Competitiveness of container terminal operating companies in South Korea and the industry–university–government network

Transportation Research Part A 80 (2015) 1–14

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Competitiveness of container terminal operating companies in South Korea and the industry–university–government network Junghyun Yoon a, Hee Yong Lee b, John Dinwoodie c,⇑ a

POSTECH Entrepreneurship Centre, POSTECH, Republic of Korea School of International Economics and Business, Yeungnam University, Republic of Korea c Plymouth Graduate School of Management, Mast House, Plymouth University, Drake Circus, Plymouth, Devon PL4 8AA, UK b

a r t i c l e

i n f o

Article history: Received 12 November 2013 Received in revised form 9 July 2015 Accepted 14 July 2015

Keywords: Container terminal operating companies (CTOCs) Competitiveness of CTOCs Antecedents of competitiveness I–U–G network

a b s t r a c t Burgeoning container port facilities have fostered intensified competition among container terminal operating companies (CTOCs). However, despite research into their survival strategies which identified antecedents of competitiveness including hard factors such as facilities, available cargo and cargo processing ability, softer factors spanning human resource management, networks and strategic alliances with universities and government agencies in industry–university–government (I–U–G) networks have been overlooked. This study aims to examine both hard and softer antecedents of competitiveness as perceived by 152 professionals in South Korean CTOCs; empirical relationships among these antecedents, I–U–G networks, and competitiveness itself; and the significance of the I–U–G network in establishing and improving competitiveness. Posited antecedents of competitiveness included human resources, facilities, service quality, customer orientation, reputation, and government support policy as independent variables; the I–U–G network as a moderating variable; and competitiveness as a dependent variable. Empirical structural relationships revealed that excepting government support policy, each variable significantly affected CTOC competitiveness. Further, the I–U–G network moderated the relationships between the antecedents of competitiveness and competitiveness. Because an effective I–U–G network was pivotal in controlling CTOC competitiveness, improved competitiveness requires not only differentiation of human resources, facilities, service quality, customer orientation, and reputation factors but also I–U–G network developments. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Recent rapid expansion of world trade and growth in container traffic has created intense competition between ports which strive for hegemony, particularly in North East Asia (Yeo et al., 2008; Yuen et al., 2012, 2013). The global pursuit of competitive advantage fostered port modernisation but because of the bespoke nature of port investment (Baird, 2000)

⇑ Corresponding author. Tel.: +44 (0) 1752 585611; fax: +44 (0) 1752 585713. E-mail addresses: [email protected], [email protected] (J. Yoon), [email protected] (H.Y. Lee), [email protected] (J. Dinwoodie). URL: http://www.plymouth.ac.uk (J. Dinwoodie). http://dx.doi.org/10.1016/j.tra.2015.07.009 0965-8564/Ó 2015 Elsevier Ltd. All rights reserved.

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the forms which change took were regionally distinct (Reveley and Tull, 2008). Further, as freight traffic consolidation increases, the analysis of business relationships that involve terminal operators has become critical to decision making in maritime container transportation (Wang and Cullinane, 2014). Corporate cooperation and integration are known to drive efficiency (Midoro et al., 2005), and extra-organisational relationships impact both intra-organisational and supply chain strategies (Yang et al., 2015). To investigate such relationships further, research is needed to explore the determinants of competitiveness within the vital context of terminal operating companies (TOC) particularly in relation to networking with industry and government. An empirical research context is required within which maritime container transportation is significant, TOCs are established and compete openly, and extra-organisational relationships attain which engage both technocrats and bureaucrats. An appropriate case-context is apparent in South Korea, an economy which handled the fourth largest global container port throughput approximating 20M TEU in 2011 (UNCTAD, 2013) and hosted intense competition between container terminal operating companies (CTOC) (Lee and Kim, 2011), alongside interesting initiatives to increase competitiveness which included ‘‘triple Helix’’ industry–university–government (I–U–G) liaisons (Etzkowitz and Leydesdorff, 2000). International transport has long embraced TOCs (de Langen and Chouly, 2009) and when multinational corporations became interested in international logistics services which generated increased competition between stakeholders in container ports, scholars seeking to examine the competitive advantages of TOCs focused on CTOCs. For instance, de Souza et al. (2003) investigated whether CTOCs could play an essential role in conducting logistics services successfully, whereas Slack and Fremont (2005) identified characteristics of CTOCs from an international standpoint. Port modernisation introduced by the government of South Korea has shaped container terminal developments and with increasing numbers of CTOCs, competition has intensified (Lee and Kim, 2011). Nevertheless, relatively few studies have analysed competition amongst CTOCs in detail, typically in a European context (de Borger et al., 2008; Reynaerts, 2010; Meersman et al., 2013). Haezendonck et al. (2006) identified the rationale for competition and the determinants of competitiveness utilising Boston Consulting Group portfolio analysis and Bang and No (2007) suggested the establishment of collaborative partnerships between CTOCs as a strategy to improve their performance. There is still no comprehensive study of factors which determine CTOC competitiveness. Government involvement in the management and operation of container terminals includes motivations regarding the management of externalities, and sustainability objectives (Iannone, 2012) but the sustainability of CTOs may also depend on agglomerative effects which government may seek to influence, perhaps through encouraging I–U–G networks. Agglomerations of similar firms often generate external economies which benefit all firms in the agglomeration through the forces of urbanisation and localization (Hoover, 1937). Localization economies accrue when one region attracts concentrations of firms in one industrial sector (Hoover, 1937) typified by geographical concentrations of for example logistics service providers offering warehousing, transportation services, third-party-logistics and related services. If an industrial cluster accumulates (Porter, 1998; Rivera et al., 2014), it may attract suppliers (Rivera et al., 2014). Within clusters, external benefits may include collective learning and tacit knowledge exchange (Keeble and Wilkinson, 2000) and if competition between firms forces cost reductions as new knowledge is created and exchanged (Delgado et al., 2010), close liaison between industry and universities would be expected. Further, to grow a cluster, industrial lobbying for a more favourable taxation and regulatory environment can extend to concerted pressure on local and central government (Rivera et al., 2014). Taken together, the forces of agglomeration imply an ever-increasing tendency for closer cooperation between industry, universities and government to harness the mutual benefits of cooperation and networking. To date, industrial clusters which have been identified include the maritime sector in Holland (de Langen, 2002), logistics in the U.S. (Rivera et al., 2014) and supply chain management in China (Wu et al., 2006). Various factors determine competitiveness including traditional factors such as facilities, location, cost, and service and softer factors including human resource, network, customers, government support policy, and reputation. Typically studies concerning the competitiveness of ports and organizations in ports tend to focus on traditional factors because non-traditional factors such as human resource and networks are either considered to be insignificant or to exhibit intangible features. Traditional modelling was based on physical or financial performance reported in secondary data which avoided the requirements to collate expensive attitude data and the inevitable ambiguities associated with interpreting constructs. Such an approach is insufficient to model competitiveness at corporate level. Non-traditional factors, including human resources management and networking with other organizations such as universities and government agencies in I–U–G networks were omitted in prior studies (e.g. Haezendonck and Notteboom, 2002) but in a knowledge-based economy an I–U–G network plays an important role in improving competitiveness. This is because government can provide useful policies and universities can provide talented workers and good research to benefit CTOCs which strive to raise their competitiveness. To increase their competitiveness, CTOCs may seek to establish an effective I–U–G network (Bang and No, 2007; Yeo and Lee, 2012) but despite its importance, minimal research has investigated the relationship between I–U–G networks and the competitiveness of CTOCs. This study aims to examine the determinants of competitiveness in South Korean CTOCs by considering both traditional and softer factors, and then to investigate empirical relationships amongst the determinants of competitiveness, I–U–G networks, and competitiveness. After reviewing the role of CTOCs in Korea, hypotheses concerning the determinants of the competitiveness of CTOCs and the moderating role of an I–U–G are discussed, ahead of reporting the research design. The model results are discussed and the hypotheses proposed are tested, before concluding.

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2. Container terminal operating companies in South Korea The TOC system aims to improve the efficiency and productivity of ports and port service quality, by adopting modern management practices for port operations (Tongzon and Heng, 2005; Song and Lee, 2007). This model implemented in South Korea was a condition of a loan contract with the International Bank for Reconstruction and Development. TOCs emulate a more generic taxonomy of logistics service providers where, as trade becomes increasingly international, service provision shifts outwards from purely local provision of local services (Lu and Dinwoodie, 2002). Geographically, service provision may become more global and an extended range of services may tend towards integrated provision (Brewer et al., 2001). In Europe de Langen and Chouly (2009) identified TOCs spanning a local terminal operator, which with geographical expansion could become a global terminal operator or with expanded service provision, a local port service provider, or with both geographical and service expansion, a global port service provider. A global terminal operator has been defined as a company involved in ‘‘international port terminal operations with a view of establishing globe-spanning network service’’ (Rodrigue and Notteboom, 2011, 10). Other definitions of TOC vary with research purpose where for instance Heaver et al. (2001) used the term ‘container terminal management company’ and Airriess (2001) the term ‘transnational port corporations’. In the process of transferring the management authority of ports in South Korea to operating companies, the scope of operations was fragmented from terminal to wharf, the physical structure at which a ship berths. The World Bank typically financed berth construction but during Gwanyang Phase II Port Development in 2002–2004 it requested the Korea Container Terminal Authority to issue Container Development Bonds. Terminal operators who purchased these bonds gained the right to operate and manage one container berth in Gwanyang and concurrently a second berth in Busan Phase IV. A container berth requires cranes and other infrastructural equipment which the World Bank also funded. Consequently, all TOCs now operate wharf units rather than terminals and many TOCs operate two or more wharves (Bang and No, 2007; Song and Lee, 2007). In 2012 Korea hosted nine container ports and 26 CTOCs. Empirically, up to six berths comprised one terminal, with up to three TOCs located in one terminal and some TOCs were owned by ten or more partners. An empirical frequency distribution of berths managed by individual TOCs does not account for berth size, which varied by a factor of ten. Given this complexity, TOCs are classified based on standards spanning globalisation and operating coverage (Fig. 1). First, South Korean TOCs can be defined geographically as global or regional, with the former operating beyond Northeast Asia, and the latter within it. Next, based on operating coverage, operating companies can be classified as operators of either one single wharf or berth; a standard two-terminal CTOC; and a multi-terminal port operating company. The study focuses on CTOCs which have a greater economic value. In Section 3, hypotheses are formulated relating to determinants of the competitiveness of CTOCs and how an I–U–G network moderates relationships between these determinants and the competitiveness of CTOCs.

3. Literature review and hypotheses 3.1. Determinants of the competitiveness of CTOCs Competition is a rivalry between organizations, caused by limited resource. Competitiveness is related to a capability that is needed to hold a dominant position in competing against competitors (Lee et al., 2010). Competitiveness can be defined as the ability to produce goods and services with higher quality and hence to provide goods and services to either international TOC (Terminal Operating Company)

Operating Coverage

Globalisation GTO (Global Terminal Operator)

Wharf (Berth) Operating Company

Container Terminal Operating Company

RTO (Regional Terminal Operator)

Multi-Terminal (Port) Operating Company

Fig. 1. A typology of Korean TOCs.

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or domestic customers (Newall, 1992). Port competitiveness is influenced by numerous factors which include security (Yeo et al., 2013) and the technical efficiency of individual terminals (González and Trujillo, 2008; Cullinane et al., 2006). Focusing on the competitiveness of individual organizations within Chinese ports, Yuen et al. (2012, 2013) divided the determinants of the competitiveness of organizations in ports into internal factors including port facilities, costs, port information system and external factors including customs and government regulation, and hinterland connection (Yap and Lam, 2006; Iannone, 2012). Various taxonomies of competitive behaviour have been identified, which identify for example whether the impact of a new entrant fundamentally shifts the competitive structure of the market (Asgari et al., 2013). In Europe, inter-port competition is intense (Sanchez Rodrigues et al., 2014; Notteboom and Yap, 2012). Useful applications have considered for example competition and cooperation strategies amongst container hub ports and shipping companies within a game theoretic design model (Anderson et al., 2008; Asgari et al., 2013), but they rarely focused on the competitiveness of particular container terminals (Saeed and Larsen, 2010). An understanding of the determinants of competitiveness assists TOCs to formulate effective strategies (Lee and Kim, 2011). Often physical factors such as port facilities are important, but softer factors such as service quality, support system, and shipping environment have been emphasised (Kim, 2003). For example, Goo et al. (2010) pointed out that variables regarding shipping and logistics service play a pivotal role in improving competitiveness. Willingale (1981) argued that an ability to provide customer-focused service is a significant factor to improve competitiveness, also noting the importance of physical resource. Grant (1991) focused on the relationship between the determinants of competitiveness and organizational performance noting core competence as a source of competitive advantage, and a significant and positive effect on organizational performance. In contrast, Porter (1985) and Barney (1995) emphasised that primary and support activities are antecedents of competitiveness. Both primary activities which include production and service quality and support activities which include human resources, logistics, research and development, determine competitiveness. Hitt and Ireland (1984) and Conant et al. (1990) identified multiple variables which affect competitiveness but more comprehensive analysis of the determinants is lacking. To understand the competitiveness of CTOCs in South Korea requires a brief review of prior local research (Yeo et al., 2008). Shin (2007) defined strategies, customer-oriented factors, and collaboration with industry related bodies as determinants of competitiveness. Customer-oriented factors can play an essential role in maintaining competitive advantage and corporate image may function as an antecedent of competitiveness. Management strategies affect corporate image, and hence image has a significant effect on reputation, which ultimately determines the level of competitiveness. Examining the determinants of market competitiveness of small and medium-sized enterprises, Chung and Choi (2009) identified that management capacity, technology capacity, trust and stability, customer management, service quality, and talented human resource could play an important role in attaining higher competitiveness. Lee et al. (2010) also argued that financial and accounting management, human resource management, marketing management, service power, and operating management acted as determinants of competitiveness. Focusing on the competitiveness of international shipping companies, Choi et al. (2010) and Sánchez et al. (2003) confirmed that supply chain management, cost management, technological capacity, human resource, and reputation played a pivotal role in determining competitiveness, and that government support policy might be correlated with competitiveness via the future research direction. Building on Lee and Kim’s (2011) work, this study argues that service quality can play an important role in improving the competitiveness of CTOCs. Finally, Aronietis et al. (2010) and Lee and Kim (2011) identified five factors which affect the level of competitiveness: human resource, physical resource (e.g., facilities), technology resource, marketing capacity and intangible resource (e.g. favourable policies). Based on the above studies, this study proposes to test hypotheses that the following factors have a positive effect on the competitiveness of CTOCs: H1.1. H1.2. H1.3. H1.4. H1.5. H1.6.

Human resources have a positive (+) effect on the competitiveness of CTOCs. Facilities have a positive (+) effect on the competitiveness of CTOCs. Service quality has a positive (+) effect on the competitiveness of CTOCs. Customer orientation has a positive (+) effect on the competitiveness of CTOCs. Reputation has a positive (+) effect on the competitiveness of CTOCs. Government support policy has a positive (+) effect on the competitiveness of CTOCs.

3.2. I–U–G network In a classic resource based theory view of innovation, if firms are resource-constrained, their propensity to seek outside resources increases. Universities which can offer firms human resources or technology become attractive partners, although they cannot meet shortfalls in corporate capital (Richardson, 1972). In a triple-helix view of the I–U–G interaction, direct university links with industry ‘‘industrialize’’ knowledge, contributing directly to economic development beyond teaching and research (Etzkowitz and Leydesdorff, 2000) but a context specific approach may be required because each developing economy has unique needs (Eun et al., 2006). Corporate membership of an I–U–G network in Korea has been observed to generate more patents, but not necessarily to raise productivity or sales. Further, participation in national projects was more likely to raise effectiveness, rather than firm size or intensity. The industrial contribution of universities through I–U–G networks may thus span improved human resources via teaching, innovations through

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research and in economies which host substantial government research institutes, highly skilled and trained researchers capable of creating innovations. The number of firms and universities in an I–U–G network defines the network size, and the frequency of mutual exchange of opinions or information defines the network intensity. For example, if a CTOC has carried out more exchanges with universities and government than other CTOCs, the intensity of its I–U–G network is stronger. To improve their competitiveness, CTOCs would like to establish a network with government agencies and universities. CTOCs, government agencies and universities must cooperate or collaborate mutually to accomplish both common and individual goals, by forming a network. Earlier work defined an I–U–G network spanning Industry–University–Government (Choi and Hwang, 2004). Local government has, for instance, to support SMEs to improve local competitiveness, CTOCs have to receive and utilise local government support and university support to heighten their competitiveness, and a university has to accept local government support and provide excellent skills and specialist research output to improve university competitiveness. H2. The I–U–G network moderates a relationship between the stated factor and the competitiveness of CTOCs. H2.1. The I–U–G network moderates a relationship between human resource and the competitiveness of CTOCs. H2.2. The I–U–G network moderates a relationship between facilities and the competitiveness of CTOCs. H2.3. The I–U–G network moderates a relationship between service quality and the competitiveness of CTOCs. H2.4. The I–U–G network moderates a relationship between customer orientation and the competitiveness of CTOCs. H2.5. The I–U–G network moderates a relationship between reputation and the competitiveness of CTOCs. H2.6. The I–U–G network moderates a relationship between government support policy and the competitiveness of CTOCs. Based on the literature discussed above, this study proposes the research model shown in Fig. 2.

4. Method 4.1. Overview of research design Within the context of South Korea, this study aims to identify factors which impact the competitiveness of CTOCs, and to examine the relationships among the factors, the I–U–G network, and the competitiveness of CTOCs. Given that studies linking the competitiveness of a CTOC and I–U–G networks are relatively recent, prior empirical work is scarce. Accordingly, a two-phase research design was adopted. First, qualitative interviews were undertaken to develop a broader understanding of CTOCs and to uncover the antecedents of their organizational competitiveness and secondly, a survey was conducted to validate findings from the qualitative phase. The survey instrument was developed in several stages, following appropriate procedures suggested by Jöreskog et al. (2000) and based on insights gained from the interviews and available literature. Literature searches informed the key constructs and scales appropriate for measuring relevant factors. Five-point scales were used to minimise executive response time and effort.

Human Resources I-U-G Network Facilities

Service Quality Competitiveness of CTOCs Customer Orientation

Reputation

Government Support Policy Fig. 2. Conceptual framework.

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4.2. Preliminary phase: identifying factors via qualitative interviews and literature reviews 4.2.1. Introduction: purpose of the preliminary phase The preliminary phase was designed to identify factors which impact the competitiveness of CTOCs, such as human resources, facilities, service quality, reputation, customer orientation, government support, and I–U–G network, using semi-structured face-to-face interviews and literature reviews. 4.2.2. Sample and data collection for the preliminary phase Factors which are known to influence the competitiveness of CTOCs, and qualitative interviews undertaken during the preliminary phase were examined to identify new factors. To identify and define these factors, seven organizations were sampled. Representatives in five CTOCs, one government agency, and one university, including two general managers, two team managers, two senior managers, and one professor, participated. Details of the CTOCs sampled are shown in Table 1. 4.2.3. Results of preliminary phase Antecedents of competitiveness. Interviewees agreed that there is a significant relationship between competitiveness and organizational performance (e.g., financial performance) and also the importance of investigating the antecedents of competitiveness in improving performance. ‘‘Competitiveness is a very important factor to improve organizational performance. This includes exploring and investigating the antecedents of competitiveness as well as relationships between the antecedents and performance. I think there certainly is a meaningful relationship between competitiveness and organizational performance. Especially, internal and external resources such as human and physical resources are determinants of competitiveness.’’ [Manager of DBCT] They noted that internal resources such as human resources and facilities, service quality, customers’ needs, brand image or company image including reputation, external environment and support policies are antecedents of the competitiveness of CTOCs. ‘‘Having outstanding human resources or talents creates a pivotal role in gaining and maintaining competitiveness. That is to say, the more talents CTOCs have, the higher their competitiveness is. For instance, a skilled worker could deal with unpredicted problems more quickly and effectively. For instance, one of our employees solved a problem quickly when a crane was not working. As a result, we saved operational costs and did not inconvenience our customers. This training improves our customers’ loyalty.’’ [Senior Manager of BNCT] ‘‘Excellent facilities give customers who are using our service convenience and satisfaction. In general, customers prefer clean conditions and handy facilities. Simply put, better facilities are a necessary factor to be able to improve competitiveness and organizational performance such as financial performance.’’ [Team Manager of KBCT] ‘‘The better the image a CTOC has, the more competitive they can be. For example, a large number of Koreans prefer XXX mobile phones as they love the good image of XXX regarding performance and design as well as a good reputation. Likewise, brand image can play an essential role in improving and maintaining competitiveness. I certainly believe that there is a significant relationship between image and competitiveness.’’ [Team Manager of HBCT] ‘‘Proper and useful support polices are factors needed to improve and maintain the competitiveness of CTOCs. If a CTOC can offer better support policies, it can gain other benefits, being able to advance into new markets more easily and use an efficient price strategy. Therefore, for improving competitiveness, government agencies need to provide useful support policies and CTOCs need to use the policies.’’ [General Manager of PNIT] Table 1 Details of sample. Organization+

Department

Position

Location

DBCT BNCT KBCT HBCT PNIT GPG KMU

Terminal operation Terminal operation Terminal operation Terminal operation Terminal operation Harbour & Logistics Maritime transport

General Manager Senior Manager Team Manager Team Manager General Manager Senior Manager Professor

Pusan, Korea Pusan new port, Korea Pusan new port, Korea Pusan new port, Korea Pusan, Korea Gyeongsangnam-do, Korea Pusan, Korea

+ DBCT (Dongbu Bisan Container Terminal); BNCT (Pusan New Container Terminal); KBCT (Korean Pusan Container Terminal); HBCT (Han-jin Pusan Container Terminal); PNIT (Pusan Newport International Terminal); GPG (Gyeongnam Provincial Government); KMU (Korean Maritime University).

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Relationship between I–U–G network and competitiveness. All three groups (CTOC, government agency, and universities) recognise that the I–U–G network plays an important role in improving the competitiveness of CTOCs. They also emphasise the role of the I–U–G network. Based on the results, it is hypothesised that the I–U–G network can moderate the relationship between the antecedents of competitiveness and a recognised level of competitiveness. In summary, human resource, facilities, service quality, reputation, government support policy, and customer orientation determine competitiveness, with the I–U–G network as a moderating variable. 4.3. Main phase: empirical investigation of relationships between the factors 4.3.1. Introduction: purpose of main phase In Phase 1, we examined whether factors derived from the interviews can function as antecedents of competitiveness of a CTOC, and investigated relationships among the factors or variables (i.e. human resource, facilities, service quality, reputation, government support policy, customer orientation, I–U–G network, and the competitiveness of CTOCs). Empirical investigation of the relationships observed in Phase 1 is now required. 4.3.2. Sample and data collection for main phase A survey instrument was developed in several stages, following appropriate procedures (e.g. Jöreskog et al., 2000) and based on insights gained from the interviews and the literature review. The literature was searched to obtain information on the key constructs and scales appropriate for measuring them (i.e., antecedents of competitiveness, I–U–G network, and competitiveness) more precisely. To increase the validity of the research, searches and reviews deployed keywords related for example to the determinants and outcomes of port competitiveness, after specialist experts had been asked to suggest the key constructs and scales. Appendix A summarises an English translation of the agreed constructs and despite apparent ambiguities even after several back-translations, experts during the piloting process in Korea were satisfied that concepts were clear and precise. This study examined CTOCs in South Korea. To enhance the validity and reliability of the research, this study used multiple sources, such as websites and addresses, to select a research sample (Nummela et al., 2004). Various database sources, such as the Ministry of Oceans and Fisheries and the SPIDC (https://www.spidc.go.kr), were employed to select CTOCs. Details of the sampling procedure are as follows. First, lists of CTOCs provided by the Ministry of Oceans and Fisheries identified 37 CTOCs suitable for this study. Second, the websites of individual firms opened by the SPIDC, a government agency for international shipping and logistics, identified 11 additional international shipping and logistics companies. Online surveys are apposite given that all TOCs are online nowadays but supplementary offline surveys were undertaken to raise response rates (Kaplowitz et al., 2004). Specifically, 200 questionnaires were distributed to the TOCs identified in March 2013. After one week, online questionnaires were despatched. Subsequently, two reminder emails (with a two-week interval) invited participation. Finally CEOs or directors were contacted in person. In total, 152 questionnaires (collection rate: 76.0%) were returned. The demographics and key characteristics are shown in Table 2. Following data collection, measures were subjected to a purification process to verify reliability and validity (Jöreskog et al., 2000). Confirmatory factor analysis was conducted in AMOS 18.0 (IBM, New York, United States) to estimate a single measurement model representing relations among all constructs and associated items. Next, the internal consistency reliabilities of the composite scales were analysed using Cronbach’s (1951) alpha coefficient. The relationship among the set of dependent variables, the set of moderating variables, and the set of independent variables was tested using construct means, standard deviations, and Pearson’s correlation coefficients of bivariate correlation analysis. To test the hypotheses, the study employed moderating effect analysis and path analysis available in AMOS 18.0. 4.3.3. Measures Determinants of competitiveness. Building on earlier work (e.g., Aronietis et al., 2010; Barney, 1995; Grant, 1991; Hitt and Ireland, 1984; Lee et al., 2010; Porter, 1985), this study suggests six factors, such as human resources, facilities, service quality, customer orientation, reputation, and government support policy, as determinants of competitiveness. Human resource is defined as the ability of human resources to manage or solve some problems and perform tasks, and an organization’s ability to retain talented human resources. Facilities must be appropriate and better than competitors offer to attract customers. Service quality is measured by service standardisation, the ability to operate a terminal effectively, the efficiency of cost management, the ability to deal with loading and unloading, maintenance, sailing management, tank cleaning, etc., and the ability to control or manage situations following accidents. Customer orientation can be a group of actions taken by a business to support its sales and service staff in considering client needs and satisfaction of their major priorities. Customer orientation might include developing a quality product appreciated by consumers, responding promptly and respectfully to consumer complaints and queries, and dealing sensitively with community issues. Reputation can be defined by a better image than competitors, trust and morality, and greater popularity. Government support policies are policies to support more effective terminal operation. To assess these variables, three, four, five, five, three, and six items respectively, were measured using a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). I–U–G network. As a moderating variable that controls the relationship between antecedents of competitiveness and competitiveness, an I–U–G network can be defined as a collaborative relationship or a network that would like to achieve both common and individual goals. I–U–G networks were measured from two perspectives, namely their scope and their intensity

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Table 2 Sample demographics. Variables

Number

Percentage

Number of employees 1–49 50–99 100–199 200–299 More than 300

10 34 50 56 2

6.6 22.4 32.9 36.8 1.3

Position CEO or Founder Senior Manager General Manager Staff

22 35 39 56

14.5 23.0 25.7 36.8

Port Pusan Gwangyang Incheon Pyeongtaek Ulsan Pohang Gunsan

51 10 41 21 17 4 8

33.6 6.6 27.0 13.8 11.2 2.6 5.3

Years since CTOC formed Less than 3 4-Mar 9-May 19-Oct More than 20

2 17 21 58 54

1.3 11.2 13.8 38.3 35.4

Years employed by CTOC Less than 1 1–4 5–7 8–10 10–19 More than 20

4 33 8 18 65 24

2.6 21.7 5.3 11.8 42.8 15.8

or level. The scope of an I–U–G network is its size indicated by the number of members in it, and its intensity reflects the frequency of mutual exchanges of opinions or information. All of the indicators were measured by employing a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). Competitiveness. Competitiveness in this study is based on an organization or firm’s competitiveness because the sample is CTOCs. It is ‘‘the ability and performance of a firm, sub-sector or country to sell and supply goods and services in a given market, in relation to the ability and performance of other firms, sub-sectors or countries in the same market.’’ Five items were used to measure the competitiveness of CTOCs. Each was measured using a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). 4.3.4. Assessing common method bias and nonresponse bias Questionnaires were collected at regular intervals to minimise common method bias, but it was impossible to avoid this type of bias completely because all items were measured using only surveys (Podsakoff and Organ, 1986). Confirmatory factor analysis (CFA) to assess the likelihood of common method bias revealed that: v2 = 6051.204 (df = 527, p = 0.000), v2 /df = 11.482, GFI = 0.349, AFI = 0.265, CFI = 0.327, TLI = 0.284, RMR = 0.150, and RMSEA = 0.194 indicating a lack of statistical significance, i.e., an extremely poor model fit. These results indicate that the study was unlikely to be affected by common method bias. In accordance with a study by Armstrong and Overton (1977), t-tests measured the likelihood of non-response bias by comparing early responses within three weeks of the mailing with late responses more than three weeks after the mailing, in terms of key variables such as competitiveness and I–U–G network. Results indicated a lack of significant differences between the two groups implying that non-response bias was not an issue in this study. 5. Analysis and results 5.1. Measurement model results Through structural equation modelling (SEM), this study investigated the relationships among the antecedents of competitiveness, I–U–G network, and competitiveness of CTOCs. SEM is a highly effective means of measuring the moderating

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effects of such factors because all of the relevant paths can be directly verified (Baron and Kenny, 1986; Spencer et al., 2005). Before performing the SEM, we assessed the measurement model, which was created to assess the reliability and validity of the constructs used. A CFA was used to evaluate the measurement model which was composed of six constructs via IBM AMOS 18.0 (New York, United States). Although the chi-squared (v2 Þ value did not indicate a high level of statistical significance (v2 = 1007.782, degrees of freedom (df) = 284, v2 /df = 3.549; Table 3), this index tends to be biased against large samples, even if the model is sound (Jöreskog and Sörbom, 1981; Bagozzi and Baumgartner, 1994), because v2 is highly sensitive to sample size. To overcome this limitation, the overall fit of the measurement model was assessed using several other indices (i.e., goodness-of-fit index (GFI), adjusted GFI (AGFI), comparative fit index (CFI), normed fit index (NF), root-mean-squared residual (RMR), and root-mean-squared error of approximation (RMSEA)) (Bollen, 1990). These tests produced the following values: the GFI (Jöreskog and Sörbom, 1981) is 0.788; the AGFI (Jöreskog and Sörbom, 1981) is 0.714; the CFI (Bentler, 1992) is 0.913; the TLI is 0.886; the RMR is 0.068 and the RMSEA is 0.127. Based on the recommended levels for these fit indices, the measurement model was acceptable considering the relatively larger number of observed indicators for the constructs. To assess the reliability of all constructs, this study utilised Cronbach’s alpha (aÞ. Nunnally (1978) recommended that Cronbach’s a values greater than 0.7 are acceptable. The computed Cronbach’s a value of all constructs in this study ranged from 0.776 to 0.948. Moreover, the construct reliabilities of each scale ranged from 0.748 to 0.968 (Fornell and Larcker, 1981). As shown in Table 3, the average variance extracted (AVE) was computed to estimate the convergent validity of all constructs. In general, an AVE value greater than 0.5 indicates that the validity of a measurement model is satisfactory. The AVE values of all variables in this study were greater than 0.501 implying that all constructs had satisfactory values and indicating that this measurement model had satisfactory convergent validity (Bagozzi and Yi, 1988). Assessments of the discriminant validity of the study using a cross-construct correlation estimate revealed that none of the two-standard-error confidence intervals included the value of one (Anderson and Gerbing, 1988), indicating satisfactory discriminant validity. 5.2. Structural model results To investigate the relationships among variables, SEM was used. This study is composed of nine latent variables (i.e., human resource, facilities, service quality, customer orientation, reputation, government support policy, a level of I–U–G network, a scope of I–U–G network, and competitiveness). As a result of investigating the structural relationships, the v2 value was 885.808 with 423 degrees of freedom. This result was not statistically satisfactory but examination of other indices, such as RMR, GFI, AGFI, TLI, CFI, and RMSEA revealed values of these indices which were within the range considered to indicate a satisfactory model fit (Table 4). 5.3. Hypothesis testing 5.3.1. Hypothesis 1 The hypotheses were assessed by interpreting the path (or structural) coefficients. We first determined which direct effects could predict a significant relationship between six variables (i.e., human resource, facilities, service quality, customer orientation, reputation, government support policy) and the competitiveness of CTOCs. Each variable excepting government Table 3 Measurement model results. Scalea

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Mean

2.9854 2.5164 3.6204 3.2165 2.6302 2.6482 2.7912 2.4453 3.2832

S.D.

1.0965 1.0380 0.9748 0.8324 0.9665 0.9221 0.9343 1.0192 0.8946

Cronbach’s a Construct reliability AVE Goodness-of-fit statistics

Cross-construct correlations (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1 0.390⁄⁄ 0.365⁄⁄ 0.596⁄⁄ 0.305⁄⁄ 0.537⁄⁄ 0.455⁄⁄ 0.457⁄⁄ 0.516⁄⁄

1 0.338⁄⁄ 0.471⁄⁄ 0.348⁄⁄ 0.574⁄⁄ 0.513⁄⁄ 0.610⁄⁄ 0.299⁄⁄

1 0.532⁄⁄ 0.420⁄⁄ 0.471⁄⁄ 0.662⁄⁄ 0.353⁄⁄ 0.727⁄⁄

1 0.468⁄⁄ 0.639⁄⁄ 0.708⁄⁄ 0.421⁄⁄ 0.643⁄⁄

1 0.336⁄⁄ 0.501⁄⁄ 0.354⁄⁄ 0.405⁄⁄

1 0.605⁄⁄ 0.585⁄⁄ 0.496⁄⁄

1 0.449⁄⁄ 0.733⁄⁄

1 0.252⁄⁄

1

0.918 0.907 0.965 0.936 0.776 0.891 0.877 .0968 0.943 0.748 0.733 0.645 .0838 0.735 0.501 2 2 v = 1007.782 (df = 284), v /df = 3.549, RMR = 0.068, GFI = 0.788, RMSEA = 0.127

0.929 0.948 0.914 0.939 0.710 0.758 AGFI = 0.714, TLI = 0.886,

0.940 0.962 0.810 CFI = 0.913,

0.933 0.935 0.743

a (1) Human Resource; (2) Facilities; (3) Service Quality; (4) Customer Orientation; (5) Reputation; (6) Government Support Policy; (7) Level of I–U–G network; (8) Scope of I–U–G network; (9) Competitiveness. ⁄ Significant at p < 0.05. ⁄⁄ Significant at p < 0.01.

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Table 4 Standardised structural estimates of the structural model. Path

Estimate

C.R.

p-value

Direct effects Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support Policy ? Competitiveness

0.170 0.128 0.467 0.264 0.086 0.046

2.924 2.413 7.305 2.429 1.981 0.598

0.003 0.016 0.000 0.015 0.048 0.550

Goodness-of-fit statistics

v2 = 885.808 (df = 423); v2 /df = 2.094; RMR = 0.070; GFI = 0.740; AGFI = 0.676; TLI = 0.891; CFI = 0.907; RMSEA = 0.090

Table 5 Parameter values of the moderating effect model (level of I–U–G network). Group

Path

Low

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

Estimate 0.059 0.187 0.447 0.414 0.089 0.079

C.R. 0.753 2.406 4.779 2.791 1.328 0.696

p-value 0.451 0.016 0.000 0.005 0.184 0.486

High

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

0.488 0.138 0.476 0.187 0.023 0.229

3.858 1.421 4.722 0.65 0.293 1.481

0.000 0.155 0.000 0.516 0.769 0.139

Table 6 Parameter difference between high and low groups (level of I–U–G network). Path

Difference of parameter

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

2.890 0.392 0.210 1.859 0.643 2.613

Table 7 Parameter values of the moderating effect model (scope of I–U–G network). Group

Path

Estimate

C.R.

p-value

Low

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

0.124 0.227 0.397 0.395 0.128 0.103

0.85 2.253 1.805 1.317 1.338 0.396

0.395 0.024 0.071 0.188 0.181 0.692

High

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

0.073 0.095 0.43 0.196 0.019 0.042

0.938 1.273 4.23 1.222 0.312 0.454

0.348 0.203 0.000 0.222 0.755 0.65

support policy had significant or meaningful effects on the competitiveness of CTOCs. Specifically, human resource, facilities, service quality, customer orientation, and reputation had significant positive (+) effects on the competitiveness of CTOCs, whereas government support policy did not have a significant effect. What is interesting is the relationship between government support policy and competitiveness. In other words, although not intended, it did not function as an antecedent of competitiveness. This result can be explained by characteristics of the policy which has focused on national-level port

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J. Yoon et al. / Transportation Research Part A 80 (2015) 1–14 Table 8 Parameter difference between high and low groups (scope of I–U–G network). Path

Difference of parameter

Human Resource ? Competitiveness Facilities ? Competitiveness Service Quality ? Competitiveness Customer Orientation ? Competitiveness Reputation ? Competitiveness Government Support ? Competitiveness

2.358 1.054 2.118 1.169 1.230 5.116

competitiveness rather than individual organization-level competitiveness. In this study the competitiveness of CTOCs was analysed from an organizational-level viewpoint. As a result, the government support policy not only has a significant effect on the competitiveness of CTOCs, but is negative because responding CTOCs perceived that the current policy is ineffective. Nevertheless, they cannot ignore Korean government agencies because they can regulate or restrict the business activities of CTOCs. Finally, these results indicate that for CTOCs to improve their competitiveness, their human resources, facilities, service quality, customer orientation, and reputation must outperform competitors. Findings demonstrate that the above variables can act as determinants of competitiveness and Hypothesis 1 (including H1.1, H1.2, H1.3, H1.4, and H1.5) was supported. 5.3.2. Hypothesis 2 Another purpose of this study is to investigate the moderating effects of an I–U–G network on the relationships between the antecedents of competitiveness (i.e., human resource, facilities, service quality, customer orientation, and reputation) and competitiveness. To achieve this purpose, the study divided I–U–G networks into two groups (i.e., a ‘‘high’’, highly integrated I–U–G network group and a ‘‘low’’ less-highly integrated I–U–G network group). The ‘‘high’’ level of group has a stronger frequency of opinion and information interchange with other counterparts than competing organisations, and vice versa. Because the moderating variable is a nonmetric variable, multiple group analysis of SEM was employed. As shown in Table 4, the model fit of the study was satisfactory. Parameter values are shown in Table 5. In detail, in the low group, facilities, service quality, and customer orientation had significant positive (+) effects on the competitiveness of CTOCs, while human resource, reputation, and government support policy did not have significant effects. In the high group, human resource and service quality had significant positive (+) effects on competitiveness, whereas facilities, customer orientation, reputation, and government support policy did not. To test difference analysis of two parameters, this study compared the label of the low group with the label of the high group. The results are shown below (Table 6). Parameter difference analysis is a method to test the difference between individual parameters measured by Z-values, where differences ±1.64 indicate a significance level of 0.1, ±1.96 a level of 0.05 and ±2.58 a level of 0.01. This study shows that the level of I–U–G network moderated the relationships between human resource, customer orientation, and government support policy, and competitiveness respectively. In other words, there is a significant difference between a high level of I–U–G network and a low level of I–U–G network. In the low group, facilities and service quality had significant positive (+) effects on the competitiveness of CTOCs, whereas human resource, customer orientation, reputation, and government support policy did not. In the high group, service quality has a significant positive (+) effect on competitiveness, while human resource, facilities, customer orientation, reputation, and government support policy did not have significant effects. Difference analysis of the two parameters compared the label of a low scope of I–U–G network group with the label of a high scope of I–U–G network group (Table 7). As shown in Table 8 the scope of the I–U–G network also moderated the relationships between human resource, service quality, and government support policy, and competitiveness respectively. In other words, there is a significant difference between a high scope of I–U–G network and a low scope of I–U–G network. To summarise, an I–U–G network can play a pivotal role in improving the competitiveness of CTOCs. It can also serve as a moderating factor that is able to control the relationships between the determinants of competitiveness, and the competitiveness of CTOCs. 6. Conclusion 6.1. Summary and implications With the goal of assisting CTOCs to improve their competitiveness, this study developed a research model that considered both soft and hard factors. These included human resources, facilities, service quality, customer orientation, reputation, and government support policy, and the network among industry, university, and government as a major variable which affected competitiveness. In addition, several hypotheses based on this model were tested empirically. The findings can be summarised as follows. First, except for government support policy, all factors, such as human resource, facilities, service quality, customer orientation and reputation, have significant effects on the competitiveness of CTOCs. Second, an I–U–G network moderated the relationships between human resource, service quality, customer orientation, and government support policy, and competitiveness.

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The results of this study imply that CEOs in CTOCs must pay more attention to both hard and soft factors in order to improve corporate competitiveness. Findings emphasise that CTOCs are more likely to improve their competitiveness if they invest in improved human resources, facilities, service quality, and reputation, as well as an increased customer orientation. If acting alone without assistance even CTOs with outstanding ratings on hard and soft factors, will find it very difficult to improve their competitiveness. Establishing a network with other useful organizations, such as government agencies and universities, is becoming more strategically important for CTOCs implying that an I–U–G network plays an important role in improving the competitiveness of CTOCs. 6.2. Limitations and future research directions This study relied mainly on an empirical, quantitative research methodology, even though it considered both qualitative and quantitative methods. Future research is likely to draw similarly on both qualitative and quantitative measures to provide a more complete and realistic representation of the issues examined. Some theoretical and methodological limitations are apparent in this work. Firstly, restricting the determinants of competitiveness to the soft and hard factors of CTOCs limited the number of variables examined. Future research could attempt to evaluate other variables that might be significantly associated with competitiveness and an I–U–G network which could include internal and external environmental factors such as regulations and green logistics. Next, this study included perceived competitiveness as a dependent variable. Even in cases where perception-based subjective measures are significantly and positively correlated with objective measures, it is better to consider both subjective and objective measures. Thus, future research could usefully combine objective measures such as financial data with subjective measures such as measures of organizational effectiveness. Finally, it may be difficult to generalise the findings of this study because it was focused on the specific business context of CTOCs in South Korea. A comparative study of two or more countries would increase the ability to generalise results. Appendix A. Measurement items

Variables

Items

Human resource

Highly skilled employees in our organization are well equipped to carry out port operational jobs effectively Our organization employs sufficient staff to carry out its work efficiently and effectively Our organizational members can deal with problems promptly and effectively

Facilities

Our organization efficiently Our organization workers Our organization effectively Our organization

has better geographic coverage and sufficient branches to deploy its services has sufficient training and education programs and facilities to secure talented has sufficient IT facilities and intranet to deal with problems smoothly and has sufficient port facilities to support its port activities

Service quality

Our organization would like to provide a better terminal operation and port operation service Our organization would like to provide better service appropriate to customer’s needs Our organization would like to provide distinguished service Our organization manages its members very well, and provides satisfactory service to customers through job standardization Our organization always considers service quality for customers satisfactorily

Customer orientation

Our organization would like to deal with customer needs and claims promptly and properly, as well as to feedback the results Our organization informs customers about situations and services as they are Our organization seeks to inform customers of the strengths and weaknesses of its services exactly Our organization would like to exceed customer expectations Our organization would like to provide better service and support, based on the customer’s perspective Our organization is renowned for its reliability and morality

Reputation

Our organization has a high and positive reputation in the port and logistics industry Our organization has heard that its terminal operation and service is highly regarded by the port and logistics industry and customers

J. Yoon et al. / Transportation Research Part A 80 (2015) 1–14

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Appendix A (continued)

Variables

Items

Government Support

Our organization shows greater flexibility in the labor market due to government support It is easier to receive permission and approval and enter into internal and external markets due to government support It is easier to secure enough funds to carry out port and logistics activities due to government financial support It is easier to secure and improve domestic and international competitiveness due to government support Our strategies tend to change according to government support policies

Competitiveness

Overall the competitiveness of our organization is improving Our organization displays greater competitiveness than its rivals Our organization displays higher service quality and competitiveness than its rivals Service quality and competitiveness is improving Organizational capabilities are improving

References Airriess, C.A., 2001. Regional production, information-communication technology, and the developmental state: the rise of Singapore as a global container hub. Geoforum 32 (2), 235–254. Anderson, C.M., Park, Y.A., Chang, Y.T., Yang, C.H., Lee, T.W., Luo, M., 2008. A game-theoretic analysis of competition among container port hubs: the case of Busan and Shanghai. Marit. Policy Manage. 35 (1), 5–26. Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 (3), 411–423. Armstrong, J.S., Overton, T.S., 1977. Estimating nonresponse bias in mail surveys. J. Mark. Res. 14, 396–402. Aronietis, R., van de Voorde, E., Vanelslander, T., 2010. Port competitiveness determinants of selected European ports in the containerized cargo market. In: European Transport Conference. Asgari, N., Farahani, R.Z., Goh, M., 2013. Network design approach for hub ports-shipping companies competition and cooperation. Transp. Res. Part A 48 (1), 1–18. Bagozzi, R.P., Baumgartner, J., 1994. The evaluation of structural equation models and hypothesis testing. Princ. Market. Res., 386–419 Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16 (1), 74–94. Baird, A.J., 2000. Port privatization: objectives, extent, process, and the UK experience. Int. J. Marit. Econ. 2 (3), 177–194. Bang, H.-S., No, Y.-J., 2007. The effect of changes of port environments on cooperation among terminal operator. Korea Trade Rev. 32 (4), 197–220. Barney, J.B., 1995. Looking inside competitive advantage. Acad. Manage. Exec. 17 (4), 49–61. Baron, R.M., Kenny, D.A., 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51 (6), 1173. Bentler, P.M., 1992. On the fit of models to covariances and methodology to the Bulletin. Psychol. Bull. 112 (3), 400. Bollen, K.A., 1990. Overall fit in covariance structure models: two types of sample size effects. Psychol. Bull. 107 (2), 256. de Borger, B., Proost, S., Van Dender, K., 2008. Private port pricing and public investment in port and hinterland capacity. J. Transp. Econ. Policy 42 (3), 527–556. Brewer, A.M., Button, K.J., Hensher, D.A., 2001. Handbook of Logistics and Supply Chain Management. Pergamon, Amsterdam. Choi, Y.J., Byeon, C.U., Lee, S.H., 2010. Determinants of export competitiveness in Korean manufacturing. J. Int. Trade Ind. Stud. 15 (3), 43–72. Choi, Y.I., Hwang, W.I., 2004. Technological innovation of venture firms in partnership with I–U–G networks. Entrep. Venture Res. 7 (2), 3–23. Chung, C.K., Choi, H.J., 2009. An analysis on determination for competitiveness of export marketing in Korea exporting companies. Int. Comm. Inform. Res. 11 (3), 217–237. Conant, J.S., Mokwa, M.P., Varadarajan, P.R., 1990. Strategic types, distinctive marketing competencies and organizational performance: a multiple measures based study. Strateg. Manage. J. 11 (5), 365–383. Cronbach, L.J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16 (3), 297–334. Cullinane, K., Wang, T.-F., Song, D.-W., Ji, P., 2006. The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis. Transp. Res. Part A 40 (4), 354–374. Delgado, M., Porter, M., Stern, S., 2010. Clusters and entrepreneurship. J. Econ. Geogr. 10 (4), 495–518. Etzkowitz, H., Leydesdorff, L., 2000. The dynamics of innovation: from national systems and ‘‘Mode 2’’ to a triple helix of university–industry–government relations. Res. Policy 29 (2), 109–123. Eun, J.-H., Keun, L., Wu, G., 2006. Explaining the ‘‘University-run Enterprises’’ in China: a new theoretical framework for university–industry relationship in developing countries and its applications to China. Res. Policy 35, 1329–1346. Fornell, C., Larcker, D.F., 1981. Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 18 (3), 382–388. González, M.M., Trujillo, L., 2008. Reforms and infrastructure efficiency in Spain’s container ports. Transp. Res. Part A 42 (1), 243–257. Goo, J.S., Hwang, K.Y., Dong, W.C., 2010. Research on the determinants of Chinese port competitiveness. Korea Trade Rev. 35 (5), 397–418. Grant, R.M., 1991. The resource-based theory of competitive advantage: implications for strategy formulation. Calif. Manage. Rev. 33 (3), 114–135. Haezendonck, E., Coeck, C., Verbeke, A., 2006. The competitive position of seaports: a strategic positioning analysis. In: Cullinane, K., Talley, W. (Eds.), Port Economics. Elsevier, pp. 141–169. Haezendonck, E., Notteboom, T., 2002. The competitive advantage of seaports. In: Winkelmans, W. (Ed.), Port Competitiveness. University of Antwerp, Antwerp, pp. 17–31. Heaver, T., Meersman, H., Van de Voorde, E., 2001. Co-operation and competition in international container transport: strategies for ports. Marit. Policy Manage. 28, 293–305. Hitt, M.A., Ireland, R.D., 1984. Corporate distinctive competence and performance: effects of perceived environmental uncertainty (PEU), size, and technology. Decis. Sci. 15 (3), 324–349. Hoover, E.M., 1937. Spatial price discrimination. Rev. Econ. Stud. 4, 182–191.

14

J. Yoon et al. / Transportation Research Part A 80 (2015) 1–14

Iannone, F., 2012. The private and social efficiency of port hinterland container distribution through a regional logistics system. Transp. Res. Part A 46 (9), 1424–1448. Jöreskog, K.G., Sörbom, D., 1981. LISREL V: Analysis of Linear Structural Relationships by Maximum Likelihood and Least Squares Methods. Department of Statistics, University of Uppsala. Jöreskog, K.G., Sörbom, D., Toit, S., Toit, M., 2000. LISREL 8: New Statistical Features. third ed. Scientific Software International, Chicago, IL. Kaplowitz, M.D., Hadlock, T.D., Levine, R., 2004. A comparison of Web and mail survey response rates. Public Opin. Quart. 68, 94–101. Keeble, D., Wilkinson, F., 2000. High-technology Clusters, Networking and Collective Learning in Europe. Ashgate, Aldershot. Kim, J.G., 2003. The evaluation analysis of competitiveness among target ports with environmental changes of global logistics. Korean Port Econ. J. 19 (2), 1– 32. de Langen, P., 2002. Clustering and performance: the case of maritime clustering in the Netherlands. Marit. Policy Manage. 29, 209–221. de Langen, P.W., Chouly, A., 2009. Strategies of terminal operators in changing environments. Int. J. Logist. 12 (6), 423–434. Lee, C.S., Kim, J.H., 2011. Effects of competition factors, customer service, and port characteristics on balanced performance of container terminals. Korea Ind. Econ. Assoc. 24 (4), 2301–2321. Lee, C.H., Ryoo, D.K., Shin, Y.J., 2010. A Study on the effects of the competitive factors and the partnership of ship management companies on their business performances. J. Hum. Resour. Manage. 17 (4), 25–52. Lu, Y., Dinwoodie, J., 2002. Comparative views of international freight forwarders in China. Transp. J. 42 (2), 17–27. Meersman, H., Van de Voorde, E., Vanelslander, T., 2013. Nothing remains the same! Port competition revisited. In: Vanoutrive, T., Verhetsel, A. (Eds.), Smart Transport Networks: Market Structure, Sustainability and Decision Making. Elgar, Cheltenham, UK, pp. 9–28. Midoro, R., Musso, E., Parola, F., 2005. Maritime liner shipping and the stevedoring industry: market structure and competition strategies. Marit. Policy Manage. 32 (2), 89–106. Newall, E., 1992. The challenge of competitiveness. Bus. Quart. 56 (4), 94–100. Notteboom, T., Yap, W.Y., 2012. Port competition and competitiveness. In: Tally, W. (Ed.), The Blackwell Companion to Maritime Economics. Blackwell, pp. 549–570. Nummela, N., Saarenketo, S., Puumalainen, K., 2004. A global mindset: A prerequisite for successful internationalisation? Can. J. Admin. Sci. 21 (1), 51–64. Nunnally, J.C., 1978. Psychometric Theory. McGraw-Hill, New York. Podsakoff, P.M., Organ, D.W., 1986. Self-reports in organizational research: problems and prospects. J. Manage. 12 (4), 531–544. Porter, M., 1998. Clusters and the new economics of competitiveness. Harvard Bus. Rev. 76 (6), 77–90. Porter, M.E., 1985. Competitive Advantage. Free Press, New York. Reveley, J., Tull, M., 2008. Port Privatisation and the Asia Pacific experience. Edward Elgar, Cheltenham, U.K.. Reynaerts, Y., 2010. Simulating mergers between stevedores. Marit. Econ. Logist. 12 (1), 8–35. Richardson, G.B., 1972. The organization of industry. Econ. J. 82 (327), 883–896. Rivera, L., Sheffi, Y., Welsch, R., 2014. Logistics agglomeration in the US. Transp. Res. Part A A59 (1), 222–238. Rodrigue, J.-P., Notteboom, T., 2011. Global networks in the container terminal operating industry Part 1: How global are global terminal operators? Port Technol. Int. 49 (1), 10–14. Sánchez, R.J., Hoffmann, J., Micco, A., Pizzolitto, G.V., Sgut, M., Wilmsmeier, G., 2003. Port efficiency and international trade: port efficiency as a determinant of maritime transport costs. Marit. Econ. Logist. 5 (2), 199–218. Sanchez Rodrigues, V., Beresford, A., Pettit, S., Bhattacharya, S., Harris, I., 2014. Assessing the cost and CO2e impacts of rerouteing UK import containers. Transp. Res. Part A 61 (3), 53–67. Saeed, N., Larsen, O.I., 2010. An application of cooperative game among container terminals of one port. Eur. J. Oper. Res. 203 (2), 393–403. Shin, G.S., 2007. A study on the determinants analysis of the port competitiveness and development strategies of Busan New Port. Korean Port Econ. J. 23 (1), 115–148. Slack, B., Fremont, A., 2005. Transformation of port terminal operations: from the local to the global. Transp. Rev. 25 (1), 117–130. Song, D.-W., Lee, S.-W., 2007. Devolution, port governance and port performance. Res. Transp. Econ. 17, 357–375. de Souza, G.A., Beresford, A.C., Pettit, S., 2003. Liner shipping companies and terminal operators: internationalization or globalization? Marit. Econ. Logist. 5 (4), 393–412. Spencer, S.J., Zanna, M.P., Fong, G.T., 2005. Establishing a causal chain: why experiments are often more effective than mediational analyses in examining psychological processes. J. Pers. Soc. Psychol. 89 (6), 845. Tongzon, J., Heng, W., 2005. Port privatization, efficiency and competitiveness: some empirical evidence from container ports (terminals). Transp. Res. Part A 39 (5), 405–424. UNCTAD, 2013. Review of Maritime Transport. UNCTAD/RMT/2013, United Nations, New York. Wang, Y., Cullinane, K., 2014. Traffic consolidation in East Asian container ports: a networkflow analysis. Transp. Res. Part A 61, 152–163. Willingale, M., 1981. The port routing behaviour of short sea ship operator theory and practices. Marit. Policy Manage. 8 (2), 109–120. Wu, L., Yue, X., Sim, T., 2006. Supply clusters: a key to China’s cost advantage. Supply Chain Manage. Rev. 10 (2), 46–51. Yang, C.-S., Yeo, G.-T., Vinh, T.V., 2015. The effects of intra- and extra-organisational integration capabilities in the container shipping freight industry. Int. J. Logist. Res. Appl. 18 (4), 325–341. Yap, W.Y., Lam, J.S., 2006. Competition dynamics between container ports in East Asia. Transp. Res. Part A: Policy Pract. 40 (1), 35–51. Yeo, G.-T., Pak, J.Y., Yang, Z., 2013. Analysis of dynamic effects on seaports adopting port security policy. Transp. Res. Part A 49 (3), 285–301. Yeo, T.D., Lee, H.Y., 2012. A case study on the Industry-Academy-Government cooperation to intensify export competitiveness of local SMEs. Int. Comm. Inform. Res. 14 (2), 411–443. Yeo, G.-T., Roe, M., Dinwoodie, J., 2008. Evaluating the competitiveness of container ports in Korea and China. Transp. Res. Part A 42 (6), 910–921. Yuen, A.C.-I., Zhang, A., Cheung, W., 2012. Competitiveness from the users’ perspective: an analysis of major container ports in China and its neighboring countries. Res. Transp. Econ. 35 (1), 34–40. Yuen, A.C.-I., Zhang, A., Cheung, W., 2013. Foreign participation and competition: A way to improve the container port efficiency in China? Transp. Res. Part A A49, 220–231.