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Article

Integration, Resilience, and Innovation Capability Enhance LSPs’ Operational Performance

by
Qining Deng
and
K. Noorliza
*
Operations Management, Universiti Sains Malaysia, Minden 11800, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1019; https://doi.org/10.3390/su15021019
Submission received: 22 November 2022 / Revised: 22 December 2022 / Accepted: 25 December 2022 / Published: 5 January 2023
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Abstract

:
In the context of the development of industry 4.0 embedded in various industries, organizations face stiffening competition from external dynamically changing and unpredictable environments. To remain competitive and sustainable in this era, organizations need resilience and innovation capability. Therefore, this paper aims to investigate the association between external integration, resilience, innovation capability, and logistics service providers (LSPs) operational performance. Moreover, this research investigates the mediating effects of sustainable logistics and innovation capability between external integration and LSPs operational performance. Based on Resource orchestration theory, a framework has been drawn. The survey of 273 Chinese LSPs was examined through the PLS-SEM technique. The findings indicate that external integration has a positive relationship with logistics resilience and innovation capability, which have a positive impact on LSPs’ operational performance. The results also show that innovation capability positively mediates the relationship between external integration and operational performance. Unexpectedly, logistics resilience has not played a mediating role between external integration and operational performance. This study makes contributions to the construction of a mechanism of LSP performance improvement by integrating the external environment, resilience, and innovation. The paper also advanced the theory of resource orchestration theory by adding these two mediators of logistics resilience and innovation capability

1. Introduction

The globalization of the economy and the boom of e-commerce have given logistics service providers (LSPs) great potential for growth and an increasing market value [1]. The services provided by LSPs are employed by more than 90% of the Fortune 500 enterprises, representing a $735 billion global business market [2]. Benefiting from specialization and economies of scale, LSPs can offer customers lower costs but with better quality logistics services, as well as provide customized services to meet customers’ needs [3,4]. Therefore, the services provided by LSPs have attracted a lot of attention. Meanwhile, the increased demand for logistics outsourcing services has placed higher demands on the relationship management between LSPs and their customers [5,6]. As the number of customers partnering with LSPs increases, the network they build will expand, involving more supply chain (SC) members and more opportunities for interaction and collaboration within the network, resulting in a more complex supply chain network [7,8]. External integration between LSPs and customers is becoming increasingly important in this context [9]. The pursuit of increasing logistics services quality and customer satisfaction rate enables LSPs to strengthen their collaborative networks and conduct external integration to achieve superior service performance [10,11].
Numerous types of research highlighted the significance of external integration for firms’ performance [12,13]. For example, Raue and Wieland [14] indicate that horizontal collaboration among LSPs can be used to gain cost advantages and improve logistics performance through resource sharing and joint activities, such as improving the efficiency of the transportation network and speeding up transportation. Consequently, external integration has emerged as a critical factor for LSPs to acquire better performance and keep their competitive edge, as well as become a significant area of academic study [15]. Given the fast-changing logistics needs and the development of LSPs in past few year, the services provided by LSPs have evolved from basic outsourced logistics services to more advanced value-added services [8]. Accordingly, the role of LSPs in the supply chain has evolved from simple participants, to integrators and orchestrators [16]. In other words, LSPs can expand their control and scope of integration within the supply chain network and contribute more to external integration [17].
Nevertheless, the positive role that LSPs play in external integration has been overlooked. Although there are substantial studies examining the external integration relationship between logistics service providers and customers, most of them start from the perspective of manufacturer-initiated SCI, lacking the exploration of external integration from the perspective of LSPs acting as integration initiators [18]. In addition, although existing research has shown that external integration helps companies improve firm performance, the research on this performance is more focused on, for example, service quality performance and financial performance, which are often understood from a single dimension, overlooking their holistic nature [19,20]. Operational performance, on the other hand, covers a variety of measures such as logistics performance, financial performance, and service capability. Therefore, this study will take operational performance as the object of study in the firm performance of LSPs.
As a result of increased specialization and globalization, enterprises’ supply networks have not only grown more complicated but also face more regular and varied unanticipated events [21]. COVID-19 also prompts many LSPs to focus on the importance of logistics resilience. The ability to be resilient to cope with risk and uncertainty is one of the key measures of firm performance for LSPs [22]. If LSPs do not respond to changes promptly, original demand will be delayed, which will lead to greater operational stress [23]. Innovation is widely recognized as a key capability for LSPs to cope with the volatile business environment, as the innovation capability helps LSPs develop new services and products which are customized based on customer needs; this unique ability is the basis for LSPs to gain an advantage in long-term market competition and is a driving force for LSPs to achieve better firm performance [24,25]. Considering the changing needs and escalating expectations of customers, as well as the intensifying competitive environment, LSPs need to improve their innovation capabilities [26]. LSPs, especially those in China, generally, lack sufficient innovation in service options or products [27]. The rapidly growing and diversified demands of customers are putting pressure on LSPs to improve their logistics services through innovative capabilities [28]. Bellingkrodt and Wallenburg [29] argue that the innovation capability helps LSPs to increase customer satisfaction, capture more market share, and achieve a first-mover edge over competitors, and consequently, the performance of LSPs is significantly improved [30]. Competing in the dynamic and uncertain environments in the logistics industry, LSPs should leverage resources with resilience and innovation capability to gain more competitiveness and improve operational performance.
The main contributions of this paper are as follows: Firstly, to bridge the gap of external integration from the perspective of LSPs-initiated and the ignorance of LSPs’ operational performance, this study examines the influence of LSPs as external integration initiators on the their operational performance [18,19,20]. Secondly, based on ROT, this research constructs and evaluates a conceptual model of the associations between external integration, logistics resilience, innovation capability, and operational performance, which extends the existing knowledge of the relationship between external integration and operational performance from the perspective of LSPs, Thirdly, this study provides practical insights for LSPs to deal with a dynamic and uncertain environment. Specifically, LSPs should highlight the external horizontal and vertical integration, and fully leverage the innovation capability during the integration process. The key research questions are raised as follows:
RQ1:
Does external integration affect logistics resilience and innovation capability?
RQ2:
Do logistics resilience and innovation capability have influences on operational performance?
RQ3:
Does external integration improve operational performance through the mediation role of logistics resilience and innovation capability?
The remaining parts of this study were structured as follows: Section 2 introduces the underpinning theory and performs the literature review on external integration, logistics resilience, innovation, and operational performance, and subsequently proposes the hypotheses to be analyzed. Then, a conceptual framework is developed. Section 3 is the research methodology, including the development of the survey instrument and data collection. In Section 4, we present the data analysis and results. Section 5 presents the discussion according to the results of data analysis. Section 6 makes an overall conclusion and discusses limitations and future research suggestions.

2. Literature Review and Hypotheses Development

2.1. Resource Orchestration Theory (ROT)

Resource orchestration theory suggests that firms can create more value by collecting, integrating, coordinating, and managing valuable, rare, inimitable, and irreplaceable resources to achieve potential advantages [31,32]. Due to the dynamic environment, firm resource base should be extended to resource orchestration theory. However, the RBV has been criticized for its oversight of dynamics capability and lack of explanation of redeployment of resources, which thereby fail to generate synergistic effects for firms [33]. Conversely, ROT focuses on the dynamic ability to holistically redeploy resources to achieve superior firm performance [34].
ROT proposes that resource utilization, rather than simple resource possession, is essential for generating greater value and improving the companies’ performance [33]. These resources not only include internal resources of the firm, but also external resources which come from the supply chain participants, such as suppliers, clients, stakeholders, competitors, as well as LSPs [35]. Resources utilization is the process of restructuring, tying up, and exploiting resources from multiple sources to enhance firm performance. This process consists of three phases: firstly, resources restructuring relates to building a portfolio of resources; secondly, resources tying up refers to managing resources to build capabilities; and finally, resources exploiting means the utilization of capabilities to create value [36,37].
According to ROT, different resource have different features and uniqueness, such that resources originating from different organizations have their complementary nature and can generate specific potential advantages in the process of resource integration [38]. It is consistent with external integration, which emphasizes achieving resource integration across organizational boundaries. Therefore, ROT is helpful for understanding the deployment of resources and abilities such as external integration [32].

2.2. External Integration

Previous studies point out that supply chain integration can be classified into three main forms: (1) internal integration and external integration, (2) forward integration and backward integration, and (3) value chain integration [39,40]. Among them, most studies tend to consider internal and external integration as the primary forms of supply chain integration [41]. Internal integration refers to the collaboration between different departments inside the enterprise, whereas external integration refers to the integration between the other supply chain partners [12,13]. External integration is defined as companies engaging in various forms of strategic collaboration with supply chain members at various levels [42]. External integration can be achieved through information-sharing, jointly decision-making, knowledge transfer, and alliance building, which exist within and across organizational boundaries [43,44]. To LSPs, external integration would normally be categorized into customer integration (CI) and logistics services providers integration (LI). Customer integration within this study is different from the previous definition, where the “customer” refers to those users seeking LSPs services, while the “logistics services providers” are the companies who aim to provide logistics services to those customers. Hence, logistics services providers integration represents the integration among peers of LSPs [45].
As the importance of LSPs has received growing attention, the studies examining the relationships between LI and its effectiveness on operational performance has increased rapidly [46]. In recent years, the functions of LSPs has gradually changed, from traditionally providing fundamental logistics services, like the transport and storage services, to expanding the scope of services [47]. Specifically, LSPs bundle their core logistics services and other value-added logistics-related services to increase more competitive advantages [48]. In addition, since LSPs are situated in the middle of SC network, they will have more collaboration with other supply chain participants. Therefore, LSPs become significant supporters of external integration and can even act as integration initiators [8]. Although the association between LSPs and external integration has been examined empirically in many researches, the characteristics of the studies are that they all take the manufacturer as the initiator of external integration, while ignoring the perspective which takes the LSP as a integrator [18,49]. Therefore, in this study, we will take the LSP as an integrator of external integration.

2.3. Logistics Resilience

Generally, most of the literature defined logistics resilience (LR) as the ability to facilitate organizations to survive, adapt, respond, as well as grow in confronting unpredictable disruptions, and, to enhance abilities to make modifications according to adversity and the changing external environment [50,51,52]. By combining and reconfiguring the firms’ available resources and capabilities, such an ability is developed [53].
Previous studies have provided various views concerning the composition of logistics resilience [52]. Jüttner and Maklan [54] conceptualized logistics resilience as four dimensions: flexibility, velocity, visibility, and collaboration. Ivanov and Dolgui [55] suggest that the key elements of logistics resilience involve redundancy, real-time monitoring, visibility, and recovery plans. Wieland and Wallenburg [56] incorporated agility and robustness as two main factors of logistics resilience. Liu et al. [57] employed risk management culture, agility, integration, and SC restructuring to illustrate the characteristics of logistics resilience. In summary, the above supply chain elements may overlap somewhat in concept, but they are all important for constructing and increasing logistics resilience.
With the extension of SC network, the entities and practices involved in logistics services have become more diverse and complex, deepening their connection [58]. At the same time, the emergence of new requirements in logistics, such as shorter lead times, the pursuit of lower costs, and diverse customer demands, as well as the occurrence of unexpected events such as inclement weather, transportation network disruptions or congestion, and incompetent service from logistics partners, have posed additional operational challenges to the logistics services provided by LSPs [45,58,59]. For LSPs, logistics resilience is a company’s capacity to respond to such changes and the ability to exceed them [60]. In other words, logistics resilience can help companies achieve better operational performance [61]. For LSPs, logistics resilience is not only the ability to recover from interruptions and sustain its stability and consistency of business operation, but also the capability to deal with changing market conditions [60].

2.4. Innovation Capability

Innovation capability (IC) refers to the ability of a company to respond to changing market demand by developing new products, services, ideas, or technologies [61,62]. The existing studies propose different categories of innovation, the widely accepted form of innovation is to classify innovations into ideas, products, services, processes, organizational, and technologies innovation [63,64]. IC is associated with the physical resources and knowledge base of LSPs, which contribute to the increasing of IC, thereby improving the firm’s performance [65]. Due to the increasingly complex supply chain network, changing market demands, and diversification of logistics services, the fiercely competitive market environment has placed higher demands on LSPs’ services. In order to meet market expectations and acquire competitive edges in the fiercely competitive environment, the logistics services offered by LSPs should be more innovative [28]. Prior literature has emphasized that IC is able to improve logistics performance and operational performance [62,66]. However, the amount of existing literature examining the logistics services of LSPs from an innovation perspective is still limited. In particular, they lack a consensus on the connotation of logistics service innovation [67]. Thus, the focus of present study is the logistics services innovation of LSPs.
Logistics services are the center of LSPs’ activities; hence, the innovation of logistics service is a critical dimension for LSPs [26]. Logistics services not only refer to the services of logistics transportation, but also to other logistics-related services that are considered novel and beneficial for LSPs and their customers, which can satisfy customers’ need and consequently increase the performance of LSPs [68]. However, Wanger [63] emphasizes that logistics service innovation is not only service innovation, but may also involve other aspects of innovation, such as technological innovation (EDI, RFID, IoT) and product innovation (consulting and financial services). Therefore, other forms of innovation construct the realization of logistics services innovative. As a result, logistics service innovation is defined as the innovatively developed or remarkably enhanced multifaceted logistics service provided by LSPs [30].
Prior studies have emphasized the significant effect of IC in the increase of competitive advantage and achievement of superior performance. However, there is limited research on the innovative logistics service capabilities of 3PL providers, in particular; the amount of literature linking it to operational performance and examining their association is insufficient [30]. In summary, the research on the link of the innovation capability to LSPs performance is still in its infancy [30,67].

2.5. Operational Performance

LSPs firms aim to meet customer needs and provide various values to customers through logistics services. Past researches have explored the knowledge of the firm performance of LSPs [45]. Concerning firm performance, previous studies into logistics systems have considered multiple dimensions, such as organizational, financial, and operational performance. The most frequently discussed performance is financial and operational performance [69,70]. As a rule of thumb, financial performance is regarded as a trailing indicator, whereas operational performance is typically considered as a leading indicator. The lagging indicators are used to measure previous performance while leading indicators serve to forecast future performance [71]. Therefore, this research will concentrate on the operational performance of LSPs.
Operational performance (PR) refers to an indicator designed to measure the effectiveness of a company in achieving its goals and objectives [72]. To better measure and understand the performance of a company in achieving its goals and outcomes, the performance needs to be measured from diversified perspectives; hence, operational performance is a multidimensional concept [73]. In current literature, different studies have different perspectives on the composition of the indicators included in operational performance. In other words, some literature argues that some of the indicators of logistics service performance have a significant degree of overlap with operational performance. According to previous literature, the indicators of operational performance should include cost, quality, flexibility, delivery, inventory turnover, and lead time [74,75,76]. Bayraktar et al. [76] reviewed the indicators of operational performance and argue that key success factors and operational performance metrics contain delivery quality and satisfaction-rate of customers. Given that the service capabilities offered to customers are the primary value that LSPs supply, we also consider the improvement of customer satisfaction as another important attribute of operational performance [20]. Consequently, within this research, these elements are employed to evaluate operational performance: delivery, flexibility, quality, and customer satisfaction.

2.6. External Integration and Operational Performance

It is now well-established from a variety of studies that external integration significantly affects operational performance. Demeter et al. [77] affirmed that external integration positively influences manufacturing firms’ PR. External integration is widely acknowledged as a key driver for improving the PR of enterprises by joining other supply chain members to reach collaboration and interaction at both strategic and tactical levels, resulting in improved PR such as lower cost, shorter lead time, and higher reliability [78]. For example, strategically, supply chain members can improve performance by sharing common goals and clarifying priorities. Tactically, they can optimize resource use and management efficiency by sharing operational data (such as the delivery schedules, routesm and vehicle availability), joint planning, and system coupling to increase service flexibility. Improved logistics collaboration capabilities can have a positive impact on PR [79]. Similarly, integration with customers improves the level of collaboration mainly through in-depth information exchange with customers, for example, to obtain more market demand and expectation in order to better serve customers’ needs quickly and accurately, thus improving the service level, thereby boosting the competitiveness and market share of firms [79]. Accordingly, the below hypotheses have been raised:
H1. 
External integration positively influences operational performance.
H1a. 
Customer integration positively influences LSPs operational performance.
H1b. 
Logistics service providers’ integration positively influences the LSPs operational performance.

2.7. External Integration and Logistics Resilience

Logistics resilience helps a company to quickly adapt to changes in its complex external business environment that is unstable and unpredictable [80,81]. Prior literature has found that external integration significantly influences logistics resilience. Hohenstein et al. [52] indicated that external integration of LSPs is associated with higher logistics resilience. By establishing mutually advantageous relationships with customers, competitors, as well as other stakeholders, a highly collaborative supply chain partner network can be constructed [82]. Accordingly, the key resources (such as data, technologies, and raw materials) will be shared within this network, the resources can be reconfigured, and coordinated operations can be conducted, resulting in increased responsiveness and quick adaptability to external demands and changes [83,84]. Thus, the following hypotheses have been developed:
H2. 
External integration has a positive impact on logistics resilience.
H2a. 
Customer integration has a positive impact on logistics resilience.
H2b. 
Logistics service providers’ integration has a positive impact on logistics resilience.

2.8. External Integration and Innovation Capability

Collaboration and resource sharing (including the sharing of information, core know-how, and technologies) across organizational boundaries that operate along the supply and value chains, encompassing supply chain participants, is the primary aspect of external integration [85]. IC is a comprehensive ability to improve and even fundamentally change outcomes of products, services, processes, and technologies [86]. Past studies underline the positive effect of external integration on achieving better IC [68]. According to ROT, external integration can help companies access and build the relational resources of supply chain network participants that are highly collaborative [32,68]. For LSPs, the information and feedback from supply chain participants can provide LSPs with new ideas or new solutions for improving current services [87,88]. Furthermore, due to its highly collaborative relationship involving different teams from supply chain members, it leads to a greater diversity of innovative ideas and helps to advance new technologies, services, and processes [89]. External integration facilitates the acquisition of new skills and ideas to develop innovative capabilities by making quality resources and information available to each other as members [30]. Specifically, most Chinese LSPs lack the resources required for IC, while such scarce resources can be obtained from its supply chain network members. This sharing of resources with its complementary nature can not only address the knowledge gaps that exist between companies but also enable value co-creation, thus developing IC, such as novel product and services development [90]. Based on the above discussion, the following hypotheses were proposed:
H3. 
External integration correlates positively with innovation capability.
H3a. 
Customer integration correlates positively with IC.
H3b. 
Logistics service providers integration correlates positively with IC.

2.9. Logistics Resilience and Operational Performance

According to ROT, logistics resilience allows LSP to continuously evolve and rebuild their capabilities through the integration and reconfiguration of resources, and transformation of operational procedures [32,91]. This dynamic capability can drive LSP to respond to dynamically changing environments promptly. Logistics resilience not only affects company’s responsiveness and adaptability to external environment but also exerts an influence on firm’s performance [8,92]. Some studies have provided empirical evidence that there is a correlation between higher logistics resilience and better firm PR [25,52,93]. Liu and Lee [45] reported that LR has positive effect on firm performance, because logistics resilience of 3PL can maximize resource utilization rate by detecting and responding to changes and threats, proposing solutions, and reallocating existing resources. As a result, customer requirements can be met, and customer satisfaction can be increased. In addition, a 3PL with a more dynamic logistics resilience can adapt to fluctuations in demand along the value chain and provide a customized experience for customers [94]. Based on these previous findings, the fourth hypothesis of the present research is:
H4. 
Logistics resilience correlates positively with LSPs’ operational performance.

2.10. Innovation Capability and Operational Performance

Innovation capability refers to the ability to enhance firms competitiveness by transforming new ideas and knowledge received into new products, technologies, services, which cater to market demands [62]. Companies with superior level of IC are more likely to adapt quickly and respond appropriately to changes in the external environment and provide various forms of innovative services to capture market opportunities, especially in the dynamic market [95,96]. Such capability to handle the growing complexity of the dynamic environment and rapid change is critical to achieving superior firm performance [97]. Wagner [63] emphasized the significance of IC to the success of LSPs. Prior researches have revealed a significant correlation between IC and PR [68,98]. The use of innovative technology enables LSPs to improve its internal operational and management efficiencies, thus contributing to the improvement of operational performance [68]. For example, the use of RFID technology, warehousing technology, and transportation management systems can improve the data process, processes optimization, and operations planning abilities of LSPs, thereby increasing the logistics reliability and operational efficiency of LSPs [99,100].
In addition, IC supports LSPs to provide better customer services, which in turn increase firm performance [68]. To be specific, innovative LSPs can offer novel services which are diversified and detailed to fulfill the requirements of their customers. LSPs that enjoy high innovation capabilities can design solutions and higher quality services that are customized to the demands and issues raised by customers, thus increasing customer satisfaction [101].
Nevertheless, some studies have found that there is no particular interplay between innovation capability and firm performance; for example, Damanpour et al. [102] revealed that continuous use of the same innovation model for many years fails to contribute to the improvement of performance outcomes. However, exploiting different models of innovation in different industries leads to different performance outcomes. Moreover, existing theories and most of the available studies indicate that innovation capability and firm performance are positively related [103]. According to above discussions, we draw the hypothesis:
H5. 
Innovation capability correlates positively with LSPs’ operational performance.

2.11. Mediating Effect of Logistics Resilience

Prior literature discussed the mediating role played by LR in different research domains. For instance, Ji et al. [104] proposed that LR mediates the relationship between supplier integration and environmental performance in manufacturing companies. This finding affirmed that logistics resilience partially performs a mediating role. Similarly, Bahrami et al. [105] examined the role of logistics resilience in mediating digital analytics and performance in Iran. The findings highlight the full mediating role of logistics resilience. Likewise, Bahrami and Shokouhyar [106] also affirmed this results. Finally, Rezaei et al. [107] concluded that logistics resilience has a mediating role between data analytics capability and competitive advantage. Supported by previous studies, we proposed the following hypotheses:
H6. 
Logistics resilience positively mediates the relationship between external integration and LSP’s operational performance.
H6a. 
Logistics resilience positively mediates the relationship between customer integration and LSP’s operational performance.
H6b. 
Logistics resilience positively mediates the relationship between LSPs integration and LSPs operational performance.

2.12. Mediating Effect of Innovation Capability

Past studies also discuss the mediating effect of IC in the different research domains. Shou et al. [68] explored how IC mediates the association between relationship resources and LSPs performance in China. This finding affirmed that IC mediates the relationships among study variables. Liu et al. [108] pointed out the mediating effect of IC between human capital and competitiveness. Hwang et al. [109] affirmed that IC is able to mediate entrepreneurial capabilities and competitiveness. Finally, AlTaweel et al. [110] concluded the mediating role played by IC between strategic agility and firm performance. According to these arguments, we proposed the seventh hypothesis. Figure 1 shows the conceptual framework in detail.
H7. 
Innovation capability positively mediates the relationship between external integration and LSP’s operational performance.
H7a. 
Innovation capability positively mediates the relationship between customer integration and LSP’s operational performance.
H7b. 
Innovation capability positively mediates the relationship between LSPs integration and LSPs operational performance.

3. Methodology

This research adopts quantitative research methodology to investigate the proposed hypotheses. This part includes the study instruments, data collection procedure, and operationalization of questionnaire measurements. Data analysis was performed by PLS-SEM, and the software we adopted is SmartPLS 3.3 [111].

3.1. Instrument and Measures

To statistically test the hypotheses, a structured questionnaire was performed which consisted of four parts: external integration (CI and LI), logistics resilience, innovation capability, and operational performance. All the measurement indicators were displayed in Table 1. The questionnaire was formulated according to the previous literature [25,37,45,56,112]. The questionnaire was designed as a five-point Likert scale ranging from “1 = strongly disagree” to “5 = strongly agree” [113]. The instrument has English and Chinese versions. This study performed pre-test to improve its readability. The pilot test on 50 LSPs confirmed its reliability. The detailed questionnaire is presented in Appendix A.

3.2. Sample and Data Collection

A sample of 1000 Chinese LSPs firms was drawn from a Chinese LSPs directory website (http://www.6-china.com/company/ accessed on 15 September 2022). This directory is reliable and frequently used by previous literature [115]. The survey was randomly emailed to the LSPs and answered by the middle and senior managers because of their deeper and comprehensive understanding of the company’s operation situation and strategic decisions. To ensure that the respondents are middle and senior managers, a filter question was set at the beginning of the questionnaire: “I am a middle or higher level management or decision maker”. After follow-up and reminder emails, a total of 290 questionnaire were returned, of which 17 were unusable due to lacking adequate information. The 273 valid responses represent a response rate of 27.3%. The profiles of the respondents are displayed in Table 2.
According to Table 2, nearly half of the target companies are medium-sized (42.12%) LSPs, and the main logistics services types provided by these companies are relatively evenly distributed. Most of them specialize in one logistics service, and only 12.28% of them are able to provide multiple logistics services. Respondents are middle and senior managers, mostly from the transportation/logistics and operations departments.

4. Results

4.1. Descriptive Statistics and Common Method Bias

This part demonstrate the value of mean, kurtosis, and skewness of constructs. Table 3 shows the descriptive analysis in detail. Specifically, the values of skewness and kurtosis did not exceed the threshold range, such as, ±2 [116].
In addition, the common method bias (CMB) was used to analyze the biasness of data. According to Podsakoff et al. [117], the procedural and statistical measures should be conducted to eliminate the CMB issue. Statistically, Harman’s one-factor test has been performed. The exploratory factor analysis underlines that the single factor should account for ≤50% of the variance. Previous studies argued that [118,119] if the value of variance inflation factor (VIF) through the full collinearity test is less than 3.3, it represents that the data is free from the CMB issue. For the results shown in Table 4, all the VIF value are lower than 3.3; therefore, CMB is unlikely to be considered as a problem in this study [118,119].

4.2. Assessment of Measurement Model

4.2.1. Reliability and Convergent Validity

The measurement model assessment consists of reliability and validity [120]. Regarding reliability, composite reliability (CR) and rho_A were adopted to measure the data reliability. Previous studies recommended that if the Cronbach’s Alpha and rho_A values ≥ 0.70, this means that the data are reliable and verified [120,121]. In terms of convergent validity, item loading and average variance extracted (AVE) are used to measure it [122]. According to Hair et al. [120], item loading exceed 0.70 is considered excellent. Nevertheless, if the loading falls between 0.4–0.7 and AVE is ≥0.5, then researchers can retain the constructs. Moreover, the threshold value of AVE is ≥0.5. Table 4 shows the results of reliability and convergent validity.

4.2.2. Discriminant Validity

According to previous studies, three main approaches are normally used to evaluate discriminant validity: Fornell-Larker, cross-loading, and Heterotrait-Monotrait ratio (HTMT). However, the previous two approaches were criticized due to their ineffectiveness in the detection of discriminant validity [123]. To be specific, Fornell-Larcker requires AVE’s square root to be higher than its highest correlation with any other constructs in the model; as for cross-loading, it requires the item-loading on its construct to be larger than its cross-loadings [124]. The HTMT criteria can accurately detect the lack of discriminant validity since it contrasts the indicator correlations between constructs with the correlations within indicators of the same construct [120]. Compared to HTMT, Fornell-Larker and cross-loading approaches lack in establishing the distinctiveness and sensitivity between constructs; the last two approaches fail to effectively determine the discriminant validity of their measures [120,123]. Consequently, this study selected HTMT approach to examine the discriminant validity. The favorable value of HTMT is less than 0.85 [120]. Table 5 displays the discriminant validity (HTMT) of all latent variables; it shows that the values of HTMT were below the recommended threshold limit (less than 0.85).

4.3. Assessment of Structural Model

Hair et al. [120] suggested that the relationship between exogenous and endogenous variables can be tested in the structural model assessment. Table 6 displays the outcomes of structural model, effect size, and the criteria of hypotheses accepted or rejected. Based on the results, the association between CI → PR (H1a: β = 0.266, t = 3.616, p < 0.05), and LI → PR (H1b: β = 0.171, t = 2.408, p < 0.05) are positive and significant. Hence, H1a and H1b have been accepted. Furthermore, the association between CI → LR (H2a: β = 0.517, t = 8.824, p < 0.05) and LI → LR (H2b: β = 0.170, t = 2.850, p < 0.05) are positive and significant. Therefore, H2a and H2b have been accepted. These results also emphasized that the relationship between CI → IC (H3a: β = 0.211, t = 3.129, p < 0.05) and LI → CI (H3b: β = 0.447, t = 6.474, p < 0.05) are significant and positive. Thus, H3a, and H3b have been accepted. However, the relationship between LR → PR (H4: β = 0.063, t = 0.783, p > 0.05) is insignificant; hence H4 has been rejected. Finally, the association between IC → PR (H5: β = 0.157, t = 2.568, p < 0.05) is significant and positive. Thus, H5 has been accepted.

4.4. Mediation Analysis

To test the mediating role played by LR and IC, we follow a relatively new analytical method. According to Nitzl et al. [125], a two-step approach for mediation analysis was adopted. In the first step, the significance of indirect relationships was examined. Then, the strength and magnitude of the mediating variable are examined through the Variance Accounted For (VAF) ratio (a × b/(a × b + c’). The value of VAF < 0.20 represents no mediating effect, 0.20 ≤ VAF ≤ 0.80 means it has partial mediating effect, and VAF > 0.80 means it has full mediating effect [122].
Table 7 shows the results of mediation analysis. In the first step, all the indirect relationships are significant. In the second step, the mediating type has been identified. Based on Table 7, first-step results indicate that LR has no indirect relationship between CI, LI, and PR. Thus, H6a and H6b have been rejected. On the other hand, the first-step result shows that IC has a mediating role between CI, LI, and PR. The second step of analysis indicate that IC has no indirect relationship between CI and PR because the value of VAF is less than 20%, and thus, H7a has been rejected, whereas IC has a partial mediation effect between LI and PR, and VAF value is more than 20%. Thus, H7b has been accepted.

4.5. Q2 (Predictive Relevance) and R2 (Explained Variance)

In the PLS-SEM technique, Q2 affirms the model’s accuracy and predictive relevance, and R2 refers to the extent to which independent variables explain the variance of dependent variables [126]. This technique accurately and efficiently demonstrates the data points of variables in the case of the reflective model. Hair et al. [127] claim that if the value of Q2 exceeds 0.0, this model would be regarded as being capable of predicting the value of the dependent variables. To be specific, Q2 ≥ 0 means small, Q2 ≥ 0.25 represents medium, and Q2 ≥ 0.50 means large predictive relevance of the PLS-path model [127]. Table 8 shows the Q2 of LR, IC, and PR. The results indicate that the value of Q2 of LR (0.245) and IC (0.223) is close to medium predictive relevance, and the value of Q2 of PR has weak predictive relevance.
In terms of R2, the value of R2 ranges from 0 to 1, with higher values indicating a greater explanatory ability [127]. According to Hair et al. [126], 0≤ R2 ≤ 0.25 shows a weak association, 0.25 ≤ R2 ≤ 0.50 moderate, and 0.50 ≤ R2 ≤ 0.75 strong. Table 8 indicates that the value of Q2 of LR (0.368) has strong association, and that of IC (0.321) and PR (0.260) has moderate association. Finally, Figure 2 shows the complete path model.

5. Discussion

This study focuses on the analysis of the direct association between external integration (customer and LSPs) and operational performance. Additionally, this research examined the mediating effect of LR and IC between external integration and operation performance of LSPs.
The first research hypothesis (H1) affirmed that external integration (CI & LI) has a significant and positive relationship with LSPs operational performance. This hypothesis is further subdivided into H1a and H1b. The results of SEM indicated that external integration (CI & LI) has a positive and significant relationship with operational performance. The finding of H1 are in accordance with previous studies. For example, Demeter et al. [77] concluded that external integration is a significant driver of operational performance in manufacturing firms. Likewise, Chen et al. [79] affirmed that both supplier and customer integration are necessary to achieve higher firm operational performance. Although the significance of external integration in enhancing operational performance has already been demonstrated by past research, but our findings further emphasize its worth for LSPs firms.
The second hypothesis (H2) affirmed that external integration has a positive association with logistics resilience. This hypothesis is further subdivided into H2a (CI → LR) and H2b (LI → LR). The empirical findings highlight that CI and LI are positively correlated with logistics resilience. Therefore, H2a and H2b have been accepted. The findings of this research are in accordance with previous studies. For example, Siagian et al. [126] argued that there is a positive and significant association between external integration and resilience among Indonesian manufacturing firms. They further argued that the positive association between SCI and logistics resilience helps organizations to respond to sudden market changes. Similarly, Liu and Lee [45] affirmed that SCI (customer and internal) is positively correlated with logistics resilience among logistics service providers. CI and LI can directly increase LR; however, CI has a stronger impact on LR. It indicates that LSPs can achieve better effects when coping with change and responding to the changing market by integrating with the customer.
The third hypothesis (H3) affirmed that external integration (CI & LI) has a positive and significant association with IC. To achieve this, H3a and H3b have been formulated. The results show that there is a significant association between external integration and IC; thus, H3a and H3b have been accepted. The study results are consistent with past literature. For example, Freije et al. [128] highlighted that SCI is positively correlated with IC among manufacturing companies. Likewise, this conclusion was also confirmed by Tian et al. [86] and Adebanjo et al. [129]. Theoretically, the results of the study suggest facing demand pressures, such as those demands from SC customers, facilitating the development of innovation capabilities, such as the development of novel products, and novel services. In fact, the results indicate that institutional forces formed based on SC relationships and integration strengths contribute to the development of LSPs’ innovation capabilities [129]. From the view of market competition, the results of this study imply that LSPs are increasingly focused on enhancing their innovation abilities. Once this capability is developed to a more advanced level, it helps to strengthen the competitiveness and performance of firms.
The fourth hypothesis (H4) affirmed that LR has a significant association with operational performance. Surprisingly, the results affirmed that logistics resilience has an insignificant association with performance. Thus, H4 has been rejected. Gupta et al. [130] argued that lack of digital resources is the barrier to promoting logistics resilience in manufacturing firms. Moreover, James et al. [131] argued that small manufacturing firms have a lack of financial resources to implement digital technology. Consequently, their logistics or supply chain is not resilient to cope the future challenges. Thus, the firm cannot gain competitive advantage. Therefore, the potential explanation for the insignificant relationship between LR and PR might be due to the lack of use of digital technology and its financial support. Hence, if enterprises aim to increase operational performance through logistics resilience, they should emphasize the role of digital technology and its financial investment.
The fifth research hypothesis (H5) affirmed that IC has significant linkages with operational performance. The empirical findings confirmed the positive relationship; thus, H5 has been accepted. The results are in line with past literature. Altaweel et al. [110] concluded that the link between IC and organizational performance is highly significant. Similarly, Liu et al. [108] affirmed that the link between IC and organizational performance is statistically significant. Companies with innovation capability are more capable to develop innovative products and services to fulfil the diverse customers’ demands and thus to improve operational performance in a changing market environment.
Apart from the direct effect, the indirect effect hypotheses were also been formulated through the examination of mediators. The sixth research hypothesis (H6) affirmed that logistics resilience has a mediating role between external integration and performance. To achieve this, H6a and H6b have been formulated. The empirical findings indicated that LR has no mediating role between external integration and performance. Thus, H6a and H6b have been rejected. The results show that the logistics resilience is insignificant for the improvement of LSPs operational performance. It might because the logistics of Chinese LSPs are not highly resilient yet. This results can be explained by a research conducted by Abeysekara et al. [112]: this study claimed that an organization’s resilience ability has no direct effect on performance. The logistics resilience abilities contain various kinds of factors; hence, increasing the positive effect of LR on performance requires LSPs to build resilience capabilities in multiple dimensions.
Finally, the seventh research hypothesis (H7) affirmed that IC has a mediating role between external integration and performance. To achieve this, H7a, and H7b have been proposed. The empirical findings indicated that IC has a partial mediating association between external integration (LR) and PR. Therefore, H7b has been accepted. The results of this study are in agreement with past research. Shou et al. [68] concluded that IC positively mediates the relationship between organizational resources and organizational performance. Likewise, AlTaweel and Al-Hawary [110] argued that IC has a mediating effect between the agility and performance of enterprise. Finally, Tian et al. [86] concluded that IC significantly mediates the relationship between SCI and organizational performance in Ghanaian SMEs manufacturing. These results proved that integration and IC are crucial success elements for dealing with change and uncertainty, which thereby increase operational performance of LSPs to achieve more advantages in the market. In particular, the results of this study show that the mediation effect of IC between customer integration and operational performance is stronger than its mediation effect between LI and operational performance. Therefore, in order to increase the operational performance, compared to the horizontal integration with other LSPs, LSPs should concentrate more on the integration with SC customers.

6. Conclusions, Contributions, and Implications

6.1. Key Findings

This study explores the mediating role of LR and IC in the framework of examining the relationship between external integration and LSPs operational performance. According to the results, external integration can positively promote the operational performance of LSPs; moreover, the two different subdivisions of external integration (CI & LI) differ in the extent to which they exert positive effects on performance. Specifically, CI has a more significant effect on the LSPs’ operational performance. IC positively mediates the relationship between external integration and operational performance, while LR fails to mediate their relationship. Overall, the findings indicated that CI and LI are the essential elements for increasing PR. Meanwhile, IC partially mediates the relationship between external integration and PR. Therefore, enhancing CI is a valuable strategy to realize better PR.

6.2. Theoretical Contributions

This study developed an integration-resilience-innovation-performance model in the Chinese LSPs context. This model is supported by the resource orchestration theory. Our findings expand the existing understanding of the connection between integration and operational performance. Firstly, the results endorse the theoretical mechanisms of how external integration facilitates in enhancing innovation capability and contributes to superior operational performance. Secondly, this research has highlighted the importance of external integration in improving LSPs’ operational performance. Thirdly, this study provides insight on the mediator role played by IC in the linkage between external integration and PR. Apart from that, this study provides empirical evidence to bridge the gap in the existing studies on external integration from the perspective of LI.

6.3. Practical Implications

Apart from research findings, this research offers several implications for logistics service providers. First, it is critical to conduct external integration which contributes to superior operational performance. Hence, LSPs need to establish closer vertical and horizontal collaborative partnerships with SC members. Particularly, this study reveals that the direct effect of CI on PR was higher than the effect of LI on PR Thus, we recommend that LSPs firms should first focus on CI, and deepen collaborative relationships with customers to guarantee a high potential for improving better PR. Second, the results highlight that IC helps to improve the PR of LSPs in China. It shows that LSPs managers may develop innovation capability concurrently with a strategic purpose, because Chinese LSPs confront increasingly fierce competition. In this situation, innovation capability can be efficient and effective strategic weapons for LSPs to grow and sustain their competitiveness. Furthermore, it helps LSPs to stand out from rivals and obtaining financial stability in the marketplace. Third, the LSPs practitioners need to understand the value of external integration in their operations to promote IC. As the results of this study revealed, external integration is capable of enhancing the IC of LSPs. Therefore, managers may dedicate themselves to the development of external integration, which serves as a source of IC. Moreover, the association between external integration, IC, and operational performance forms a robust system that allows the organization to anticipate quick market changes and minimize disruption to enhance competitiveness. Finally, these findings assist LSPs practitioners to better understand the importance of integration and innovation in a holistic manner to improve firms’ operational performance. This highlights that each external integration is a prerequisite of IC to enhance operational performance.

6.4. Limitations and Implications for Future Research

Although this study addressed critical constructs of logistics management, there were several limitations which lay the foundation for future studies. Firstly, this model only contributes 26% to the LSPs operational performance, which means that, in future studies, more factors should be included in the improvement of operational performance. In addition, surprisingly, logistics resilience fails to mediate the association between external integration and operational performance. In the current situation, the Chinese LSPs fail to have a high logistics resilience, resulting in the difficulty of enhancing operational performance. Therefore, for future study, how to guide LSPs and other relevant or similar industries in other countries to develop more resilient logistics to improve resource deployment capabilities and operational performance in a dynamic and changing environment could be a direction for future research.

Author Contributions

Conceptualization, Q.D. and K.N.; methodology, Q.D. and K.N.; validation, Q.D. and K.N.; formal analysis, Q.D. and K.N.; investigation, Q.D. and K.N.; data curation, Q.D. and K.N.; writing—original draft preparation, Q.D. and K.N.; writing—review and editing, Q.D. and K.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universiti Sains Malaysia, Grant RU 1001/PMGT/8016031.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The author is grateful to the Universiti Sains Malaysia for the fund given.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Construct ItemSources
Customer integrationCI1Comprehensive plan with customers Liu and Lee, 2018 [45]
CI2Communicate with customers
CI3Have a long-term contract
CI4Exchange ideas with key customers
CI5Integrates its operations with customers
CI6Integrates its information with customers
CI7Establishes a fast order system with key customers
Logistics service providers integrationLI1Cooperate with other LSPs Liu and Lee, 2018 [45]
LI2Works with other LSPs on a long-term basis
LI3Exchange ideas with key LSPs
LI4Integrate its operations with other LSPs
LI5Integrates its information systems with those of crucial LSPs
Logistics resilienceLR1Can cope with changes brought by the supply chain disruptionAmbulkar et al., 2015 [114]; Chunsheng et al., 2020 [37]
LR2Can adapt to the supply chain disruption easily
LR3Can able to provide a quick response to the supply chain disruption
LR4Can maintain high situational awareness at all times
LR5Can extract meaning and useful knowledge from disruptions and unexpected events
Innovation capabilityIC1Adopts new skills and technologyDovbischuk, 2022 [25]
IC2Introduces new products or services
IC3Incorporates information about our industry, customers and competitors
IC4Seeks creative solutions
IC5Seeks new ideas and opportunities
Operational performancePR1Can provide on-time delivery rate Wieland and Wallenburg, 2013 [56]
PR2Has high customer satisfaction
PR3Has problem-solving capabilities for customers
PR4Has low customer complaint rate

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Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
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Figure 2. PLS path model.
Figure 2. PLS path model.
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Table
Table 1. Measurements construction of questionnaire.
Table 1. Measurements construction of questionnaire.
VariablesNumber of MeasurementsReferences
Independent variablesCustomer integration7[45]
LSPs integration5[45]
Mediator Logistics resilience5[37,114]
Innovation capability5[25]
Dependent variablesOperational performance4[56]
TOTAL26
Table
Table 2. Respondents’ profile.
Table 2. Respondents’ profile.
CharacteristicsTypesNumberPercentage
Company Size2 and less than 20 employees8129.67
20–100 employees11542.12
More than 100 employees7728.21
Logistics service typeLand freight4516.48
Sea freight3713.55
Air freight4917.95
Multimodal transport3512.82
Warehousing4014.65
Distribution3813.92
Others2910.63
PositionGeneral Manager8029.30
Director5921.61
Department Manager7928.93
Other5520.16
DepartmentWarehousing3010.99
Transportation/Logistics8832.23
Information system2107.69
Operation7025.65
Other6423.44
Table
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
ConstructsNMeanKurtosis Skewness
CI2733.681−1.057−0.189
LI2734.061−1.232−0.121
LR2734.188−1.196−0.036
IC2733.649−0.965−0.334
PR2733.983−0.859−0.188
Table
Table 4. The results of reliability and convergent validity.
Table 4. The results of reliability and convergent validity.
DimensionsItemsLoadingVIFReliabilityAVE
CRrho_A
Customer IntegrationCI10.7611.6550.8870.8510.568
CI20.7781.797
CI30.7311.671
CI40.7451.641
CI50.7591.703
CI60.7471.711
Logistics Services Providers IntegrationLI10.8782.4910.9310.9030.771
LI20.8782.522
LI30.8822.633
LI40.8752.598
Logistics ResilienceLR10.8272.0710.9140.8870.681
LR20.7921.943
LR30.8121.965
LR40.8582.389
LR50.8332.112
Innovation CapabilityIC10.8302.2370.9240.9020.709
IC20.8132.062
IC30.8542.335
IC40.8612.581
IC50.8502.572
Operational PerformancePR10.7521.5350.8530.7840.593
PR20.8101.663
PR30.8181.589
PR40.6941.373
Table
Table 5. Discriminant Validity (HTMT).
Table 5. Discriminant Validity (HTMT).
ConstructsCILILRICPR
CI0.754
LI0.4630.878
LR0.6730.4170.825
IC0.4450.5890.2930.842
PR0.5340.4620.3920.4360.770
Table
Table 6. Structural model and effect size.
Table 6. Structural model and effect size.
RelationshipsPath Coefficient (β)t-Valuep-ValueDecision
H1a: CI → PR0.2663.6160.000Accepted
H1b: LI → PR0.1712.4080.010Accepted
H2a: CI → LR0.5178.8240.000Accepted
H2b: LI → LR0.1702.8500.003Accepted
H3a: CI → IC0.2113.1290.001Accepted
H3b: LI → IC0.4476.4740.000Accepted
H4: LR → PR0.0630.7830.219Rejected
H5: IC → PR0.1572.5680.007Accepted
Table
Table 7. Mediation Analysis.
Table 7. Mediation Analysis.
RelationshipBetap-Valuea × bVAFDecision
Step-1H6a: CI → LR → PR0.0330.242
H6b: LI → LR → PR0.0110.178
H7a: CI → IC → PR0.0330.048
H7b: LI → IC → PR0.0700.009
Step-2H7aPath a: CI → IC0.2110.0010.0339%No Mediation
Path b: IC → PR0.1570.001
Path c: CI → PR0.3320.001
H7bPath a: LI → IC0.4470.0000.07022%Partial mediation
Path b: IC → PR0.1570.001
Path c: LI → PR0.2520.000
Table
Table 8. The results of Q2 and R2.
Table 8. The results of Q2 and R2.
ConstructsR2Q2
LR0.3680.245
IC0.3210.223
PR0.2600.147
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Deng, Q.; Noorliza, K. Integration, Resilience, and Innovation Capability Enhance LSPs’ Operational Performance. Sustainability 2023, 15, 1019. https://doi.org/10.3390/su15021019

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Deng Q, Noorliza K. Integration, Resilience, and Innovation Capability Enhance LSPs’ Operational Performance. Sustainability. 2023; 15(2):1019. https://doi.org/10.3390/su15021019

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Deng, Qining, and K. Noorliza. 2023. "Integration, Resilience, and Innovation Capability Enhance LSPs’ Operational Performance" Sustainability 15, no. 2: 1019. https://doi.org/10.3390/su15021019

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