlations to evaluate the discriminant validity of the constructs (see Table 5). The HTMT ratio measures the correlation between two constructs, indicating their distinctiveness. The findings showed that all constructs had HTMT values under 0.9, consistent with [75], who suggested that an HTMT value under 0.9 is necessary to establish satisfactory discriminant validity. Additionally, the Fornell–Larcker criterion was applied to examine discriminant validity by computing the square root of the average variance extracted for each construct and comparing it with the inter-construct correlations. Discriminant validity is considered sufficient if the square root of a construct’s AVE surpasses its correlations with other constructs [95,96]. The subsequent table draws the findings of this analysis, summarizing the square root of AVE values for each latent component concerning their correlations with other latent variables. By examining these values, researchers can determine whether the constructs are adequately distinguished from each other.

4.4. Hypotheses Testing

4.4.1. Individual Impact

The outcomes of the structural model, as illustrated in Table 6 and Figure 2, reveal the relationships among the study variables. The findings indicate that reward management (RM), training and development (TD), job appraisal (JA), and teamwork (TW) have a significant and positive impact on logistics agility (AG). These outcomes provide robust statistical support for H2 (β = 0.205, t = 3.111, p < 0.01), H3 (β = 0.358, t = 5.013, p < 0.01), H4 (β = −0.163, t = 1.99, p < 0.05), and H5 (β = 0.220, t = 3.92, p < 0.01). These findings offer strong empirical evidence that the identified factors enhance logistics agility, thereby validating the proposed hypotheses and reinforcing their theoretical foundations. However, the relationship between recruitment and selection (RS) and logistics agility (AG) was found to be statistically insignificant, as the results did not provide sufficient evidence to support H1 (p > 0.05). These findings suggest that recruitment processes and selection criteria may not significantly influence logistics agility, implying that other factors are likely to be more critical in driving such outcomes. Consequently, H1 was rejected due to a lack of empirical support, highlighting the need to explore alternative determinants that contribute more substantially to enhancing logistics agility.
Similarly, the study confirms that artificial intelligence (AI) significantly enhances logistics agility (AG), supporting H8 (β = 0.258, t = 3.587, p < 0.01), as detailed in Table 6 and Figure 2. These findings highlight the transformative impact of AI in optimizing logistics operations, showcasing its capacity to drive efficiency, improve decision-making, and promote adaptability. By accepting H8, the study reinforces AI as a critical enabler of agility in logistics, aligning with its growing recognition as a transformative technology. The results emphasize the value of integrating AI into logistics strategies to boost agility and operational efficiency in dynamic business environments.

4.4.2. Collective Impact

The findings are further reinforced by the significant statistical values presented in Table 6 and illustrated in Figure 2, which strongly validate H6 (β = 0.544, t = 8.375, p < 0.01) and H7 (β = 0.781, t = 29.688, p < 0.01). These results highlight that effective sustainable HRM practices like recruitment and selection, training and development, reward management, job appraisal, and teamwork collectively play a pivotal role in enhancing logistics agility and enabling the adoption of artificial intelligence in logistics operations. The robust statistical evidence obtained underscores the importance of these SHRM practices in driving improvements in logistics agility, mainly when supported by AI technologies. This suggests that organizations strategically implementing these SHRM practices are better positioned to leverage AI capabilities, optimize their logistics processes, and achieve greater operational efficiency. The validation of these hypotheses confirms the proposed relationships within the model and emphasizes the synergistic effect of SHRM practices and AI in advancing logistics agility. These insights contribute to the broader understanding of how human resource strategies and technological innovation can work in tandem to enhance organizational performance in the logistics industry.

4.4.3. Mediation Analysis

The study highlights the significant direct and indirect effects of SHRM practices on logistics agility, as depicted in Figure 2. Specifically, the indirect impact of HRM practices on logistics agility, mediated by artificial intelligence (AI), is positive and statistically significant, with a beta value of 0.201, a t-statistic of 3.464, and a p-value of 0.001, as detailed in Table 7. This significant relationship underscores the pivotal role of AI technologies in amplifying the influence of sustainable HRM practices on logistics agility. The confidence intervals further substantiate the significance of the mediation effects, providing robust evidence that AI acts as a key mediator in the relationship between SHRM practices and logistics agility within the logistics industry. In line with the recommendation by Nitzl and Roldan [97], indirect effects are deemed meaningful when the confidence intervals for the lower and upper bounds of the indirect paths exclude zero. The confidence intervals in this study meet this criterion, offering strong support that AI significantly mediates the association between sustainable HRM practices and logistics agility. These findings highlight the value of incorporating AI into SHRM strategies to boost agility and operational efficiency in the logistics industry, emphasizing the potential of AI in driving organizational success.

4.5. Discussions

This study examines how sustainable human resource management (SHRM) practices influence logistics agility, with the mediating role of artificial intelligence. The rapid advancement of AI technologies, in combination with continuously evolving business dynamics, has fundamentally transformed logistics operations, compelling firms to enhance operational continuity and responsiveness. Prior research indicates that logistics firms integrate sustainable HRM practices into their policies to achieve superior competitive performance [8,74], underscoring the need to identify key factors driving logistics agility.
Building on this foundation, we developed a conceptual model incorporating five core dimensions of sustainable HRM practices, such as recruitment and selection, reward management, training and development, job appraisal, and teamwork. Using structural equation modeling, we analyzed these relationships in three phases. First, we assessed the individual impacts of each SHRM component on logistics agility. Second, the collective effect of all five SHRM dimensions was determined. Finally, the mediating role of AI between sustainable HRM practices and logistics agility was identified. The key findings of this study reveal several important insights, as follows:
  • Recruitment and selection (H1—not supported): The empirical results revealed a statistically insignificant relationship between recruitment and selection and logistics agility. The results suggest that recruitment criteria and selection processes may not significantly enhance logistics agility. Instead, logistics agility appears to be influenced more considerably by dynamic organizational capabilities, including technology integration, supply chain collaboration, and process adaptability, rather than the individual competencies of employees recruited through mechanisms such as relationship strength, long-term affiliations, or word-of-mouth referrals [32,98]. Furthermore, our findings are consistent with those of Wong and Wong [99], who posited that organizations emphasizing a culture rooted in relationship building or guanxi may experience diminished agility, irrespective of their recruitment processes or selection criteria. This underscores the notion that agility is less contingent on hiring practices and more dependent on organizational dynamics and cultural orientations.
  • Reward management (H2—supported): Reward management has a positive and statistically significant effect on logistics agility. The results suggest that organizations with well-established reward systems play a critical role in enhancing logistics agility by aligning employee incentives with key organizational values such as flexibility, innovation, responsiveness, and collaboration. From the perspective of logistics firms, a strategically designed reward system drives individual performance and fosters an organizational culture that prioritizes agility [37]. Notably, these findings align with those of Menon [39] and Kam and Tsahuridu [16], who emphasize the importance of reward management as a key driver of logistics agility.
  • Training and development (H3—supported): The research findings revealed a positive influence of training and development on logistics agility. The outcomes suggest that industries with comprehensive and well-structured training programs are better equipped to develop and sustain the essential components of logistics agility. In an era where logistics firms are confronted with rapid technological advancements, evolving market demands, and shifting customer expectations, effective T&D strategies are critical to empowering employees with the necessary skills, competencies, and adaptive mindsets to respond swiftly to changes. These findings are backed by Adeniran, Efunniyi [42], and Lieb [100], who emphasized that such strategies ensure employees are adequately prepared to perform in dynamic environments, thereby enabling organizations to maintain competitiveness and responsiveness to market fluctuations [39,45].
  • Job appraisal (H4—supported): The study identified the positive impact of job appraisal on logistics agility. The findings suggest that organizations that systematically evaluate and review employee performance concerning their job responsibilities, objectives, and overall contributions are more likely to enhance logistics agility. This relationship highlights effective job appraisals’ critical role in fostering individual and organizational development. Specifically, well-executed appraisals contribute to growth by clarifying performance expectations, highlighting strengths and development areas, and aligning employee goals with organizational priorities. These findings not only substantiate H4 but also align with Cho and Lee [49], who highlighted the significance of job appraisal as a driver of logistics agility.
  • Teamwork (H5—supported): The empirical findings substantiated a significant favorable influence of teamwork on logistics agility. This relationship underscores that teamwork within logistics organizations entails a synergistic integration of transparent communication, coordinated cooperation, mutual accountability, and collective responsibility among stakeholders. Such dynamics facilitate adaptive decision-making and operational fluidity, which are critical for navigating complex supply chain environments. Menon’s [39] seminal work aligns with these results, positing that cohesive teamwork not only strengthens interdepartmental alignment but also optimizes broader supply chain performance by nurturing resilience and responsiveness.
  • Overall SHRM effects (H6—supported): This study identified a comprehensive positive role of sustainable HRM practices in fostering logistics agility within the logistics industry. The findings reveal that logistics firms prioritizing sustainable HRM practices like recruitment and selection, reward management, training and development, performance appraisal, and teamwork are better equipped to harness logistics agility as a driver of enhanced operational performance. These practices are instrumental in attracting, retaining, motivating, and upskilling talent, ensuring that organizations possess the requisite human capital with the competencies and motivation to achieve strategic objectives [8]. By cultivating a workforce aligned with organizational goals, these SHRM practices amplify the positive relationship between HRM and logistics agility, enabling firms to respond swiftly and effectively to dynamic market demands. The study’s outcomes align with Fong, Ooi [57], and Haq, Asadullah [58], who emphasized that HRM practices were critical in shaping the employee lifecycle, from initial recruitment and onboarding to continuous development, performance evaluation, and career progression. These findings accentuate the strategic value of HRM practices as a foundational enabler of logistics agility, positioning them as critical for achieving competitiveness in the logistics industry.
  • SHRM and AI relationship (H7—supported): The findings of this study corroborate the positive impact of overall SHRM practices on artificial intelligence. The results indicate that as logistics firms increasingly adopt AI technologies, HR departments play a pivotal role in ensuring effective and ethical implementation. This, in turn, contributes to enhanced performance for both logistics firms and their employees [101]. Integrating SHRM practices with AI can boost operational efficiency, mitigate biases, and offer more profound insights into employee performance and organizational requirements [102,103]. These findings are consistent with Malik and Budhwar [104], who posit that AI is revolutionizing HRM in ways that yield positive outcomes for both employees and organizations.
  • AI and logistics agility link (H8—supported): This study underscores the significant positive effect of artificial intelligence on logistics agility. AI has revolutionized logistics agility by facilitating faster decision-making, enabling predictive analytics, delivering real-time tracking, and deriving operational efficiency [20,21]. These findings are consistent with prior research by Joel, Oyewole [61], and Pasupuleti, Thuraka [62], who emphasize that AI significantly boosts logistics agility by enhancing predictive capabilities, optimizing operational processes, and allowing rapid data-driven decision-making [62]. Further, adopting AI into the logistics sector delivers numerous benefits, such as greater efficiency, reduced cost, better customer satisfaction, and boosted economic and operational productivity [64,65].
  • AI’s mediating role (H9—supported): Ninth, the study investigated the mediating role of artificial intelligence in the relationship between sustainable HRM practices and logistics agility. The outcomes highlighted that AI significantly enhances the positive link between SHRM practices and logistics agility. By acting as a mediator, AI bridges the gap between SHRM strategies and logistics operations, enabling organizations to adapt more swiftly and effectively to dynamic changes in the supply chain. Through the integration of AI, HRM practices can ensure that the workforce is well-resourced to respond to the evolving demands of the logistics environment, thereby boosting overall organizational flexibility. These results are supported by Thangaraja, Maharudrappa [23], who underscored the mediating role of AI and are further corroborated by Krishnan, Govindaraj [24], and Vijaykumar, Mercy [69], who emphasized how AI optimizes HRM practices and reinforces logistics agility.
These findings underscore the potential for logistics firms to enhance operational agility by strategically integrating critical drivers such as reward systems, training and development, job appraisals, collaborative teamwork, and artificial intelligence. To maximize impact, policymakers should prioritize embedding these elements into strategic frameworks when formulating new policies, ensuring a cohesive approach that bridges human capital development with technological innovation. By fostering these factors, logistics firms can cultivate adaptive, future-ready operations while aligning stakeholder efforts with broader industry advancements.

5. Conclusions and Policy Implications

While many Chinese organizations increasingly adopt Western HRM practices, logistics firms encounter difficulties customizing these practices to align with China’s unique business context. Addressing the shortage of logistics expertise in China’s logistics industry, this study investigates the role of sustainable HRM practices in fostering logistics agility within Chinese logistics firms. Specifically, it examines the impact of key SHRM practices such as recruitment and selection, reward management, training and development, job appraisal, and teamwork on logistics agility. The study also explores the mediating role of artificial intelligence between overall HRM practice and logistics agility. The results revealed that reward management, training and development, job appraisal, and teamwork significantly enhance logistics agility by cultivating a skilled, motivated, and adaptable workforce. In contrast, recruitment and selection were found to have an insignificant impact, as logistics firms often rely on informal methods to build their talent pool. Collectively, these factors and AI explained 61% of logistics agility variance (R2 = 0.610), indicating a strong predictive relationship. These results underscore the essential contribution of sustainable HRM practices in empowering logistics firms to achieve competitive advantage and thrive in a dynamic global market.
The study also highlights a significant positive relationship between SHRM practices and logistics agility and between SHRM practices and AI. AI significantly positively mediates the relationship between SHRM practices and logistics agility. By automating key processes, real-time insights, and predictive analytics, AI amplifies the effectiveness of SHRM practices, optimizing workforce management and operational efficiency. This integration optimizes workforce management and operational efficiency, enabling firms to respond swiftly to market changes.
These findings suggest logistics firms should invest in AI-driven sustainable HRM tools like intelligent recruitment platforms, personalized training systems, and performance analytics tools. HR managers should develop strategies that align AI with SHRM practices to enhance collaboration, flexibility, and innovation. Such integration boosts operational adaptability, reduces costs, and improves customer satisfaction in logistics firms, securing long-term competitiveness in a dynamic industry. For Chinese logistics firms, these insights underscore the need to strengthen core sustainable HRM practices, including reward systems, training, job appraisal, and teamwork, while leveraging AI for process optimization. A critical step involves modernizing recruitment through structured, competency-based hiring, which will improve talent acquisition. The strategic combination of HRM and AI will enhance both workforce adaptability and operational responsiveness, helping firms thrive in China’s unique market and the global logistics sector.

Limitations and Future Research Directions

This study has certain limitations that need to be recognized. First, the research was confined exclusively to the logistics industry in China. Future investigations could broaden the scope by examining findings in other sectors, such as manufacturing, services, and construction. This would help assess the generalizability of the results and determine whether the observed relationships remain consistent across different contexts. Second, the study was conducted within China’s unique cultural and industrial environment. Researchers can replicate this study in developing and developed countries to enhance the understanding of cross-cultural applicability. Third, this study examined only five components of sustainable HRM practices, i.e., recruitment and selection, reward management, training and development, job appraisal, and teamwork, in relation to logistics agility. Future research could incorporate additional HRM dimensions, such as social support, employee empowerment, employee relations, and performance management, to better understand how HRM practices influence agility. Finally, this study utilized cross-sectional data, which limits its capacity to establish causal relationships. Future research could mitigate this by adopting longitudinal data to observe trends and changes. This approach would provide more robust evidence for causality and offer deeper insights into the dynamic interplay between sustainable HRM practices, artificial intelligence, and organizational agility.

Author Contributions

Conceptualization, S.J.; data curation, M.H.; formal analysis, S.J.; funding acquisition, R.X.; methodology, S.J.; project administration, R.X.; resources, A.I.; software, S.J., A.I. and M.H.; supervision, R.X.; validation, R.X.; writing—original draft preparation, S.J.; writing—review and editing, A.I. and M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by the Humanities and Social Science Project of the Ministry of Education of China (23 YJA630060), the Philosophy and Social Science Planning Project of Guangdong Province (GD22CGL01), the Guangzhou Science and Technology Planning Project (2024A03J0315), and Guangzhou Government postdoc startup grant number 624021-68.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to IRB Policy Statement of Guangzhou University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be provided upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 17 03099 g001
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Individual and collective impacts of sustainable HRM practices.
Figure 2. Individual and collective impacts of sustainable HRM practices.
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Table 1. Measurement items.
Table 1. Measurement items.
Recruitment and SelectionCode
1Interview panels are typically used as a main selection method in our organization.RS1
2We rely on word of mouth to fill our vacant positions.RS2
3We offer flexible working hours to employees.RS3
4Recruitment and selection play an important role in improving SCP because SC’s performance is dependent on its employees. RS4
5Our organization’s recruitment and selection strategy is highly integrated with our company’s overall strategy.RS5
6Our organization selects and evaluates applicants effectively.RS6
Reward Management
1We offer attractive salaries to our employees.RM1
2We offer attractive welfare packages to our employees.RM2
3The incentive system is fair in rewarding people.RM3
4Persons (and/or teams) who achieve the goals are rewarded the same as those who do not.RM4
Training and Development
1We provide job-related training to employees.TD1
2We provide career development opportunities to employees.TD2
3We have employee retention policies to retain skilled employees.TD3
Job Appraisal
1Annual performance appraisal results in our organization determine salary increases.JA1
2Annual performance appraisal results in our organization determine promotion.JA2
3We undertake annual performance appraisals of our employees.JA3
Teamwork
1Our company forms teams to solve problems.TW1
2Employees are encouraged to participate in teams.TW2
3Our company gets all team members’ opinions and ideas before making a decision.TW3
4Supervisors encourage employees to work as a team.
Artificial Intelligence
1This logistics firm uses artificial intelligence to track products.AI1
2This logistics firm measures uncertainty through artificial intelligence.AI2
3Artificial intelligence enables employees to make quick decisions.AI3
Logistics Agility
1This firm has the ability to deliver products quickly during disruption.AG1
2This firm meets evolving customer needs and quickly responds to changes.AG2
3This firm is capable of meeting customers’ needs without any interruption.AG3
Table 2. Demographic analysis.
Table 2. Demographic analysis.
DemographicsClassificationsSample SizeProportion (%)
GenderMale21663.34%
Female12536.65%
Age20–309929.03%
31–4016648.68%
41 and above7622.28%
EducationIntermediate319.09%
BS14843.40%
Masters9226.97%
MS/MPhil7020.52%
PositionLower Management15545.45%
Middle Management13439.29%
Upper Management5215.24%
Experience1–4 Years13940.76%
5–8 Years11533.72%
More than 9 Years8725.51%
Total 341100%
Table 3. Reliability and validity.
Table 3. Reliability and validity.
ConstructsItemsLoadingsVIFAlphaRho_aCRAVE
Logistics AgilityAG10.8221.5500.810.810.8880.726
AG20.8651.990
AG30.8671.999
Artificial IntelligenceAI10.8141.5490.7970.8050.8810.711
AI20.8441.841
AI30.8701.798
Job AppraisalJA10.8852.3000.8430.8450.9060.762
JA20.8251.697
JA30.9072.642
Reward ManagementRM10.8351.8310.780.8020.8580.603
RM20.8331.752
RM30.7531.456
RM40.6741.359
Recruitment and SelectionRS10.7141.7760.8470.850.8860.565
RS20.7962.310
RS30.7832.261
RS40.7611.942
RS50.7331.605
RS60.7211.577
Training and DevelopmentTD10.8892.2740.8290.8290.8980.745
TD20.8311.632
TD30.8692.109
TeamworkTW10.9222.9220.880.8820.9260.806
TW20.8932.420
TW30.8772.233
Sustainable HRM PracticesSHRM 0.9350.9400.9420.565
Notes: VIF, variance inflation factor; alpha, Cronbach’s alpha; CR, composite reliability; AVE, average variance extracted.
Table 4. R2, Q2, F2, and SRMR.
Table 4. R2, Q2, F2, and SRMR.
ConstructR-Square (R2)Q-Square Predict (Q2)F-Square (F2)
Relationships
AG0.5810.552AI -> AG0.061
AI0.6100.606SHRM -> AG0.277
SHRM -> AI1.563
SRMR 0.07≤0.08
Note: AG = logistics agility; AI = artificial intelligence; HRM = human resource management practices.
Table 5. Heterotrait–monotrait (HTMT) and Fornell–Larcker criteria.
Table 5. Heterotrait–monotrait (HTMT) and Fornell–Larcker criteria.
FactorAGAIJARMRSTDTW
AG
AI0.839
JA0.7260.852
RM0.7900.7880.738
RS0.7100.6760.6630.905
TD0.7790.7200.9080.6980.674
TW0.7570.8970.7450.6580.6330.605
Fornell–Larcker criteria
AG0.852
AI0.6810.843
JA0.5990.7050.873
RM0.6370.6370.6020.777
RS0.5960.570.5720.7920.752
TD0.6380.5890.7990.5610.5710.863
TW0.640.7570.6430.5610.560.5170.898
Note: AG = logistics agility; AI = artificial intelligence; JA = job appraisal; RM = reward management; TD = training and development; RS = recruitment and selection; TW = teamwork.
Table 6. Direct paths.
Table 6. Direct paths.
RelationshipsHypothesesBetaSample Mean(STDEV)t-Statp-ValuesRemarks
Individual
RS -> AGH10.0520.0540.0720.7160.474Rejected
RM -> AGH20.2050.2030.0663.1110.002Accepted
TD -> AGH30.3580.3580.0715.0130.000Accepted
JA -> AGH40.1630.1620.0821.9900.047Accepted
TW -> AGH50.2200.2160.0563.9200.000Accepted
AI -> AGH80.2580.2550.0723.5870.000Accepted
Collective
SHRM -> AGH60.5440.5460.0658.3750.000Accepted
SHRM -> AIH70.7810.7810.02629.6880.000Accepted
Note: Authors constructed the hypotheses and table.
Table 7. Mediation effect.
Table 7. Mediation effect.
RelationshipsHypothesisβ(STDEV)t-StatBCIp-ValuesRemarks
2.50%97.50%
HRM -> AI -> AGH90.2010.0583.4640.0980.3270.001Accepted
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Jahangir, S.; Xie, R.; Iqbal, A.; Hussain, M. The Influence of Sustainable Human Resource Management Practices on Logistics Agility: The Mediating Role of Artificial Intelligence. Sustainability 2025, 17, 3099. https://doi.org/10.3390/su17073099

AMA Style

Jahangir S, Xie R, Iqbal A, Hussain M. The Influence of Sustainable Human Resource Management Practices on Logistics Agility: The Mediating Role of Artificial Intelligence. Sustainability. 2025; 17(7):3099. https://doi.org/10.3390/su17073099

Chicago/Turabian Style

Jahangir, Sayeda, Ruhe Xie, Amir Iqbal, and Muttahir Hussain. 2025. "The Influence of Sustainable Human Resource Management Practices on Logistics Agility: The Mediating Role of Artificial Intelligence" Sustainability 17, no. 7: 3099. https://doi.org/10.3390/su17073099

APA Style

Jahangir, S., Xie, R., Iqbal, A., & Hussain, M. (2025). The Influence of Sustainable Human Resource Management Practices on Logistics Agility: The Mediating Role of Artificial Intelligence. Sustainability, 17(7), 3099. https://doi.org/10.3390/su17073099

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