No. 005/2020/POM

Performance Implications of Logistics Information Technology Adoption for 3PL
Firms

Haidi Zhou
Department of Innovation, Entrepreneurship, and Strategy
The School of Management, Xi’an Jiaotong University (XJTU)

Liang Wang
Department of Economics and Decision Sciences
China Europe International Business School (CEIBS)

Qiang Wang*
Department of Innovation, Entrepreneurship, and Strategy
The School of Management, Xi’an Jiaotong University (XJTU)

Xiande Zhao
Department of Economics and Decision Sciences
China Europe International Business School (CEIBS)

February 2020*

Corresponding author: Qiang Wang (qiangwang@xjtu.edu.cn). Address: Department of Innovation, Entrepreneurship, andStrategy, School of Management, Xi’an Jiaotong University (XJTU), No. 28 Xianning West Road, Beilin District, Xi’an, China
710049.

Performance Implications of Logistics Information Technology Adoption for 3PL
Firms
Abstract
Purpose— The third-party logistics (3PL) firms increasingly rely on information
technology (IT) to improve the supply chain process and firm performance in thecontext of the globalized and fiercely competitive market. The purpose of this study is
to investigate how logistics IT adoption as a standardized resource affects firm
performance. Moreover, we explore the mediating role of customer collaboration andthe moderating role of government policy support between logistics IT adoption andfirm performance from the resource-based view and socio-technical perspective.
Design/ methodology/ approach— Survey data acquired from a sample of 235 3PL
firms in China were analyzed using partial least squares structural equation modeling(PLS-SEM).
Findings—The empirical results show that logistics IT adoption has a positive effecton both financial and operational performance by strengthening customer
collaboration. Additionally, government policy support amplifies the positive effect ofcustomer collaboration on operational performance, rather than on financial
performance.
Originality/value—This study offers rich empirical insights to the growing body of
SCM and 3PL literature. And the findings contribute to our understanding of thetechnological and developmental issues of 3PL firms both theoretically and1practically.
Keywords: information technology adoption, financial performance, operational
performance, customer collaboration, government policy support
Introduction
With the fierce market competition and increasingly complex logistics service needs,a growing number of firms are relying on third-party logistics (3PL) to centralize
main business, reduce inventory, and improve operational efficiency (Sum et al.,2001;
Selnes and Sallis,2003; Hsiao et al., 2010; Stank et al., 2017). 3PL has been rapidly
expanding and becomes the fundamental industry that supports economic and social
development, with the development of China’s manufacturing industries. According
to China Federation of Logistics & Purchasing (2020), the total logistics revenue was
10.3 trillion RMB in 2019, with the growth rate of 9%, and the total logistics
expenditures were 14.6 trillion RMB, accounting for 14.7% of GDP. In recent decades,with the development of Internet and mobile Internet, information technology (IT) has
injected new vitality into service industries and becomes a new driving force to propel
the development of the logistics firms (Choy et al., 2014).
Drawing on previous literature, the use of IT has a significant influence on
business performance (Karia, 2018; Evangelista et al., 2012, Li et al., 2009;
Bharadwaj, 2000). Logistics information technologies, e.g. data acquisition
technologies (e.g. RFID), information technologies (e.g. EDI), warehousing
technologies and transportation technologies (e.g. GPS, GIS) help firms integrate
information across the supply chain and thus speed the delivery time (Hazen and Byrd,2

2012; Lin, 2007; Yang et al., 2009). There is a trend that logistics firms focusing on IT
tend to better meet customer requirements (Tripathy et al., 2016) and lead tocompetitive advantage (Lai et al., 2006). However, there are many logistics firms
failing to achieve better financial performance by investing in logistics IT (Lai et al.,2007). Due to the high cost of advanced technology, the difficulty of establishingcomplex information systems, and the lack of IT experts’ guidance, it is easy forlogistics firms to lose cost advantages when adopting IT (Jeffery and Leliveld, 2004).
Besides, as a kind of standardized resource, specific technologies are easily duplicatedby other competitors, which makes it difficult for firms adopting IT to maintain asustained competitive advantage. As such, given the rapid development of 3PL
industries based on logistics IT, an in-depth investigation of the underlying
mechanism via which logistics IT adoption affects the financial and operational
performance of 3PL firms, especially within the current dynamic social-economic
environment, is called for.
To address this research gap, we develop a theoretical model of logistics IT
adoption and empirically test it using data from a sample of 235 3PL firms in China.
Although IT may fail to create competitive advantage when acting alone, its value canbe realized when it is associated with organizational capability along the supply chain(Wong et al., 2015; Xu, et al., 2014; Powell and Dent-Micallef, 1997). From therelational view of strategic management and prior studies, collaboration withcustomers along supply chains is a strategic resource or capability (Dyer and Singh,
1998; Stank et al., 2001; Leuschner et al., 2013; Xu et al., 2014; Flynn et al., 2010;3

Zhao et al., 2011; Sehgal and Gupta, 2020). Thus drawing on the resource-based view(RBV) and socio-technical perspective, we propose that customer collaboration as anorganizational capability, can work as a catalyst in converting IT into higher value forfirms. Moreover, this relationship needs to be re-examined in the context of 3PL withthe changing environment dynamics, especially when it comes to government policies
and regulations in emerging economies. In this study, we examine the influential
contingency effect of government policy support in moderating the relationshipbetween customer collaboration and firm performance. Integrating the socio-technicalperspective and RBV as the theoretical underpinnings provide a strong foundation tounderstand how 3PL firms pursue better financial and operational performance when
faced with changing technological and social dynamics. Practically, our findings shedlight on how IT adoption helps 3PL providers, highlight the important role ofcustomer collaboration when transforming logistics IT investments into firm
performance, and encourage the government to provide supportive policies tostrengthen the performance improvements from IT-enabled customer collaborations.
The remainder of this paper is structured as follows. In the next part, we review theliterature about theoretical background and propose research hypotheses. And thenresearch methodology is presented, followed by the analysis and result section.
Finally, we close with conclusions and limitations.
Theoretical background and research hypotheses
RBV and socio-technical perspective
The resource-based view highlights the importance of unique resources and capacities
4and regards them as the most important source to achieve better corporate
performance (Barney, 1991). The core of RBV suggests that only if the resources andcapabilities are valuable, rare, inimitable and non-substitutable, they can constitute asource of sustainable competitive advantage (Wernerfelt, 2016). Thus, a firm should
possess certain intangible resources that competitors cannot copy or buy easily to gain
a competitive advantage in the market. Resources and capabilities are two important
tools used by firms to develop and implement their strategies, and how to create andsustain competitive advantage by leveraging them has become the focus ofresearchers (Srivastava et al., 1998; Hunt and Morgan, 1995; Srivastava et al., 2001).
While in the field of information systems, physical IT as a standardized resource
may not create sustainable competitive advantage when acting alone from theperspective of RBV (Mata et al., 1995) because competitors can acquire the similar IT
easily. Therefore, how IT can enhance firms’ performance has become a hot topic in
recent research. Bharadwaj (2000), Hazen and Byrd (2012), and Karia (2018)suggested synchronizing and associating sophisticated technologies with an integratedorganizational capability through resource complementarity and co-specialization toachieve information advantage and synergistic benefits. According to strategic
management perspective, the ability to manage inter-organizational relations should
be regarded as a strategic resource, while partners in the supply chain should be
regarded as capabilities (Dyer and Singh, 1998; Paulraj et al., 2008; Rungtusanatham
et al., 2003). Huo (2012) and Xu et al. (2014) considered collaboration withcustomers as relational capability and competency. Sehgal and Gupta (2020) posited5

that firms who integrate customers into production activities have greater potential tostay innovative and inimitable. As such, in this paper, we aim to examine the adoptionof logistics IT in 3PL firms and propose that logistics IT can benefit 3PL firms’
financial performance and operational performance by being associated with customer
collaboration in order to acquire complementary capabilities and expertise that are
unique and inimitable.
The concept of socio-technical perspective originated in the British coal mining
industry (Trist, 1981), and it argued that advanced technologies, whether accompaniedor predicted by regulatory action, have become the driving force in addressing
complex inter- and intra-organizational problems. However, it cannot function
effectively to improve corporate performance and practices when acting alone(Fischer and Herrmann, 2011). In this regard, the main insight of socio-technicalperspective is that the technical factors and social factors are interdependent within
the whole social-economic environment (Trist and Murray, 1993), which indicatesthat technology could not be effectively exploited without fully understanding thesocial environment (Trist and Bamforth, 1951; Emery, 1959; Jaffe et al., 2003). In
general, technical factors include technology, machines, or equipment, and they are
produced and continuously modified to provide a reliable experience for individual
users and serve the needs of the organization. Social factors include social
relationship that needs to coordinate, control, and continuously evolve to manage
emerging risks in the organization (Carayon et al., 2015; Mumford, 2006).
Social-economic environment refers to the policy implemented by the government. It
6has been posited that social factors behave differently from technical factors due tothe nature of people in social networks which are more unobservable and subtle(Walker et al., 2008). The two factors are both affected by the changes in the wholesocial-economic environment no matter it is regulated or formal, as social-economic
environment can create constraints or incentives that influence the process oftechnological change (Kerr and Newell, 2003).
In this paper, the socio-technical perspective is applied to the logistics industry as
technical factors, social factors, and social-economic factors are all essential factors
for 3PL firms’ long-term success in rapidly developing supply chains (Chen andPaulraj, 2004). Logistics IT and customer collaboration are technical and social
factors respectively to disentangle the mechanism that explains 3PL firms’
performance. We propose that logistics IT can have a great effect on firm performance
via customer collaboration, and the relationships between customer collaboration andfirms’ operational and financial performance are moderated by the government
policies and regulations within a dynamic social-economic environment.
Conceptual model
Based on RBV and socio-technical perspective, we propose a conceptual model of therelationships among logistics IT adoption, customer collaboration, firm performanceand government policy support grounded in (see Figure 1). In this model, logistics IT
adoption affects firm performance through customer collaboration, and therelationships between customer collaboration and firm performance will be moderated
by government policy support. In particular, firm performance includes financial
7performance and operational performance, which is consistent with supply chain
management (SCM) literature (e.g., Wu et al., 2006; Sanders, 2007, 2008).
--- Insert Figure 1 about Here --Research hypotheses
Logistics IT adoption and customer collaboration
IT adoption is the extent to which a firm embeds a certain set of information
technologies in its operations and makes them fully functioned (Li et al., 2009). In
this paper, we mainly focus on three common logistics information technologies:
Internet of Things technology, big data technology and logistics information
management platforms, in which Internet of Things technology includes Radio
Frequency Identification (RFID), Bar codes, Satellite positioning system or othertracking technology (GPS), Data logger, Machine learning, Cloud computing and soon; big data technology can manage to monitor business operation data, real –timedata of business flow and logistics nodes; logistics information management platforms
include Enterprise Resource Planning (ERP), Electronic Ordering System (EOS),
Transportation Management System (TMS), Warehouse Management System (WMS),
Freight Forwarding Management System (FMS), Order Management System (OMS)
and so on.

Nowadays, information technologies permeate many parts of the supply

chain processes and convey information about related exchange activities. Increasing
investments of logistics IT has become a trend in logistics firms to promote theircompetitive advantages (Lai et al., 2010). On the one hand, managers use different
information technologies to properly allocate and utilize existing resources to improve8

the efficiency and effectiveness of day-to-day logistics operations in the short term;on the other hand, the IT systems can help to analyze business information moreaccurately and intelligently to support decision making in the long term (Elbashir etal., 2008).
Customer collaboration refers to the degree of provider-buyer collaboration
through mutual sharing of resources, information, strategy, and knowledge in thesynchronized process of operations (Stevens and Johnson, 2016). It includes
collaboration of materials, human capital and equipment, from placing orders tofollow-up orders (Sahin and Robinson, 2002). Successful customer collaboration
requires accurate and timely information shared between 3PL firms and theircustomers (Stock et al., 2000). Efficiently managing information flow is the key
weapon to gain the leading edge for 3PL firms.
Logistics IT can improve 3PL providers’ ability to manage the information flow
and strengthen their collaborations with customers (Brandyberry et al., 1999).
Logistics IT adoption can coordinate and deliver information, materials, andorganizational capacities more accurately and timely, which can reduce theoperational redundancy and resource waste and create added value for 3PL firms.
These make a 3PL provider different from its competitors and thus more reliable andattractive to customers. In return, customers would be more willing to collaborate
with 3PL firms to share their planning and needs.
From the transaction cost economics (Coase, 1937; Stoeken, 2000), logistics IT
adoption also helps to reduce the transaction costs in the relationship between 3PL
9firms and their customers. Logistics IT has great potential to improve the transparency,
accuracy and reliability of the information sharing with customers, and 3PL managers
can make more efficient decisions with lower costs and uncertainties. Therefore, we
propose the following hypothesis:
H1: Logistics IT adoption has a positive effect on customer collaboration.
Logistics customer collaboration and firm performance
In logistics, point-of-sale data can be shared and operational consistency along the
supply chain can be achieved by cooperating with customers (Turkulainen, 2017). It
helps to reduce information uncertainty and lower costs (Chen et al., 2000; Bayraktar
et al., 2009; Ryu et al., 2009; Coppini et al., 2010; Wangphanich et al., 2010), which
contributes to 3PL firms’ competitive advantage. It has been pointed out that a close
relationship with customers lowers costs by 12 to 23 percent (Lee et al., 2000), andLin et al. (2002) reported greater inventory cost savings through sharing demand
information. Meanwhile, collaborating with customers also creates superior customer
value by finding new and better ways to serve them (Ittner and Larcker, 1998;
Reichheld, 2001). Information sharing and exchanging facilitate 3PL firms to better
understand and meet customer needs in the dynamic markets (Lee et al., 1997). Thisis an effective way to promote service delivery innovation for 3PL providers(Hammer, 2004; Srivastava et al., 1999) and differentiate themselves fromcompetitors. All of these may enhance customer satisfaction and loyalty, increase
sales, and enlarge market shares (Fabbe-Costes and Jahre, 2008). As such, customer
collaboration helps to reduce costs and increase sales, and financial performance of103PL firms will thus be improved.
Additionally, information sharing and joint decision-making with customers
enable 3PL firms to improve the operational efficiencies and make the supply chain
more agile, adaptable and aligned (Lee, 2004). Resources would be fully utilized andallocated; operational processes would be optimized; duplicated routines andnon-value-added activities would be eliminated through reasonable segmentation andbetter solving related operational (delivery, service and flexibility) issues together(Esper et al., 2010). Walmart, for example, has managed to increase inventory
turnover and improve operational efficiency by sharing point-of-sale information andsales and inventory data (Nguyen, 2017). Thus, customer collaboration positively
impacts a firm’s operational performance due to improved transactional efficiency
throughout the supply chain, which helps 3PL firms to achieve superior operational
performance (Davis and Spekman, 2004; Marn and Rosiello, 1992).
Based on the above, we propose the following hypotheses:
H2: Customer collaboration has a positive effect on (a) 3PL firms’ financial
performance and (b) operational performance.
The moderating role of government policy support
From the socio-technical perspective, government regulatory as the essential factor in
the dynamic environment plays an important role in affecting technical and social
factors through policy instruments (Carayon et al., 2015). Due to the underdeveloped
market mechanisms, government policy support, which is regarded as one of the key
business priorities, often has a greater impact on corporate operations in emerging
11

economies than in developed countries (Hong et al., 2015). Government policy
support refers to the extent to which local governments provide supportive policies tobusiness entities in the market or a specific industry, and active support may include
regulatory or financial assistance, as well as the provision of efficient services such as
licensing and customs clearance, etc.
The value of customer collaboration may increase with the strong government
policy support. Strong policy support provides 3PL firms with legitimacy in bothsocial and environmental context (Sheng et al., 2011). The Chinese government not
only introduces favorable policies for logistics industries to build open
communication platforms and create benign business trading environment but also
matches related useful resources for their cooperation. With the social andenvironmental legitimacy (Bai et al., 2016), the utility of customer collaboration
would be amplified by the industry-university-research interactions encouraged by thegovernment (Wang et al., 2016a). This government-backed and customer-supported
social capital can weave a safety net (Sheng et al., 2011) to protect 3PL firms fromfinancial and market fluctuations in a long term. As such, the positive effects ofcustomer collaboration on financial and operational performance will be strengthened.
In contrast, the lack of support or even more strict control from the government is anegative signal to market, and it will jeopardize the positive performance implications
from existing partnerships between customers and 3PL providers.
Based on the above, we propose the following hypotheses:
H3: Government policy support strengthens the relationships between customer
12

collaboration and (a) financial performance and (b) operational performance.
Research Methodology
Sampling and data collection
We conducted a nationwide survey across 23 cities in China to test our hypotheses,
and a large professional survey company was employed to collect the data. The
survey was conducted from 2017 to 2018. The stratified sampling method was used,
690 companies in 23 cities were randomly selected, and the sampling pool consistedof 6254 logistics companies in the A-level directory of China Federation of Logistics& Purchasing. During the data collection process, we first called every sampledcompany to inform them of our purpose before sending questionnaires. The
questionnaires were then sent to the respondents via email or postal mail, with apostage-paid return envelope to the complete sample of 690 companies. Ten days later,
follow-up telephone calls were used to remind them of answering the questionnaires.
At last, the final sample contained 235 usable responses, yielding a usable response
rate of 34.06%.
Measures
The measures were developed by adapting items from well-established constructs
applied in previous studies and we consulted logistics professionals to make sure they
are appropriate for the 3PL and Chinese context. Specifically, we invited four leading
operations management professors in Chinese and Western universities to assist withthe design of measurement items. The items were then sent to a group of logistics
experts and practitioners for discussion and modification. Then we conducted a pilot
13

study in 30 firms before running the final survey. The final scales are shown inAppendix A.
In this study, the scales of logistics IT adoption were adapted from previous
research (Evangelista et al., 2012) which emphasizes the extent to which logistics IT
is applied and well functioned. Customer collaboration was measured by a four-item
scale developed from Danese and Romano (2011). Government policy support wasmeasured by four items adapted from Sheng et al. (2011). Operational performance
was adapted from Laumer et al. (2015) and Neely et al. (2005), including delivery,
service quality and flexibility. The items were measured using a seven-point Likert
scale with 1=strongly disagree and 7=strongly agree. Respondents were asked toindicate their degree of agreement regarding statements.
Financial performance was measured in terms of return on investment, return onsales, revenue growth, and market share suggested by Sum and Teo (1998).
Respondents were asked to compare their company’s financial performance with thatof their major competitors on a 7-point scale, with 1 indicating that the firm wassignificantly worse than competitors and 7 indicating that it was significantly better.
Respondent profile
A wide range of 3PL firms in China were included, and respondents were mainly top
management and logistics managers who are familiar with firms’ logistics activities.
The sample covered a variety of 3PL firms, as shown by the annual operating income
in Table 1.
--- Insert Table 1 about Here --14

Results
Partial least squares-structural equation modeling (PLS-SEM) was used for data
analysis in this study. PLS is a second-generation modeling technique thatsimultaneously evaluates the quality of research structures and the proposed
relationship (Fornell and Bookstein, 1982). It has been widely used (Peng and Lai,2012) and has a strong ability to handle complex models (Hair et al., 2013).
PLS-SEM technologies are well documented across disciplines (Ringle et al., 2012;
Hair et al., 2012). We used SmartPLS (version 3.2.8) software to assess themeasurement and structural models (Ringle et al., 2015). A bootstrapping estimation
procedure was used to test the hypotheses. The results of the measurement model andstructural model are described as follows.
Non-response bias and common method bias
We compared the difference of early and late (after several rounds of calls) responses
for logistics services, number of employees, annual sales, and variables used in thisstudy (Armstrong and Overton, 1977; Stank et al., 2001) and then conducted the t-test
to address the non-response bias. The result showed no significant differences exist,
indicating that non-response bias would not affect the empirical result in this study.
As to the potential issue of common method bias for one informant-reported
questionnaire, we conducted both program control and statistical control to minimize
its impact on the test results. First, we adopted the method suggested by Podsakoff etal. (2003) with fully considering the psychological feelings of the respondents in theprocess of item setting and dividing adjacent variables in the conceptual model into15

different parts. Second, to confirm the effectiveness of this strategy, we conducted a
test, as recommended by Podsakoff et al. (2003). Accordingly, two measurement
models were compared following the analytical procedure proposed by Liang et al.
(2007), with one measurement model including all of the traits and the other model
adding a method factor. The results showed that the path coefficients were
insignificant and subtle. Third, the correlations, as shown in Table 3, between thelatent variables involved in this study were all lower than 0.9, less than the thresholdsuggested by Pavlou et al. (2007). Therefore, the influence of common method bias
does not appear to be a major concern in this study.
Reliability and validity
Rigorous procedures were used to develop and validate the survey instruments. We
followed two commonly used methods to test construct reliability. First, we conducted
exploratory factor analyses (EFA) using both orthogonal and oblique rotations toensure high loadings on the hypothesized factors and low loadings on cross-loadingsin the datasets. All of the items loaded onto the expected factors without significant
cross-loadings. Then, the reliability of each construct was tested using Cronbach’s
alpha. The Cronbach’s alpha values, shown in Table 2, were over 0.7 for all of theconstructs, indicating that all of the constructs were reliable.
Next, convergent validity and discriminant validity were tested (O’Leary-Kelly
and Vokurka, 1998). As shown in Table 2, the CRs of all the factors were greater thanthreshold 0.80, and the average variance extracted (AVE) for all of the latent variables
were greater than the threshold 0.50 (Bagozzi and Yi, 1988). Thus, convergent validity16was achieved. Further, the squared correlations between each pair of constructs (Table3) were all lower than their AVEs, which provides strong evidence supporting
discriminant validity.
--- Insert Table 2 about Here ----- Insert Table 3 about Here --Hypotheses testing results
Figure 2 shows the empirical results of the structural model. As indicated by pathcoefficients, logistics IT adoption has a significantly positive effect on customer
collaboration. The path coefficient was 0.151 (t = 2.711, p < 0.01), providing supportfor H1. Customer collaboration has a positive and significant impact on financial
performance and operational performance. The path coefficient was 0.385 (t = 5.856,
p < 0.001) and 0.505 (t = 7.762, p < 0.001) respectively, hence H2 (a) and H2 (b)were both supported. Further, we also conducted post hoc analysis to examine themediating effect of customer collaboration between logistics IT adoption and firm
performance. The direct effects of IT adoption on financial and operational
performance were significant in a simple model (b = 0.197, p < 0.01 and b = 0.174, p< 0.01 respectively) but not significant in the full model when customer collaboration
is included (b = 0.068, p = 0.356 and b = 0.014, p = 0.817 respectively). The indirect
effect from logistics IT adoption to financial performance through customer
collaboration was 0.057 (t = 2.400, p < 0.05) and the percentile 95% confidence
interval was from 0.016 to 0.108. The indirect effect from logistics IT adoption tooperational performance through customer collaboration was 0.076 (t = 2.455, p <17

0.05) and the percentile 95% confidence interval was from 0.022 to 0.143. Therefore,customer collaboration positively mediated the relationship between logistics IT
adoption and financial and operational performance.
Regarding the moderating effects of government policy support, as shown in
Figure 2, government policy support positively moderates customer collaboration’s
impact on operational performance. The path coefficient was 0.193 (t = 3.357, p <0.01), providing support for H3 (b). However, it does not significantly moderate theeffect of customer collaboration on financial performance as the path coefficient was
0.047 (t = 0.780, p = 0.436), which suggests that H3 (a) was not supported. The
moderating effect was shown in Figure 3.
--- Insert Figure 2 about Here ----- Insert Figure 3 about Here --Discussion and conclusion
To understand the influence mechanism of how logistics IT adoption as a strategic
priority affects 3PL firms’ financial and operational performance, this study proposesa conceptual model from the resource-based view and socio-technical perspective.
Some important findings and implications emerge as follows.
Major findings
The empirical results suggest that logistics IT adoption has a significantly positiveeffect on customer collaboration, which indicates that 3PL firms with advanced
logistics IT such as big data, logistics information platform, and the Internet of things
have the great potential to manage information flow more accurately and timely. This18

helps to reduce transaction barriers in the supply chain and build up the closer links
between 3PL providers and customers that supports collaboration (Brandyberry et al.,1999).
Additionally, our findings show that customer collaboration positively enhanceboth financial performance and operational performance, which can be explained by
RBV. Collaboration with actors along the supply chain, especially customers, as anorganizational capability helps firms to achieve better performance including financial
performance and operational performance (Barney, 1991; Wang et al., 2016b). In thissense, firms who adopt IT can only achieve better performance when the investments
on IT are transformed into valuable, rare, inimitable and non-substitutable resources
such as customer collaboration. For 3PL firms, logistics IT may not create sustainable
competitive advantage when acting alone (Mata et al., 1995) as it is easily imitated,
and they have to use IT systems and platforms to better understand and meet customer
requirements (Tripathy et al., 2016) and build strategic assets such as inter-firm
relationship (Johnson, 1999).
Finally, this study examines the moderating effect of government policy support
on the relationship between customer collaboration and firm performance. Althoughgovernment policy support significantly amplifies the positive effect of customer
collaboration on operational performance, it does not significantly influence that on
financial performance as predicted. A possible explanation might be the inherent
nature of the 3PL business, whose main focus is the efficiency of operations, which isalso emphasized by the government. The relevant supportive policies from the19

government are aimed to create a benign market environment for the whole logistics
industry, which influence operational performance directly, such as better public
logistics infrastructures and related training and services provided by the government.
However, the market itself could still be competitive, and 3PL firms as players need towork harder to improve their financial performance.
Theoretical implications
The findings contribute to our understanding of the technological and developmental
issues of 3PL firms theoretically. First, a significant contribution of this study is thatthe theoretical underpinning of combining socio-technical perspective and RBV
provides a strong foundation for further understanding how 3PL firms pursue better
financial and operational performance. Within the socio-technical perspective, IT
adoption (technical factor) and customer collaboration (social factor) are
interdependent to influence firm performance under the dynamic social-economic
environment, as the relationship between customer collaboration and firm
performance is affected by the government policy regulation. RBV suggests linking
logistics IT with an integrated organizational capability through resourcecomplementarity and co-specialization to achieve information advantage andsynergistic performance. This study integrates the socio-technical perspective andRBV to build up solid linkage between logistics IT adoption and firm performance
under the specific boundary condition.
Further, although previous research has studied the relationship between IT andsupply chain performance (Devaraj and Kohli, 2003; Jin, 2006; Allenby and Rossi,
20

1991), this study aims to explore the role of customer collaboration and themoderating role of government policy support between IT adoption and firm
performance in an integrated conceptual model. While more and more 3PL firms arelooking for effective ways to improve financial and operational performance and often
invest heavily in IT systems, it is unclear how IT adoption influences firm
performance. This model verifies the important role of customer collaboration inconverting logistics IT to firm performance (Devaraj et al., 2007; Swink et al., 2007)
and examines the moderating role that government policy support strengthens theeffect of customer collaboration on operational performance. With supportive
government policies, customer collaboration can create more value to reduce
information asymmetry, improve the service quality and supply chain flexibility andeventually contribute to better operational performance (Laumer et al., 2015; Stank etal., 2017).
This study offers rich empirical insights to the growing body of SCM and 3PL
literature. Several studies have outlined the importance of collaboration along the
supply chain (e.g., Chang, 2016; Li et al., 2009), but there is limited understanding on
how IT investments can be transformed into competitive advantage and how
government should support the growing and prosperity of the whole industry,
especially in emerging economies. Our findings enrich 3PL literature by explaining
how logistics providers achieve better performance in China.
Managerial implications
Our findings also have important managerial implications. It emphasizes the essential
21

role that IT plays in the operations of 3PL business. Now in the era of artificial
intelligence, proactive adoption of advanced IT systems, such as Radio Frequency
Identification (RFID), Data logger, Machine learning, Automated Storage andRetrieval System (ASRS) and automatic guided vehicle (AGV), will enable 3PL firms
to manage their operations more effectively and efficiently. 3PL firms should invest to
improve their IT capability and strengthen their IT collaborations with customers.
Then our findings validate the importance of customer collaboration in converting
logistics IT into financial and operational performance. The significant impacts ofcustomer collaboration on financial and operational performance suggest that 3PL
firms should attach great importance to strategies that foster collaboration andintegration with customers to obtain the competitive edge against their competitors.
The moderation effects of government policy support suggest that 3PL managers
should balance their operational and financial objectives when utilizing the supportive
government policy. While for policy makers, our results also encourage governmentsof emerging economies to implement supportive policies, such as providing
professional training and services related to logistics and information technologies,
improving the effectiveness of laws and regulations, and making full use of online
information platforms to modernize the logistics industry.
Limitations and future research
The limitations of this study and the future research directions are notified as follows.
First, this study focuses on the impact of IT adoption in general, rather thanclassifying IT into different types of systems. Future work may consider to investigate
22the impact of different IT systems on different types of collaboration in the supply
chain so that managers can find more appropriate forms of IT to adopt. Second, thedata in this study were mainly obtained from “developed” cities in China, although
the Chinese economy as a whole is still considered as an emerging economy. There
are still rural areas in which IT investments and adoptions are very basic andpremature, thus initial investments may not contribute to firm performance in the
short term, and governments may need to provide more supportive policies toencourage IT adoptions of logistics firms. Further research is therefore needed to
examine whether the findings reported here are applicable to the underdeveloped
regions of China and to other emerging economies. Finally, cross-sectional data wereused to analyze the relationships, and future longitudinal studies are called for toverify the causal relationships dynamically over time.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Table 1. Respondent profile
Annual operating income (RMB)

Percentage (%)

Under 500,000

0500,000 to 10 million

26.38

10 to 20 million

10.64

20 to 50 million

17.02

50 to 100 million

12.77

100 to 500 million

20.85

More than 500 million

12.34

Table 2. Construct reliability and validity
Item

Factor loading

Logistics IT adoption
LITA1

0.810

LITA2

0.724

LITA3

0.840

Customer collaboration
CC1

0.794

CC2

0.854

CC3

0.866

CC4

0.843

Financial performance
FP1

0.925

FP2

0.871

FP3

0.863

FP4

0.857

Operational performance
OP1

0.787

OP2

0.865

OP3

0.780

OP4

0.800

Government policy support
GPS1

0.800

GPS2

0.766

GPS3

0.864

GPS4

0.890

32

AVE

CR

Cronbach’s alpha

0.628

0.835

0.702

0.704

0.905

0.860

0.774

0.932

0.903

0.654

0.883

0.823

0.692

0.899

0.852

Table 3. Correlation between the constructs
LITA

CC

FP

OP

Logistics IT adoption (LITA)

1

Customer collaboration (CC)

0.151

1

Financial performance (FP)

0.128

0.382

1

Operational performance (OP)

0.133

0.517

0.289

1

Government policy support (GPS)

0.182

0.298

0.128

0.293

33

GPS

1

Government
policy support

Financialperformance
LogisticsIT
adoption

Customercollaboration
Operational
performance

Figure 1. Conceptual model

Government policy
support

0.038.

0. 208***Financialperformance

0. 389 ***Logistics IT adoption

Customer
0. 151 **collaboration
0. 483***Operational
performance***p<0. 001 ; **p<0.01 ; *p<0.05

Figure 2. Structural estimation

34

Figure 3. Moderating effect35

Appendix A. Measurement Items
Logistics IT adoption:
LITA1: Our firm has widely adopted Internet of Things technology.
LITA2: Our firm has widely adopted big data technology.
LITA3: Our firm has widely adopted logistics information management
platforms.

Customer collaboration:
CC1: We shared information with key customers about the plans and capabilitiesof the services we provide.
CC2: We worked with key customers to forecast demand and resolve operational
issues through joint programs.
CC3: We worked with key customers to improve overall cost-effectivenessthrough joint decision-making.
CC4: We worked with key customers to customize the bill processing.
Government policy support:
GPS1: The municipal government strongly supports the development of thewhole logistics industry in the city.
GPS2: The municipal government can make full use of the online information
platform to provide related services to logistics enterprises (e.g., electronic customs
clearance, approval, licensing, etc.).
GPS3: Municipal logistics industry association can effectively report the current
status and challenges of the industry to the city government.
GPS4: Municipal logistics industry association can effectively strive forgovernment support to implement or improve logistics-related policies.
Operational performance:
OP1: Our delivery is faster.
OP2: Our delivery is more punctual.
OP3: Our service is more convenient.
OP4: We are more flexible to handle customers’ specific requirements.
Financial performance:
Compared to your major competitors, please evaluate your company’s
performance in the following areas:
FP1: Average growth rate of turnover over the past two years.
FP2: Average growth rate of market share over the past two years.
FP3: Average rate of return on sales over the past two years.
FP4: Average rate of return on investment growth over the past two years.

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