Mediating effects of service recovery on liner shipping users

Accepted Manuscript Mediating effects of service recovery on liner shipping users Shih-Liang Chao, Ru-Yan Lin, Yu-Han Sun PII:

S0967-070X(17)30836-3

DOI:

https://doi.org/10.1016/j.tranpol.2018.12.016

Reference:

JTRP 2126

To appear in:

Transport Policy

Received Date: 30 November 2017 Revised Date:

25 November 2018

Accepted Date: 28 December 2018

Please cite this article as: Chao, S.-L., Lin, R.-Y., Sun, Y.-H., Mediating effects of service recovery on liner shipping users, Transport Policy (2019), doi: https://doi.org/10.1016/j.tranpol.2018.12.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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How Service Recovery Affects Liner Shipping Customer Loyalty Shih-Liang Chao* Department of Shipping and Transportation Management

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National Taiwan Ocean University, Keelung, Taiwan, ROC 2, Pei-Ning Road, Keelung, 20224 Taiwan, ROC. Tel: 886-2-24622192 ext 3422 Fax:886-2-24631903

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Ru-Yan Lin

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E-mail: [email protected]

Department of Shipping and Transportation Management

National Taiwan Ocean University, Keelung, Taiwan, ROC 2, Pei-Ning Road, Keelung, 20224 Taiwan, ROC.

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E-mail: [email protected]

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Yu-Han Sun

Department of Shipping and Transportation Management

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National Taiwan Ocean University, Keelung, Taiwan, ROC 2, Pei-Ning Road, Keelung, 20224 Taiwan, ROC. E-mail: [email protected]

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Corresponding author.

ACCEPTED MANUSCRIPT How Service Recovery Affects Liner Shipping Customer Loyalty Abstract

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Offering appropriate service recovery is important for service providers to retain their existing customers. Given that service failures are both common and inevitable when using liner shipping services, in this study, a model was established by which to examine the impact of service recovery on customer loyalty. An empirical questionnaire survey was conducted, and structural equation modeling (SEM) was applied to test the hypotheses proposed in this study. The results showed there to be a

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significant positive impact from service recovery on the customer loyalty of liner shipping users, which was found to be mediated via customer satisfaction and satisfaction after recovery.

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Keywords: liner shipping, service recovery, customer loyalty, structural equation modeling (SEM)

1. Introduction

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Liner shipping has been playing an important role in the global cargo transportation industry for more than fifty years (Heaver, 2002). Specially-designed

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containers enable convenient cargo transshipment between trucks, ships, and even trains. This so-called door to door service has become more popular because containers can easily be trucked to and from customer premises. Moreover, efficient transshipment also has become the predominant method by which liner shipping

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companies (LSCs) enlarge their sailing networks, which has in turn formed a complete, efficient global transportation infrastructure. In 2016, world container traffic was estimated to have reached 140 million TEUs (UNCTAD, 2017). It’s worth noting that it’s very costly to maintain such a huge transportation system. LSCs have to deploy a large number of ships to sail between ports and must also provide empty containers for shippers to load cargo in. In addition, even when a ship is not fully loaded, it has to depart on time according to the announced schedule. This so-called perishability of service increases the pressure on LSCs to strengthen the loyalty of their customers, most of whom are frequent users of liner shipping services and include manufacturers, trading companies, department stores, wholesalers, logistics companies, freight forwarders, etc. This is why LSCs are happy to sign service contracts with customers that are intended to maintain long-term relationships and stable cargo volumes for each voyage. Several prior studies (e.g. Chao and Chen, 1

ACCEPTED MANUSCRIPT 2015) investigated the determinants of customer loyalty for LSCs and found that service quality and satisfaction have a positive impact on customer loyalty. However, to the authors’ knowledge, issues related to service failure and recovery for LSCs have not as yet been discussed. The aim of this study is twofold. First, the main types of service failure experienced by liner shipping users were investigated and analyzed by conducting an empirical questionnaire survey. Second, a research model was

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established and validated using structural equation modeling (SEM) to examine the impact of service recovery on customer loyalty in an LSC context. To our knowledge, this study is the first to include service quality, customer satisfaction, and satisfaction after recovery as three mediating variables to investigate the impact of service

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recovery on customer loyalty. The remainder of this paper is organized as follows: Section two reviews literature focusing on service recovery and customer loyalty, which were the two core constructs used to establish our research model. The methodology used in this study is described in Section three. Section four presents the results of a study based on an empirical questionnaire survey. The conclusions and implications for future research are summarized in the last section. 2. Theoretical background and hypotheses

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In this section, we first explain the idea that led to the development of our research model. Afterwards, the hypotheses are established based on the related theoretical background. 2.1 Research model development

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The aim of this study is to examine the impact of service recovery on customer loyalty in an LSC context. Accordingly, service recovery and customer loyalty were included in our model as two core constructs. Given that these two constructs have

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been investigated in a number of prior studies, it was necessary to comprehensively review studies related to these two constructs before building our research model. According to our review, four frameworks have been commonly applied to investigate the impact of service recovery on customer loyalty. The first directly examines the relationship between service recovery and customer loyalty. This framework has only been used in a few studies (e.g. Chou, 2015) because it didn’t take into account other factors that might simultaneously influence customer loyalty. In terms of the antecedents of customer loyalty, customer satisfaction has been included in a large number of studies across various fields due to its significant impact on customer loyalty. Therefore, the second framework was applied in a few studies (e.g. Kim, 2007; Cheng and Mansori, 2017) by including customer satisfaction as a mediating variable between service recovery and customer loyalty. Because customer satisfaction might be influenced by other factors other than service recovery, some studies further 2

ACCEPTED MANUSCRIPT developed a third framework by including a construct called satisfaction after recovery (e.g. Del Río-Lanza et al., 2009; Kim et al., 2012; Kuo and Wu, 2012; Chang and Chang, 2010) in their models. Because satisfaction after recovery can exclusively measure the impact of specific recovery actions implemented by service providers, we also separated customer satisfaction into customer satisfaction and satisfaction after recovery when designing our model. It’s worth mentioning that an important construct,

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service quality, has been included in a number of studies related to customer satisfaction (e.g. Yuen and Thai, 2015; Chao and Chen, 2015) and customer loyalty (e.g. Chao and Chen, 2015; Fu et al., 2018). The comprehensiveness of a model measuring the impact of service recovery on loyalty can be increased if service

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quality is included. This idea, which is the fourth framework, can be found in Kuo et al. (2013). They included service recovery as a moderating variable while examining the effect of service quality on loyalty. Based on the above discussion, we first set up two core constructs, service recovery and customer loyalty, in our model. Afterwards, customer satisfaction and satisfaction after recovery were included as two mediating constructs between the two core constructs. Lastly, service quality was inserted between service recovery and

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customer satisfaction as the third mediating variable. In total, there are five constructs in our proposed research model (see Fig. 1). The hypotheses that validate the impact between the constructs and the indirect impact of service recovery on customer loyalty are established in Section 2.2. < Insert Figure 1 about here >

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2.2 Hypotheses establishment

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Based on the constructs developed in Section 2.1, the hypotheses are established in this subsection in two phases. In the first phase, the direct impacts between constructs are hypothesized. After that, the second phase hypothesizes the indirect impacts from service recovery on customer loyalty. (1) Direct impacts between constructs Parasuraman et al. (1988) suggested that service quality is highly correlated with customer satisfaction. Antony et al. (2004) conducted a survey on the relationship between service quality and satisfaction in British hotels and found that service quality is one of key factors related to customer satisfaction. Shanka (2012) measured the service quality of the Ethiopian banking sector and found a positive correlation between service quality and customer satisfaction. When a customer is using a liner shipping service, if an LSC can deliver containers to the right destination on time 3

ACCEPTED MANUSCRIPT without damage, has a frequent schedule, offers a convenient place to receive and deliver containers, delivers documents accurately, and handles customer problems promptly, customer satisfaction may be increased. Accordingly, we propose the following hypothesis: H1: There is a positive relationship between service quality and customer satisfaction

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with LSCs.

Ganiyu et al. (2012) investigated whether customer satisfaction can be an indicator of customer loyalty. The results of their study supported the premise that

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customer satisfaction has a strong positive relationship with customer loyalty. Mohsan et al. (2011) conducted a survey on 120 clients of banks in Pakistan in an attempt to identify the impact of customer satisfaction on customer loyalty. The results showed customer satisfaction and customer loyalty to be positively correlated. Deng et al.

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(2010) examined the determinants of customer satisfaction and loyalty taking instant phone messages as an example. The result proved that customer satisfaction and customer loyalty are positively correlated. In the case of an LSC, a customer with a high degree of satisfaction may support the LSC continuously or even recommend it to others. Accordingly, the following hypothesis is proposed:

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H2: There is a positive relationship between customer satisfaction and customer loyalty toward LSCs.

Ellyawati et al. (2012) investigated service recovery according to three dimensions: distributive justice, procedural justice, and interactional justice. The

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positive impacts from these three dimensions on customer satisfaction after recovery were all proven to be significant using a survey of retail stores. Suprapto and Hashym (2010) found a positive relationship between recovery strategies and customer satisfaction after recovery by conducting an empirical survey in the hotel industry. By conducting a large-scale survey in Johannesburg, Kruger et al. (2015) found the positive impact of service recovery on satisfaction after service recovery to be significant. Given that most liner shipping customers are frequent users, shipper complaints and negative emotions may be reduced if LSCs can offer effective service recovery after service failures occur. Accordingly, we propose the following hypothesis: H3: There is a positive relationship between service recovery and customer satisfaction after recovery for LSC customers. 4

ACCEPTED MANUSCRIPT By conducting a survey of 1,878 customers of a large Brazilian bank, Matos et al. (2013) found that satisfaction with service recovery was significantly associated with

hypothesis is proposed:

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loyalty. Oliver (1997) pointed out that mouth of word and repurchase have a positive relationship with satisfaction after recovery. Given that service failures are inevitable due to weather and other factors, LSCs should try their best to increase satisfaction after service recovery in order to keep their customers. Hence, the following

H4: There is a positive relationship between customer satisfaction after recovery and customer loyalty toward LSC customers.

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A number of studies (e.g. Antony et al., 2004 and Shanka, 2012) have been conducted to investigate the impact of service quality on satisfaction, but few of them have discussed the effect of service recovery on service quality when service failures

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have occurred. Kuo et al. (2013) investigated how service quality affects customer loyalty in the travel agency industry by conducting an empirical survey in Taiwan. The mediating effect of service recovery on the impact of service quality was also examined in the structured model they established. Their results proved that the mediating effect was significant. When using a liner shipping service, the service

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quality perceived by customers may be decreased if an LSC cannot deal effectively with service failures. Accordingly, we propose the following hypothesis: H5: There is a positive relationship between service recovery and service quality for LSC customers.

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Cengiz et al. (2007) empirically studied the banking industry and found that customer satisfaction after recovery has a direct impact on overall satisfaction. Focusing on the airline industry, Chang and Chang (2010) conducted an empirical study to validate the impact of service recovery on satisfaction and customer loyalty. The results showed that customer satisfaction after recovery has a significant influence on customer satisfaction. In the case of an LSC, if recovery after service failure can lead to customer satisfaction after recovery, perceptions of satisfaction with the LSC as a whole may be increased. Accordingly, we propose the following hypothesis: H6: There is a positive relationship between customer satisfaction after recovery and LSC customer satisfaction. Gu and Ye (2010) surveyed the customers of a Chinese online service and found 5

ACCEPTED MANUSCRIPT that service recovery had a significant impact on customer satisfaction. Their results showed that rapid, quality service recovery leads to a greater degree of customer satisfaction. Given that service recovery is a common issue for LSCs, prompt, appropriate service recovery may be helpful for LSCs to increase the degree of satisfaction of their customers. Therefore, we propose the following hypothesis:

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H7: There is a positive relationship between service recovery and LSC customer satisfaction.

(2) Indirect impacts of service recovery on customer loyalty

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In addition to the above hypotheses, which investigate the direct impacts among constructs, we further proposed four hypotheses to identify the indirect impact of service recovery on customer loyalty. Abbas et al. (2015) investigated the impact of

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service recovery on customer satisfaction and customer loyalty. Based on a review of related literature, they indicated that service recovery might impact customer satisfaction and that the effect would be mediated towards customer loyalty. They suggested that future studies involve empirical surveys to validate these hypotheses. Meanwhile, in the first part of Section 2.2, we proposed seven hypotheses (i.e. H1 to H7 in Fig. 1) describing the impact between the five constructs. In terms of Figure 1 as a whole, the only independent variable is service recovery, the impact of which may

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following four paths:

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be mediated by three mediating variables towards customer loyalty. Therefore, based on hypotheses H1 to H7 and the suggestion in Abbas et al. (2015), we hypothesize that the impact from service recovery on customer loyalty will be mediated by the

H8: The positive impact from service recovery will be mediated by customer

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satisfaction towards customer loyalty for LSC customers. H9: The positive impact from service recovery will be mediated by satisfaction after recovery towards customer loyalty for LSC customers.

H10: The positive impact from service recovery will be mediated by service quality and customer satisfaction towards customer loyalty for LSC customers. H11: The positive impact from service recovery will be mediated by satisfaction after recovery and customer satisfaction towards customer loyalty for LSC customers.

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ACCEPTED MANUSCRIPT 3. Methodology 3.1 Questionnaire design and construct measures

The questionnaire designed for the purpose of collecting samples in this study comprised two parts. The first part was used to collect the demographic characteristics of the respondents, including gender, age, and work experience. In addition, basic

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information about the firms for which the respondents were working, i.e. firm history, monthly transportation volume, and number of employees, was also collected. In the second part of the questionnaire, the question items were designed in groups to collected the respondents’ opinions on different constructs. To ensure the content

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validity, all constructs and question items were sourced from prior studies (see Table A1 in the Appendix). Additionally, a pilot test was conducted in October 2013, in which two professors and five practitioners in the liner shipping industry were invited to review the draft of the questionnaire. All question items were improved based on their suggestions.

In term of construct measures, unlike physical products in the manufacturing industry, the service offered by LSCs is intangible and as such, is not easily measured. Therefore, the five evaluation dimensions of the SERVQUAL model, which have been applied widely in service-related studies, were used in this study to measure LSC services. To measure service quality, the question items used in this study were

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sourced from Brooks (1990) and Premeaux (2002). These items were categorized in terms of tangibility, reliability, responsiveness, assurance, and empathy, which are the five dimensions in the SERVQUAL model proposed by Parasuraman et al., 1988. The construct for customer satisfaction, which has appeared in many service-related studies, was measured using items sourced from Miller (2000) and Lin (2007). The items for measuring customer loyalty were sourced from Oliver (1999), Gronholdt et

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al. (2000) and Kheiry and Alirezapour (2012). The construct for service recovery was measured using items sourced from Hoffman et al. (1995). The items for measuring satisfaction after recovery in this study were sourced from Tax et al. (1998) and Maxham III and Netemeyer (2002). All constructs in our proposed model were measured with items used commonly in previous similar studies although some minor modifications were made to fit the items to the study context. The question items used in the questionnaire were grouped according to above mentioned constructs to help the respondents understand the logic (Venkatesh and Davis, 2000). All respondent opinions were measured on Likert-type, multiple item scales, ranging from 1 for ‘strongly disagree’ to 5 for ‘strongly agree.’ The measurement items are listed in the Appendix (see Table A1). 3.2 Sampling and data collection 7

ACCEPTED MANUSCRIPT The population of this empirical study comprised the top 500 import and export firms as identified by the Ministry of Economic Affairs in Taiwan. Questionnaires were sent to 500 of these firms who had used liner shipping services from December 2013 to January 2014. A total of 211 out of the 500 questionnaires were returned, of which 17 questionnaires were deemed invalid; hence, the remainder accounted for an effective rate of 38.8%. By analyzing the basic information in the returned samples

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listed in the Appendix (see Table A2), we found that 48.4% of the respondents had been working for their companies for more than ten years, of which those who had more than 20 years of experience accounted for 11.3%. 76.8% of the companies had been in operation for more than ten years. Most companies were considered frequent

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users since 58.5% of them moved more than 100 twenty-foot equivalent units (TEUs) of containers monthly, of which 33.5% moved more than 200 TEU per month. In addition, 46.9% of the companies had more than 500 employees at the time of the study. According to above data, the respondents were experienced and knowledgeable and had worked for a specific period of time, showing that the results of our empirical study were reliable. In this study, we summarized six types of service failure that are commonly experienced while using liner shipping services, including schedule delays,

3.3 SEM analysis

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cargo loss and damage, documentation errors, container shortages, mislandings and shutouts. The service failures experienced by the samples in our empirical study are summarized in Fig. 2.

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Anderson and Gerbing (1988) suggested that an SEM analysis can be divided into two steps. The first step is a measurement model that tests the relationship between the observed and latent variables. The second step is a structural model that tests the relationships between constructs. In this study, this two-step concept was used to test the reliability and validity of the collected samples and to validate the proposed hypotheses. < Insert Figure 2 about here >

4. Results 4.1 Measurement model analysis

The results of the reliability test are provided in Table 1. All construct reliabilities in the measurement model were found to be greater than 0.7, exceeding the recommended level of 0.7 (Hair et al, 1998), and the AVE value of each construct exceeded the recommended level of 0.5 (Bagozzi and Yi, 1988), which indicates that 8

ACCEPTED MANUSCRIPT all latent constructs had good reliability. < Insert Table 1 about here > The results of the convergent validity test are also presented in Table 1. All factor loadings of the constructs exceeded 0.5, which ensured the desired convergent

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validity of our empirical study (Kline, 1998). Accordingly, we confirmed sound convergent validity between the collected data and the measurement model in this study. Table 2 presents the results of the discriminant validity tests. It is obvious that discriminant validity was achieved because all χ 2 differences were significant. A

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greater difference indicates less of a relationship between the latent constructs. That is, the latent constructs in this study were deemed representative.

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< Insert Table 2 about here >

Regarding the goodness-of-fit measures of the measurement model, the normalized χ 2 value was 1.487, which was acceptable because it was less than three. Five other measures, the goodness-of-fit index (GFI) (value of 0.894), adjusted GFI (AGFI) (value of 0.855), the normed fit index (NFI) (value of 0.901), the comparative

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fit index (CFI) (value of 0.965), and the root mean square residual (RMR) (value of 0.019) all achieved their acceptance thresholds (Bagozzi and Yi, 1988; Koufteros, 1999) (see Table 3). Therefore, there was sound fitness between the data and the latent constructs of the measurement model.

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< Insert Table 3 about here >

4.2 The structural model analysis

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The structural model analysis was used to test the relationships between the latent constructs. As Table 4 illustrates, six goodness-of-fit indexes were tested, and all fitness indexes between the returned samples and the structural model achieved their acceptance thresholds. Therefore, these measures demonstrated sound fitness between the collected data and the structural model. < Insert Table 4 about here > In this study, the critical ratio (CR) outputted from the AMOS 18 package was used to test the path coefficients between the constructs. A CR value greater than 1.96 or less than -1.96 reflects the significance of the path. According to results shown in Table 5, all hypotheses reflecting direct impacts were significant and supported 9

ACCEPTED MANUSCRIPT statistically except service recovery toward customer satisfaction (H7). The values of the squared multiple correlations (R2) were used in this study to examine the amount of variance of each construct accounted for by other constructs. With a range between zero and one, a high value of R2 shows that the amount of variance of a construct is well accounted for by other constructs. The results of the structural model analysis are shown in Fig. 3. As a whole, the results of structural model analysis reflected that the

< Insert Table 5 about here >

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< Insert Figure 3 about here >

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model had good explanatory power because the R2 value of customer loyalty reached 0.726.

4.3 Research hypotheses test

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The hypotheses proposed in this study are discussed in this subsection in two phases. The first phase discusses the direct impacts (i.e. H1 to H7) based on the results shown in Table 5. After that, the indirect impacts (i.e. H8 to H11) are tested and analyzed. In terms of the direct impacts, Table 5 lists the results of the statistical tests, in which the path coefficient of service quality on customer satisfaction was

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significant and reached 0.737, meaning that the respondents felt that service quality as provided by an international LSC has a positive impact on customer satisfaction. Therefore, H1 was supported. The path coefficient of customer satisfaction toward customer loyalty was 0.676 and significant, showing that the satisfaction of shippers

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can affect their loyalty. Therefore, H2 was supported. That is, if a shipper is satisfied with services provided by an LSC, the chance that the shipper will switch to other LSCs will be lower. The path coefficient of service recovery toward satisfaction after recovery was as high as 0.909 and was thus statistically significant. Such a high value means that the respondents felt that service recovery can affect satisfaction after recovery for LSCs. Therefore, H3 was supported. That is, if an LSC can offer effective and timely service recovery, the satisfaction after recovery of its customers will be increased. The path coefficient of satisfaction after recovery toward customer loyalty was only 0.227 but was still significant. This means that the respondents felt that satisfaction after recovery can affect customer loyalty to some degree. Therefore, H4 was supported. Because most users of liner shipping services are frequent shippers, this result indicates that if an LSC can increase satisfaction after recovery, then its shippers will be more willing to support it continuously. As for the impact of service recovery on service quality, the significant coefficient of 0.727 indicated a strong positive relationship. Accordingly, H5 was supported. Such a result is notable for LSCs, who should pay attention to offering appropriate service recovery in addition to 10

ACCEPTED MANUSCRIPT focusing on improving the quality of their service. The path coefficient of satisfaction after recovery toward customer satisfaction was 0.272, which exceeded the recommended level, meaning that the respondents felt that satisfaction after recovery can affect customer satisfaction to some degree. Therefore, H6 was supported. Lastly, H7 was not supported because the path coefficient of service recovery toward customer satisfaction was not statistically significant.

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In terms of the indirect impact of service recovery on customer loyalty, we first tested the mediating effects via customer satisfaction (H8) and via satisfaction after recovery (H9), respectively. Because there was only one mediating variable in these two paths, respectively, their effects could be tested using Sobel tests. The z-value in

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the Sobel test of the mediating effect via customer satisfaction was only -0.227, which didn’t reach the acceptable threshold (i.e. an interval between -1.96 and 1.96) suggested by Sobel (1982) (see Table 6). Accordingly, H8 was rejected. However, H9 was supported because its z-value in the Sobel test was 2.291, indicating that the effect from service recovery towards customer loyalty via satisfaction after recovery was significant. Finally, the two indirect effects of service recovery on customer loyalty through

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two mediators were tested using the PROCESS 2.16 package. The bootstrapping process was applied to estimate the confidence interval (C.I.) (Shrout and Bolger, 2002). Significance is achieved if the C.I. doesn’t include zero. According to this criterion, the first path in Table 7 was insignificant because its C.I. included zero. Therefore, H10 was not supported, indicating that the effect from service recovery didn’t extend to customer loyalty via this path. However, the second path in Table 7

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was significant because its C.I. didn’t include zero. Therefore, H11 was supported, meaning that the respondents felt that the effect of service recovery would be

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mediated toward customer loyalty via satisfaction after recovery and customer satisfaction. The results of all hypotheses tests in this study are summarized in Table

< Insert Table 6 about here > < Insert Table 7 about here > < Insert Table 8 about here >

Lastly, because the results might have been influenced by including samples with different attributes, we conducted a one-way analysis of variance (ANOVA) to test whether there were any statistically significant differences between the means of each question item collected from the different sample groups. In this study, firm size was the most representative attribute by which to categorize all samples into five groups, 11

ACCEPTED MANUSCRIPT i.e. less than 50, 50-100, 101-300, 301-500, and more than 501 employees. Accordingly, the ANOVA analysis was conducted based on firm size. The results are summarized in Table 9. In total, nine latent variables were tested in which five were SERVQUAL dimensions used to measure service quality. Since all p-values were not larger than the threshold (i.e. 0.05), the differences between the means of each latent variable collected from different samples groups were not significant. That is, it was

< Insert Table 9 about here >

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4.4 Discussion and implications

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determined that the results of our empirical study would not be influenced by different firm sizes.

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According to the results of our empirical study, some findings and implications can be drawn and are discussed as follows: First, maintaining and strengthening customer loyalty is an important issue for LSCs because they need long-term support from customers to maintain high loading factors in their ships. Although factors that may influence customer satisfaction and loyalty toward LSCs have been investigated in a number of studies in the field of liner shipping (e.g. Shin and Thai, 2014; Thai,

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2015; Chao and Chen, 2015; Yuen and Thai; 2015), service recovery has never been included in these models. Due to weather, ship issues, equipment, and various other issues, service failures are both common and inevitable when running an LSC. The results of our empirical study proved that service recovery has a significant positive

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impact on the loyalty of LSC customers, indicating that executives of LSCs should endeavor to implement service recovery and in turn strengthen the loyalty of their

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customers. Second, instead of directly surveying the impact between only service recovery and loyalty (e.g. Chou, 2015), our study identified the complicated nature of the relationship by including service quality, customer satisfaction, and after satisfaction recovery as three mediating variables. To our knowledge, our model is the first to propose such a framework to measure the impact of service recovery on customer loyalty, which suggests more managerial implications. For example, satisfaction after recovery played a key role in the two paths transmitting the impact of service recovery on customer loyalty (i.e. H9 and H11). It not only mediated the impact of service recovery on customer loyalty, but also significantly mediated the effect on customer satisfaction. Therefore, LSCs cannot ignore the importance of 12

ACCEPTED MANUSCRIPT increasing satisfaction after recovery. Third, although the impact of service recovery on customer loyalty via service quality and customer satisfaction was found to be insignificant, the direct positive impact of service recovery on service quality was proven to be significant. Given that service failures inevitably occur when customers use liner shipping services, the results indicated that LSCs should also pay attention to

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implementing service recovery. That is, since most users of liner shipping services have regular shipment demands, and many have encountered service failures, LSCs must seriously assess recovery strategies in addition to service quality.

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5. Conclusions

This study contributes to a comprehensive model that can be applied by LSCs and future researchers to investigate the impact of service recovery on customer

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loyalty. Compared to the previous literature, the contribution of this study can be highlighted in terms of three aspects. First, the relationship between service recovery and customer satisfaction has been discussed in previous studies (e.g. Dabholkar and Spaid, 2012). However, to our knowledge, no previous study has investigated the impact of service recovery on customer loyalty toward LSCs. In this study, by

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conducting an empirical survey of the users of LSCs, we proved the positive impact of service recovery on customer loyalty toward LSCs. In practice, customer loyalty can be easily damaged when service failures occur, but the positive relationship between service recovery and customer loyalty implies that customer loyalty toward

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an LSC can be restored if service recovery can be implemented effectively. Second, instead of deeming service quality to be an independent latent variable (e.g. Chao and

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Chen, 2015), the results of this study first suggest that service recovery is an antecedent of service quality for LSC customers. Because service failures are both common and inevitable in this service context, our findings suggest that service recovery should be included in research models related to LSC service quality in future studies. On the other hand, although the impact of service recovery on customer loyalty via service quality and customer satisfaction was found to be insignificant in this study (i.e. H10), future researchers may wish to test this indirect impact because the test results for H10 in this study were very close to the acceptance threshold. Empirical studies can be conducted in different service industries in addition to the liner shipping industry. Third, our results proved that the positive effect 13

ACCEPTED MANUSCRIPT of service recovery is significantly mediated by customer satisfaction and satisfaction after recovery toward customer loyalty. This finding further highlights the importance of service recovery for LSCs. This is because the continuous support of customers is quite important for LSCs due to costly service perishability. Therefore, enhancing customer loyalty must be a serious concern of most LSCs to maintain the high loading

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rates for their large ships. The results of this study indicate that enhancing service recovery can help strengthen customer loyalty due to its significant mediating effect.

In addition to the findings and contributions, a few limitations still exist which suggest directions for future studies. First, due to time and data constraints, this study

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identified the effects of service recovery on service quality, customer satisfaction, satisfaction after recovery, and customer loyalty. Given that some studies have

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decomposed service recovery into three constructs, distributive justice, procedural justice, and interactional justice, to investigate their effects on other industries, future studies may apply these constructs to further identify their impact on customer satisfaction and customer loyalty for LSC customers. Second, some shippers are using liner shipping through ocean freight forwarders or shipping agents instead of contacting LSCs directly. Therefore, it is also worth surveying the service recovery

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performed by ocean freight forwarders or shipping agents to increase the completeness of investigating the effects of service recovery on the liner shipping industry. Third, due to time constraints, the samples collected this study were firms

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located in Taiwan. Future researchers may wish to collect samples from different countries and compare the results obtained from different sample sets because a

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cross-cultural analysis would provide additional insights.

Acknowledgement The authors would like to thank Professor Andrew F. Hayes for providing

PROCESS 2.16 package online.

References Abbas, M.R., Abdullateef, A. O., Mokhtar, S. S. M., 2015. Does service recovery lead to customer satisfaction and loyalty in airline industry? A perceived justice 14

ACCEPTED MANUSCRIPT theory approach. World Applied Sciences Journal. 33 (2), 256-262. Anderson, J.C., Gerbing, D. W., 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin. 103 (3), 411-423. Antony, J., Antony, F.J., 2004. Evaluating service quality in a UK hotel chain: a case study. International Journal of Contemporary Hospitality Management. 16 (6),

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380-384. Augusto de Matos, C., Luiz Henrique, J., de Rosa, F., 2013. Customer reactions to service failure and recovery in the banking industry: the influence of switching costs. Journal of Services Marketing. 27 (7), 526-538.

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Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation model. Journal of Academy of Marketing Science. 16 (1), 74-94. Brooks, M.R., 1990. Ocean carrier selection criteria in a new environment. Logistics and Transportation Review. 26 (4), 339. Cengiz, E., Er, B., Kurtaran, A., 2007. The effects of failure recovery strategies on customer behaviours via complainant. perceptions of justice dimensions in banks. Banks and Bank Systems. 2 (3), 174-188.

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Chang, Y.W., Chang, Y.H., 2010. Does service recovery affect satisfaction and customer loyalty? An empirical study of airline services. Journal of Air Transport Management. 16 (6), 340-342. Chao, S.L., Chen, B.C., 2015. Effects of switching costs on customer loyalty in the

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liner shipping industry. Maritime Economics & Logistics. 17(3), 341-358. Cheng, B.L., Mansori, S., Chuan, G.C., Imrie, B.C., 2017. Hotel Service Recovery and Service Quality: Influences of Corporate Image and Generational Differences in the Relationship between Customer Satisfaction and Loyalty.

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Journal of Global Marketing, 30(1), 42-51. Chou, P.F., 2015. An analysis of the relationship between service failure, service recovery and loyalty for Low Cost Carrier travelers. Journal of Air Transport Management. 47, 119-125. Dabholkar, P.A., Spaid, B.I., 2012. Service failure and recovery in using technology-based self-service: effects on user attributions and satisfaction. The Service Industries Journal, 32(9), 1415-1432. de Matos, C.A., Henrique, J.L. and de Rosa, F., 2013. Customer reactions to service failure and recovery in the banking industry: the influence of switching costs, Journal of Services Marketing, 27(7), 526-538. Del Río-Lanza, A.B., Vázquez-Casielles, R., Díaz-Martín, A.M., 2009. Satisfaction with service recovery: Perceived justice and emotional responses. Journal of Business Research. 62 (8), 775-781. 15

ACCEPTED MANUSCRIPT Deng, Z., Lu, Y., Wei, K.K., Zhang, J., 2010. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International Journal of Information Management. 30 (4), 289-300. Ellyawati, J., Purwanto, B.M., Dharmmesta, B. S., 2012. The effect of perceived justice on customer satisfaction in the service recovery context: Testing mediating variables. Journal of Service Science (Online). 5 (2), 87

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Fu, X.M., Zhang, J.H., Chan, F.T.S., 2018. Determinants of loyalty to public transit: A model integrating Satisfaction-Loyalty Theory and Expectation-Confirmation Theory. Transportation Research Part A: Policy and Practice. 113. 476-490. Ganiyu, R.A., Uche, I.I., Elizabeth, A.O., 2012. Is Customer Satisfaction an Indicator

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of Customer Loyalty? Australian Journal of Business and Management Research. 2 (7), 14-20. Gronholdt, L., Martensen, A., Kristensen, K., 2000. The relationship between customer satisfaction and loyalty: cross-industry differences. Total Quality Management. 11 (5), 509-516. Gu, B., Ye, Q., 2010. The Impact of Online Service Recovery on Customer Satisfaction: Empirical Evidences from Service Operations in China. McCombs

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Research Paper Series. No. IROM-01-10. Hair, J.F., Tatham, R.L., Anderson, R.E., Black, W., 1998. Multivariate Data Analysis with Readings, 5th ed. Upper Saddle River, NJ: Prentice-Hall. Heaver, T.D., 2002. The Evolving Roles of Shipping Lines in International Logistics. International Journal of Maritime Economics. 4, 210–230. Hoffman, K.D., Kelley, S.W., H. M. Rotalsky., 1995. Tracking service failures and

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employee recovery efforts. Journal of Services Marketing. 9 (2), 49-61. Kheiry, B., Alirezapour, M., 2012. The Effect of Satisfaction, Trust and Switching Barriers Service Provider on Customer Loyalty (Mobile Phone Users of Iran Cell Campany In Iran). Australian Journal of Basic & Applied Sciences. 6 (12),

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52-60. Kim, G.S., 2007. The Service Recovery Strategies, Customer Satisfaction, Customer Loyalty. Asian Journal on Quality. 8(1), 76-86. Kim, T., Yoo, J.J.E., Lee, G., 2012. Post‐recovery customer relationships and customer partnerships in a restaurant setting. International Journal of Contemporary

Hospitality Management. 24(3), 381-401. Kline, R.B., 1998. Principles and Practice of Structural Equation Modelings. New York: The Guilford Press. Koufteros, X.A., 1999. Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. Journal of Operations Management. 17, 467-488. 16

ACCEPTED MANUSCRIPT Kruger, L.M., Mostert, P., De Beer, L. T., 2015. Relationship intention and satisfaction following service recovery: The mediating role of perceptions of service recovery in the cell phone industry. South African Journal of Economic and Management Sciences. 18 (4), 608-628. Kuo, N.T., Chang, K.C., Cheng, Y.S., Lai, C.H., 2013. How service quality affects customer loyalty in the travel agency: The effects of customer satisfaction,

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service recovery, and perceived value. Asia Pacific Journal of Tourism Research. 18 (7), 803-822. Kuo, Y.F., Wu, C.M., 2012. Satisfaction and post-purchase intentions with service recovery of online shopping websites: Perspectives on perceived justice and

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emotions. International Journal of Information Management. 32(2), 127-138. Lin, C.C., 2007. The Relationship among Customer Personality Traits, Service Expectation, Perceived Service Quality, and Customer Satisfaction: A Study of Real Estate Brokerage. National Cheng Kung University International Master of Business Administration (IMBA) Program Master’s Thesis. Maxham III, J.G., Netemeyer, R.G., 2002. Modeling customer perceptions of complaint handling over time: the effects of perceived justice on satisfaction and

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intent. Journal of retailing. 78 (4), 239-252. Miller, J.L., Craighead, C.W., Karwan, K.R., 2000. Service recovery: a framework and empirical investigation. Journal of Operations Management. 18 (4), 387-400. Mohsan, F., Nawaz, M.M., Khan, M.S., Shaukat, Z., Aslam, N., 2011. Impact of

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Customer Satisfaction on Customer Loyalty and Intentions to Switch: Evidence from Banking Sector of Pakistan, International Journal of Business and Social Science 2 (16): 230-245. Oliver, R.L., 1997. Satisfaction: A Behavioral Perspective on the Consumer. New

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York, McGraw-Hill. Oliver, R.L., 1999. Whence consumer loyalty. Journal of Marketing. 63, 33-44. Parasuraman, A., Zeithaml, V. A., Berry, L. L., 1988. Servqual. Journal of retailing. 64 (1), 12-40. Premeaux, S.R., 2002. Motor carrier selection criteria: perceptual differences between shippers and motor carriers. Transportation Journal. 28-38. Shanka, M.S., 2012. Bank Service Quality, Customer Satisfaction and Loyalty in Ethiopian Banking Sector. Journal of Business Administration and management Science Research. 1 (1), 1-9. Shin, Y., Thai, V.V., 2014. 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. 17

ACCEPTED MANUSCRIPT Shrout, P.E., Bolger, N., 2002. Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological methods. 7 (4), 422-445. Sobel, M.E., 1982. Asymptotic confidence intervals for indirect effects in structural equation models. Washington DC, American Sociological Association. Suprapto, B., Hashym, G.Y., 2010. Service recovery Strategy and customer

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satisfaction: evidence from Hotel industry in yogyakarta-indonesia. Kinerja, Jurnal Bisnis dan Ekonomi. 14 (2), 118-130. Tax, S.S., Brown, S.W., Chandrashekaran, M., 1998. Customer evaluations of service complaint experiences: Implications for relationship marketing. Journal of

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Marketing. 62 (4), 60-76. Thai, V.V., 2015. The impact of port service quality on customer satisfaction: The case of Singapore. Maritime Economics & Logistics. 18(4), 458-475. UNCTAD, 2017. Review of Maritime Transport 2017. United Nations publication. Sales No. E.17.II.D.10 New York and Geneva. Available at http:// http://unctad.org/en/PublicationsLibrary/rmt2017_en.pdf (accessed 11 November 2017). Venkatesh, V., Davis, F.D., 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science. 46(2), 186-204.

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Yuen, K.F., Thai, V.V., 2015. Service quality and customer satisfaction in liner shipping. International Journal of Quality and Service Sciences, 7(2/3), 170-183.

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Table 1 The factor loading, stand errors, construct reliability and average variance extracted.

ACCEPTED MANUSCRIPT S.E.a

T

0.694

0.089

RES

0.824

0.094

Construct reliabilityb

AVEc

0.894

0.630

Service quality

REL

0.783

0.087

A

0.832

0.087

E

0.826

-

Customer satisfaction

0.748

SA1

0.772

0.089

SA2

0.595

0.148

SA3

0.743

-

Customer loyalty 0.748

CL2

0.745

0.118

CL3

0.568

0.132

CL4

0.756

-

Service recovery SR1

0.702

SR2

0.866

SR3

0.717

SR4

0.656

Satisfaction after recovery

SAR2 SAR3 SAR4

0.547

0.884

0.656

0.112

0.099

-

0.079

0.086

0.813

0.092

0.793

-

S.E. is an estimate of the standard error of the covariance.

Construct reliability = (sum of factor loadings)2/[(sum of factor loadings)2 + (sum of error variances)]. c AVE signifies average variance extracted.

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b

0.827

0.097

0.832

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a

0.802

0.502

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SAR1

0.117

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CL1

0.501

SC

0.799

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factor loading

Measures

Table 2 Results of the χ2 difference tests between the latent construct pairs

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Service quality Service quality Service quality Service quality Customer satisfaction Customer satisfaction Customer satisfaction Customer loyalty Customer loyalty Service recovery

Customer satisfaction Customer loyalty Service recovery Satisfaction after recovery Customer loyalty Service recovery Satisfaction after recovery Service recovery Satisfaction after recovery Satisfaction after recovery

55.504 81.247 70.807 30.368 21.131 32.823 14.898 33.955 26.008 46.832

59.845 148.112 196.963 136.835 25.065 70.755 35.770 143.941 87.625 82.351

20 27 27 27 14 14 14 20 20 20

significant at the P<0.05 level, **significant at the P<0.01 level, ***significant at the P<0.001 level.

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*

19 26 26 26 13 13 13 19 19 19

Constrained χ2 df

∆χ2 4.341*** 66.865*** 126.156*** 106.467*** 3.934** 37.932** 20.872* 109.986** 61.617* 35.519***

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Unconstrained χ2 df

Construct pairs

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Table 3 Goodness-of-fit measures of the measurement model

Ratio of χ2 to degrees of freedom (χ2 /d.f.) Goodness of Fit Index (GFI) Adjusted Goodness of Fit Index (AGFI) Normed fit index (NFI) Comparative Fit Index (CFI)

Recommended value ≤3.00 ≥0.8 ≥0 8 ≥0.9 ≥0.9 ≤0.05

Test result

1.487 0.894 0.855 0.901 0.965

accepted accepted accepted accepted accepted

0.019

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Root Mean Square Residual (RMR)

Value

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Goodness-of-fit measure

accepted

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Table 4 Goodness-of-fit measures of the structural model Recommended value ≤3.00

Value 2.063

Test result accepted

Goodness of Fit Index (GFI)

≥0.8

0.845

accepted

Adjusted Goodness of Fit Index (AGFI)

≥0.8

0.802

accepted

Comparative Fit Index (CFI)

≥0.9

0.947

accepted

Normed fit index (NFI)

≥0.9

0.902

accepted

Root Mean Square Residual (RMR)

≤0.05

0.023

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Goodness-of-fit measure Ratio of χ to degrees of freedom (χ2 /d.f.) 2

accepted

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Table 5 Path coefficients and critical ratio (direct impact) Hypotheses

Variables

S.E.a

Critical ratios

***

0.130

5.900

Estimates

H1

Service quality→customer satisfaction

0.737

H2

customer satisfaction→ customer loyalty

0.676***

0.127

5.305

***

0.081

8.574

0.097

2.108

0.130

5.900

0.057

2.176

0.172

-0.156 b

H3

Service recovery→satisfaction after recovery.

0.909

H4

satisfaction after recovery→ customer loyalty

0.227*

H5

Service recovery→Service quality

0.727

H6

satisfaction after recovery→ customer satisfaction

0.272*

H7

Service recovery→customer satisfaction

-0.039

S.E. stands for standard error b; C.R. stands for critical ratio.

b

Insignificant at the P<0.05 level.

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a

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***

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Table 6 Results of the Indirect Effects of Service Recovery on Customer Loyalty (H8 and H9)

Hypotheses H8

SR→ CS → CL

H9

SR→ SAR → CL

Insignificant at the P<0.05 level.

Sobel test (Z-value)

-0.026

-0.227 a

0.206

2.291

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a

Effects

Paths

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SR: Service quality; CS: customer satisfaction; SAR: satisfaction after recovery; CL: Customer Loyalty

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Table 7 Results for the Indirect Effect of Service Recovery on Customer Loyalty (H10 and H11) Effects

Paths

LLCI

ULCI

H10 SR → SQ → CS → CL

0.0071

-0.0010

0.0249

H11 SR → SAR → CS → CL

0.0381

0.0179

0.0722

LLCI: Lower level for confidence interval.

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ULCI: Upper level for confidence interval.

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SR: Service quality; CS: customer satisfaction; SAR: satisfaction after recovery; SQ: Service quality

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Table 8 The results of the hypotheses test Research hypothesis

Result

H1: There is a positive relationship between service quality and customer satisfaction with LSCs H2: There is a positive relationship between customer satisfaction and customer loyalty toward LSCs. satisfaction after recovery for LSC customers.

H4: There is a positive relationship between customer satisfaction after recovery and customer loyalty toward LSC customers.

H5: There is a positive relationship between service recovery and service quality for LSC customers.

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H6: There is a positive relationship between customer satisfaction after recovery and LSC customer satisfaction.

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H7: There is a positive relationship between service recovery and LSC customer satisfaction.

Supported Supported

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H3: There is a positive relationship between service recovery and customer

Supported

H8: The positive impact from service recovery will be mediated by customer satisfaction towards customer loyalty for LSC customers.

H9: The positive impact from service recovery will be mediated by satisfaction after recovery towards customer loyalty for LSC customers.

Supported Supported Supported Rejected Rejected Supported

H10: The positive impact from service recovery will be mediated by service customers of LSCs.

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quality and customer satisfaction towards customer loyalty for the

Rejected

H11: The positive impact from service recovery will be mediated by satisfaction after recovery and customer satisfaction towards customer loyalty for the

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customers of LSCs.

Supported

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Table 9 The results of one-way ANOVA analysis based on firm size

ANOVA Tangibility

Responsiveness

MS

F

Sig.

Between Groups 1.244 4 0.311 1.138 0.340 Within Groups 51.652 189 0.273 Total 52.896 193 Between Groups 0.275 4 0.069 0.275 0.894 Within Groups 47.254 189 0.250 Total 47.529 193 Between Groups 0.925 4 0.231 1.037 0.389 Within Groups 42.109 189 0.223

Reliability

Total 43.034 193 Between Groups 0.958 4 0.239 1.071 0.372 Within Groups 42.245 189 0.224 Total 43.203 193

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Assurance

Between Groups 0.765 4 0.191 0.662 0.619 Within Groups 54.618 189 0.289 Total 55.383 193

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Service Recovery

Between Groups 0.644 4 0.161 0.463 0.763 Within Groups 65.686 189 0.348 Total 66.330 193 Between Groups 1.124 4 0.281 1.163 0.329 Within Groups Total Between Groups Within Groups Total Between Groups

45.682 46.806 0.189 48.685 48.874 0.514

Within Groups Total

38.434 189 0.203 38.947 193

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Customer Satisfaction

Satisfaction after Recovery

DF

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Service Quality

SS

AC C

Customer Loyalty

189 193 4 189 193 4

0.242 0.047 0.183 0.947 0.258 0.128 0.631 0.641

Note: SS = the sum of squares, DF = the degrees of freedom, MS = the mean sum of squares, F = the F-statistic, Sig. = the P-value

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Appendices Table A1:

Dependent variables used to measure latent constructs

Description

Sources

Service quality (Sixteen items)

Tangibility (Four items) T1: The containers provided by the carrier we are using are in good condition. T2: The transit time of the carrier we are using is competitive. T3: The carrier we are using offers online billing and container tracing service. T4: The carrier we are using offers an extensive service network.

Premeaux (2002)

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Constructs

Premeaux (2002) (items 1 ,2 and 3) Developed in this study (items 4)

Reliability (Four items) Rel1: The carrier we are using picks up and delivers our containers on time. Rel2: The carrier we are using is financially stable. Rel3: The carrier we are using has a reputation for reliability. Rel4: The carrier we are using has the ability to handle special cargos.

Brooks (1990) (items 1 and 3) Premeaux (2002) (items 2 and 4)

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Responsiveness (Four items) Res1: The carrier we are using can offer flexible schedules. Res2: The carrier we are using can respond during emergencies or unexpected situations. Res3: The carrier we are using provides online information quickly and correctly. Res4: The carrier we are using never ignores our problems because its staff is too busy.

Brooks (1990) (items 1) Premeaux (2002) (items 2 and 4) Developed in this study (items 3)

Empathy (Four items) E1: The carrier we are using is willing to carry small shipments. E2: The carrier we are using can provide flexible rates or discounts. E3: The carrier we are using makes it easy to make claims for cargo loss or damage. E4: The carrier we are using offers more flexible rates than its competitors.

Premeaux (2002)

Customer Satisfaction (Three items)

SA1: We are happy with our decision to use our current carrier. SA2: Compared to the carrier we are using, no other carriers can achieve the same level of service. SA3: Overall, we are very pleased with the services offered by this carrier.

Miller (2000) (items 1) Developed in this study (items 2) Flint et al. (2011) (items 3)

Customer Loyalty (Four items)

CL1: We will continuously support the carrier we are using. CL2: We will recommend the carrier we are using to other companies. CL3: I have said positive things about the carrier we are using to professional colleagues. CL4: I intend to stay with the carrier we are using.

Oliver (1999) (items 1) Gronholdt et al. (2000) (items 2) Kheiry and Alirezapour (2012) (items 3 and 4)

SR1: The carrier personnel were very patient when explaining the reasons for our problems. SR2: The carrier we are using gave us a quick, heartfelt apology for our problems. SR3: The carrier we are using tried to solve any problems as quickly as possible. SR4: The carrier we are using provided us with adequate remedies for our problems.

Wen (2013) (items 1, 2 and 4) Del Río-Lanza (2009) (items 3)

SAR1: The carrier we are using gave a positive assessment of the response of the carrier to a mistake. SAR 2: In my opinion, the carrier provided a satisfactory resolution on this particular occasion. SAR 3: The handling of complaints by the carrier’s staff can reduce the dissatisfaction of our company. SAR 4: Regarding this particular event, I am satisfied with the carrier.

Tax et al. (1998) (items 1 and 3) Maxham III and Netemeyer (2002) (items 2 and 4)

Satisfaction After Recovery (Four items)

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Service Recovery (Four items)

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Assurance (Four items) A1: The carrier we are using has experience with handling cargo loss and damage. A2: The carrier we are using honors shippers’ routing requests. A3: The representative of the carrier we are using is willing to understand our needs. A4: The representative of the carrier we are using has knowledge of shippers’ needs.

Table A2: Returned sample statistical distribution

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Percentage

68 126

35.1% 64.9%

12 46 52 45 39

6.2% 23.7% 26.8% 23.2% 20.1%

11 34 43 27 79

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15.5% 36.1% 23.7% 13.4% 11.3%

SC

30 70 46 26 22

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Frequency

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Item Gender Male Female Age 20-25 years 26-30 years 31-35 years 35-40 years More than 40 years Work experience Less than 5 years 6-10 years 11-15 years 15-20 years More than 20 years Year founded Less than 5 years 6-10 years 11-15 years 15-20 years More than20 years Monthly transportation volume Less than 50 TEU 50-100 TEU 101-150 TEU 151-200TEU More than 201 TEU Number of employees Less than 50 employees 50-100 employees 101-300 employees 301-500 employees More than 501 employees

5.7% 17.5% 22.2% 13.9% 40.7%

40 40 31 18 65

20.6% 20.6% 16.0% 9.3% 33.5%

38 25 31 39 23

19.6% 12.9% 15.9% 20.1% 11.9%

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Figure 1 The research model

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shutout (34) mislanded (16)

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container shortage (47) documentation errors (81) cargo loss and damage (94)

100

150

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50

Frequency 200

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Figure 2 Common types of service failures when using liner shipping services

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0

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schedule delay (153)

SC

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Figure 3 Results of the structural model analysis

ACCEPTED MANUSCRIPT Highlights We found the positive impact of service recovery will be mediated to loyalty for liner customers. We included service quality, customer satisfaction, and satisfaction after recovery as mediating variables.

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We proved service recovery has a positive impact on service quality for liner customers.