The Influence of Strategic Human Resource Management and Artificial Intelligence in Determining Supply Chain Agility and Supply Chain Resilience

By inergency On Mar 25, 2024

1. Introduction

The constantly changing environment and rising global competition have made it necessary for logistics firms to establish novel strategies that bring agility and resilience to their logistics operations. During the recent COVID-19 pandemic, it was noted that logistics firms showcasing the characteristics of resilience and agility showed continuity in their operations [1,2]. Therefore, it is essential to examine the factors that bring agility and resilience in supply chain operations. Supply chain agility can be seen as a firm’s ability to ensure continuity in its logistics operations, while resilience indicates the adaptive ability of a logistics firm to return to normal operations after a disruption [3,4]. Despite the exponential growth in the literature on supply chains, empirical insights into how logistics firms achieve supply chain agility are yet to be obtained [5]. Although the literature has established a positive association between human resource practices and supply chain resilience [6,7], little research is available that discusses the relationship between strategic human resource management and supply chain agility. To bridge this gap, in the current study, we develop a research framework that combines factors such as leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence to determine logistics firms’ agility and resilience.

The authors of [8] studied strategic human resource management as a single factor in determining employee commitment and competitive advantage. Using an approach differing from that of the current study, they summarized strategic human resource management as five core dimensions, namely leadership, employee skills, organizational culture, competitive intensity, and human capital development, and they investigated supply chain agility. The term “leadership” refers to a leader’s role in nurturing a positive, conducive, and supportive environment in an organization and boosting employees’ self-motivation and commitment towards their tasks during a disruption [5,9]. Employee skills are defined as employees’ abilities to learn new concepts, their familiarity with the latest technologies, and their readiness to deal with unprecedented disruptions [10]. Organizational culture is recognized as a core predictor in measuring the performance of a logistics firm during disruptive events [11]. Furthermore, the competitive intensity factor enables logistics firms’ activity and assists firms in adapting to market demands [12]. Human capital can be defined as the collective sum of attributes, including experience, knowledge, enthusiasm, energy, and creativity, that employees invest at their workplace [13]. Besides strategic human resource management, the artificial intelligence dimension is recognized as an important technology enabler that allows logistics managers to manage logistics operations quickly and adequately and enhance their agility in these operations [14]. Thus, these factors are conceptualized to determine supply chain agility and resilience.

4. Data Analysis and Results

The study data were analyzed through structural equation modeling, following a two-step process [54]. The first step of SEM ensures factor reliability, validity, and convergent and discriminant validity. The hypotheses are then tested in the second step of SEM. Following the guidelines provided by [54], indicator reliability is considered to be achieved when the loadings of the factors are greater than 0.60. For factor reliability and validity, the values of α and CR must be higher than 0.70 to be considered satisfactory [54]. Convergent validity is established with a threshold value of 0.50, indicating the satisfactory convergent validity of the factors [54]. The study data were analyzed, and the PLS algorithm revealed satisfactory values for α and CR. Similarly, indicator reliability was achieved, as the loading values were higher than 0.60 [54]. Finally, the results revealed that the average variance extracted values were higher than 0.50, thus establishing the convergent validity of the factors. Table 1 depicts the loading, α, and CR values obtained and the average variance extracted.

In order to ensure discriminant validity, cross-loading and Fornell–Larcker analyses were incorporated. The cross-loading criterion suggests that the loadings of the indicators must be higher than other factors’ loadings. The results of the cross-loading analysis showed satisfactory loading values, thus establishing the discriminant validity of the factors. The results of the cross-loading analysis are exhibited in Table 2.

Fornell–Larcker analysis is another prominent analysis technique employed in data analysis and can be used to assess factors’ discriminant validity [55]. The average variance extracted must be higher than that of other factors [55,56]. Our findings confirmed the adequate discriminant validity of the factors, as the square root of the average variance extracted was higher in comparison to other factors. The results of the Fornell–Larcker analysis are exhibited in Table 3, comprising the AVE square root values and factor correlations.

4.1. Hypothesis Analysis

In the second step of SEM, we analyzed the hypotheses through a bootstrapping procedure. According to the author of [54], the bootstrapping procedure reduces data normality issues and must therefore be incorporated in data analysis. Moreover, multi-collinearity is addressed through the variance inflation factor [54]. None of the VIF values were higher than 3.3, thus establishing that multi-collinearity was not likely to be an issue in our data. The data were bootstrapped and revealed the positive beta values, t-statistics, and significance of the hypotheses. Table 4 depicts the results of the hypothesis analysis and the coefficients of determination.

The results of the hypothesis analysis demonstrated that leadership was positively associated with supply chain agility, strengthened by the statistical results of β = 0.449 and a t-statistic of 7.440, significant at p = 0.000; hence, H1 is accepted. Employee skills showed a positive impact on supply chain agility, supported by β = 0.119 and a t-statistic of 2.108, significant at p = 0.0018; thus, H2 is confirmed. Organizational culture showed an influence on supply chain agility, as confirmed by β = 0.215 and a t-statistic of 3.552, significant at p = 0.0000; thus, H3 is accepted. Moreover, competitive intensity was positively associated with supply chain agility, confirmed by β = −0.175 and a t-statistic of 3.057, significant at p = 0.0001; therefore, H4 is established. Human capital development showed a positive influence on supply chain agility, confirmed by β = −0.175 and a t-statistic of 4.928, significant at p = 0.0000; hence, H5 is accepted. Artificial intelligence showed a positive impact in determining supply chain agility, supported by β = 0.089 and a t-statistic of 1.715, significant at p = 0.0043; therefore, H6 is confirmed.

Supply chain agility showed a positive impact on resilience, confirmed by β = 0.693 and a t-statistic of 15.293, significant at p = 0.0000; therefore, H7 is accepted. These findings reveal that the outlined exogenous factors are positively related to supply chain agility, with satisfactory beta and t-values. Moreover, the collective variance explained was assessed through the coefficient of determination, $R^{2}$ . Altogether, leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence explained substantial variance ( $R^{2}$ ) of 80% in supply chain agility. Supply chain resilience, assessed through supply chain agility and organizational flexibility, explained substantial variance ( $R^{2}$ ) of 68.7% in supply chain resilience. To summarize, these results indicate that, statistically speaking, all our hypotheses are acceptable in describing supply chain agility and resilience. Moreover, our findings show the substantial variance explained in predicting supply chain agility and supply chain resilience, thus legitimizing the outlined supply chain research model.

4.2. Factor Effect Size Analysis

All our hypotheses were accepted; however, the effect of each factor must be examined through

f^{2}

effect size analysis. The

f^{2}

value in effect size analysis demonstrates three effect sizes: large, medium, and small. Values of

f^{2}

higher than 0.35 denote a large effect size, values between 0.35 and 0.15 correspond to medium, and values between 0.15 and 0.02 represent a small effect size of the factor in measuring the endogenous factor. The results of the effect size analysis revealed that organizational leadership had a medium effect size with regard to supply chain agility. The effect sizes of all the other factors were found to be small with regard to agility. Moreover, logistics agility showed a large impact in determining supply chain resilience, but organizational flexibility showed a small effect towards resilience. The resulting

f^{2}

values are presented in Table 5 for the factors of both supply chain agility and supply chain resilience.

4.3. Post Hoc Analysis

The positivist research paradigm recommends examining phenomena through robust forms of statistical analysis. Therefore, importance performance analysis was employed to obtain a macro perspective on the supply chain model. The first step in importance performance analysis is to select the outcome factor. Therefore, supply chain resilience was selected as the outcome factor to reveal the factors’ importance and performance. The data were analyzed, and the results indicated that supply chain agility was the most important factor due to its high total effect. Leadership was identified as the second most important factor determining supply chain resilience. Nevertheless, the importance of human capital development and organizational flexibility was sizeable in predicting supply chain resilience. Moreover, factors like organizational culture and competitive intensity were found to be important in measuring supply chain resilience. In terms of the performance index, the results showed high values for competitive intensity and organizational flexibility; therefore, policymakers should focus on these factors. The results of the post hoc analysis can be seen in Table 6.

4.4. Moderation Analysis

Organizational flexibility was hypothesized as a moderating factor between logistics agility and resilience. For the computation, the product indicator approach was adopted, consistent with prior studies [41,53,57]. The data were bootstrapped, and the results revealed a significant moderating influence of organizational flexibility towards supply chain agility and resilience, supported by β = 0.048 and a t-statistic of 1.799, significant at p = 0.036. Therefore, it is confirmed that organizational flexibility moderates the relationship between supply chain agility and supply chain resilience; hence, H8 is accepted. The result of the moderating analysis is exhibited in Figure 2.

In addition, a simple slope analysis was considered to examine the trend of moderation and determine whether organizational flexibility positively or negatively moderated the relationship between supply chain agility and supply chain resilience. As depicted in Figure 3, the simple slope analysis demonstrated an uphill trend with OFL at + 1SD. This indicates that a higher level of organizational flexibility in decision making enhances a logistics firm’s agility and resilience during disruptive events.

5. Discussion

The recent COVID-19 pandemic and dynamic changes in the business environment have entirely changed logistical operations. Now, logistics firms are striving for better continuity in their logistics operations. Previous studies have revealed that logistics firms that incorporate characteristics of resilience into their policies perform better in the competitive market [41,46,58]. Therefore, it is important to identify the factors that impact supply chain resilience. In this study, we established a model underpinned by strategic human resource management factors and artificial intelligence. We summarized strategic human resource management with five core dimensions, namely leadership, employee skills, organizational culture, competitive intensity, and human capital development, and we investigated the impact of these factors on supply chain agility and resilience. In order to test the relationships among these factors, the data were analyzed via the structural equation modeling approach. The results demonstrated that leadership was positively associated with supply chain agility, which was consistent with prior studies [5,9]. Similarly, employee skills were positively associated with logistics firms’ agility, which was also in line with prior studies [5,9,16]. Moreover, organizational culture was positively related to supply chain agility, consistent with prior studies [11,21].

Another dimension of strategic human resource management, namely competitive intensity, was positively associated with logistics firm agility, consistent with prior studies [13,23,24,26]. Similarly, human capital development was positively associated with supply chain agility, consistent with prior research work [13]. Artificial intelligence presented a positive impact in determining supply chain agility, consistent with prior studies [31,32]. Additionally, supply chain agility showed a positive impact on resilience, in line with prior studies [36,40,41]. Another important finding of this research was the concept of organizational flexibility as a moderating factor. This study confirmed the significant moderating influence of organizational flexibility in the relationship between supply chain agility and resilience, thus supporting the arguments developed by the authors of [36]. These findings suggest that logistics firms can improve the agility and resilience in their operations through leadership, employee skills, organizational culture, competitive intensity, human capital development, artificial intelligence, and organizational flexibility; therefore, policymakers should consider these factors when developing new strategies.

5.1. Contributions to Theory

This study contributes to the literature in numerous ways. Primarily, this study schematized strategic human resource management into five core dimensions, namely leadership, employee skills, organizational culture, competitive intensity, and human capital development. In addition, this study conceptualized the relationship between strategic human resource management and supply chain agility, thus contributing to the literature on human resources and logistics. Another unique theoretical contribution of this study is the integration of artificial intelligence as a single factor within strategic human resource management and its relation to supply chain agility, which is a novel contribution to the literature on information systems. Moreover, organizational flexibility has rarely been considered as a moderating factor between supply chain agility and supply chain resilience. This study confirmed the moderating effect of organizational flexibility and revealed that an increase in organizational flexibility increases supply chain resilience, thus enriching the literature on logistics. Aside from the significant impact of exogenous factors on supply chain agility and supply chain resilience, the results revealed substantial coefficients of determination ( $R^{2}$ ). For instance, leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence were associated with substantial variance explained ( $R^{2}$ value) of 80% in supply chain agility, confirming the validity of the research model. Similarly, supply chain agility and organizational flexibility were associated with substantial variance explained ( $R^{2}$ value) of 68.7% in supply chain resilience, confirming the validity of the extended model. The substantial coefficients of determination showed the high robustness of the research model. This model and its results enrich the literature, especially in the three domains of information systems, strategic human resource management, and supply chain resilience.

5.2. Contributions to Practice

In terms of practical implications, this research showed that factors like leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence positively influence supply chain agility and therefore need policymakers’ attention. More precisely, the effect size ( $f^{2}$ ) analysis suggested that, within the integrated research model, the leadership factor had a greater effect size with regard to supply chain agility. This indicates that, in logistics firms, supply chain agility is strongly linked to leadership values. The effect size analysis revealed that supply chain agility has a large impact in determining supply chain resilience, and this indicates that, for greater supply chain resilience, it is essential that logistical operations have the characteristic of agility. Aside from strategic human resource factors, in this study, we found that artificial intelligence was positively associated with supply chain agility. Therefore, managers should develop supply chain designs that are backed by artificial intelligence to improve the supply chain agility in turbulent environments. Another vital practical contribution of this study is that it revealed the importance of organizational flexibility. This study established the moderating effect of organizational flexibility and showed that an increase in organizational flexibility increases supply chain resilience. Therefore, if policymakers seek organizational agility and resilience in their operations during unprecedented situations, they should improve the organizational flexibility in the workplace. Achieving supply chain resilience with minimal resources is the key challenge for policymakers. The importance performance index values represent a macro perspective on the factors underpinning the current research framework and can assist managers in achieving supply chain resilience with minimal resources. According to the importance performance index, the factors of supply chain agility, leadership, human capital development, organizational flexibility, organizational culture, and competitive intensity are influential in predicting supply chain resilience; hence, managers and other policymakers must consider these factors when developing supply chain strategies.

6. Conclusions

Despite the exponential growth in supply chain research, there are few empirical insights into how logistics firms can achieve supply chain agility. To bridge this gap, we developed a research framework that combines factors such as leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence in order to examine supply chain agility. The results indicate that leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence collectively have substantial variance explained ( $R^{2}$ value) of 80% in supply chain agility. Moreover, supply chain agility and organizational flexibility have substantial variance explained ( $R^{2}$ value) of 68.7% in supply chain resilience. The results of the effect size analysis revealed that organizational leadership has a medium effect size with regard to supply chain agility. However, the effect sizes of all other exogenous factors were small with regard to supply chain agility. Moreover, the importance performance index analysis revealed that, within the integrated research model, supply chain agility, leadership, human capital development, and organizational flexibility have greater importance in determining supply chain resilience. As part of its contribution, this study schematized strategic human resource management into five core dimensions, namely leadership, employee skills, organizational culture, competitive intensity, and human capital development. In terms of practical applications, our results showed that factors like leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence are positively associated with supply chain agility and therefore need policymakers’ attention. The moderating effect of organizational flexibility between supply chain agility and supply chain resilience was tested. The results established that organizational flexibility enhances supply chain agility and supply chain resilience. This finding suggests that policymakers should improve the organizational flexibility in the workplace in order to boost logistics firms’ resilience. This study is unique as it integrates artificial intelligence, organizational flexibility, and strategic human resource management to investigate supply chain agility and supply chain resilience. In summary, the findings of this research will assist managers in developing resilient supply chain strategies to bring harmony to logistics operations and boost logistics firms’ resilience.

Limitations and Future Research Directions

Although this study makes strong contributions to theory and practice, it has some limitations that suggest future research directions. Firstly, this study summarized strategic human resource management as five core dimensions, namely leadership, employee skills, organizational culture, competitive intensity, and human capital development. However, there are some other HR practices, such as staffing, recruitment, training, and development, that could play important roles in measuring supply chain agility and resilience. Therefore, extending the current research model with staffing, recruitment, training, and development could disclose useful findings. Secondly, artificial intelligence was conceptualized as a single factor in this study. However, future researchers are encouraged to extend the current research model with big data analytics to obtain further insights into the agility in logistics operations and supply chain resilience. Additionally, to reduce the complexity of the research model, only direct relationships among hypotheses were tested. Future researchers may examine the mediating effect of supply chain agility between strategic human resource management and supply chain resilience. Lastly, this study was cross-sectional and examined the phenomenon at a single point in time. Future researchers are encouraged to test the current research model in a longitudinal context to obtain further insights into the impacts of strategic human resource management and artificial intelligence on supply chain agility and supply chain resilience.