Relational bonding strategies, customer satisfaction, and loyalty in the container shipping market

Abstract

Purpose - In recent years, the business of container lines has faced severe challenges such as overcapacity and low-profitability. To survive in such a competitive market, container lines need to maintain long-term customer relationships by enhancing the satisfaction and loyalty of customers. This study adopts a social exchange theory approach and investigates the impact of relational bonding strategies on the satisfaction and loyalty of customers in container shipping.

Design/methodology/approach – Drawing on social exchange theory, a theoretical model that specifies the relationships between relational bonding strategies, customer satisfaction and loyalty was proposed. Survey data were collected from 175 freight forwarders. The obtained data were analyzed using structural equation modelling.

Findings – The results indicate that financial bonding strategies have the most significant direct effects on customer satisfaction, while social bonding strategies have the strongest direct impact on customer loyalty. Financial bonding strategies, on the other hand, have the strongest total effects on customer loyalty. Intermodal and basic operations are found to have the equal total effects on customer loyalty.

Research limitations/implications - By identifying the most effective relational bonding strategies for enhancing customer satisfaction and loyalty, this study’s findings allow container lines to better allocate their resources and implement effective relational marketing policies to satisfy and retain their customers.

Originality/value - This research analyses and validates the determinants of customer satisfaction and loyalty from a relational lens and empirically contributes to the field of relational marketing in the container shipping industry.

Keywords: Container shipping; relationship marketing; social exchange theory; relational bonding; loyalty; customer satisfaction

Introduction

Container shipping is vital for both international trade and globalization. Bernhofen et al. (2016) argued that container shipping is the “driver of 20th-century economic globalization.” The container shipping market has grown significantly since its inception in the 1950s. However, container lines have recently experienced overcapacity and low or even negative profitability due to fierce competition (Glave et al., 2014).

To cope with overcapacity and reduce costs, container lines perform route planning and capacity optimization (Ting and Tzeng, 2003), form strategic alliances, and share vessels. However, vessel-sharing also affects competition due to perceived commoditization in the market (Maloni et al., 2016; Balci et al., 2018). In the container shipping market, customers pursue the best price offer because the product or service characteristics of competitors appear homogeneous (Reimann et al., 2010). Therefore, the container shipping market is likely to face price wars, which cause low profitability or even bankruptcies.

1

In addition to developing cost minimization and capacity utilization methods, container lines also need to enhance customer satisfaction and build long-term relationships with customers and investors to survive in a highly competitive market (Balci and Cetin, 2017). Reckoning the low profitability of the market, establishing and maintaining the relationships with investors and customers play a key role for business survival. Otherwise, the situation can be quite dramatic as in the case of Hanjin Bankruptcy in 2016.

Focusing on the relationship with customers, shipping lines can partly protect themselves from price competition by building long-term relationships, which are essential for customer retention. Increased customer retention and satisfaction can boost profitability and increase market share (Williams and Naumann, 2011). Similarly, customer satisfaction and customer loyalty, which are related to each other, positively affect profitability and financial performance (Xie et al., 2017). Therefore, achieving customer satisfaction and loyalty is vital for container lines to assure their survival in a competitive market.

The container shipping literature examined the determinants of customer satisfaction and loyalty. Several studies reported that customer satisfaction can be influenced by the interaction between the customer and service encounter (Panayides and Gray, 1997), integrated logistics services (Zacharia and Mentzer, 2004), services of the sales and marketing department, stable relationships (Durvasula et al., 2007), quality management practices (Cheng and Choy, 2013), corporate social responsibility (Shin and Thai, 2015), service quality (Yuen and Thai, 2017a), organizational capability (Lun et al., 2016), and sustainable management practices (Shin et al., 2017). Other studies showed that customer loyalty may be enhanced by sustainable shipping practices (Yuen et al., 2018), logistics service quality and relationship quality (Jang et al., 2013), customer satisfaction, perceived service quality, and switching costs (Chao and Chen, 2015). However, very limited research has focused on the effects of relationship bonding strategies on customer satisfaction and loyalty in container shipping.

Anchored on social exchange theory (SET), this study examines the effects of container lines’ relationship marketing strategies on customer satisfaction and loyalty. The theory suggests that individuals’ or organizations’ intentions to continue using a service or product are taken by evaluating the rewards and costs of a transaction. Hence, developing relationships with customers can potentially reduce the costs and enhance the rewards of a transaction. In a business-to-business (B2B) services market such as container shipping, relationship marketing strategies are particularly relevant because these services are intangible. In addition, service markets are usually more complex than consumer markets (Yuen et al., 2017b). According to Balci et al. (2018), relationships can be useful differentiation tools in the container shipping market. By reviewing the existing relationship marketing literature, this study proposes three relational marketing strategies: financial, social, and structural strategies (Berry, 1995; Lee et al., 2015), which are considered to be effective drivers of customer satisfaction and loyalty. Container lines need to evaluate which strategy is more effective in ensuring the satisfaction and loyalty of their customers. However, limited research on container shipping services has so far examined the relationship between relational bonding strategies, customer satisfaction, and customer loyalty.

Container lines have two primary customers: freight forwarders and direct shippers. With the increase in logistics provider outsourcing, freight forwarders have become prevalent in the market, shipping more than 80% of the total global container traffic (Ho et al., 2017). Considering the large volumes handled by freight forwarders, stable relationships between container lines and freight forwarders can help the former maintain large shipment volumes. Thus, container lines should acknowledge the impact of their relational bonding strategies on the satisfaction and loyalty of freight forwarders. Such

2

awareness may enable them to better adjust their relationship efforts and investment to achieve sound customer satisfaction and long-term customer relationships and more efficiently use their marketing resources.

The paper has significant contributions to the literature on container shipping, relationship marketing, and SET. The paper enriches our knowledge on SET by identifying which relational bonds (financial, structural, and social) has greater influence on the satisfaction and loyalty of freight forwarders in the container shipping market. The study investigates the relationship between container lines and their customers, and extends marketing-perspective literature in the container shipping market, which has not gained the attention it deserves.

The remainder of this study is organized as follows. Section 2 reviews the existing literature and develops the research hypotheses. Section 3 describes the data collection method. Section 4 reports the results, and Section 5 discusses the main findings. Section 6 provides our concluding remarks.

Conceptual Development and Hypotheses

Relationship Marketing and Social Exchange Theory

Establishing and maintaining long-term and profitable relationships is essential for ensuring the competitiveness and profitability of businesses (Nguyen and Mutum, 2012). The relationship marketing (RM) approach posits that companies should aim at building and maintaining permanent relationships with their customers instead of achieving single transactions (Gummesson, 2017). RM is especially relevant in B2B markets, in which services are intangible and complex and require frequent relational exchanges between buyer and seller companies (Balci et al., 2018). Exchanges in B2B relations are central to marketing activities, and the interactions between organizations involve both economic and social exchanges (Zhou et al., 2015).

A key motivation for practicing RM is grounded in SET, developed by Homans (1958), Thibault and Kelley (1959), and Blau (1964), which is considered an appropriate theory to examine inter-organizational relational exchanges (Toth et al., 2015). SET has been used by several disciplines including economics, sociology, and psychology and is also commonly utilized by marketing researchers. For instance, Shiau and Luo (2012) investigated the factors that affect online purchase intention and satisfaction based on SET. Schakett et al. (2011) argued that RM and SET are complementary as both focus on long-term relational exchanges. According to Jeong and Oh (2017), SET is also a suitable theory to understand how business relations are continued or terminated in B2B markets.

Recent contributions in the shipping and logistics literature have discussed SET in the context of relationship exchanges. Yuen et al. (2018) addressed SET and argued that a positive link exists between the sustainable practices of container shipping companies and the loyalty of shippers. Huo et al. (2016) investigated opportunistic behavior in third-party logistics outsourcing relations based on SET and transaction cost economics.

SET suggests that individuals or organizations begin and continue or terminate a relation by evaluating the rewards and costs of that relationship (Homans, 1958). If one believes that a relationship guarantees more rewards than an alternative connection, she/he continues the relationship. Likewise, if the relationship guarantees fewer rewards than an alternative connection, she/he switches to the alternative relationship (Thibault and Kelley, 1959). Homans (1958) named the result of the rewards and cost equation as profit. He further proposed that if the perceived profit is minimum, an individual

3

is very likely to terminate that relationship. The theory indicates that the rewards are not only monetary or tangible. Intangible social rewards such as friendship, reputation, and the feeling of belonging also exist and can be more significant than economic incentives for the decision to maintain a relation (Lambe et al., 2001). According to SET, the resources exchanged in a relationship can be love, status, information, money, goods, and services (Cropanzano and Mitchell, 2005).

SET posits that companies can build and enhance their relationships by exchanging several benefits. In line with the RM literature and SET, this study proposes that shipping companies can strengthen their relations with customers through three relational bonding strategies: financial, structural, and social relationships. Financial bonding strategies refer to direct monetary incentives such as discounts or deferred payments (Lee et al., 2015). Structural bonding strategies are valuable and customer-specific services that customers consider difficult and/or expensive to find (Rodriguez and Wilson, 2002). Finally, social bonding strategies aim to increase the hedonic value of buyers through social ties and interpersonal relations (Schakett et al., 2011).

Whether these relational bonding strategies were originally designed based on SET is unclear. However, they include the economic and social exchange resources attributed to SET. Financial and structural bonding strategies resemble the economic and tangible rewards mentioned in SET, while social bonding strategies are identical to the intangible social rewards mentioned in SET. Schakett et al. (2011), for instance, investigated the impact of social bonds on several relational outcomes such as satisfaction and loyalty by referring to SET.

The Impact of Relational Bonding Strategies on Customer Satisfaction and Loyalty

The primary aim of RM is to satisfy customers and gain their loyalty, which eventually increases the profitability of seller companies (Miquel-Romero et al., 2014). Customer satisfaction is a reliable indicator and evaluation criteria for product and service quality and considered as a valid measurement metric of marketing efforts (Jang et al., 2013). Customer satisfaction also has a positive impact on loyalty, which is a significant asset for companies struggling to survive in a highly competitive arena (Chang and Yeh, 2017). However, customer satisfaction does not always lead to customer loyalty and needs to be tested in different industries or customer groups (Jiang and Zhang, 2016).

SET suggests that relational bonding strategies can help seller organizations increase the perceived reward of customers, increase their satisfaction with the relationship, and gain their loyalty. Different branches of the literature also demonstrated the positive influence of relationships and bonding strategies on customer satisfaction and loyalty (e.g., the telecommunication industry in Akroush and ElSamen, 2012; the insurance industry in Ansari and Riasi, 2016; airfreight transportation in Yang and Chao, 2017).

Consistent with the literature on SET, the current research adopts three bonding strategies for relationship marketing. We posit that the proposed strategies can help container shipping companies increase the perceived profit of freight forwarders in the relationship exchange and enhance their satisfaction and loyalty levels. The research hypotheses of the current study are built on this assumption.

Financial bonding strategies

Financial bonding strategies aim to increase the perception of the utilitarian value and enhance customer satisfaction and loyalty through monetary incentives (Chiu et al., 2005). They can include price incentives and volume discounts (Lee et al., 2015). Chaabane and Volle (2010) found that monetary-based relational efforts are the best predictors of loyalty. However, other scholars argued that financial bonding strategies are not as effective as structural and social strategies (Wang et al., 2006; Lee et al., 2015).4

Although no previous study has directly investigated the impact of relational bonding strategies in container shipping, several studies indirectly examined the importance of financial bonding. For instance, some authors found that appropriate freight rate offerings of container carriers may predict customer loyalty intentions (He et al., 2008; Lin et al., 2017). Similarly, Chen et al. (2017) found that container shipping companies must offer price incentives to gain customer loyalty. Considering financial strategies for customer retention and relationship building in container shipping, customers expect to obtain exclusive price offerings and discounts from carriers (Lin et al., 2017; Maloni et al, 2016).

Several other studies found that the freight rates of container carriers are one of the essential decision criteria of freight forwarders (e.g., Kannan et al., 2011; Ho et al., 2017). Durvasula et al. (2007) investigated the service attributes that affect customer satisfaction in the ocean freight industry and reported that competitive freight rates are one of the most significant determinants of satisfaction. Besides, the customers of container lines have become more sensitive to price in recent years as they find the services of container lines being identical due to increasing vessel sharing agreements (Balci et al, 2018). Financial bonding strategies are envisaged to influence satisfaction and loyalty in such a market condition. Thus, the following research hypotheses are proposed:

H1: The financial bonding strategies of container lines have a positive impact on the satisfaction of freight forwarders;

H2: The financial bonding strategies of container lines have a positive impact on the loyalty of freight forwarders.

Structural bonding strategies

Structural bonding strategies involve valuable and customer-specific services. These activities impact the efficiency, productivity, and quality perceptions of customers (Zhao and Stank, 2003). Lee et al. (2015) investigated food service franchises in South Korea and revealed that structural strategies, together with social strategies, have a significant positive impact on utilitarian benefits (satisfaction, willingness to renew the contract, willingness to recommend, and long-term orientation). Wang et al. (2006) argued that structural bonding has the most significant impact on relationship quality and positively affects the loyalty of customers. The literature also indicates that structural bonds increase switching costs and decrease customer churn likelihood, thus building customer loyalty (Chiu et al., 2005).

The container shipping literature points to several variables potentially affecting structural or operational bonding. Customs clearance and value-added services, such as offering e-commerce alternatives only to selected customers (Penaloza et al., 2007), are effective service elements that can create bonds with customers. Customers also attach significant importance to the priority given in finding empty containers and space in the vessel (Lu, 2003; Kannan et al., 2011; Wen and Lin, 2016). Intermodal services, such as the ability to arrange inland transportation (Kannan et al., 2011), combining inland and maritime operations, and offering door-to-door and port-to-door services (Ho et al., 2017), are essential structural bonding strategies in the container shipping market used to develop or enhance relationships. According to Zacharia and Mentzer (2004), integrated logistics services are a source of competitive advantage and customer satisfaction. Jang et al. (2013) found that container lines’ operational services, together with relational services, have a direct positive impact on South Korean freight forwarders’ satisfaction.

In container shipping, the content of structural bonding strategies is complex because the services offered by container lines include several layers. There are elements of basic services in port-to-port 5

transportation, such as providing empty containers, and elements of extended services, such as door-to-door transportation or warehousing. We propose that the structural bonding of container lines consists of two dimensions: basic services, which are related to port-to-port transportation of containers, and intermodal services, which involve the services provided in door-to-door transportation. Hence, we propose the following hypotheses:

H3: The basic operations of container lines have a positive impact on the satisfaction of freight forwarders;

H4: The basic operations of container lines have a positive impact on the loyalty of freight forwarders;

H5: The intermodal operations of container lines have a positive impact on the satisfaction of freight forwarders;

H6: The intermodal operations of container lines have a positive impact on the loyalty of freight forwarders.

Social bonding strategies

Social bonding strategies include relational capabilities that help develop seller-buyer closeness through social ties focused on interpersonal interactions (Shou et al., 2017; Huo et al., 2017). This strategy is based on activities aimed at communicating with customers to develop fruitful relationships. In this way, the satisfaction and loyalty of customers can be achieved. For instance, Schakett et al. (2011) examined the impact of social bonding in B2B markets and found that social bonding positively affects customers’ loyalty, satisfaction, trust, and perceived service quality.

In the shipping literature, Panayides and Gray (1997) found that direct interaction between customer and service encounter is critical to develop relationships and generate customer satisfaction. Durvasula et al. (2007) found that relationships with customers have a strong positive impact on satisfaction. Lin et al. (2017) found a strong relationship between a close relationship with freight forwarders and customer retention likelihood. Similarly, Chen et al. (2017) reported that customer relationships and personal selling increase the likelihood of customer retention.

The literature on container shipping refers to several other social bonding practices. Some authors suggested that maintaining and developing existing relationships through regular visits and calling customers are essential social practices of a container shipping company (Kannan et al., 2011; Wen and Lin, 2016). Another social strategy is based on implementing social responsibility projects that involve customers, which may help shipping companies develop a better corporate image while enhancing existing relationships (Wen and Lin, 2016). Sending gifts to selected customers is another social practice adopted by container shipping lines (Kannan et al., 2011). Balci et al. (2018) also reported that customer relationships, including frequent visits and calls and good interpersonal relations with shippers, are important differentiators of container lines. Thus, we propose the following hypotheses:

H7: The social bonding strategies of container lines have a positive impact on the satisfaction of freight forwarders;

H8: The social bonding strategies of container lines have a positive impact on the loyalty of freight forwarders.

Impact of Satisfaction on Loyalty

6

Apart from addressing several dimensions that affect the satisfaction and loyalty of shippers, the present study proposes that satisfied shippers are more likely to remain loyal to a specific liner operator. Empirical studies on container shipping showed considerable support for the link between satisfaction and loyalty. For example, Lu (2003) reported that shippers who are highly satisfied are more willing to continue the existing relationships. A strong and positive link has been found between satisfied shippers and freight forwarders in South Korea and their repurchase intentions from the same container carrier (Shin et al., 2017). Likewise, Chao and Chen (2015) found a strong positive correlation between the satisfaction level of liner operators’ customers and their loyal behavior. Chao et al. (2015) investigated the mediating impact of service recovery in liner shipping services and found that customer satisfaction positively influences loyalty. Hence, we propose the following hypothesis:

H9: The satisfaction of freight forwards positively affects their loyalty towards container lines.

Mediating Effects of Customer Satisfaction

The hypotheses suggest that the effects of relational bonding strategies on customer loyalty are both direct, and indirect which is channeled via customer satisfaction, suggesting mediation.

The direct effect can be explained from the perspective of value. According to this stream of research, a rational customer (or shipper) will continue to use a service (i.e. exhibiting loyalty) rather than other alternatives if the service provides the customer with most benefits at the least cost (Yuen et al., 2018). The benefits can be measured in the form of economic (e.g., financial rewards), functional (e.g. quality and transport performance improvement), hedonic (e.g. emotional rewards) and social (e.g. environmental improvement benefits or externality reduction) benefits. Relational bonding strategies, for instance, financial bonding strategies such as providing discounts on freight rates can improve the perceived economic benefits of using the container line’s service. In a similar vein, structural bonding strategies such as giving priority for empty containers and value-added services can improve the perceived functional benefits of using the service. Further, social bonding strategies such as offering gifts and celebrating special days with shippers can improve their perceived hedonic and social benefits.

Next, according to Oliver (2010), repeated satisfaction is a key condition for the initial development of loyalty. In other words, satisfied shippers are more likely to exhibit loyalty. The review above suggests that the effects of relational bonding strategies on customer loyalty are direct, as well as indirect through customer satisfaction.

Therefore, based on the above review, the following discussion is proposed:

H10: The satisfaction of freight forwards mediates the effects of relational bonding strategies on their loyalty.

Based on discussions above, the research model is as below (Figure 1).

7

Financial strategies

Basic operations

Intermodal operations

Social strategies

Satisfaction Loyalty

H1

H3

H5

H7

H2

H4

H6

H8

H9

Figure 1. Conceptual Model

Note: The model suggests a mediation relationship (i.e. H10).

Methodology

Measurement Items

This study surveyed freight forwarders in Turkey to measure the influence of relational bonding strategies on customer satisfaction and loyalty in container shipping. The paper conducts structural equation modelling by using LISREL program. We utilized both a literature review and interviews with sector experts to create questionnaire variables. More specifically, we first generated some key variables based on the literature review and, then, we conducted semi-structured interviews with a total of five container lines and four freight forwarders. All interviewees operate in managerial-level positions in their company (Table 1). Furthermore, they have ten years or more experience in the container shipping industry.

Table 1. Profile of interviewees

Company Position of the interviewee

Total work experience

of the interviewee

Company Position of the interviewee

Total work experience of the interviewee

Line Branch manager 27 Forwarder Branch manager 10Line Branch manager 32 Forwarder Owner 29Line Sales manager 18 Forwarder Owner 22

8

Line Sales manager 15 Forwarder Sales manager 16Line Sales manager 11

As a result of the data collection process, relational bonding, satisfaction, and loyalty items were obtained and included in the questionnaire (Table 2). This study adapted relational bonding strategies to SET and RM and utilized customer-specific service offerings of container lines. The variables that could not be customized were excluded in the survey (e.g., transit time, schedules, and frequency, among the others). Thus, in the measurement scale, instead of expressions like “this line has a short transit time,” we reported statements such as “this line gives priority to us for empty equipment.” Although some studies focused on service attributes and service quality in container shipping, there is a lacuna in customized/personalized service offerings. Therefore, we obtained the majority of the relational bonding variables through the interviews with industry experts. Global measures for satisfaction and loyalty were adapted from Lam et al. (2004).

Table 2. Measurement Scale

Variables Abbreviation SourceRelational bonding strategiesFinancial strategiesThis line ….offers special deferred payment opportunities for us. Deferred payment Intervieweesoffers special discounts on freight rates for us. Discountprovides a discount in local port expenses for us. Local expensesprovides flexibility in demurrage free time for us. Free timeBasic operations (Structural strategies)gives priority to us for the space in the vessel. Space Wen and Lin, 2016;

Kannan et al., 2011; gives priority to us for empty equipment. Empty containerhelps us find special equipment (Open-Top, Reefer, 45'). Special equipment Lu (2003)Intermodal operations (Structural strategies)offers door-to-door and port-to-door services exclusively to us.

Door-to-door service

Interviewees

offers value-added services in the destination exclusively to us.

Value-added service

helps us with customs operations in the destination. Customsprovides convenience to us in finding barges, trains, and trucks in destinations.

Vehicle

Social strategiescelebrates special days with us. Celebration Intervieweesgives us gifts. Gifthas lunch or dinners with us. Dinnerjoins social responsibility projects together with us. Social

responsibilityThis line and we attend special events (sports, cocktails, concerts, and exhibitions) together.

Event

Outcomes SatisfactionMy company is satisfied with the performance of this line. Performance Lam et al. 2004My company is satisfied with its relationship with this line. RelationshipThe service of this line meets our expectations. Expectation

LoyaltyMy company is willing to do more business with this carrier in the coming years.

More business Lam et al. 2004

9

My company has never considered changing this line. ChangingWe see this line as our partner. Partner

Survey Design and Administration

The survey, online self-administered, comprises four sections. Section one describes the objective of the study. Section two contains company demographic questions relating to the source of capital, age of the firm, number of employees, product category most frequently exported to Northern Europe, and the number of containers annually shipped. Section three investigates the demographics of respondents as well as their position, total work experience, and experience in the current position. Section four involves measurement scale variables (see Table 2). The items in this section were evaluated using a five-point Likert-type scale ranging from “strongly disagree” (1) to “strongly agree” (5).

The expert interviews revealed that the strength of the relationship between a container line and a freight forwarder varies based on the route. The container line interviewees stated that their relational bonding strategies change depending on the routes they serve. Similarly, freight forwarders also stated that their satisfaction levels with each container line vary based on the destination. Therefore, we asked freight forwarders to answer the Likert-type questions by considering a single route only, specifically, the Turkey-Northern Europe route because of its high container traffic volume and variety of container lines. The interviewees also suggested that this route is characterized by the highest level of competition between container lines for shipments from Turkey. In addition, freight forwarders were asked to answer these questions by considering the container line with which they ship the largest container volume on this route.

Regarding the sampling of respondents, since no comprehensive list of forwarders operating in Turkey exists, we used the freight forwarder list of a major container line in Turkey to define our population size. We obtained a comprehensive list of forwarders, which handle seaway shipments. We identified a total of 413 freight forwarders operating in Turkey. A reasonable limitation was adopted in the data collection process: since we only focused on “sea exports,” the respondents should be aware of ocean operations. Therefore, respondents were explicitly requested to hold seaway shipment-related job specifications or be a manager or owner. A self-administered online survey link was sent to the personal e-mail addresses of respondents. In addition, the LinkedIn social platform was also used to identify and connect with the interviewees in the respondent companies. Data collection was carried out between January 2018 – May 2019. At the end of the data collection process, we obtained a total of 175 valid responses, which correspond to 42.3% of the total population.

Profile of Respondents

The results indicate that almost 34% of the freight forwarding firms are foreign enterprises, while 66% of are local companies, and 85% of firms have ten or more employees. As shown in Table 3, more than half of the respondent companies are older than 11 years, 40% have a total annual export volume of 4,500 TEUs or more, and 45% have an annual export volume to Northern Europe of 1,000 TEUs or more. Finally, the most commonly exported products from Turkey to Northern Europe are food, machinery and spare parts, and marble and minerals. It should be noted that we are unable to compare demographics of sampled companies with the original 413 freight forwarders since the demographics of the companies were not available.

Table 3. Company Information

Frequency % Frequency %Source of Capital Domestic 116 66.3 Total Exports 1500 and less 61 34.9

10

Foreign 59 33.7 (TEU) 1501-3000 21 12.0Total 175 100 3001-4500 23 13.1

Employees 1-9 26 14.9 4501 and more 70 40.010-24 42 24.0 Total 175 10025-49 49 28 Northern Europe 999 and less 96 54.950 and more 58 33.1 Exports (TEU) 1000 and more 79 45.1Total 175 100 Total 175 100

Age of the 1-5 38 21.7 Products Food 53 30.3firm 6-10 34 19.4 (North Europe) Machinery and spare part 31 17.7

11-15 28 16.0 Marble & Minerals 25 14.315-20 25 14.3 Textile 24 13.721 and more 50 28.6 Chemistry and plastics 22 12.6Total 175 100 Others 20 11.4

Total 175 100

In addition to company demographics, the respondents’ individual demographics are presented in Table 4, which reports their position in the company, total work experience, and tenure. The majority (46.9%) of the 175 respondents hold managerial level positions in the company, such as owner, general manager, director, board member, co-founder, export manager, sales manager, or regional manager, while 27.4% hold operational level positions, and25.7% hold positions related to marketing and sales.

Table 4. Respondent Profiles

Frequency %Position Managerial level 82 46.9

Operational level 48 27.4Marketing & Sales 45 25.7Total 175 100

Total work experience 2-5 48 27.56-10 47 26.911-15 36 20.616 and more 44 25.1Total 175 100

Experience in the current position

1-5 65 37.26-10 47 26.911-15 28 16.016 and more 35 20.0Total 175 100

The results indicate that the respondents are experienced: almost half of them have an overall work experience of 11 years or more, while approximately 40% gained their experience in their current position during this period.

Results

Exploratory and Confirmatory Factor Analyses

Since the measurement scale was developed using both the existing literature and interviews with experts, the reliability and validity of the measures were verified. First, explanatory factor analysis (EFA) was conducted to explore the underlying structure among the factors (Hair et al., 2006). Principal component analysis (with eigenvalues greater than one) was used as the extraction method,

11

while Varimax with Kaiser Normalization was employed as the rotation method. The analysis was carried out using the IBM SPSS 20.0 software package. Kaiser-Meyer-Olkin (KMO) test of sampling adequacy was 0.911, while the significance of Bartlett’s Test of Sphericity was less than 0.001, thus indicating that the sample is adequate for analysis. The EFA produced a total of six factors.

Following the EFA, the reliability and validity of the measures were tested by confirmatory factor analysis (CFA). As shown in Table 5, all the standardized loadings were above the recommended cut-off value of .60 (Bagozzi and Yi, 1998), with significant t-values, between 7.64 and 16.36 (critical t-value = 2.576 for the 0.01 confidence interval). In addition, the model fit indices were also satisfactory. χ2 was used for testing the difference between observed and estimated covariance matrices (Hair et al., 2008). The data are considered to have a good fit with the model if χ2/df ≤2 (Bentler, 1988), and the values for our model guarantee a good fit (p= 0.01, χ2

=247.62, χ2/df= 1.3). Hair et al. (2008) recommended using at least one absolute and one incremental index in addition to the χ2 statistic for assessing the model fit. For structural equation modeling (SEM), the absolute badness-of-fit measure (RMSEA) and the comparative incremental fit index (CFI) are the most broadly accepted indices in the literature (McDonald and Ho, 2002). The results of this study obtained satisfactory scores, with an RMSEA value of 0.044 (good fit; Browne and Cudeck, 1993), and a CFI value of 0.97 (good fit; Hu and Bentler, 1999). Thus, the validity of the proposed measurement model is confirmed, and the proposed approach can be employed in further analyses.

Table 5. Measurement Model Results

Construct Indicator Standardized Loading

Composite Reliability

AVE

Financial strategies (A) Deferred payment 0.65 0.80 0.51Discount 0.68Local expenses 0.71Free time 0.80

Basic operations (B) Space 0.93 0.88 0.71Empty container 0.96Special equipment 0.60

Intermodal operations (C)

Door-to-door service 0.82 0.85 0.60

Value-added service 0.80Customs 0.75Vehicle 0.73

Social strategies (D) Celebration 0.68 0.87 0.57Gift 0.81Dinner 0.71Social responsibility 0.76Event 0.82

Satisfaction (E) Performance 0.89 0.93 0.81Relationship 0.91Expectation 0.91

Loyalty (F) More business 0.93 0.88 0.72Changing 0.72Partner 0.88

To achieve convergent validity, we calculated average variance extracted (AVE) values and composite reliability (CR) scores manually using Microsoft Excel. All the latent variables show high CR scores (between .80 and .93), exceeding the accepted .70 ratio (Bagozzi and Yi, 1988). AVE scores of

12

constructs are also above the recommended threshold of .50 (Hair et al. 2006), values between .51 and .81.

Table 6 shows the AVEs of the constructs on the main diagonal, and the off-diagonal entries are the correlations and squared correlations of the constructs. The results suggest discriminant validity since the squared correlations of the constructs are lower than their AVE. This indicates that the measurement items explain a larger proportion of the variance in their loaded constructs compared to other elements.

Table 6. Discriminant Validity Analysis

Construct A B C D E FA 0.51 0.48 0.45 0.17 0.44 0.37B 0.0.69 0.71 0.19 0.09 0.21 0.34C 0.67 0.44 0.60 0.27 0.30 0.2D 0.41 0.30 0.52 0.57 0.16 0.26E 0.66 0.46 0.55 0.40 0.81 0.56F 0.61 0.58 0.57 0.51 0.75 0.72

Note: The values on the main diagonal are AVEs. The values below the main diagonal are the correlations. The values above the main diagonal are squared correlations.

Since the obtained data were collected using surveys which are perceptual in nature, common method bias might be an issue. Harman’s single factor test was conducted (Podsakoff et al., 2003). The test reveals that the variance of the one factor model accounts for only 39% of all the indicators. Therefore, common method bias is not a major issue.

Evaluation of the Structural Model and Path Results

Figure 2 presents the results of the structural model regarding the impact of shipping lines’ relational bonding strategies on freight forwarders’ satisfaction and loyalty. Demographic variables which include total exports (TEUs), firm age (years) and firm size (number of employees) were incorporated in the model as control variables. Specifically, their effects on the endogenous variables which include satisfaction and loyalty were estimated.

The red lines represent insignificant paths. The model provides a good fit of the data according to the results of SEM: χ2/df is 1.46, RMSEA is 0.051, and CFI is 0.96. The t-values of the paths are higher than the critical levels (critical t-value= 2.576 for the 0.01 significance level; critical t-value= 1.96 for the 0.05 significance level), except for the path from social strategies to satisfaction. Disregarding the control variables, the SEM analysis indicates that eight out of nine paths are statistically significant.

Since Figure 2 suggests the presence of mediation effects, a bootstrapping technique was utilized, as suggested by Zhao et al. (2010). The hypothesized paths in the model presented in Figure 1 were bootstrapped based on a predefined sample distribution. The estimates of all direct, indirect, and total effects were generated based on random sampling with replacement with 5,000 reiterations. Based on the bias-corrected two-tail significance tests, all the direct and indirect effects of the relational bonding strategies on loyalty are statistically significant (p<0.05). Hence, H10 which posits a mediated model is accepted.

13

Financial strategies

Basic operations

Intermodal operations

Social strategies

Satisfaction Loyalty

0.46**

0.30*

0.30**

0.16 ns ns

0.32**0.37*

0.37*

0.41**

0.77*

Total exports

Firm age

Firm size

Control Variables

0.00ns 0.01ns 0.00ns

0.02ns

-0. 07ns

0.02 ns

Figure 2. Structural Path Estimates

* statistically significant (p<0.05); ns statistically not significant (p>0.05)

As shown in Table 7, the model results indicate that the financial efforts, basic operations, and intermodal operations of shipping lines explain freight forwarders’ satisfaction, with effect sizes of .46, .30, and .30, respectively. Financial strategies have the greatest impact on satisfaction, while basic and intermodal operations have relatively lower level of impact on satisfaction. No significant relationship was found between social strategies and satisfaction.

Table 7 Structural paths of the model

Structural Paths Path t-value Result HypothesisFinancial strategies → Satisfaction 0.46 3.41 Significant Accepted (H1)Financial strategies → Loyalty 0.32 2.42 Significant Accepted (H2)Basic operations → Satisfaction 0.30 2.54 Significant Accepted (H3)Basic operations → Loyalty 0.37 3.05 Significant Accepted (H4)Intermodal operations → Satisfaction 0.30 2.42 Significant Accepted (H5)Intermodal operations → Loyalty 0.37 2.92 Significant Accepted (H6)Social strategies → Satisfaction 0.16 1.17 Non-significant Rejected (H7)Social strategies → Loyalty 0.41 2.89 Significant Accepted (H8)Satisfaction → Loyalty 0.77 10.04 Significant Accepted (H9)

Significant Accepted (H10)χ2/df = 1.46, RMSEA = 0.051, CFI = 0.96

14

Satisfaction was found to exert the most significant direct influence on freight forwarders’ loyalty. Regarding relational bonding strategies, social bonding practices exert the highest influence, with a loading of .41. Basic and intermodal operations have a slightly lower but still significant impact on loyalty, with each has a loading of .37. The results indicate that financial bonding strategies had the lowest direct impact (.32) on customer loyalty in our sample.

Table 8 The direct, indirect, and total effects

Satisfaction LoyaltyDirect effectsFinancial strategies 0.46 0.32Basic operations 0.30 0.37Intermodal operations 0.30 0.37Social strategies - 0.41Satisfaction - 0.77

Indirect effectsFinancial strategies - 0.35Basic operations - 0.23Intermodal operations - 0.23Social strategies - -Satisfaction - -

Total effectsFinancial strategies 0.46 0.67Basic operations 0.30 0.60Intermodal operations 0.30 0.60Social strategies - 0.41Satisfaction - 0.77

Relational bonding strategies also have indirect effects on loyalty through satisfaction. To determine the total effect of each relational bonding strategy on loyalty, their indirect effects need to be calculated, based on Kline (2016). The total effect of each strategy is calculated by summing its direct and indirect effects. The results are presented in Table 8. In our study, the total effect of the financial programs offered by a shipping line on loyalty is .67. Basic and intermodal operations’ total impacts on loyalty are the same, .60. No indirect relationship was found between social bonding strategies and loyalty through satisfaction because no significant relationship exists between social efforts and customer satisfaction. Therefore, the total effect of social strategies on loyalty remains .41. Overall, the results suggest that financial strategies have the greatest total impact on loyalty, followed by basic operations and intermodal operations. Social bonding strategies have the lowest total effect on customer loyalty.

Effect Size Analysis

This study employs the method proposed by Christopher (2010) to determine the minimum sample size for our structural equation modelling analysis. By conventions, we specified the desired statistical power (1 – β) to 0.80 and alpha value (α) to 0.05. With reference to our model (refer to Figure 1), the number of latent and observable variables are 6 and 22 respectively. Consequently, based on the information, we estimate the minimum sample sizes required to detect significant effect sizes of 0.1, 0.3, and 0.5. Accordingly, the required sample size to detect these effect sizes are 1713, 161 and 40.

15

The current sample size of our study (n=175) is sufficient to detect significant effects with estimates of 0.3 and above, which generally covers all estimated relationships between the key constructs presented in Figure 2. More importantly, it indicates that caution should be exercised when interpreting non-significant relationships with effect size lesser than 0.3.

As shown in Figure 2, there are seven non-significant effects. One emanates from social strategies to satisfaction whereas the remaining six emanate from the control variables with effect sizes ranging from 0.001 to 0.076. To confidently ascertain that these effects are truly non-significant, a sample size of approximately 1,713 or more is needed. This is not possible given the current sample size and even if we have the data from the entire population.

Nevertheless, as these effect sizes are considered small (≤ 0.16) and most do not directly relate to the hypotheses of this study, we can view them as inconsequential and have little effects on distorting the key parameter estimates of our model. However, this study suggests caution when interpreting the non-significant results in this study as a larger sample size is needed to ascertain the significance of their effect size.

Discussion

The results of the present study indicate that all of the relational bonding strategies have direct or indirect positive effect on the loyalty of freight forwarders. The significant direct effects corroborate the concept of value because it is well established that a rational customer (or freight forwarder) will remain loyal to a shipping line if a container line’s services offer the best value over other competitors’ services. The significant indirect effects are consistent with the study of Oliver (2010) who proposed that repeated satisfaction with a service can lead to loyalty. Further, our analysis shows that the control variables do not have any significant effects on satisfaction and loyalty. This indicates that these control variables are not as important as those variables from the relational bonding strategies in terms of explaining customer satisfaction and loyalty.

Among the four bonding strategies, financial bonding strategies have the largest total impact on loyalty. Considering the current trends in the container shipping market, it is not surprising that the financial bonding strategies have the highest impact on satisfaction and the highest total impact on loyalty. Container lines have recently paid great attention to minimize unit cost, such as investing heavily in giant containerships and signing vessel sharing agreements to operate these giant vessels more efficiently. However, container lines have largely ignored the flexibilities and customized services, which has caused the services of container lines to become identical and deepened the price-based competition in the market. In addition, the price sensitivity of customers has also increased due to escalating global competition. This situation can explain why financial bonding strategies have the highest total impact on loyalty.

Structural bonding strategies, which include basic and intermodal operations, also significantly affect satisfaction and loyalty of freight forwarders. This result is expected because these two structural bonding strategies can offer significant rewards for customers and lead to higher profit. Structural bonds have considerable potential to increase efficiency and productivity of customers in container shipping. For instance, in basic operations, container lines support their customers’ operations by giving priority in usual or special equipment supply and space in vessel. These service elements significantly affect the reliability and timeliness of services specifically in countries where empty container or congestion problem exist. This also helps customers of container lines reduce their inventory costs by eliminating delays. Considering the importance of reliability and timeliness in the

16

logistics of companies, it makes sense that basic operations have a significant effect on the loyalty of freight forwarders.

The significant impact of intermodal operations on loyalty is also reasonable as door-to-door privileges help freight forwarders differentiate themselves and provide value-added services to shippers, thus gaining a competitive advantage. Since port-to-port operations are very difficult to differentiate due to increasing vessel sharing agreements, value added services given in land operations can help in sustaining long-term relations. The increasing importance of one-stop-solution strategies through door-to-door transportation is also confirmed by the recent activities of container lines. Vertical integration in land operations is becoming widespread in the industry. For instance, the acquisition of CEVA Logistics by CMA CGM indicates how container lines attach importance to land operations for achieving competitive advantage. Another reason for the strong impact of intermodal practices may be that shippers in Europe attach importance to the intermodal transportation that arises from the geographical accessibility of the region, and this element needs further investigation.

Our study also reveals that the direct effect of social bonds is more effective than that of other bonds in building loyalty with customers. The significant impact of social bonding on loyalty is not surprising because the complex structure of container shipping service often necessitates personal communication between container lines and their customers. Moreover, container shipping is also an industry which has diverse environmental and social issues such as emissions, marine pollution, and gender equality on board and offices. This situation forces the actors in container shipping to have frequent social interactions and manage corporate social responsibility activities. Besides, social bonding strategies, including interpersonal relations, are essential determinants of loyalty, especially in B2B markets (Schakett, 2011).

Although our study finds a positive effect of social bonding on loyalty, we did not find any significant relationship that exists between social bonding and satisfaction. This finding may arise due to the indicators in our satisfaction construct which include – aside from overall satisfaction in the relationship – two business-wise variables namely performance level and meeting expectations. On the other hand, social bonding strategies are mostly addressed at personal level and social events rather than business-wise performances and expectations. This can explain the insignificant relationship between social bonding and satisfaction. However, this assumption needs a further investigation.

Compared to the previous literature, our findings have similarities and dissimilarities. Regarding the effect of financial strategies on loyalty, our findings are consistent with Chaabane and Volle (2010) who found that monetary-based relational efforts are the best predictors of loyalty. It is also consistent with findings of previous studies in container shipping which indicate the importance of freight rates on relationship building and loyalty (Chen et al.,2017; Maloni et al., 2016). However, our findings are not parallel with Wang et al. (2006) who showed that structural and social bonding strategies are more effective than financial strategies. As for the structural bonding strategies, our results are parallel with the findings of relational bonding literature in which significant relationships are observed between structural strategies and loyalty (Chiu et al., 2005; Lee et al., 2015). Our results also confirm the literature in container shipping which underlines the importance of structural bonding dimensions such as door-to-door transportation, availability of equipment, and other operational services (Jang et al., 2013; Wen and Lin, 2016).

Regarding the effects of social bonding, our results are partially in parallel with Schakett et al. (2011) who demonstrated that social bonding strategies have a positive impact on the satisfaction and loyalty of customers. Although our study confirms the positive impact of social bonding on loyalty, our analysis does not find a positive effect of social bonding on the satisfaction of forwarders. The results

17

also confirm the positive effect of satisfaction on loyalty, which is consistent with the literature of container shipping (Chao and Chen, 2015; Shin et al., 2017).

Theoretical and Managerial Implications

The results of this study have significant theoretical and managerial implications. With respect to the theoretical contributions, the present analysis enriches the literature on SET and RM. The positive relations between relational bonds and loyalty provide empirical support to SET. In addition, SET suggests that organizations continue or terminate a relationship by evaluating the rewards of the relation. These rewards can be social, financial, and operations or services. Based on SET, customers become more satisfied and loyal if the rewards are substantial compared to those received from other suppliers. Our study helps clarify which rewards are more effective to achieve customer satisfaction and loyalty. To the best of our knowledge, so far, only one study has examined relational strategies using SET (Schakett et al., 2011). However, the authors have mainly tested the impact of social bonding. In contrast, our study addresses three relational strategies and examines their relative effectiveness in achieving customer satisfaction and loyalty.

This study also contributes to the container shipping literature on marketing. The container shipping market faces severe business conditions because of overcapacity and low profitability. Along with cost minimization policies, container lines need to find new ways to increase customer demand. Hence, marketing policies have recently become crucial in container shipping. The lack of relationship marketing in the container shipping literature is mentioned by Jang et al. (2013) as well. Our study contributes to filling this gap in the literature by investigating relationship marketing policies for container shipping, explicitly focusing on relational bonding strategies.

The content of relationship marketing not only varies between B2C and B2B markets but also between different industries within the B2B market. This study represents the first attempt to investigate relational bonding strategies within the container shipping domain. Unlike previous studies focusing on other industries, the structural bonding strategies addressed in this study consist of two dimensions (intermodal and basic operations) and are based on measurements obtained through EFA and CFA. Container shipping services have unique characteristics. The service is complex and includes several layers, such as pre-carriage, sea transportation, and inland transportation at the destination. As Maskey et al. (2018) also mention, a large variety of latent variables related to shipping services can be found in the literature. Therefore, structural bonding strategies of container lines involves two dimensions.

The results of this study have managerial implications for container shipping lines as well. The fact that financial strategies have the highest total impact on loyalty requires container lines to focus on monetary incentives for building long term relationship with their customers. However, given the low-profit structure of the container shipping market in recent years, gaining loyalty of customers via monetary privileges may not be feasible for container lines. Container lines can pursue cost reduction for being able to offer monetary privileges, but the conventional cost reduction methods such as employing larger vessels or slow steaming are already being employed. It is, therefore, difficult for container lines to offer significant financial privileges in port-to-port services without sacrificing profitability. However, when carriers cannot offer significant differences in the freight rate of port-to-port transportation, they can expand their door-to-door services to uncover new opportunities such as warehousing and inland transportation. In this way, container lines may apply financial bonding strategies to the whole transportation network. Besides the direct transportation costs, container lines can also indirectly generate a cost advantage for freight forwarders. For instance, an extended demurrage-free period at the destination port may be an ideal strategy for container lines. By this way,

18

freight forwarders can also offer a longer period of demurrage-free time to their customers, which eventually creates a cost advantage to cargo owners.

Since forwarders’ expectation of receiving privileged structural services also drives customer loyalty significantly, container lines should focus on intermodal and basic structural bonding practices as well. The focus on intermodal practices can create better protection against the competitive moves of other shipping lines. This is coherent with the recent business practices of container lines, which have begun to heavily invest in land operations. Container lines usually have more advantages in their origin countries or nearby regions regarding intermodal services. For example, they may have better connections and coordination with local logistics companies and authorities. Thus, shipping lines have incentives to identify the freight forwarders that send large volumes to their origin countries and offer better intermodal services targeting these forwarders.

Intermodal operations offer other significant opportunities to container lines as well. For instance, the visibility of the cargo is essential for customers in the current market environment. Most container lines only offer container tracking services for port-to-port operations, and tracking systems do not show the real-time position of the cargo within door-to-door operations. Besides, door-to-door operations usually comprise several steps as they require the exchange of documents and information between many different organizations. Therefore, container lines that offer smooth door-to-door operations, which may be traced in real-time, can gain a significant competitive advantage in intermodal services. Adopting modern technologies such as blockchain, which is anticipated to enable faster and more traceable transactions through smart contracts, may help container lines accomplish this in intermodal operations. In this way, shipping lines may increase the loyalty of their customers. Moreover, focusing on one-stop solution can help container lines stay closers to their customers and understand their needs and wants thoroughly, which, eventually, nurtures the relationship with their customers.

Container lines should also focus on developing social bonds with their customers considering their significant direct impact on loyalty. Shipping industry possesses various opportunities to generate a social bonding. In addition to well-known applications of social bonding strategies such as interpersonal communication, container lines are able to organize social responsibility events to increase awareness in various issues such as climate change or gender equality on board of vessels. These events do not only enhance the social bonds with customers but also reinforce the engagement with stakeholders. However, the managers of container lines should be cautious when designing social bonding strategies as the impact of social bonding may vary depending on the national culture. For instance, Rodriguez and Wilson (2002) found that US managers attach more importance to structural bonding for building trust, while Mexican managers attach more importance to social bonds. Moreover, the ideal tools for developing social bonds may vary among customers and should be carefully investigated.

Limitations and Future Research

This study suffered several limitations. First, the present analysis only focuses on a single route and one type of container line customer, although the impact of each relational bonding strategy may vary by country, destination, and customer type. For instance, the strong influence of intermodal operations may be explained by the geographical suitability of Europe for intermodal transportation, while another destination or region may result in different findings. Further studies should compare the differences between shippers across different countries and/or regions. Another limitation is that the impact of trust, a widely used latent variable in the relationship marketing literature, has not been examined. However, the primary aim of this study was to investigate the impact of different relational

19

strategies on satisfaction and loyalty. A future study can address the mediator or moderator role of trust in this equation.

This study acknowledges the importance of building relationships with investors as well. Relationships with investors can be as effective as relationships with customers for sustaining the business. The investors are the ones who provide necessary resources for companies to survive in such a challenging market. Moreover, the relationship with other partners also play a key role in ensuring the loyalty of customers. For instance, we mentioned how cost reduction is vital for offering financial privileges. It should be noted that cost reduction of container lines cannot be achieved by only focusing on their internal endeavors. The lines can achieve cost reduction mostly by increasing productivity, and the success of productivity depends on the performance of their partners such as other container lines, ports, and trucking companies. For instance, container lines can partly decrease their costs by reducing turnaround time at ports. At this point, the relationships built with ports can allow for better performance. Likewise, the relationships built with trucking and warehousing companies enable container lines to provide more effective intermodal operations. However, despite the importance of relationships with investors and other partners, our paper only focuses on the relationships with freight forwarders. A future study can investigate relationship building of container lines with other stakeholders.

The results of this study should be carefully generalized to other regions and industries. For instance, there has been currently an empty container problem in Turkey due to decreasing levels of import. Therefore, freight forwarders in Turkey attach significant importance to availability of empty equipment. This might explain why basic operations have a significant impact on the satisfaction and loyalty of customers. The effect of basic operations may be lower in other countries where no empty equipment problem exists. The relative weights of the relational bonding can also differ in other B2B industries based on the competition level and complexity of the services. For instance, our results contradict findings of Wang et al. (2006) who found that structural and social bonding strategies are more effective than financial strategies. This assumption needs further confirmation with a comparative future study.

Conclusions

Based on relationship marketing and SET, this study investigated the impact of relational bonding strategies on customer satisfaction and customer loyalty in the relationship between container shipping lines and freight forwarders. Exploring the impact of each relational strategy on satisfaction and loyalty may help container lines realize the relative effectiveness of these strategies. Such knowledge enables shipping lines to introduce more effective marketing policies for achieving customer satisfaction and long-term customer relationships and more efficiently use their marketing resources. The results of this study demonstrate that the considered relational bonds (financial, intermodal operations, basic operations, and social bonds) have direct or indirect positive impacts on loyalty. Among the considered bonding strategies, financial bonding strategies have the greatest total impact on loyalty.

References

Akroush, M. N., & Abu ELSamen, A. A. (2012). An empirical investigation of the mediating role of relationship marketing skills on the relationship between customer satisfaction and customer loyalty. International Journal of Internet Marketing and Advertising, 7(1), 1-30.

20

Ansari, A., & Riasi, A. (2016). Modelling and evaluating customer loyalty using neural networks: Evidence from startup insurance companies. Future Business Journal, 2(1), 15-30.

Bagozzi, R.P. and Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science. 16(1): 74-94.

Balci, G., & Cetin, I. B. (2017). Market segmentation in container shipping services: a qualitative study. Management Research Review, 40(10), 1100-1116.

Balci, G., Cetin, I. B., & Tanyeri, M. (2018). Differentiation of container shipping services in Turkey. Transport Policy, 61, 26-35.Bentler, P. M (1988). Theory and implementation of EQS: A structural equation program. California: Sage

Bernhofen, D. M., El-Sahli, Z., & Kneller, R. (2016). Estimating the effects of the container revolution on world trade. Journal of International Economics, 98, 36-50.

Berry, L. L. (1995). Relationship marketing of services—growing interest, emerging perspectives. Journal of the Academy of marketing science, 23(4), 236-245.

Blau, P. M. (1964). Exchange and power in social life. NY: John Wiley & Sons.

Browne, M. W. and Cudeck, R. (1993). Alternative ways of assessing model fit. Testing structural equation models (pp. 136−162). Newbury Park, CA: Sage.

Chaabane, A.M. and Volle, P. (2010). Perceived benefits of loyalty programs: Scale development and implications for relational strategies. Journal of Business Research. 63:32-37.

Chang, Y. H., & Yeh, C. H. (2017). Corporate social responsibility and customer loyalty in intercity bus services. Transport Policy, 59, 38-45.

Chao, S. L., & Chen, B. C. (2015). Effects of switching costs on customer loyalty in the liner shipping industry. Maritime Economics & Logistics, 17(3), 341-358.

Chao, S. L., Lin, R. Y., & Sun, Y. H. (2018). Mediating effects of service recovery on liner shipping users. Transport Policy.

Chen, K. K., Chiu, R. H., & Chang, C. T. (2017). Using beta regression to explore the relationship between service attributes and likelihood of customer retention for the container shipping industry. Transportation Research Part E: Logistics and Transportation Review, 104, 1-16.

Cheng, T. C. E., & Choy, P. W. (2013). A study of the relationships between quality management practices and organizational performance in the shipping industry. Maritime Economics & Logistics, 15(1), 1-31.

Chiu, H. C., Hsieh, Y. C., Li, Y. C., & Lee, M. (2005). Relationship marketing and consumer switching behavior. Journal of Business Research, 58(12), 1681-1689.

Christopher Westland, J., 2010. Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications 9(6), 476-487.Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of management, 31(6), 874-900.

Durvasula, S., Lysonski, S., & Mehta, S. C. (2007). Competitive positioning in international logistics: Identifying a system of attributes through neural networks and decision trees. Journal of Global Marketing, 20(1), 75-89.

Glave, T., Joerss, M., Saxon, S., 2014. The Hidden Opportunity in Container Shipping. Available at: http://www.mckinsey.com/business-functions/strategy-and-corporatefinance/ our-insights/the-hidden-opportunity-in-container-shipping. (Accessed 10 May 2016).

Gummesson, E. (2017). From relationship marketing to total relationship marketing and beyond. Journal of Services Marketing, 31(1), 16-19.21

Gwinner, K. P., Gremler, D. D., & Bitner, M. J. (1998). Relational benefits in services industries: the customer’s perspective. Journal of the academy of marketing science, 26(2), 101-114.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2006. Multivariate Data Analysis (Pearson Prentice Hall Upper Saddle River, NJ).

Hair, J.F., Wolfinbarger, M.F., Ortinau, D.J., and Bush, R.P. (2008). Essentials of Marketing Research. New York: McGraw-Hill.

He, Y., Chan, L. K., & Tse, S. K. (2008). From consumer satisfaction to repurchase intention: The role of price tolerance in a competitive service market. Total Quality Management, 19(9), 949-961.

Ho, T. C., Chiu, R. H., Chung, C. C., & Lee, H. S. (2017). Key influence factors for ocean freight forwarders selecting container shipping lines using the revised DEMATEL approach. Journal of Marine Science and Technology, 25(3), 299-310.

Homans, G. C. (1958). Social behavior as exchange. American journal of sociology, 63(6), 597-606.

Huo, B., Liu, C., Chen, H., Zhao, X., 2017. Dependence, trust, and 3PL integration: an empirical study in China. International Journal of Physical Distribution & Logistics Management 47(9), 927-948.

Hu, L. T. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 6(1):1-55

Huo, B., Fu, D., Zhao, X., & Zhu, J. (2016). Curbing opportunism in logistics outsourcing relationships: The role of relational norms and contract. International Journal of Production Economics, 182, 293-303.

Jang, H. M., P. B. Marlow, and K. Mitroussi. 2013. The Effect of Logistics Service Quality on Customer Loyalty through Relationship Quality in the Container Shipping Context. Transportation Journal 52 (4): 493–521.doi:10.5325/transportationj.52.4.0493.

Jeong, M., & Oh, H. (2017). Business-to-business social exchange relationship beyond trust and commitment. International Journal of Hospitality Management, 65, 115-124.

Jiang, H., & Zhang, Y. (2016). An investigation of service quality, customer satisfaction and loyalty in China's airline market. Journal of air transport management, 57, 80-88.

Kannan, V., S. K. Bose, and N. G. Kannan. 2011. “An Evaluation of Ocean Container Carrier Selection Criteria: An Indian Shipper’s Perspective.” Management Research Review 34 (7): 754–772. doi:10.1108/01409171111146661.

Kline, R. B. (2016). Principles and practice of structural equation modeling, 4th edition, The Guilford Press, New York and London.

Lam, S. Y., Shankar, V., Erramilli, M. K., & Murthy, B. (2004). Customer value, satisfaction, loyalty, and switching costs: an illustration from a business-to-business service context. Journal of the academy of marketing science, 32(3), 293-311.

Lambe, C. J., Wittmann, C. M., & Spekman, R. E. (2001). Social exchange theory and research on business-to-business relational exchange. Journal of Business-to-Business Marketing, 8(3), 1-36.

Lee, Y. K., Kim, S., Kim, M. S., Lee, J. H., & Lim, K. T. (2015). Relational bonding strategies in the franchise industry: The moderating role of duration of the relationship. Journal of Business & Industrial Marketing, 30(7), 830-841.

Li, Y. H., & Huang, J. W. (2017). The moderating role of relational bonding in green supply chain practices and performance. Journal of Purchasing and Supply Management, 23(4), 290-299.

22

Lin, L. H., Che, K. K., & Chiu, R. H. (2017). Predicting Customer Retention Likelihood In The Container Shipping Industry Through The Decision Tree Approach. Journal of Marine Science and Technology, 25(1), 23-33.

Lu, C.-S. 2003. The Impact of Carrier Service Attributes on Shipper-Carrier Partnering Relationships: A Shipper’s Perspective. Transportation Research Part E: Logistics and Transportation Review 39 (5): 399–415.

Lun, Y. V., Shang, K. C., Lai, K. H., & Cheng, T. C. E. (2016). Examining the influence of organizational capability in innovative business operations and the mediation of profitability on customer satisfaction: An application in intermodal transport operators in Taiwan. International Journal of Production Economics, 171, 179-188.

Maloni, M. J., Gligor, D. M., & Lagoudis, I. N. (2016). Linking ocean container carrier capabilities to shipper–carrier relationships: a case study. Maritime Policy & Management, 43(8), 959-975.

Maskey, R., Fei, J., & Nguyen, H. O. (2018). Use of exploratory factor analysis in maritime research. The Asian Journal of Shipping and Logistics, 34(2), 91-111.

McDonald, R. P. and Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods. 7(1):64-82.

Miquel-Romero, M. J., Caplliure-Giner, E. M., & Adame-Sánchez, C. (2014). Relationship marketing management: Its importance in private label extension. Journal of Business Research, 67(5), 667-672.

Morgan, N., Anderson, E. and Mittal, V. (2005). Understanding firms’ customer satisfaction information usage. Journal of Marketing. 69:131–151.

Nguyen, B., & Mutum, D. S. (2012). A review of customer relationship management: successes, advances, pitfalls and futures. Business Process Management Journal, 18(3), 400-419.

Panayides, P. M., & Gray, R. (1997). Marketing the professional ship management service. Maritime Policy and Management, 24(3), 233-244

Penaloza, E., M. Brooks and S. Marche (2007). Case study analysis of the impacts of electronic commerce on the strategic management of container shipping companies. Maritime Policy and Management 34(1), 37-45.

Reimann, M., Schilke, O., & Thomas, J. S. (2010). Toward an understanding of industry commoditization: Its nature and role in evolving marketing competition. International Journal of Research in Marketing, 27(2), 188-197.

Rodríguez, C. M., & Wilson, D. T. (2002). Relationship bonding and trust as a foundation for commitment in US-Mexican strategic alliances: A structural equation modeling approach. Journal of International Marketing, 10(4), 53-76.

Schakett, T., Flaschner, A., Gao, T., & El-Ansary, A. (2011). Effects of social bonding in business-to-business relationships. Journal of Relationship Marketing, 10(4), 264-280.

Shiau, W. L., & Luo, M. M. (2012). Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior, 28(6), 2431-2444.

Shin, Y., & Thai, V. V. (2015). The impact of corporate social responsibility on customer satisfaction, relationship maintenance and loyalty in the shipping industry. Corporate Social Responsibility and Environmental Management, 22(6), 381-392.

Shin, Y., Thai, V. V., Grewal, D., & Kim, Y. (2017). Do corporate sustainable management activities improve customer satisfaction, word of mouth intention and repurchase intention? Empirical evidence from the shipping industry. The International Journal of Logistics Management, 28(2), 555-570.

23

Shou, Y., Shao, J., Chen, A., 2017. Relational resources and performance of Chinese third-party logistics providers: The mediating role of innovation capability. International Journal of Physical Distribution & Logistics Management 47(9), 864-883.

Thibaut, J. W. & Kelley, H. H. (1959). The social psychology of groups. NY: John Wiley &Sons.

Ting, S. C., & Tzeng, G. H. (2003). Ship scheduling and cost analysis for route planning in liner shipping. Maritime Economics & Logistics, 5(4), 378-392.

Tiwari, P., H. Itoh and M. Doi (2003). Shippers’ port and carrier selection behavior in China: a discrete choice analysis. Maritime Economics and Logistics, 5(1), 23-39.

Tóth, Z., Thiesbrummel, C., Henneberg, S. C., & Naudé, P. (2015). Understanding configurations of relational attractiveness of the customer firm using fuzzy set QCA. Journal of Business Research, 68(3), 723-734.

Oliver, R.L., 2010. Satisfaction: A Behavioral Perspective on the Consumer, 2nd ed. M.E. Sharpe, Inc.

Wang, W. H., Liang, C. J., & Wu, Y. D. (2006). Relationship bonding strategies, relationship quality and customer behavioral loyalty--behavioral sequence in Taiwan's information services industry. Journal of Services Research, 6(1).

Wen, C. H., & Lin, W. W. (2016). Customer segmentation of freight forwarders and impacts on the competitive positioning of ocean carriers in the Taiwan–southern China trade lane. Maritime Policy & Management, 43(4), 420-435.

Williams, P., & Naumann, E. (2011). Customer satisfaction and business performance: a firm-level analysis. Journal of services marketing, 25(1), 20-32.

Xie, X., Jia, Y., Meng, X., & Li, C. (2017). Corporate social responsibility, customer satisfaction, and financial performance: The moderating effect of the institutional environment in two transition economies. Journal of Cleaner Production, 150, 26-39.

Yang, C. C., & Chao, C. C. (2017). How relationship marketing, switching costs, and service quality impact customer satisfaction and loyalty in Taiwan’s airfreight forwarding industry?. Transportmetrica A: Transport Science, 13(8), 679-707.

Yuen, K.F., Thai, V.V., Wong, Y.D., 2016. The effect of continuous improvement capacity on the relationship between of corporate social performance and business performance in maritime transport in Singapore. Transportation Research Part E: Logistics and Transportation Review, 95, 62-75.

Yuen, K.F., Thai, V., 2017a. Service quality appraisal: a study of interactions. Total Quality Management & Business Excellence, 28(7-8), 730-745.

Yuen, K.F., Thai, V.V., Wong, Y.D., 2017b. Corporate social responsibility and classical competitive strategies of maritime transport firms: A contingency-fit perspective. Transportation Research Part A: Policy and Practice 98, 1-13.

Yuen, K. F., Wang, X., Wong, Y. D., & Zhou, Q. (2018). The effect of sustainable shipping practices on shippers’ loyalty: The mediating role of perceived value, trust and transaction cost. Transportation Research Part E: Logistics and Transportation Review, 116, 123-135.

Zacharia, Z. and J. Mentzer (2004). Logistics salience in a changing environment. Journal of Business Logistics 25(1), 187-210.

Zhao, M., & Stank, T. P. (2003). Interactions between operational and relational capabilities in fast food service delivery. Transportation Research Part E: Logistics and Transportation Review, 39(2), 161-173.

24

Zhao, X., Lynch Jr, J.G., Chen, Q., 2010. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.

Zhou, Y., Zhang, X., Zhuang, G., & Zhou, N. (2015). Relational norms and collaborative activities: roles in reducing opportunism in marketing channels. Industrial Marketing Management, 46, 147-159.

25