Revisiting Customer Complaint Intention: A Case Study of Mobile Service Users in Vietnam

Abstract

In the mobile industry, customer complaints play a significant role in retaining customer loyalty to the services provided. Thanks to user complaints, mobile service providers can effectively identify problems and then propose solutions to adapt or improve their services. Hence, it's critical to understand the relationship between consumer complaints and satisfaction with mobile services. While several studies have shown that customer satisfaction is an intermediary variable that explains customer complaint intention, there have been few studies on the relationship between pre-determinants of their satisfaction, leaving a gap in our understanding of customer complaint intention. To demonstrate an in-depth approach to this matter, authors revisit justice theory and suggest trust and perceived responsibility variables be combined into a research model. A cross-section survey was conducted to collect data from 265 mobile services users of the three biggest mobile service providers in Kien Giang Province, Vietnam. This study employed Structural Equation Modeling (SEM) method to analyze the samples collected. The result showed that customer complaint intention is affected by distributive justice, interactional justice, trust but not procedural justice. Additionally, the moderating role of the perceived responsibility variable to the relationship between customer satisfaction and complaint intention is also proved.

1. Introduction

Complaining is important to retain customers and business profits (Holloway et al., 2005). According to Breazeale (2009), the cost of convincing a new consumer to try a service is five times more than the cost of retaining a current customer. The vital function of resolving customer complaints has increased in the context of increasing competition in the mobile services business, as we have witnessed today (Cho, 2018).

Hart et al. (1990) stated that there is a distinct difference between companies whose customer complaints are well managed and others. Halim and Christian (2013) suggested that the managers should support consumers to raise their complaints directly to the company. Accordingly, successful companies often welcome their customers’ complaints and responsive actions from their staff to resolve these complaints, since they believe these activities illustrate positive cooperation. Companies who inadequately manage customer complaints, on the other hand, fail to recognize the value of customers’ voices (Firnstahl, 1989), and hence their brand or services will not improve.

Today, businesses are gradually realizing the value of customers’ complaints about any issues that cause them to be dissatisfied. However, few studies have been undertaken to emphasize the importance of this tendency to business (Voorhees & Brady, 2005), and in a broader sense, managers in all service industries should investigate consumer intention in the complaint-making process. This reflects the situation in Vietnam, where studies on the subject are scarce and opinions on the negative side of customer complaint-making continue to feature prominently. Previously conducted studies tended to focus on the prediction of purchasing intent rather than complaint intention. (Cronin et al., 2000). In this context, understanding of how customers intend to make a complaint about a bad experience with services used, as well as what management may do to encourage customers to voice their discontent, is limited. As a result, this paper can be considered one of the first steps in determining the nature of a customer’s complaint. The findings of this study, by providing concrete evidence, can help telecommunication service providers in Kien Giang enhance their current services.

2. Literature Review

Mobile service is unique in that it is delivered over a telecommunications network with no physical or human connection between users and service providers. Hence, customers of mobile services experience higher levels of service uncertainty than users of traditional goods or services. This is the major difference between the mobile service business and other industries in terms of recognizing consumer complaint intent.

On a worldwide scale, earlier customer complaint research focused on justice theory and traditional market products/services to better understand the nature of consumer complaints. The authors will use justice theory and extend the research model with the trust variable in this study. This is to make the model’s justification for complaint intention clear.

2.1. Justice Theory

The definitions of justice are often regarded in research about exchanges/transactions in society, with or without commercial purpose (Wu, 2013). Accordingly, social exchanges/transactions are often viewed by scholars as a framework to evaluate justice as well as emphasize the role of justice in forming successor transactions (Smith et al., 1999). In this essence, Colquitt et al. (2001) classified justice into 3 main categories after observing 183 relating projects, namely distributive justice, procedural justice, and interactional justice.

Firstly. distributive justice is defined as personal feeling. This type of justice is produced by a person’s personal assessment of a transaction’s fairness based on a comparison of the price paid and the advantages gained from that transaction (Martinez-Tur et al., 2006). According to Smith et al. (1999), distributive justice affects customer satisfaction and impacts their complaint intention. Badawi et al. (2021) also confirmed the positive correlation between distributive justice and customer satisfaction. From these point of view, a hypothesis can be extracted that:

H1: There is a positive relationship between distributive justice and the satisfaction of mobile service users.

Second, procedural justice refers to the principle of fairness in the development of standards and policies (Voorhees & Brady, 2005). As Leventhal (1980) points out, the definition of procedural fairness is based on a set of six criteria: (1) appropriate: the same procedure shall be applied to all; (2) fairness: one who has power in making decisions will not be impacted by any individual benefits while making decisions; (3) correctness: the information collected serving for decision making must be correct; (4) amendment: refer to the availability of mechanisms aiming to correct wrongful decisions; (5) representativeness: all opinions of related parties, who are impacted by the decision, should be recorded and considered; (6) ethics: respecting all ethical standards of society. Research also showed that individuals who believe in procedural justice are also more satisfied with the outcome, even if it is not in their favor (Lind & Tyler, 1988). (Lind & Tyler, 1988). From this light, a hypothesis can be formed to describe the relationship between procedural justice and customer satisfaction.

H2: There is a positive relationship between procedural justice and the satisfaction of mobile service users.

Third, individual-related viewpoints should be separated in procedural justice to develop the idea of interactional justice, as described by Bies and Moag (1986). Customers will perceive justice through staff behaviors when making decisions (Martinez-Tur et al., 2006; Son & Kim, 2008), and there are four elements to determine interactive justice: (1) justification for the action; (2) faithfulness; (3) respect and (4) level of privilege. Research in the field of the Internet identified interactional justice as an extent to which online customers can perceive the faithfulness and reliability of companies in respecting their commitments toward protecting personal privacy (Son & Kim, 2008).

Many studies in services stated the relationship between perceived justice and customer satisfaction (Holloway et al., 2005). In the hospitality sector, Martinez-Tur et al. (2006) showed that distributive justice has the most significant impact comparing to the others. Maxham and Netemeyer (2002) admitted that 3 categories of justice have a certain impact on customer satisfaction while conducting their research in construction activities. However, the result of analyzing data illustrated that procedural and interactional justice dominated distributive justice in predicting customer satisfaction. This relationship is also confirmed by other scholars (Voorhees & Brady, 2005), but its impact is different depending on industries as well as geographic regions. Hence, the authors suggested that the relationship between interactional justice and customer satisfaction should be re-examined.

H3: Interactional justice has a positive effect on customer satisfaction.

2.2. Trust

The role and function of trust have been shown in many social exchange studies (Kelley, 1979). Trust is recognized as a common mechanism to reduce risk in transactions by increasing the participants’ expectation of a positive outcome while making social exchange (Grabner-Kraeuter, 2002).

In TRA theory (Ajzen & Fishbein, 1980), trust is defined as the buyer’s belief that the seller would conduct transactions properly and ethically, whereas Pavlou and Fygenson (2006) defined trust as the buyer’s conviction that the seller will conduct transactions properly and ethically. In an e-commerce study, Morgan and Hunt (1994) found that trust is one of the most important factors in predicting customer satisfaction, while Lin and Wang (2006) found that trust has a positive association with customer satisfaction. Customers rarely communicate directly with mobile service providers, although many complaints may arise, such as a miscalculation in the fare of offered services or a failure to meet service quality commitments. When these scenarios arise, customers’ faith in service providers may be undermined. As a result, this study proposes that to improve the research model’s explainability, the trust variable should be examined along with the three types of justice. Accordingly, the proposed hypothesis will be:

H4: Trust has a positive relationship with customer satisfaction in using mobile services.

2.3. The Relationship of Customer Satisfaction and Complaint Intention

Research in service sectors provided that there has been a link between customer dissatisfaction and complaint intention (Zeelenberg & Pieters, 2004). Particularly, Thogersen and Juhl (2009) argued that one of the factors influencing directly customer’s complaint was their dissatisfaction with the malfunctions or shortcomings of products or services. According to Voorhees and Brady (2005), the satisfaction of service has a reciprocal impact on the complaint intention in the future. This means that if customer satisfaction increases, he or she has less intention to make a complaint in the future. After studying health care, fast food, and entertainment services, Cronin et al. (2000) concluded that the influence of services on behavioral intent can be impacted by an intermediary variable, which is customer satisfaction. In this line, the results of the studies provided show that the more customers are unsatisfied with mobile services, the more their intention to make complaints. The suggested hypothesis is thus:

H5: Customer dissatisfaction has a negative relationship with the customer’s complaint intention in using mobile services.

2.4. Moderating Variable

Apart from the variables above, the findings of theoretical research revealed that other moderating variables may underpin the reciprocal relationship between satisfaction and complaint intention. Customers’ complaint intention is facilitated by perceived responsibility, according to the authors, as a moderating variable if they are unsatisfied with the services (Tax et al., 1998). Perceived responsibility is the customer’s perception of the readiness of services providers in resolving problems (Richins, 1987). In addition to this, the research of Voorhees and Brady (2005) and Wu (2016) also indicated the moderating role of perceived responsibility in the relationship between customer satisfaction and complaint intention. From this point of view, this research propose:

H6: The higher the perceived responsiveness is, the stronger the relationship between customer satisfaction and complaint intention.

2.5. Research Model

In line with the proposed hypothesis and the basis of justice theory, this research suggests a research model, which integrates some additional variables (Figure 1).

Figure 1: Proposed Research Model

3. Research Methodology

3.1. Sample Selection

To acquire primary data, the authors used quantitative methodologies and a field survey. To overcome the disadvantage of resource scarcity, observed samples were chosen using a purposeful sampling strategy. Accordingly, samples were collected from users of all mobile telecommunication service providers in Kien Giang province, Vietnam, which are Mobifone, Viettel, and Vinaphone. In 265 observed samples collected, 142 are males (53,6%) and the remaining 123 are females with 46, 4%. The respondents with the age from 31–40 took the largest percentage in the survey with 43%, while the second group was 21–30 with 27.9%. Ones with the age of 41–50 was17% and people under 20 were 9.1%, while only 8 people who are over 50 joined the survey (3%). In terms of time using mobile services, only 7 people responded that they are new users with the time of use is under 1 year, while the other groups have been experienced mobile services for 1–3 years (32,1%), 4–6years (30, 9%) and over 6 years with 34,3%.

Before answering the main questions, respondents were required to describe in general their personal experiences about problems that they had recently encountered within 6 months. Throughout the survey, the authors were ready to explain and answer any related questions to avoid misunderstandings and maintain the respondents’ focus. The input data, which is the outcome of 265 final samples, is analyzed using SPSS 20 and AMOS 7.0 software.

3.2. Measurement

The questionnaire was designed in line with Likert’s scale from ‘1-strongly disagree’ to ‘5-strongly agree’. Additionally, measurement scales for distributive justice, procedural justice, and integrative justice were adapted from Martinez-Tur et al. (2006), Turel et al. (2008), Chiu et al. (2009), and Wu (2013). Trust’s measurement scales were adapted from Wu (2013) and Turel et al. (2008). The measurement scales applied to measure customer satisfaction, complaint intention, and perceived responsibility were adapted from Wu (2013) and Voorhee and Brady (2005).

The survey was conducted in Kien Giang, a big province in the south of Vietnam. The authors employed an expert interview method to minimize all discrepancies in terms of languages, cultural and personal opinions while translating the measurement scales from English to Vietnamese. Prior to the main survey, a preliminary study with 50 samples was undertaken to ensure that the questionnaire was appropriate and to assess any potential difficulties.

4. Results

4.1. Testing the Reliability and Analysis of EFA’s Factor

Cronbach’s alpha is considered to test the consistency and reliability of suggested measurement scales and Cronbach’s alpha with a value over 0.6 is good for successor tests. After analyzing, Cronbach values of the measurement scales were: complaint intention (0.949); customer’s satisfaction (0.878); distributive justice (0.905); procedural justice (0.865); interactional justice (0.904) and trust (0.910).

Then, EFA factor analysis was conducted based on exploiting method with principal Axis Factoring and Promax rotation. The result showed that KMO is 0.927, and observed variables in measurement scales of independent variables, which are procedural and interactional justice, could be grouped together into one group. This result may surprise some, however, it can be explained by a study released by Bies and Moag (1986), wherein interactional justice and procedural justice were originally separated.

Based on the result of data analysis, this research proposes that observed variables of procedural justice and interactional justice measurable scales should be grouped and thus form a new independent variable with the name that remains procedural justice and denoted as PROIN. After grouping and simultaneously eliminating Items Projus01, Projus03, and Projus04, the coefficient of KMO is 0.924 and higher than the minimum of 0.7. Moving forward with removing Items Projus02 and Sastic05, the results of EFA analysis of all the variables in the proposed research model showed that the KMO coefficient is 0.911, sig = 0 and other values meet the statistical requirements. Therefore, the variable Projus was completely eliminated in this case of study.

In the end, the authors conducted an EFA analysis after removing Items Projus02 and Sastic05, and the results showed that KMO is 0.9 and 5 factors were totally extracted. The indicators were thus satisfactory to continue the next steps of analysis (Table 1).

Table 1: Analysis Pattern Matrix^a

Extraction Method: Principal Axis Factoring.

Rotation Method: Promax with Kaiser Normalization.

^aRotation converged in 6 iterations.

4.2. Confirmatory Factor Analysis CFA

Confirmatory Factor Analysis (CFA) was conducted to confirm the suitability of the measurement scales (Figure 2). In this study, CFA checked the model fit of the model with AMOS software.

Figure 2: CFA Results

Figure 2 shows the results of CFA with model fit indicators shown on the top right of the figure. This confirmed that the fit indicators of the model are all good enough and these can be used for further analysis.

4.3. Structural Equations Model (SEM)

To test the hypotheses about relationships between variables in this research, the authors used a modeling technique of structural equations. The analytical results showed that the model provided a good fit index, matching the criteria of statistical regulations (Figure 3).

Figure 3: SEM Results

Table 2 shows that distributional justice and interactional justice have positive effects on satisfaction, the β indexes are 0.345, 0.287, and all are good P-values. This means that the hypothesis H1 and H3 are accepted. Trust serves explanatory meaning for customer satisfaction with β = 0.204, thus hypothesis H4 is accepted. The analytical results also show that satisfaction is an important intermediate variable to shape the complaint intention (β = -0.632; P-value = 0.025). Through these intermediate variables of satisfaction, the prefixes of distributional justice, interactional justice, and trust affected complaint intention. Therefore, hypothesis H5 is also accepted.

Table 2: SEM Results of the Final Model

Regarding the moderator role of perceived responsibility mentioned in H6, the authors used the multigroup structural equation modeling technique. The observed samples were divided into two groups by the median-split method, namely the group with a high sense of responsibility and the group with a low sense of responsibility. Next, the χ² difference test was conducted to evaluate the difference between the two sample groups as mentioned. The results showed that the moderator role of the perceived responsibility variable in the relationship between satisfaction and complaint intention is also appropriate (Table 3).

Table 3: Chi-square Test Results with Stats Tools Package

5. Discussion

Many studies have been undertaken on customer behavior intention, and many deliverables have been created, but complaint intention has just lately been discussed and examined (Voorhees & Brady, 2005; Wu, 2013), and so remains an issue that needs to be resolved. More essentially, there have been few studies focusing on this topic in the telecommunication service industry. As a result, this study may be considered one of the first attempts to investigate the knowledge of consumer complaint intention based on some empirical evidence. Although some perspectives were limited when constructing the survey template, the research’s contribution is to extend the paradigm of justice theory and apply it to explain the issue. The results indicated that distributive justice, interactional justice, and trust have certain roles in impacting positively customer satisfaction. Because of this impact, their complaint intention toward the services they receive is contributively formed. In general, the conclusions of this study are consistent with those of Smith et al. (1999) and Martinez-Tur et al. (1999) on the production of goods (2006).

In all transactions, customers often tend to compare the quality of goods/ services and the price they pay to see if such payment is value for money, and there is no exception for mobile services. According to distributive justice, if the value they receive is less than the price paid, or furthermore their expectation, they will be unsatisfied and thus the possibility or level of their complaint intention will be high. This result relates to economic theories that maximizing profit and minimizing cost rationally motivate people in social transactions. Furthermore, a key element of mobile services is that users have less opportunity to interact physically with telecommunication providers to address problems, enhancing the role of distributive justice. Thus, the customer’s primary focus is cost and benefit analysis. Interactional justice is also a key component of satisfaction. Companies can use key measures to create support systems to boost high-quality readiness while servicing clients if they understand this fairness. Despite the fact that experimental research has stressed the interaction between employees and customers, communications services still have a gap. This can be attributed to a lack of physical touch between clients and telecom service providers. As a result, having a rapid, efficient, and accessible support system that is fully operational at any moment when customers need it is critical. This study found no evidence of the effects of procedural justice on customer satisfaction. This can be explained by the communications industry’s incorporating characteristics. Notably, being a recently established industry, the telecommunications sector can benefit from the preceding ones to raise its standards in providing mobile services. This means that mobile communications services have been created with standardized and well-defined procedures and policies, such as transaction regulations, payment, and so on, since 1993. Furthermore, mobile service operational processes have been coded or incorporated into the operational system, allowing them to run autonomously with little or no human interaction or bias. Therefore, clients may quickly and simply understand the policies of service providers and select the most appropriate services for them. Hence, because customers make their own decisions, they are less likely to be dissatisfied with business policies.

The findings of the study showed that trust is positively linked to customer satisfaction, which in turn influences the likelihood of making a complaint. Customers’ satisfaction can be improved in this line if they have a positive perception of mobile service providers. Customers develop trust in mobile service providers when they believe they are transparent, clear, and consistent in what they promise while providing services.

Finally, the moderation role of perceived responsibility shows that mobile service providers can encourage customers to voice complaints about their dissatisfaction. Perceived responsibility allows customers to have the sense that their voices are heard and their issues taken into account. Otherwise saying, the customer will feel that their contributions are valuable and being treasured by service providers. This is an essential factor that mobile service providers should care about to constantly improve their service quality from the perspective of customer understanding.

6. Conclusion

Departing from the Justice Theory approach, this research contributed scientific evidence for the predeterminants affecting customer complaint intention. Accordingly, the perception toward justice in transactions should be emphasized to gain customer satisfaction, and thereby reduce their complaint intention.

The results generated from this research can be applied in the course of business practice to improve the experience of users in mobile services. Accordingly, mobile service providers can consider their price strategy while designing a service with more attention. This is because users are interested in maximizing any benefit that they can receive and on the other hand, they often want to minimize to the most any payment they make to the mobile services used. With this in mind, one of the key concepts the managers should strive for is to provide quality services at competitive prices, and this will absolutely be the core strategy of any institution in the future to gain customer satisfaction. Besides, companies should diversify their communication channels since these channels will facilitate regular, timely, and accurate interactions for mutual understanding and gaining trust between customers and firms. It is easier than ever in today’s world, where the development of information communication technology (ICT) allows services providers to interactively connect with their users promptly and conveniently. Thus, taking advantage of ICT can help to resolve chronic problems in terms of treating users’ feedbacks and provide them with the sense of being cared for and listened to. Moreover, although complaining is an inevitable issue in the service management system, how the mobile service providers handle this issue does matter. Users will provide the company with essential and constructive information about the shortcomings of their services once they get the feeling that their complaint or their opinion contribution, will be treasured. As a result, this is not only an issue of handling complaints but also contributes to building a company’s reputation in consumer awareness.

Finally, in view of the paucity of research on the topic of customer complaint intention in mobile services, the authors want to provide the groundwork for future research on the subject. Despite considerable efforts, there are several limitations to this study. First, the data for analysis was collected only within Kien Giang province, so it can be a hurdle to apply the results of this research in other areas. Second, due to some practical limitations in conducting this research, the target sampling method was applied despite the fact that this is a non-probability sampling method. This creates some drawbacks in generalizing the results in other cases. Third, since respondents were asked to describe the problems that occurred in experiencing mobile services before answering key questions, this could create a negative bias and thus affect their responses. The authors hope that further studies will address the limitations in this research and improve the stereotype of the companies, which often view customer complaint behavior as a negative signal. Customer complaints should be viewed as a gift and great opportunities to increase and maintain customer loyalty (Voorhees & Brady, 2006) rather than a problem for companies (Kendall & Russ, 1975).