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Article

Does Intellectual Capital Boost Firm Resilience Capability? Conceptualizing Logistic Service Quality as a Moderating Factor Between Resilience Capability and Firm Performance

by
Omima Abdalla Abass Abdalatif
and
Mohammad Ali Yousef Yamin
*
Department of Human Resources Management, College of Business, University of Jeddah, Jeddah 23218, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(17), 7948; https://doi.org/10.3390/su17177948 (registering DOI)
Submission received: 28 May 2025 / Revised: 4 July 2025 / Accepted: 12 July 2025 / Published: 3 September 2025
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Abstract

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained R 2 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained R 2 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance.

1. Introduction

Organizations around the globe have suffered from catastrophic events such as natural disasters, pandemics, sudden terrorist attacks, and political and economic instability. Although these events have negatively influenced logistic operations, they also provide opportunities for policymakers to re-think and re-design operational strategies to meet customers’ demands. Firms with resilience capabilities have survived the COVID-19 pandemic [1,2]. The authors of Ref. [3] postulated that meeting customers’ demands under the shadow of war and sudden outbreaks such as the COVID-19 pandemic is critical, and that such events even halted business growth. Therefore, policymakers need to design strategies that boost a firm’s resilience capability and enable it to confront catastrophic events [4]. Firm capability denotes the firm’s ability to operate in the face of massive disruptions, make a timely and cost-effective recovery, and move to a post-disruption state with or without a decrease in firm performance [5]. Studies have pointed out that resilience capability is an important strategic weapon in uncertain business environments in general and requires managerial attention [1,5,6]. However, there has been little discussion on resilience capability, especially in the context of logistic services [7]. To fill this research gap, this study outlined factors such as intellectual capital, HR practices, risk management capability, and artificial intelligence to investigate the resilience capability of logistic firms.
Intellectual capital comprises three main dimensions: human, relational, and structural capital. These dimensions have substantial support from the literature for predicting firm resilience capability and maintaining firm performance during catastrophic events [5,8,9]. Similarly, HR practices are outlined in the research model with the assumption that HR practices boost employees’ confidence and enable them to better deal with crises during a disaster [3,10,11]. Furthermore, factors such as risk management capability have shown a positive impact on firm resilience capability [12]. Similarly, studies have revealed that, in logistic operations, artificial intelligence assists employees in processing, predicting, retrieving, computing, and analyzing data quickly, and enhancing firm resilience capability [7,13,14]. Another dimension of this study is examining the moderating impact of logistic service quality on the relationship between resilience capability and firm performance. Thus, the research model of this study investigated firm resilience capability with multiple factors and provides useful insights for managers seeking to boost resilience capability, which will in turn improve firm performance. This study is original in terms of its uniqueness, as it developed an amalgamated research model and investigated logistic firm resilience capability with intellectual capital, HR practices, artificial intelligence, and logistic service quality. The remainder of this paper comprises a literature review, the methodology, data analysis, a discussion, the study’s implications, the conclusions, research limitations, and future research directions.

2. Literature Review

2.1. Intellectual Capital

Organizations around the globe have been affected by catastrophic events such as political instability, natural disasters, energy crises, terrorism, and pandemics. Managing supply chain disruption and the smooth running of logistic operations in the face of these remains a critical issue among manufacturing firms. The authors of [5] stated that firm resilience is a strategic weapon for dealing with supply chain disruption, which can be achieved through intellectual capital. The term intellectual capital denotes the sum of intangible knowledge and resources within a firm [15]. In the academic literature, intellectual capital is studied in terms of three core dimensions: human, relational, and structural capital [9,15,16]. Human capital encompasses the characteristics of employees’ skills and knowledge of supply chain operations. Relational capital denotes the quality of relationships between employees and other stakeholders involved in supply chain operations. Structural capital is identified as non-human knowledge embedded in a firm’s routine operations, processes, and copyrights [9,15,16]. There is a wide consensus in the academic literature that intellectual capital boosts firm resilience and performance [5,8,9]. For instance, Ref. [8] revealed that firms with adequate human capital are in a better position to respond quickly to supply chain disruption, resulting in better firm performance. Similarly, quality relationships among stakeholders improve information-sharing and enable managers to deal with disruptions. This implies that the timely sharing of information not only improves coordination in the supply chain, but also assists firms in dealing with crises in the long term [9]. Similarly, studies have revealed that established operations, processes, and databases significantly influence firms’ agility and analytical capability [9,17]. Therefore, we established the following hypothesis regarding intellectual capital:
H1: 
Human capital is positively related to firm resilience capability.
H2: 
Relational capital is positively related to firm resilience capability.
H3: 
Structural capital is positively related to firm resilience capability.

2.2. HR Practices and Risk Management Capability

Although the literature has revealed that organizational resources and quality relationships among stakeholders enable employees to confront disruption prudently, the importance of human resource practices cannot be ignored when measuring logistic firm resilience [18]. According to Ref. [3], human resource management systems enable employees to effectively deal with unprecedented situations and reduce external contingencies, resulting in long-term business survival in a competitive environment. Therefore, in this study, HR practices were linked to resilience capability. Studies have shown that HR practices comprise autonomy, training, job rotation, and flexible work hours, which encourage employees to better control operations during a disaster [3,4,10,11,19]. Similarly, HR practices have been proven to motivate employees during crisis periods. The literature has also emphasized team-oriented task management. For instance, Ref. [10] states that teamwork improves employees’ abilities and promotes resilience. Another study conducted by Ref. [11] revealed that the exchange of information and knowledge sharing among stakeholders, including employees, suppliers, and customers, could effectively improve organizational resilience. Therefore, this study conceptualized that HR practices positively influence firm resilience capability [3,10,11]. Risk management is identified as an essential factor that boosts firms’ resilience capabilities. The literature summarizes risk management into four categories: identification, assessment, risk mitigation, and risk control [6,20]. Risk identification assists managers in detecting risk; therefore, the assessment process reveals the severity and vulnerability of a disruption. The risk-mitigation process comprises details of the implementation and contingency plans. Therefore, a risk control policy indicates a firm’s readiness to deal with disruption. Thus, it is assumed that a firm with risk management capability is in a better position to deal with catastrophic events [12]. Thus, the following hypotheses were proposed:
H4: 
HR practices are positively related to firm resilience capability.
H5: 
Risk management capability is positively related to firm resilience capability.

2.3. Artificial Intelligence

With the surge in technology, artificial intelligence (AI)-enabled tools have attracted the attention of policymakers. According to Ref. [13], AI enables employees to process big data; predict, retrieve, compute, and analyze information quickly; and enhance supply chain capability. Prior to artificial intelligence, logistic firms relied on traditional solutions and remained unsuccessful in predicting the foreseeable crisis. Nevertheless, AI-enabled technologies have proven the ability to develop strategies that manufacturers need to boost their firm resilience. The literature provides clear evidence that artificial intelligence plays a significant role in improving supply chain agility, risk management, production resilience, and resilience [7,13,14]. Therefore, in the current research, it was assumed that the deployment of artificial intelligence in the logistic decision process could enhance a firm’s resilience capability, resulting in better firm performance. Authors such as Ref. [14] revealed that artificial intelligence positively impacts employee performance and work engagement behavior. Another study conducted by Ref. [7] discussed artificial intelligence readiness and the impact of AI on production system resilience. Extending this, artificial intelligence reduced the losses that occurred during the pandemic crisis in manufacturing firms [21]. Another study conducted by Ref. [7] proved that AI enhances firm response capability, recovery capability, and resistance capability. Furthermore, Ref. [13] confirmed the impact of AI in measuring the supply chain agility and risk management that lead to supply chain resilience. Therefore, it is appropriate to assume that artificial intelligence boosts a logistic firm’s resilience capability [7,13,14]. Thus, the following hypothesis pertaining to artificial intelligence was established:
H6: 
Artificial intelligence is positively related to firm resilience capability.

2.4. Logistic Service Quality

To boost a firm’s resilience capability and firm performance, it is essential to accentuate the importance of logistic service quality. Although the literature has established that service quality enriches customer experiences and increases firm performance, logistic service quality has rarely been discussed as a moderating factor between resilience capability and firm performance [22,23]. The term service quality is defined as customers’ judgment and evaluation of how the service they receive meets their expectations [24,25]. More precisely, logistic service quality is defined as the degree to which a firm’s ability to distribute a product is measured by different performance factors and an evaluation of the delivery process in accordance with customers’ requirements and expectations [26]. Several studies have shown that logistic service quality enhances the operational efficiency of logistic firms [1,26,27]. A recent study by Ref. [28] revealed that ensuring high logistic service quality leads to a high level of customer satisfaction and customer purchase intention, resulting in better firm performance. Similarly, Ref. [26] postulated that logistic service quality could enhance a firm’s logistic capabilities and customer satisfaction. Therefore, in this study, logistic service quality was conceptualized as a moderating factor between resilience capability and firm performance. With the support of prior literature, this study assumed that an increase in logistic service quality would increase a firm’s resilience capability and performance [1,26,27,29]. Thus, the following hypotheses were established:
H7: 
Firm resilience capability is positively related to firm performance.
H8: 
Logistic service quality moderates the relationship between firm resilience capability and firm performance.
The proposed research model, which summarizes the hypothesized relationships (H1–H8), is illustrated in Figure 1.

3. Methodology

3.1. Research Methods and Scale Development

This study’s research methodology is based on a positivist research paradigm. The positivist paradigm is grounded in the assumption that reality is objective, observable, and measurable. It relies on empirical evidence and statistical analysis to test the hypotheses and establish generalizable findings. Therefore, the research objectives were achieved through various assumptions. The research model comprises multiple factors explored in the current research context with the help of the literature. The factors underpinning the current research model were conceptualized to measure the logistic firm resilience capability and firm performance. These factors were further examined with empirical observations collected through structured questionnaires. The survey questionnaire included the demographic characteristics of the respondents and scale items. The scale items were adapted from previous studies in the same field. The adoption method was adequate because these scale items had the highest alpha values. Human capital was measured using scale items adapted from Ref. [4]. Therefore, scale items for the construct of relational and structural capital were adapted from Refs. [4,8,30]. The scale items for HR practices were adopted from Ref. [3] and slightly adapted to the current research setting. Risk management capability items were adapted from Ref. [12]. Another important construct, artificial intelligence, was measured with scale items adapted from prior studies conducted by Refs. [14,31]. The scale items for construct resilience capability were adapted from Ref. [32]. The scale items for the construct firm performance were adapted from Ref. [1]. Likewise, scale items for the logistic service quality factor were adapted from Refs. [1,26,33]. These items are measured on seven-point Likert scale ranging from 1 to 7, wherein 1 indicates “strongly disagree” and 7 stands for “strongly agree”, consistent with prior studies [34,35].

3.2. Sampling and Data Collection

The sample in this study was computed using a priori power analysis. According to Ref. [36], a priori power analysis can reveal the exact sample required for factor analysis. Following the guidelines provided by Ref. [36], a priori power analysis was conducted using the G-power software (version 3.1.9.7, Heinrich University, Düsseldorf, Germany). The results indicated that a minimum sample of 160 respondents would be satisfactory for factor analysis. The purposive sampling approach was selected for data collection. This approach was selected because the researcher wanted to collect observations from firm managers only, and it would be consistent with prior studies in the same field [29,37]. Moreover, logistic managers possess a comprehensive understanding of firm-level strategies, resource allocation, and resilience capabilities, making them the most appropriate informants for assessing the constructs in our study. A total of 359 questionnaires were distributed among logistic firm managers. These managers were requested to fill out questionnaires based on their understanding of the logistic firm’s resilience capability. Participation in the survey was voluntary. The survey questionnaires were distributed physically among managers. After several phone calls and follow-ups, 213 questionnaires were retrieved, with a response rate of 59%. These responses were further evaluated using a missing value analysis, the results of which revealed that the data were missing completely at random and valid. Similarly, the data common method bias was tested using Harman’s single-factor analysis. Harman’s factor analysis revealed that the maximum variance explained by the first factor was <40%. This indicates that common method bias was not a likely issue in this study. To further ensure that common method bias was not an issue, the questionnaire items were jumbled up prior to distribution. This method is highly recommended and has been widely used in past studies [18,19,21,22,23,24,25,26,27,28,29,30,32,33,34,35,36,37,38,39,40,41].

3.3. Ethical Considerations

This study did not involve any intervention, treatment, or collection of sensitive personal data from human subjects. It relied solely on anonymized survey responses collected for academic research purposes. Therefore, according to the local and national research guidelines, and in accordance with the principles outlined in the Declaration of Helsinki, ethical approval from an Institutional Review Board (IRB) or Ethics Committee was not required. The authors confirm that all the participants were informed about the voluntary nature of their participation and that their responses were kept confidential and used exclusively for research purposes.

4. Data Analysis

4.1. Structural Equation Modeling Approach

The data were analyzed using variance-based structural equation modeling with SmartPLS (version 4.0, SmartPLS GmbH, Bönningstedt, Germany). There are two types of structural equation modeling (SEM): variance-based and co-variance-based. Both types are important and widely acknowledged. The variance-based SEM is adopted when the research objective is to test a new model instead of testing an existing model [39]. VB-SEM was consistently selected in this study because the research model was new and exploratory in nature. In the first stage, factor reliability was established with composite reliability and α with a threshold value of 0.70, as recommended by prior researchers [36,39]. Therefore, convergent validity was confirmed with the average variance extracted following the threshold value 0.50 [29,36]. Moreover, indicator reliability was established following a threshold value of 0.60 as recommended by prior studies [35,36,40]. Table 1 depicts the satisfactory values of α, composite reliability, loadings, and average variance extracted.
After establishing factor reliability and validity in data analysis, it is essential to ensure the discriminant validity of the factors. The discriminant validity of the factors was tested using a cross-loading analysis and the Fornell–Larcker method [35,42]. The cross-loading analysis was conducted following the standard that the values of the loadings had to be higher than other factor loadings. The results of the cross-loading are depicted in Table 2, demonstrating that the loadings are higher than the corresponding factor loadings, hence confirming factor discriminant validity.
Another method, namely Fornell–Larcker analysis, is applied to test the discriminant validity of factors [42]. This method assesses discriminant validity through the average variance extracted and is highly recommend by prior researchers [35]. The Fornell–Larcker analysis suggests that the square root value of the average variance extracted must be greater than other factors’ correlation [42]. Data were estimated, and the results confirmed that the square root value of the average variance extracted was higher, hence establishing the discriminant validity of the factors. The results of the Fornell–Larcker analysis are exhibited in Table 3.

4.2. Hypothesis Testing

The structural model assessed the hypotheses through a bootstrapping procedure. According to Ref. [35], the bootstrapping procedure is highly recommended, as it mitigates the data normality issue. Data were bootstrapped with a dummy dataset of 10,000 as recommended by prior researchers [35,36,43]. The results of the hypothesis analysis are shown in Table 4, comprising beta values, t-statistics, and the significance of the hypotheses.
The relationship between hypotheses was tested, and results revealed a significant impact of human capital on firm resilience capability, supported by β = 0.191 and a t-statistic of 2.786, significant at p 0.003; hence, H1 is accepted. Relational capital is positively associated with logistic firm resilience capability, statistically supported by β = 0.146 and a t-statistic of 1.973, significant at p 0.024; thus, H2 is accepted. Similarly, structural capital showed a positive impact in measuring logistic firm resilience capability, statistically supported by β = 0.190 and a t-statistic of 2.657, significant at p 0.004; therefore, H3 is accepted. The role of human resource practices was found to be positive in measuring firm resilience capability, statistically confirmed by β = 0.181 and a t-statistic of 2.438, significant at p 0.007; thus, H4 is accepted. Nevertheless, factors such as risk management capability showed insignificant impacts on firm resilience capability; hence, H5 is rejected due to a lack of statistical support (β = 0.111 and a t-statistic of 1.566, significant at p 0.059). Artificial intelligence is another important factor and showed a positive impact in determining firm resilience capability, statistically supported by β = 0.194 and a t-statistic of 2.715, significant at p 0.003; hence, H6 is accepted. The relationship between resilience capability and firm performance was found to be significant and statistically confirmed by β = 0.288 and a t-statistic of 4.883, significant at p 0.000; thus, H7 is accepted. Overall, exogenous factors were strongly associated with resilience capability except risk management capability. The results for the hypotheses with respective paths and significance are shown in Appendix A.

4.3. Importance Performance Matrix

The research model has multiple factors and, therefore, requires a macro view of the underlying factors. Therefore, an importance performance matrix analysis was incorporated to determine the importance and performance level of the factors. Prior studies have encouraged the analysis of data using IPMA [39]. Logistic firm performance was selected as the outcome factor, and IPMA analysis was then performed. The results reveal that logistic service quality is the most important factor in measuring logistic firm performance, while resilience capability is the second most important. Nevertheless, other factors such as artificial intelligence; HR practices; and human, structural, and relational capital are less important when compared with logistic service quality. The results of the importance–performance matrix are listed in Table 5.

4.4. Effect Size Analysis and Variance R 2

The importance–performance analysis exhibits a macro view of the factors within an integrated model. Nevertheless, the effect size estimation with f 2 analysis discloses the impact of each factor in measuring the outcome variable. The threshold values of f 2 analysis were 0.35, 0.15, and 0.02, representing large, medium, and small effect sizes, respectively [44]. The results indicate that artificial intelligence, human capital, structural capital, relational capital, and HR practices had small effect sizes in measuring firm resilience capability. Therefore, risk management capability showed no effect size in determining a firm’s resilience capability. Logistic service showed a substantial impact in measuring logistic firm performance. Moreover, resilience capability revealed a medium-sized effect in determining logistic firm resilience. Similarly, the collective impact of the factors was tested using coefficient of determination R 2 analysis. The results revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained R 2 86.5% of the variance in the resilience capability of logistic firms. Likewise, logistic service quality and firm resilience capability explained R 2 79.5% of the variance in logistic firm performance. The results of the coefficient of determination and f 2 analyses are presented in Table 6.

4.5. Moderation Analysis

The moderation analysis was conducted using a product indicator approach, as recommended by prior studies [35]. At the initial stage, data were bootstrapped to disclose the path coefficient and t-statistics. Therefore, in the second stage of moderation analysis, the strength of the moderating effect was determined through a simple slope analysis. This analysis revealed that logistic service positively moderates the relationship between firm resilience capability and firm performance and is statistically supported by β = 0.082, a t-value of 2.486, and a significance level of p 0.006. These findings clearly indicate that logistic service quality moderates the relationship between firm resilience capability and firm performance; hence confirming H8. Moreover, the strength of the moderation was tested using a simple slope analysis, as depicted in Figure 2. This analysis shows that the logistic service quality LSR at + 1SD is the highest and exhibits an upward trend. This trend shows that an increase in logistic service quality boosts firm resilience capability and logistic firm performance.

5. Discussion

Organizations around the globe manage their operations in complex and uncertain environments. Moreover, with the surge in catastrophic events such as political instability, natural disasters, energy crises, terrorism, and recent pandemics, constant smooth operations are a daydream. Nonetheless, these events not only disrupt logistic operations but also highlight the importance of resilience. Authors such as Ref. [1] imply that firms with resilience capability would have survived the recent pandemic. Therefore, logistic firms should design strategies to boost firm resilience and confront disruptive events. Another study conducted by Ref. [3] demonstrated that operating under the shadow of war, sudden outbreaks like the COVID-19 pandemic, and economic gloom and doom created challenging situations for business growth. Therefore, the current research has developed a logistic model that enhances firm resilience capability and empowers managers to deal with disruptions. The research model of this study considers that intellectual capital could influence a firm’s resilience capability, which is hence outlined in the model. To understand how intellectual capital works, this study divided intellectual capital into three core dimensions: human, relational, and structural capital. The empirical findings of this research suggest that all three dimensions are positively related to firm resilience capability, consistent with prior studies [9,17].
The importance of human resource practices cannot be overlooked, especially during crises. HR practices were considered and showed a positive influence on firm resilience capability, in line with prior research findings [3,10]. Further statistical results showed that risk management capability does not positively influence resilience capability, which is inconsistent with previous findings [6,20]. This outcome is because many logistic firms may possess formal risk-management structures, but these are often reactive and not strategically integrated. This reduces their practical effectiveness in enhancing their resilience capability. Another possible overshadowing effect is that artificial intelligence and human capital play a more dominant role in dynamic decision making and crisis response, thereby reducing the observable influence of traditional RMC practices. The impact of artificial intelligence is found to be positive in measuring a firm’s resilience capability, which is consistent with prior studies [7,13,14]. Finally, the moderating effect of logistic service quality on the relationship between firm resilience capability and firm performance was tested. The results revealed that logistic service quality boosts firm resilience capability and firm performance and confirm arguments developed by prior studies [1,26,27]. High service quality in logistic services is also essential due to the industry’s reliance on timely, reliable, and flexible operations, which ultimately intensify its influence on resilience and performance. Finally, the results revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained R 2 86.5% of the variance in logistic firm resilience capability, which is substantial. Likewise, logistic service quality and firm resilience capability explained R 2 79.5% of the variance in logistic firm performance, ensuring the validity of the research model. Appendix B provides a summary of the results.

Implications for Theory, Methods, and Practice

The current study contributes to theory, methods, and practice in several ways. Theoretically, this study contributes to the resource-based view by integrating intellectual capital, HR practices, artificial intelligence, and risk management capability as strategic intangible resources that enhance a firm’s resilience capability and performance. This extends the resource-based view by showing how these resources interact, particularly in the context of logistic firms facing disruptions. This study developed a research model that comprises factors such as intellectual capital, HR practices, and artificial intelligence and investigated the resilience capability of the logistic firm. To the best of our knowledge, this study is the first to develop an integrated model that covers HR policies, discloses the intellectual capital of a firm, and conceptualizes artificial intelligence to investigate a firm’s resilience capability. Therefore, developing a completely new model and conceptualizing these factors for a firm’s resilience capability enriches the logistic literature. In addition, this research used the latest statistical methods to compute datasets, such as the structural equation modeling approach and prior power analysis for sample size computation. Practically, this study can direct policymakers to focus on improving the intellectual capital of logistic firms, which will in turn boost resilience capability. In other words, logistic firms with adequate processes, quality relationships, and skilled workers will be in a better position to confront crises. Similarly, our findings suggest that appropriate HR practices and the use of artificial intelligence boost employees’ confidence and improve efficiency in the workplace. Therefore, logistic managers could introduce adaptable HR practices and artificial intelligence in workplaces to increase the resilience capability of logistic firms. Another important implication of this study is the confirmation of the moderating effect of the logistic service quality. This study showed that an increase in logistic service quality strengthens the relationship between resilience capability and firm performance. This finding suggests that managers can achieve the desired level of performance by improving logistic service quality. The IPMA analysis disclosed a macro view of the underpinned factors. This analysis illustrates that logistic firm performance is linked to the resilience capability and logistic service quality of the firm because of the high importance of these factors in the IPMA index. Similarly, the importance of artificial intelligence; human resource practices; and human, structural, and relational capital in achieving firm performance during catastrophic events is also notable.

6. Conclusions

Although prior studies have long debated resilience and operational performance, few have highlighted factors that boost firm resilience capabilities. Moreover, with the surge in catastrophic events, logistic firms are keen to develop strategies that enhance firm resilience. To fill this research gap, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. Similarly, this study examined the moderating impact of logistic service quality on the relationship between resilience capability and firm performance. The results revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained R 2 86.5% of the variance in logistic firm resilience capability. It is interesting to note that the relationship between risk management and resilience capabilities was insignificant. Moreover, logistic service quality and firm resilience capability also explained R 2 79.5% of the variance in logistic firm performance and ensured the validity of the research model. The effect size analysis suggests that logistic service quality shows a substantial effect size in measuring logistic firm performance. The moderating effect analysis revealed that an increase in logistic service quality boosts firm resilience capability and logistic firm performance. Therefore, managers and policymakers should focus on improving logistic service quality during the distribution process. In terms of research contributions, this study integrated HR practices, intellectual capital, and artificial intelligence for resilience capability and enriches the logistic literature. For managers, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. To the best of our knowledge, this study is the first to investigate logistic firms’ resilience capability in relation to intellectual capital, HR practices, and artificial intelligence. Likewise, conceptualizing the moderating effect of logistic service quality between logistic firm resilience capability and firm performance makes this research more attractive and valuable.

Research Limitations and Future Directions

This study has a few limitations that should be underscored for future research guidance. For instance, intellectual capability was studied with three core dimensions: structural, human, and relational capital. However, these dimensions can be extended to other factors, such as social capital. Similarly, to reduce model complexity, this study examined HR practices using a single construct. However, future researchers should use multidimensional scales for HR practices. Resilience capability was studied as a single factor in these studies, but future researchers may add other dimensions of resilience capability, such as robustness capability and learning capability. Although the research model showed substantial variance in firm resilience capabilities, we do not claim that this study included all the factors that boost firm resilience capabilities. Extending the current research model to include factors such as leadership, employee voice behavior, and big data analytics could reveal interesting findings. Another limitation of this research is the number of respondents. This study was limited to logistic managers and considered their opinions only to understand a firm’s resilience capability. Capturing the opinions of other stakeholders such as customers and suppliers could expand the scope of a future study. Similarly, this study was cross-sectional and investigated problems at one point in time. Longitudinal research could enhance the scope and validity of the research model.

Author Contributions

Conceptualization, O.A.A.A. and M.A.Y.Y.; Methodology, O.A.A.A. and M.A.Y.Y.; Software, O.A.A.A. and M.A.Y.Y.; Validation, O.A.A.A. and M.A.Y.Y.; Formal analysis, O.A.A.A. and M.A.Y.Y.; Investigation, O.A.A.A. and M.A.Y.Y.; Resources, O.A.A.A. and M.A.Y.Y.; Data curation, O.A.A.A. and M.A.Y.Y.; Writing—original draft, O.A.A.A. and M.A.Y.Y.; Writing—review and editing, O.A.A.A. and M.A.Y.Y.; Visualization, O.A.A.A. and M.A.Y.Y.; Supervision, O.A.A.A. and M.A.Y.Y.; Project administration, O.A.A.A. and M.A.Y.Y.; Funding acquisition, O.A.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-24-DR-20271-1). Therefore, the authors thank the University of Jeddah for its technical and financial support.

Institutional Review Board Statement

In accordance with the National Committee of Bioethics (NCBE) regulations under the Law of Ethics of Research on Living Creatures (Royal Decree No. M/59, 14/9/1431H), research involving surveys, interviews, or educational tests that poses only minimal risk to participants and does not collect identifiable or sensitive data may be considered “Exempt Research” and therefore does not require formal Institutional Review Board (IRB) approval. “Research involves any of the following categories: educational tests, surveys, interviews, public behavior monitoring, and both of the following conditions apply: (1) the information is recorded in a manner that does not reveal the identity of the source person, and (2) the research should not expose the participant to criminal or civil liability or jeopardize their financial position or career”. (Source: National Committee of Bioethics/KAUST IBEC FAQs: KAUST Research Compliance—IBEC FAQs). Accordingly, our study, which was based solely on a questionnaire with verbal consent, qualifies under this Exempt Research category.

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Sustainability 17 07948 g0a1
Figure A1. Path coefficient and significance.
Figure A1. Path coefficient and significance.
Sustainability 17 07948 g0a1

Appendix B

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Figure A2. t-statistics and path coefficient.
Figure A2. t-statistics and path coefficient.
Sustainability 17 07948 g0a2

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Sustainability 17 07948 g001
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Output of simple slope analysis.
Figure 2. Output of simple slope analysis.
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Table 1. Indicator reliability.
Table 1. Indicator reliability.
IndicatorsLoadingsαCRAVE
AIN1: Artificial intelligence assists me to retrieve right data information.0.8660.8540.9110.774
AIN2: Artificial intelligence assists me to analyze complex data.0.883
AIN3: Artificial intelligence helps me to take important decision.0.891
HCP1: This firm has highly skilled workers to manage operations.0.9070.8900.9320.820
HCP2: Employees in this firm are expert to manage complex problems.0.919
HCP3: Employees in this firm are considered best in industry. 0.890
HRP1: Firm offers range of training programs to develop employee skills.0.8040.8860.9210.746
HRP2: Firm offers job rotation opportunity to develop multiple skills.0.907
HRP3: Firm hires employees based on diverse experience to perform variety of tasks.0.876
HRP4: Firm improves employee skills through broader job design.0.864
LSR1: Firm has ability to deliver goods in accordance with customer requirement.0.8860.8630.9160.785
LSR2: Firm delivers error free and undamaged goods to end customers.0.896
LSR3: Firm has support system to respond customer queries quickly.0.876
PER1: In comparing this firm is more profitable.0.8990.8760.9240.802
PER2: This firm has superior reputation in industry.0.893
PER3: This firm has visible market share growth.0.894
RCP1: Employees feel comfortable while sharing information with each other. 0.8810.8670.9180.789
RCP2: This firm is active in developing long term relationship with stakeholders.0.897
RCP3: Employees extensively collaborate with customers and suppliers to develop new solution.0.887
REC1: This logistic firm is able to deal with unprecedented situation.0.8730.8780.9250.804
REC2: This logistic firm is able to respond quickly to disruption.0.905
REC3: This logistic firm has ability to adapt and recover quickly.0.911
RMC1: Firm is capable to find potential supply chain risk regularly.0.8430.8330.8990.749
RMC2: Prior experience assists employee to minimize frequency of the risk.0.877
RMC3: Employees are able to analyze risk and its impact on operations.0.875
SCP1: Knowledge is kept in proper manuals, archives and databases.0.8780.8500.9090.769
SCP2: Firm uses intellectual property rights to store knowledge.0.871
SCP3: Firm is capable to protect knowledge and useful information.0.882
Table 2. Factor loadings.
Table 2. Factor loadings.
Factors AINHCPHRPLSRPERRCPRECRMCSCP
AIN10.8660.6610.6910.5370.5270.6600.7430.6970.658
AIN20.8830.6510.6670.5740.5760.6870.7200.6420.679
AIN30.8910.6900.6940.5380.5730.7240.7230.7400.705
HCP10.6660.9070.6940.5720.6110.7200.7560.7300.718
HCP20.6790.9190.7770.6090.6440.7880.8020.7520.807
HCP30.7160.8900.6970.5580.6040.7960.7660.7200.752
HRP10.6250.6150.8040.5000.5130.6350.6390.6610.632
HRP20.6730.7350.9070.6090.6250.7230.7680.7250.778
HRP30.6800.7380.8760.5880.6400.6940.7460.7510.744
HRP40.7040.6680.8640.5950.6430.7020.7870.7370.761
LSR10.5380.5250.5870.8860.8420.5570.5750.5660.572
LSR20.5880.5880.6140.8960.7320.5730.6120.6040.634
LSR30.5360.5960.5690.8760.7210.5580.5630.5790.612
PER10.5890.6710.6550.7540.8990.6720.6700.6370.671
PER20.5810.6500.6510.7560.8930.6530.6650.6250.701
PER30.5360.5210.5870.8190.8940.5510.5610.5400.556
RCP10.7020.7510.7660.5770.6280.8810.7910.7740.780
RCP20.6850.7470.6970.6010.6390.8970.7390.7320.714
RCP30.7020.7650.6620.5140.5920.8870.7560.7460.748
REC10.7400.7720.7260.5840.6160.7380.8730.7590.723
REC20.7410.7720.7980.5940.6350.7940.9050.7630.810
REC30.7480.7600.7730.5920.6460.7770.9110.7760.808
RMC10.6240.6460.6410.5610.5430.6530.6740.8430.650
RMC20.6720.6690.7390.5810.5610.7120.7160.8770.709
RMC30.7390.7780.7710.5660.6280.8160.8160.8750.832
SCP10.7120.7650.7290.5600.6160.7940.8320.7700.878
SCP20.6720.7210.7470.6150.6210.6980.7080.7290.871
SCP30.6460.7170.7550.6250.6500.7150.7400.7340.882
Table 3. Discriminant validity.
Table 3. Discriminant validity.
Factors AINHCPHRPLSRPERRCPRECRMCSCP
AIN0.880
HCP0.7590.905
HRP0.7770.7990.864
LSR0.6250.6410.6660.886
PER0.6350.6850.7040.8680.895
RCP0.7840.8490.7990.6350.6970.889
REC0.8280.8560.8540.6580.7050.8590.897
RMC0.7880.8110.8330.6570.6700.8460.8550.865
SCP0.7730.8390.8470.6820.7170.8420.8710.8500.877
Table 4. Hypothesis analysis.
Table 4. Hypothesis analysis.
Hypothesis PathβSTDEVt-StatisticsSignificanceDecision
H1HCP → REC0.1910.0692.7860.003Supported
H2RCP → REC0.1460.0741.9730.024Supported
H3SCP → REC0.1900.0712.6570.004Supported
H4HRP → REC0.1810.0742.4380.007Supported
H5RMC → REC0.1110.0711.5660.059Rejected
H6AIN → REC0.1940.0712.7150.003Supported
H7REC → PER0.2880.0594.8830.000Supported
Table 5. Importance performance matrix.
Table 5. Importance performance matrix.
Firm Performance Outcome Factor
Construct Total Effect Performance Level
Artificial intelligence0.05671.886
Human capital0.05572.390
Human resource practices0.05270.671
Logistic service quality0.76775.238
Relational capital0.04273.917
Resilience capability0.28872.728
Risk management capability0.03271.650
Structural capital0.05573.823
Table 6. Factor effect size f 2 and collective variance R 2 .
Table 6. Factor effect size f 2 and collective variance R 2 .
Construct Resilience CapabilityImpact
Artificial intelligence0.083Small effect
Human capital0.058Small effect
Human resource practices0.052Small effect
Relational capital0.029Small effect
Risk management capability0.017No-effect
Structural capital0.047Small effect
Firm performance
Logistic service quality1.392Substantial
Resilience capability0.200Medium-effect
Coefficient of determination R 2
Resilience capability R 2 86.5%Substantial
Firm performance R 2 79.5%Substantial
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Abdalatif, O.A.A.; Yamin, M.A.Y. Does Intellectual Capital Boost Firm Resilience Capability? Conceptualizing Logistic Service Quality as a Moderating Factor Between Resilience Capability and Firm Performance. Sustainability 2025, 17, 7948. https://doi.org/10.3390/su17177948

AMA Style

Abdalatif OAA, Yamin MAY. Does Intellectual Capital Boost Firm Resilience Capability? Conceptualizing Logistic Service Quality as a Moderating Factor Between Resilience Capability and Firm Performance. Sustainability. 2025; 17(17):7948. https://doi.org/10.3390/su17177948

Chicago/Turabian Style

Abdalatif, Omima Abdalla Abass, and Mohammad Ali Yousef Yamin. 2025. "Does Intellectual Capital Boost Firm Resilience Capability? Conceptualizing Logistic Service Quality as a Moderating Factor Between Resilience Capability and Firm Performance" Sustainability 17, no. 17: 7948. https://doi.org/10.3390/su17177948

APA Style

Abdalatif, O. A. A., & Yamin, M. A. Y. (2025). Does Intellectual Capital Boost Firm Resilience Capability? Conceptualizing Logistic Service Quality as a Moderating Factor Between Resilience Capability and Firm Performance. Sustainability, 17(17), 7948. https://doi.org/10.3390/su17177948

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