Examining the Role of the Internet of Things and Coordination Human Resource Practices Towards Service Engagement and Firm Performance: The Moderating Effect of Digital Capability

Abstract

Digital transformation in logistics requires sustainable service engagement; however, limited research has examined how technological and organizational factors jointly influence service engagement and firm performance. This study investigates the effects of the Internet of Things, information pervasiveness, shared decision-making, market orientation, and coordination-oriented human resource practices on service engagement, and examines the moderating role of digital capability in the relationship between service engagement and firm performance. Data were collected through a structured questionnaire from 312 logistics managers and analyzed using partial least squares structural equation modeling. The results indicate that IoT, information pervasiveness, market orientation, and coordination-oriented human resource practices positively influence service engagement, whereas shared decision-making has an insignificant effect. Digital capability strengthens the positive effect of service engagement on firm performance significantly. The model explains 52.6% of the variance in service engagement. This study contributes by integrating technological, organizational, and managerial factors into a unified framework, and demonstrates the contingent role of digital capability. Practically, the findings emphasize investments in digital capabilities, IoT, market-oriented strategies, and coordination-oriented human resource practices to enhance sustainable engagement, organizational resilience, and firm performance.

1. Introduction

The Fourth Industrial Revolution is transforming business operations with automation and highly integrated manufacturing technologies, thereby enabling more sustainable and digitally integrated service ecosystems. Similarly, with the advent of integrated applications and pervasive information, customers are now more demanding and pickier than ever before [1]. Thus, engaging customers in business operations is a key challenge for service providers [1,2]. Service engagement is the extent to which care is delivered by the provider to receivers in a way that enriches customer experience [3]. Service engagement is essential, as it creates better coordination and understanding between customers and service providers, and boosts value co-creation processes. Moreover, prior studies have established that service engagement enhances agility and flexibility in service operations, which, in turn, improves firm performance and supports long-term organizational sustainability [1,2]. Although previous studies have examined service engagement in different contexts, including healthcare [4], limited attention has been devoted to explaining how technological and organizational factors jointly influence service engagement and firm performance in the logistics industry [5]. In particular, the combined effects of the IoT, information pervasiveness, shared decision-making, market orientation, and coordination-oriented HR practices remain underexplored. This study addresses this research gap by developing an integrated service engagement framework for the logistics industry.

According to ref. [1], services are highly intangible; therefore, service engagement can be enhanced through cognitive and relational factors. Consistent with the above argument, the research model of this study underpinned factors such as the Internet of Things, information pervasiveness, shared decision-making, market orientation, and coordination-oriented human resource practices to investigate individual behavior towards service engagement. These factors have substantial support from the resource-based view theory and dynamic capability theory. For instance, the resource-based view suggests that firms achieve competitive advantages by possessing and effectively utilizing valuable, rare, inimitable, and non-substitutable resources. In the context of digital transformation, technological resources such as Internet of Things (IoT) capabilities and information accessibility represent important organizational resources that enable firms to collect, process, and utilize information more effectively. However, technological resources alone cannot automatically generate superior service outcomes. Their value depends on the organization’s capability to integrate resources with strategic orientation and internal management practices [6].

Building on the RBV perspective, dynamic capability theory explains how firms develop the ability to adapt, integrate, and reconfigure their resources in response to changing environmental conditions. In the logistics industry, where customer expectations, technological developments, and market conditions are rapidly evolving, digital capability represents an important dynamic capability that allows firms to transform digital resources into improved service processes and performance outcomes [7]. Therefore, digital capability is expected to strengthen the relationship between service engagement and firm performance by enabling organizations to effectively utilize digital technologies and respond to market demands. Furthermore, service-dominant logistics emphasize that value creation increasingly occurs through interactions among multiple actors rather than through the unilateral delivery of services by firms. From this perspective, service engagement represents an important mechanism through which organizations, customers, and technological systems jointly contribute to value creation. In logistics operations, effective service engagement requires not only technological support, but also organizational coordination, market responsiveness, and human resource capabilities [8]. Accordingly, this study examines the effects of the Internet of Things, information pervasiveness, shared decision-making, market orientation, and coordination-oriented HR practices on service engagement while investigating the moderating role of digital capability in the relationship between service engagement and firm performance.

The Internet of Things (IoT) is increasingly regarded as a critical enabler of service engagement, as it facilitates interactive, personalized, and value-enhancing service experiences that attract and retain customers. Previous studies [5] have stated that IoT in service ecosystems has unlocked unprecedented opportunities for service innovation and customer engagement. Similarly, information pervasiveness and shared decision-making have shown a positive impact on improving service engagement [4,9,10]. In addition, this study established that market orientation and coordination-oriented HR practices boost individual behavior towards service engagement [5,11,12,13]. Based on the above theoretical arguments, this study proposes that technological resources (IOT and information accessibility) and organizational capabilities (market orientation and coordination-oriented human resource practices) contribute to service engagement by improving information processing, organizational responsiveness, and service coordination. Furthermore, digital capability strengthens firms’ ability to transform service engagement into improved firm performance. Thus, the proposed framework extends existing research by explaining not only whether these relationships exist, but also why and under what conditions these relationships occur within the logistics industry context. Furthermore, the proposed framework contributes to the sustainability literature by explaining how digital and organizational capabilities support sustainable service engagement and long-term firm performance in logistics firms. The remainder of this paper includes a detailed literature review, methodology, data analysis, discussion, contributions to theory and practice, conclusions, limitations, and future research directions.

2. Literature Review

2.1. Theoretical Foundation

Digital transformation has fundamentally changed how firms create value, deliver services, and achieve a competitive advantage. These digital transformations have also become essential for achieving sustainable service operations and long-term organizational competitiveness. In this environment, organizational success depends on the ability to effectively integrate technological resources, information capabilities, and managerial practices [14]. Drawing on the resource-based view and dynamic capability theory, this study suggests that firms can enhance performance by developing valuable resources and building capabilities to effectively deploy and adapt to changing environments. IoT enables real-time connectivity, data exchange, and improved operational visibility, allowing firms to enhance service responsiveness and provide more efficient customer solutions [15]. Information pervasiveness improves access to timely and accurate information across organizational levels, supporting better coordination, decision-making, and customer responsiveness. Shared decision-making facilitates collaboration and knowledge integration, enabling organizations to improve problem-solving, employee commitment, and service quality [4]. Similarly, market orientation helps firms to understand customer needs and market changes, allowing them to create superior value and strengthen customer relationships. Coordination-oriented HR practices enhance employee collaboration, knowledge sharing, and service capabilities by aligning workforce skills with organizational objectives.

Furthermore, these technological and organizational capabilities contribute to stronger service engagement by improving responsiveness, interaction quality, and value co-creation with customers. Service engagement is an important mechanism through which organizational resources influence a firm’s performance. Service engagement enables firms to achieve improved performance outcomes by fostering stronger customer relationships, improving service experiences, and enhancing operational effectiveness [16]. Furthermore, digital capability plays a critical role in determining how effectively firms utilize their technological and organizational resources. Firms with stronger digital capabilities are more able to integrate digital technologies, process information, and transform existing resources into strategic advantages [17]. Digital capability strengthens the influence of these resources on service engagement and firm performance. Therefore, the theoretical foundation establishes the relationship between the study variables, and highlights how digital and organizational capabilities contribute to sustainable service engagement, enhanced firm performance, and long-term organizational sustainability. Based on these theoretical arguments, the following section discusses conceptual linkages that explain how these factors influence service engagement and foster organizational performance.

2.2. Internet of Things and Information Pervasiveness

Organizations are looking for innovations in their business processes to compete in a turbulent market. Nevertheless, the arrival of cutting-edge technology such as the Internet of Things (IOT) has made business operations more agile and smooth [5]. The term IoT is defined as a technology that is interconnected with objects and can send or receive data and information through internet networks. Prior studies have revealed that with disruptive technologies such as the Internet of Things, organizations can better control their business processes [5,18]. Moreover, authors such as [18] have asserted that product conception is largely connected with technology; therefore, the Internet of Things could assist customers in participating in the product development process. Therefore, this study conceptualizes that the IoT enhances service engagement. Such technological capabilities enable logistics firms to deliver more sustainable and responsive service operations. Concerning information pervasiveness, the literature reveals a strong relationship between information pervasiveness and service engagement [4,19]. The term information pervasiveness regards the extent to which information is developed and gathered with IOT devices and inserted into a physical system to fulfill customer needs [4]. In addition, information pervasiveness assists devices in connecting with each other and responding to customer queries in a timely manner. Similarly, information pervasiveness allows end users to monitor data from heterogeneous sources and engage customers in business processes. Moreover, prior studies have clearly demonstrated that information pervasiveness relates to the extraction of information from complex databases and improves service experience. Therefore, it is assumed that with pervasive information, customers will be more engaged in services, resulting in a better customer experience, sustainable service engagement, and improved firm performance. Thus, the following hypothesis is proposed:

H1. 

Internet of Things is positively related to service engagement.

H2. 

Information pervasiveness is positively related to service engagement.

2.3. Shared Decision Making and Market Orientation

The role of shared decision-making is inevitable in improving business processes. Shared decision-making involves the consent of both parties, such as sellers and buyers, accepting or rejecting services. Alternatively, shared decision-making could be a part of feedback from customers [10]. Prior literature has revealed that a shared decision-making process enables customers to participate in business operations, thereby improving customer satisfaction [4,9,10]. In other words, it is assumed that with shared decision-making, customers will be more engaged in services, and this will improve firm performance. Nevertheless, studies have revealed that only well-informed customers participate in the shared decision-making process [10,13]. Therefore, market orientation is identified as an important factor that updates customers regarding product features. The term market orientation is extracted from marketing, and is defined as a marketing concept that enables a firm to beat rivals by identifying customers’ needs and wants [13,20]. In the literature, market orientation has been summarized into three core dimensions: generation, dissemination, and responsiveness. The purpose of these dimensions is to extract information through market intelligence and respond quickly to market changes. As services are intangible, market orientation assists service providers in understanding customers’ actual needs [5,13]. In addition, customers have opportunities to understand product features and engage in business processes. Therefore, one can assume that individuals with market orientation will show more inclination towards service engagement. This customer-oriented approach supports sustainable value creation by strengthening long-term customer relationships. Thus, the following hypotheses are proposed:

H3. 

Shared decision-making is positively related to service engagement.

H4. 

Market orientation is positively related to service engagement.

2.4. Coordination-Oriented HR Practices and Digital Capability

Another important factor that could engage customers in service is the firm’s human resource practices. Nevertheless, the literature has emphasized coordination-oriented human resource practices instead of traditional HR practices [11,12]. Coordination-oriented HR practices are seen as practices effectively that engage HR personnel and other stakeholders in actions and policies that achieve organizational goals. Although human resource practices have been widely studied in measuring customer behavioral and effective engagement, few studies have discussed coordination-oriented HR practices in relation to service engagement. Therefore, this study assumes that with the implementation of coordination-oriented HR practices, employees will be more empowered to make decisions, which, in turn, enhances responsiveness and improves logistic firm performance. Moreover, a timely response to customers is recognized as a useful enabler in engaging customers in services. Therefore, it is assumed that the coordination of human-oriented HR practices brings positive changes in customer attitudes and enhances service engagement. To investigate customer behavior in service engagement, this study studied digital capability and linked it with service engagement and firm performance. Digital capability indicates a firm’s digital ability to perform a task [21]. Such coordinated HR practices also support sustainable service delivery by improving collaboration and organizational responsiveness. Nevertheless, this study assumes that digitally capable firms could achieve enhanced service engagement and firm performance. Therefore, the moderating effects of digital capability are conceptualized such that a higher level of digital capability strengthens the relationship between service engagement and firm performance, thereby supporting sustainable organizational performance. The conceptual relationships proposed in this study are illustrated in Figure 1. Thus, the following hypothesis is proposed:

H5. 

Coordination-oriented HR practices are positively related to service engagement.

H6. 

Service engagement is positively related to firm performance.

H7. 

Digital capability moderates the relationship between service engagement and firm performance.

3. Research Methods

3.1. The Research Methods and Sampling

The current research is empirical; therefore, assumptions are evaluated using quantitative data. The following quantitative research framework was designed. The research model of this study was established using a literature review and past studies in the same field. After reviewing the literature, the following assumptions were made. Assumptions are tested with empirical data; therefore, the first research population in this study was assessed. With the help of prior studies, this study concluded that logistic firm managers are considered the most suitable population for this research [22,23]. Nevertheless, a sample size was required for data collection. Therefore, the sample of this study was computed according to the guidelines provided by [24], which suggested that the items of the factors must be multiplied 5 times minimum or 10 times maximum to reveal the exact sample size. Nevertheless, this study comprised 30 items that were multiplied five times (150) and ten times (300). This method suggests that a sample of 300 responses is satisfactory for the factor analysis.

The data were collected using a convenience sampling approach. Although convenience sampling may limit the generalizability of the findings, this approach is widely adopted in organizational and logistics research involving managerial respondents, particularly when access to organizational decision-makers is limited [22,23,24]. Prior to the main survey, a pilot study was conducted to ensure that the contents of the survey questionnaire were easy to understand. Experts have suggested adding the word “logistics” in scale items to bring more clarity in survey questionnaire. After slight amendments, the final research survey was conducted. A total of 325 logistics managers were approached by phone and email and invited to participate in an online survey conducted between May and June 2024. Of these, 312 valid responses were obtained and used in the empirical analysis.

Participation was voluntary and respondents were informed of the academic purpose of the study prior to participation. All participants were adults (18 years or older) and were free to withdraw from the survey at any stage, without any consequences. The study collected only anonymous and non-identifiable data related to organizational practices, and involved minimal risk to the participants. Informed consent was obtained electronically from all the participants before participation in the survey. This study was conducted in accordance with the ethical principles for research involving human participants, including the principles outlined in the Declaration of Helsinki.

3.2. Survey Questionnaire and Scale Development

The research model was tested using empirical observations retrieved from the respondents. The questionnaire was divided into three parts. The first part comprised information about the research survey and the purpose of the research. Therefore, the second part comprised respondents’ information, including age, gender, and education. The third part contained scale items that were adopted from prior literature. For instance, four scale items of the IoT were adapted from [25,26]. Similarly, four scale items of information pervasiveness were adapted from [4,26]. Shared decision-making factors were measured using four items adapted from [3,4]. Moving further to market orientation was measured using four scale items adapted from [27]. Coordination-oriented human resource practices were measured using four scale items adapted from [28]. Next, service engagement was measured using four scale items adapted from [4]. Firm performance was measured using three scale items adapted from [29]. Similarly, digital capability was measured using three scale items adapted from [21]. The Likert scale was used for measurement, with a value of 7 representing strongly agree and 1 representing strongly disagree.

4. Data Analysis

4.1. Common Method Bias

This study used primary data for hypothesis testing. As data were collected through structured questionnaires at one point in time, it is expected that the data could be affected by common method bias issues [24,30]. Nevertheless, this issue can be resolved using procedural and statistical measures. The process of procedural remedies suggests that scale items must be mixed prior to data collection. Nonetheless, statistical measures have been suggested to test the first-factor variance through an un-rotated factor solution [31]. This study followed both procedures to reduce the common method bias issue in the dataset. The results of the statistical analysis suggest that the variance explained by the first unrotated factor solution was only 27% and less than the threshold value, that is, 40% [30,32,33]. Therefore, it was established that the data had no common method bias, and were valid for further statistical analysis.

4.2. Structural Equation Modeling

The structural equation modeling (SEM) approach is the latest statistical approach used for data estimation. The structural equation modeling (SEM) approach has the ability to test complex models, and is therefore improved in this study. There are two types of SEM, namnely, variance-based VB-SEM and covariance-based CB-SEM [34]. Nevertheless, in this study, VB-SEM was adopted as the objective to establish a new research model instead of testing an existing model [30]. Following structural equation modeling, the data were first refined through a measurement model, and then the hypotheses were tested through structural assessment. In the first stage, factor loadings were confirmed using a threshold value of 0.60 [34]. Similarly, factor reliability was ensured through composite reliability and alpha values, following a threshold value of 0.70 [30]. Moving further, convergent validity was tested with average variance extracted following a threshold value of 0.50 [30]. The findings of the measurement model showed satisfactory loadings, except for IOT2. The loading of IOT2 was below the recommended threshold of 0.60; therefore, the indicator was removed to improve construct reliability and convergent validity while preserving the conceptual meaning of the IoT construct. These results have also shown the satisfactory factor reliability and convergent validity of the factors, as shown in

Table 1. Measurement model.

Items	Loadings	(α)	CR	AVE
CHR1: Employees working in this firm are well informed so that they can be deployed quickly.	0.764	0.816	0.878	0.643
CHR2: This firm updates the information system regularly so that employees get informed and can handle disruptions.	0.821
CHR3: Employees are encouraged to share their views and suggestions to improve service operations.	0.795
CHR4: Employees gets group-based compensations that encourage them to coordinate with other core employees in a timely and effective manner.	0.826
DCA1: Digital capability allows this firm to modify services according to requirements.	0.824	0.757	0.861	0.673
DCA2: This firm has integrated digital services including CRM and digital payment services.	0.843
DCA3: This firm makes data digitally available to counterparts.	0.793
FPE1: This firm has improved service quality as compared to the competitors.	0.881	0.851	0.910	0.770
FPE2: This firm has more in return on investment as compared to the rivals.	0.889
FPE3: Sales are increasing in this firm as compared to the rivals.	0.863
IOT1: This firm has stable connectivity with IOT devices.	0.730	0.722	0.844	0.645
IOT3: Employees get effective coordination with the help of IOT devices.	0.878
IOT4: Employees assimilate new information and knowledge with the help of IOT technology and make effective decisions.	0.795
IPE1: IOT devices reveal sensitive information with employees.	0.827	0.861	0.906	0.706
IPE2: IOT devices comprise enough information to detect future events.	0.856
IPE3: Sharing information with IOT devices is easy from remote areas.	0.833
IPE4: Employees get timely notifications with the use of IOT devices.	0.844
MOR1: This firm frequently reviews changes in the market.	0.842	0.866	0.908	0.713
MOR2: If change occurs in the market this firm gets information in a short time period.	0.831
MOR3: This firm is fast to detect customer preferences.	0.873
MOR4: Departments are well integrated and periodically plan to respond to changes.	0.830
SDM1: Services provider offers me options to choose the right service.	0.767	0.767	0.850	0.587
SDM2: Services provider encourages my suggestions and assists me to find the relevant service.	0.747
SDM3: Together the services provider and customers set goals to achieve successful operations.	0.795
SDM4: Services provider involves customers in the planning and execution process.	0.755
SEN1: The level of care service of this firm is excellent.	0.844	0.876	0.915	0.729
SEN2: This firm has adopted stimulating policies to engage customers.	0.862
SEN3: Combined efforts from the services provider and customers improve the level of engagement.	0.867
SEN4: The empathetic style of the service provider gives me a feeling of care.	0.841

.

The measurement model also ensured discriminant validity through a cross-loading analysis. To confirm that factors are discriminant, it is mandatory that the cross-loadings of the factors must be higher than other factor loadings. The results indicate that all loadings are adequate and higher than the other factor loadings. Therefore, it is established that factors that measure distinct concepts in nature are discriminant. The results for discriminant validity are shown in

Table 2. Cross-loadings.

Items	CHR	DCA	FPE	IOT	IPE	MOR	SDM	SEN
CHR1	0.764	0.313	0.390	0.310	0.262	0.233	0.259	0.443
CHR2	0.821	0.347	0.424	0.345	0.265	0.246	0.248	0.474
CHR3	0.795	0.303	0.426	0.278	0.217	0.170	0.237	0.520
CHR4	0.826	0.346	0.481	0.346	0.299	0.291	0.302	0.626
DCA1	0.365	0.824	0.352	0.386	0.239	0.232	0.371	0.365
DCA2	0.302	0.843	0.311	0.335	0.276	0.210	0.399	0.306
DCA3	0.334	0.793	0.312	0.422	0.237	0.166	0.522	0.325
FPE1	0.469	0.335	0.881	0.279	0.449	0.419	0.236	0.633
FPE2	0.525	0.362	0.889	0.379	0.456	0.401	0.285	0.676
FPE3	0.424	0.351	0.863	0.236	0.423	0.389	0.180	0.578
IOT1	0.293	0.365	0.283	0.730	0.195	0.224	0.345	0.283
IOT3	0.321	0.401	0.285	0.878	0.196	0.167	0.347	0.347
IOT4	0.348	0.355	0.262	0.795	0.203	0.184	0.284	0.324
IPE1	0.325	0.240	0.434	0.206	0.827	0.557	0.210	0.397
IPE2	0.278	0.243	0.431	0.198	0.856	0.553	0.181	0.422
IPE3	0.293	0.290	0.431	0.211	0.833	0.583	0.236	0.382
IPE4	0.200	0.254	0.401	0.212	0.844	0.636	0.234	0.384
MOR1	0.236	0.222	0.393	0.149	0.626	0.842	0.161	0.363
MOR2	0.272	0.208	0.369	0.246	0.583	0.831	0.237	0.369
MOR3	0.291	0.225	0.423	0.229	0.571	0.873	0.185	0.403
MOR4	0.191	0.181	0.362	0.164	0.558	0.830	0.122	0.334
SDM1	0.265	0.533	0.244	0.370	0.239	0.253	0.767	0.296
SDM2	0.212	0.321	0.189	0.302	0.137	0.082	0.747	0.217
SDM3	0.265	0.397	0.179	0.274	0.156	0.115	0.795	0.243
SDM4	0.258	0.314	0.204	0.278	0.232	0.167	0.755	0.250
SEN1	0.618	0.346	0.554	0.296	0.358	0.354	0.301	0.844
SEN2	0.534	0.331	0.535	0.330	0.337	0.365	0.286	0.862
SEN3	0.493	0.355	0.616	0.316	0.435	0.392	0.281	0.867
SEN4	0.581	0.354	0.726	0.404	0.467	0.377	0.269	0.841

.

This study established the discriminant validity of the factors using the Fornell and Larcker criterion [35]. Discriminant validity was assessed using the square root value of the average variance extracted. To ensure that the factors are discriminant, the square root value of the average variance extracted must be higher than other values. Nevertheless, results have shown that the square root values of AVE are higher, establishing that factors measure distinct concepts and are valid for inferential analysis. The results of the Fornell and Larcker analysis are presented in

Table 3. Fornell and Larcker analysis.

Factors	CHR	DCA	FPE	IOT	IPE	MOR	SDM	SEN
CHR	0.802
DCA	0.408	0.820
FPE	0.541	0.398	0.878
IOT	0.399	0.465	0.343	0.803
IPE	0.327	0.305	0.505	0.246	0.840
MOR	0.296	0.248	0.459	0.235	0.692	0.844
SDM	0.329	0.522	0.269	0.404	0.255	0.210	0.766
SEN	0.654	0.407	0.719	0.398	0.472	0.437	0.332	0.854

.

The measurement model assessment also included an HTMT analysis to ensure the discriminant validity of the factors. Previous studies [36,37,38] have stated that the HTMT method is the most reliable method to ensure the discriminant validity of the factors, and must be considered in data analysis. Therefore, data were assessed with HTMT analysis following the threshold value that the HTMT ratio must be lower, reaching either <0.85 or <0.90 [30,33,36]. Nonetheless, the results reveal that none of the values were greater than 0.90, thus confirming the discriminant validity of the factors.

Table 4. The HTMT analysis.

Factors	CHR	DCA	FPE	IOT	IPE	MOR	SDM	SEN
Coordination-oriented HR practices
Digital capability	0.516
Firm performance	0.640	0.494
Internet of Things	0.520	0.630	0.436
Information pervasiveness	0.387	0.379	0.589	0.313
Market orientation	0.345	0.304	0.533	0.300	0.803
Shared decision-making	0.408	0.673	0.325	0.539	0.308	0.244
Service engagement	0.757	0.496	0.821	0.494	0.537	0.498	0.400

presents the results of the HTMT analysis.

4.3. Structural Model Assessment

In this study, hypotheses were tested with a structural model following the path coefficient, significance, and t-statistics values [34]. These values were obtained using a bootstrapping procedure. The bootstrapping procedure is a widely recommended method that has been proven to mitigate data normality issues [30,34]. Therefore, the data were bootstrapped to confirm the results. The results of the structural assessment are given in

Table 5. Hypotheses testing.

Hypothesis	Path	β	STDEV	t-Statistics	Significance	Result
H1	IOT → SEN	0.102	0.045	2.253	0.012	Accepted
H2	IPE → SEN	0.182	0.064	2.839	0.002	Accepted
H3	SDM → SEN	0.054	0.041	1.323	0.093	Not-accepted
H4	MOR → SEN	0.128	0.061	2.105	0.018	Accepted
H5	CHR → SEN	0.498	0.038	13.106	0.000	Accepted

, which contains the beta values, standard deviation, t-statistics, and path significance.

The research model is tested using multiple hypotheses, as shown in Table 5. Statistical findings have revealed that the IoT is positively related to service engagement and confirmed by β = 0.102, with t-statistics 2.253 significant at 0.012; hence, H1 is accepted. Information pervasiveness has a positive influence on service engagement, hence confirming H2, with statistical backing of β = 0.182 and t-statistics 2.839 significant at 0.002. The results indicate that the relationship between shared decision-making and service engagement is not significant, and H3 is rejected following the statistics of β = 0.054, with t-statistics 1.323 significant at 0.093. Further, the relationship between market orientation and service engagement is found to be significant and confirmed by β = 0.128, with t-statistics 2.105 significant at 0.018; therefore, H4 is accepted. Coordination-oriented HR practices have shown a positive impact towards service engagement, and are supported by β = 0.498 with t-statistics 13.106 significant at 0.000; hence, H5 is accepted. Alongside hypotheses testing, the results have shown that factors such as the Internet of Things, information pervasiveness, shared decision-making, market orientation, and coordination-oriented HR practices explained a substantial variance (R² = 0.526) in measuring service engagement. Similarly, service engagement and digital capability revealed substantial variance (R² = 0.546) in measuring firm performance.

4.4. Importance Performance Analysis

Although the hypothesis relationships reveal a significant association between predictor and outcome factors, the importance of factors has yet to be evaluated through importance performance matrix analysis. The results of the importance–performance analysis shown in

Table 6. Importance performance analysis results.

Service Engagement
Factors	Total Effect/Importance	Performance
Coordination oriented HR practices	0.333	67.951
Digital capability	0.134	70.890
Internet of things	0.068	69.267
Information pervasiveness	0.122	73.143
Market orientation	0.085	73.963
Shared decision making	0.036	71.114
Service engagement	0.668	66.902

reveal that service engagement achieves the highest importance in determining firm performance. Therefore, coordination-oriented HR practices are found to be the second most important in integrated models. Similarly, the importance of digital capability comes third, followed by information pervasiveness. Nevertheless, the IoT, shared decision-making, and market orientation were found to be the least important. In terms of managerial implications, these results suggest that coordination-oriented HR practices, digital capability, information pervasiveness, and service engagement are important for measuring firm performance, and therefore must be considered when developing new strategies.

4.5. Factors Effect Size

The effect size analysis reveals the actual impact sizes of the factors individually. Understanding the actual effect size within an integrated research model assists managers in choosing relevant and potential factors. Therefore, the data were estimated to reveal the actual effect size f² of the factors following threshold values of 0.35, 0.15 and 0.02, indicating large, medium, and small effect sizes, respectively. The findings of the effect size f² analysis reveal that coordination-oriented HR practices have a large effect size in measuring service engagement. Therefore, other factors had a small effect size. Although IoT, information pervasiveness, shared decision-making, and market orientation exhibited relatively small individual effect sizes, their statistically significant effects indicate that they make complementary contributions within the integrated research model, rather than acting as dominant independent predictors. Similarly, service engagement has a large effect size when measuring firm performance; however, digital capability has a small effect size. These findings indicate that if policymakers want to boost service engagement, they must pay attention to coordination-oriented HR practices. Therefore, if the objective is to boost firm performance, policymakers should focus on service engagement, which in turn increases firm performance. The IPMA findings further indicate that coordination-oriented HR practices and information pervasiveness should be prioritized, because they combine relatively high importance with strong performance. These results suggest that improving coordination mechanisms and information availability can generate greater managerial value than focusing exclusively on factors of relatively smaller practical importance.

Table 7. Factors effect size f² analysis.

Constructs	Service Engagement	Effect Size ${\mathit{f}}^2$
Internet of Things	0.017	Small effect
Information pervasiveness	0.035	Small effect
Market orientation	0.018	Small effect
Shared decision-making	0.005	No-effect
Coordination-oriented HR practices	0.397	Large effect
Firm performance
Digital capability	0.033	Small effect
Service engagement	0.821	Large effect

presents the results of the effect size analysis.

4.6. Moderating Analysis

The moderating hypothesis is tested using the product indicator approach. To ensure that digital capability moderates the relationship between service engagement and firm performance, the first set of data was bootstrapped. The results of the bootstrapping verify that digital capability positively moderates the relationship between service engagement and firm performance, and is supported by the path coefficient β = 0.119, SE 0.039, with t-statistics 3.069 significant at 0.001; hence H6 is accepted. Appendix A presents the results of the moderating analysis. In the second stage, the strength of the moderating factor was tested using simple slope analysis. A simple slope map is shown in Figure 2, which illustrates that the DCA at + ISD shows an upward trend. This indicates that an increase in digital capability strengthens the relationship between service engagement and firm performance. Therefore, policymakers could boost service engagement and firm performance by improving a firm’s digital capability.

5. Discussion

Global access to products and services has completely transformed business operations. Moreover, with the proliferation of technology, customers are choosier and more demanding than ever. Therefore, the ongoing challenge for service providers is how to engage customers in services to enrich their experience. Simultaneously, service providers are increasingly expected to deliver these services in ways that support sustainable operational performance and long-term customer value. To address this issue, current research has developed a research model that combines factors such as the Internet of Things, information pervasiveness, shared decision-making, market orientation, and coordination-oriented human resource practices, and investigates customer behavior in relation to service engagement. The research model of this study was empirically tested using a structural equation modeling approach. The findings provide empirical evidence that integrating technological and organizational capabilities contributes to sustainable service engagement in logistics firms. In addition, a comparison was made to understand how the findings of the current study differ from those of prior studies. This study has confirmed the positive impact of the IoT in measuring service engagement, which is consistent with prior studies [5,18]. Similarly, information pervasiveness has shown a positive influence on service engagement, which is in line with previous research [4,19]. Nevertheless, this study has revealed an insignificant relationship between decision-making and service engagement, and hence, negates arguments developed by prior studies [4,9,10].

Pointing to marketing factors, this study has revealed the positive impact of market orientation towards service engagement, which is consistent with prior studies [5,13]. Likewise, the relationship between coordination-oriented HR practices and service engagement was found to be significant and consistent with previous studies [11,12]. The research model is further evaluated, and statistical analysis has revealed that digital capability positively moderates the relationship between service engagement and firm performance, supporting arguments established by prior researchers [21]. A comparison was also made to determine the effect sizes of the factors. Nevertheless, this study revealed that coordination-oriented HR practices have a large effect size f² in measuring service engagement. Therefore, if policymakers want to engage customers in services, they must adopt coordination-oriented HR practices to boost employee confidence, resulting in increased service engagement. Moreover, the effect size f² has also demonstrated that, for logistics, firm performance service engagement is the core factor. Therefore, improvements in service engagement increase logistics firm performance. These findings further suggest that strengthening service engagement enables logistics firms to improve their operational sustainability through better responsiveness, coordination, and customer value creation. Another important analysis, namely, IPMA analysis, was conducted to obtain an overview of the outlined factors. The IPMA analysis results have suggested that coordination-oriented HR practices, digital capability, information pervasiveness, and service engagement have an attractive level of importance in measuring firm performance, and therefore must be considered while developing new strategies. Overall, this study provides new insights into customer behavior related to service engagement, while demonstrating how technological, organizational, and digital capabilities jointly support sustainable firm performance in the logistics industry.

5.1. Theoretical Contributions

The overarching objective of this study is to advance the understanding of how technological, organizational, and strategic capabilities shape service engagement and subsequently enhance firm performance in the logistics context. Drawing on the Resource-Based View and Dynamic Capability Theory, this study develops a theoretical framework to explain how firms can leverage digital resources, information capabilities, managerial practices, and human resource systems to strengthen service engagement and achieve superior performance outcomes. First, it contributes to the service and digital transformation literature by explaining the mechanisms through which technological capabilities influence service engagement. Specifically, the findings demonstrate that the Internet of Things (IoT) and information pervasiveness are not merely technological investments, but strategic resources that improve information availability, operational responsiveness, and service interaction quality. By highlighting how digital technologies facilitate value creation through enhanced service engagement, this study extends the existing research that has primarily examined technology adoption from an operational perspective. Second, this study contributes to the understanding of the organizational and strategic antecedents of service engagement by demonstrating the role of shared decision-making and market orientation. Shared decision-making reflects the importance of managerial and organizational processes in integrating knowledge and improving responsiveness, whereas market orientation highlights how customer-focused strategic practices facilitate value co-creation. By positioning these factors as drivers of service engagement, this study provides a broader theoretical explanation of how internal capabilities and external market focus jointly influence service-related outcomes. Third, this study extends human resource management literature by introducing coordination-oriented HR practices as an important antecedent of service engagement. Previous studies have examined supportive and flexible HR practices; however, limited attention has been paid to how coordination-focused HR systems enhance collaboration, employee confidence, and the ability to deliver effective services. This study advances the existing knowledge by explaining the role of coordinated human resource practices in strengthening service engagement within logistics firms. Furthermore, this study contributes to the literature by conceptualizing digital capability as an important boundary condition that determines the extent to which service engagement translates into firm performance. The findings indicate that firms with stronger digital capabilities are more able to convert service engagement into improved performance outcomes, highlighting the strategic importance of digital capability beyond direct operational benefits. Collectively, the findings provide empirical support for the proposed framework, with the antecedent factors explaining the substantial variance in service engagement (R² = 0.526), while service engagement and digital capability explain the substantial variance in firm performance (R² = 0.546). These results reinforce the theoretical relevance of the model, and demonstrate how technological, organizational, and strategic capabilities collectively contribute to service engagement and sustainable firm performance in a logistics setting. In addition, the findings extend sustainability literature by demonstrating that sustainable firm performance can be achieved through the integration of digital technologies, organizational capabilities, and coordination-oriented human resource practices.

5.2. Contribution to Theory

The findings of this research could help policymakers recognize the factors that boost service engagement and firm performance. Current research has summarized that factors such as the Internet of Things, information pervasiveness, market orientation, and coordination-oriented human resource practices are key factors that enhance service engagement and enrich customer experiences. This study revealed that the coordination of human resource practices has a large effect size in measuring service engagement. The strong effect size of coordination HR practices has also demonstrated that with strong coordination, employees get ample knowledge to entrain customers and hence secure better customer engagement in services. This finding indicates that policymakers could enhance service engagement through the implementation of coordinated human resource practices in logistic firms. Moreover, firm performance was predicted using digital capability and service engagement. Nevertheless, the statistical findings confirm that firm performance can be achieved with a high level of service engagement. Although hypothesis testing revealed the nature of the relationship, the research framework was examined further with IPMA analysis. The results show that factors such as coordination-oriented HR practices, digital capability, information pervasiveness, and service engagement have considerable importance in measuring firm performance, and therefore, must be considered in developing new strategies. Moving further, this study has also instructed policymakers to enhance service engagement and firm performance through the intervention of digital capability. For example, the moderating effect of digital capability shows that a higher level of digital capability of a firm will boost service engagement and logistics performance. Finally, this research has shown that factors such as the Internet of Things, information pervasiveness, market orientation, and coordination-oriented human resource practices are core factors that enhance service engagement. Therefore, policymakers should consider these factors to improve the performance of the logistics firm. These managerial actions can also support more sustainable logistics operations by strengthening long-term service quality, digital responsiveness, and organizational competitiveness.

6. Conclusions and Policy Recommendations

The rising intensity of global competition and rapid advances in artificial intelligence (AI) and digital technologies have significantly transformed business operations, particularly within the logistics sector. Customers now have greater access to products and services across geographical boundaries and, consequently, have increasingly demanding expectations regarding service quality, responsiveness, accessibility, and overall experience. In this changing business environment, logistics firms face the continuing challenge of identifying and developing technological, organizational, and human resource capabilities that can strengthen customer service engagement, while simultaneously improving long-term firm performance. To address this issue, this study developed and empirically tested a research model integrating IoT, information pervasiveness, shared decision-making, market orientation, and coordination-oriented human resource practices as antecedents of service engagement. In addition, this study examines the moderating role of digital capability in the relationship between service engagement and firm performance.

The empirical findings lead to several important conclusions. The hypothesis testing results demonstrate that IoT, information pervasiveness, market orientation, and coordination-oriented human resource practices have significant effects on service engagement, whereas shared decision-making does not demonstrate a significant effect. These findings suggest that technological and organizational capabilities play an important role in strengthening service engagement within logistics firms. Furthermore, the five exogenous factors collectively explain the substantial variance in service engagement, with an R² value of 0.526. This indicates that the proposed model explains 52.6% of the variance in service engagement, and provides substantial empirical support for understanding the factors that contribute to customer-oriented service engagement in the logistics context.

The findings further demonstrate that service engagement is an important determinant of a logistics firm’s performance. Service engagement and digital capability jointly explain the substantial variance in firm performance, with an R² value of 0.546. Moreover, moderation analysis confirms that digital capability strengthens the positive relationship between service engagement and firm performance. Thus, logistics firms with stronger digital capabilities are better positioned to convert service engagement into improved organizational performance. The effect size analysis further demonstrates that coordination-oriented human resource practices have a large effect on service engagement, while service engagement has a large effect on firm performance. These results highlight the importance of integrating technological capabilities with organizational and human resource capabilities, rather than relying on a single source of competitive advantage.

These findings also have important implications for policymakers. First, policymakers should encourage and facilitate the adoption of coordination-oriented human resource practices within logistics firms. The large effect size identified for coordination-oriented HR practices demonstrates their importance in strengthening service engagement. Effective coordination among employees can improve information-sharing, knowledge exchange, communication, and employees’ ability to understand and respond to customer requirements. Employees with timely and relevant knowledge are more able to interact with customers, address service needs, and provide responsive solutions. Therefore, policymakers and relevant industry authorities can promote human resource development frameworks, training initiatives, interdepartmental coordination mechanisms, and organizational practices that strengthen employee collaboration and knowledge-sharing within logistics firms.

Second, policymakers should support the development and adoption of IoT technologies in the logistics sector. The significant relationship between IoT and service engagement indicates that connected technologies can contribute to a better service experience. IoT-enabled logistics systems can facilitate real-time monitoring, tracking, information exchange, and operational visibility, thereby improving service responsiveness and customer confidence. Accordingly, policymakers could develop supportive digital infrastructure, technology adoption programs, industry standards, and incentives that encourage logistics firms, particularly smaller ones, to adopt IoT-enabled operational systems. Such interventions could contribute to more responsive and transparent logistics services.

Third, policymakers should prioritize information pervasiveness as an important component of digital logistics development. The empirical results identify information pervasiveness as a significant factor in enhancing service engagement, while the IPMA results further indicate its importance for firm performance. Policymakers could, therefore, encourage logistics firms to establish systems that ensure accurate, timely, accessible, and widely available information across relevant organizational and customer interfaces. Policies supporting data integration, information-sharing standards, digital information platforms, and interoperability among logistics stakeholders can improve the availability and usefulness of information throughout the service process.

Fourth, policymakers should encourage logistics firms to strengthen their market orientations. The significant influence of market orientation on service engagement indicates that firms that continuously identify customer needs, monitor market development, and respond to changing customer expectations are more capable of creating meaningful service experiences. Policymakers and industry institutions can support market-oriented development through sector-specific market intelligence initiatives, customer-service training, industry benchmarking, and programs that help logistics firms better understand evolving customer expectations. Such measures can strengthen firms’ responsiveness to changing market conditions, and contribute to sustainable service quality.

Fifth, the findings highlight the importance of digital capabilities in improving logistics firm performance. This significant moderating effect demonstrates that higher digital capability strengthens the relationship between service engagement and firm performance. Therefore, policymakers should consider digital capability development as a strategic priority in logistics sector policies. This may include support for digital transformation programs, employee digital skills development, technology training, digital infrastructure, and initiatives that help firms integrate digital technologies into their core operations. Strengthening digital capabilities can enable logistics firms to respond more effectively to customers and translate service engagement into stronger organizational performance.

Sixth, the IPMA results provide further guidance for prioritizing strategic interventions. The analysis indicates that coordination-oriented HR practices, digital capability, information pervasiveness, and service engagement are important in explaining firm performance. Therefore, these factors should receive particular attention when policymakers and industry stakeholders formulate new strategies for improving the logistics sector’s competitiveness. Rather than focusing exclusively on technological investment, policy initiatives should adopt an integrated approach that combines digital transformation with employee capabilities, organizational coordination, information accessibility, and customer engagement.

Finally, empirical evidence demonstrates that service engagement is a central mechanism connecting organizational and technological capabilities with firm performance. Although shared decision-making did not show a significant direct effect on service engagement in the present study, the findings do not diminish the broader importance of organizational participation; rather, they indicate that, within the specific research context and empirical model, other capabilities demonstrated stronger explanatory power. Policymakers and logistics-sector stakeholders should, therefore, prioritize the factors supported by empirical evidence while continuing to evaluate organizational practices in different contexts.

Overall, the present study demonstrates that integrating technological, organizational, market-oriented, and human resource capabilities can enable logistics firms to strengthen service engagement and improve firm performance in an increasingly digital business environment. These findings provide a basis for policymakers to formulate targeted interventions focusing on IoT adoption, information accessibility, market orientation, coordination-oriented HR practices, and digital capability development. These policy directions can support logistics firms in improving their service quality, customer responsiveness, digital adaptability, and organizational competitiveness. In the long term, such interventions can also contribute to more sustainable logistics operations by strengthening service quality, organizational resilience, digital responsiveness, and competitiveness. Thus, the present study provides empirical evidence regarding the determinants of service engagement and firm performance, as well as actionable implications for policymakers and industry stakeholders seeking to develop a more digitally capable, customer-oriented, and sustainable logistics sector.

There are some limitations to this study that are acknowledged and should be addressed in future studies. First, this research has focused on IoT technology; however, big data analytics and artificial intelligence are other important factors that need to be investigated. Therefore, future researchers should examine the role of big data analytics and artificial intelligence in improving service engagement. Second, although the coordination of human resource practices addresses the key role of HR practices in the management context, there are still some other important factors that need consideration, such as employee empowerment and supportive leadership. Moreover, the research model of this study has tested direct hypotheses and moderating paths to analyze the research model; however, adding a mediating factor could reveal more interesting findings. Future research should investigate the mediating role of service engagement between exogenous and endogenous factors. Regarding the research methodology, the use of a probability sampling approach could enhance the strength of the data; therefore, future researchers should choose sampling methods derived through probability sampling. In terms of the time horizon, this research is cross-sectional; however, a longitudinal method could enhance the generalizability of this research. As this study employed a cross-sectional design, the findings should be interpreted as associative rather than definitive causal relationships.