The relationship of customer complaints, satisfaction and loyalty_ Evidence from China’s mobile phone industry

Dec. 2009, Volume 8, No.12 (Serial No.78)

China-USA Business Review, ISSN 1537-1514, USA

The relationship of customer complaints, satisfaction and loyalty: Evidence from China’s mobile phone industry* KANG Jian1, ZHANG Xin2, ZHENG Zhao-hong3 (1. Customer Satisfaction Measurement Center, China National Institute of Standardization, Beijing 100088, China; 2. College of Management, Tianjin Normal University, Tianjin 300387, China; 3. China National Institute of Standardization, Beijing 100088, China)

Abstract: Customer satisfaction and loyalty is subject to the influence of different types of customer complaints and will be with different results. In this paper, according to the hypotheses about the relationship of complaint, satisfaction and loyalty, a Structural Equation Model based on PLS (Partial Least 2 Squares) is built. By using the PLS Graph, these hypotheses are empirically tested by the data from China’s mobile phone consumers. The study suggests that direct complaint should have a positive effect on satisfaction. However the degree of effect depends on the result of dealing with complaints. So the number of direct complaints should be controlled within the enterprise’s ability of dealing with complaints. Meanwhile, comparing with directly complaining customers, indirectly complaining customers are more likely to repeat the purchasing and become loyal customers. Key words: customer complaints; customer satisfaction; customer loyalty; structural equation model; PLS

1. Introduction The way to retain customers is to treat them well. However, none firm can always achieve 100% customer satisfaction. There will always be some customer dissatisfaction due to the variety of causes (including, at least for certain types of products, customer desire for variety) (Claes Fornell & Birger Wernerfelt, 1987). According to Hirschman’s “exit-voice” theory, management discovers its failure to provide satisfaction via two feedback mechanisms, exit and voice. Exit implies the customer stop buying from the firm. Voice is the customer’s complaint that expresses the dissatisfaction directly to the firm (Hirschman & Albert O., 1970). Singh conceptualized consumer complaint behavior (CCB) is a set of multiple (behavioral & non-behavioral) responses, some or all of which are triggered by perceived dissatisfaction with a purchase episode (Singh J., 1988). In essence, “exit” is an escape from an objectionable state of affairs and “voice” is an attempt by the customer to change the practices, or offerings of the firm and to seek some remedies. *

Acknowledgment: We wish to acknowledge the National Natural Science Foundation of China (70472064). We gratefully acknowledge the three anonymous reviewers for their valuable advice about further improvement of this manuscript. We also wish to thank Dr. Wynne Chin of the University of Houston for kindly permitting us to use his PLS-Graph package. KANG Jian, Ph.D., Customer Satisfaction Measurement Center, China National Institute of Standardization; research fields: customer asset and customer satisfaction, use of statistical modeling in marketing research. ZHANG Xin, Ph.D., lecturer, College of Management, Tianjin Normal University; research fields: service quality measurement, reverse logistics and operational research. ZHENG Zhao-hong, associate professor, China National Institute of Standardization; research fields: customer asset and customer satisfaction, use of statistical modeling in marketing research.

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The relationship of customer complaints, satisfaction and loyalty: Evidence from China’s mobile phone industry

In fact, customers who expressed their dissatisfaction were communicating with the firms, which could be regarded as a chance to improve firm’s work. So customers’ complaints should be appreciated and be taken as presents from customers, though firms are not ready to meet these negative feedbacks (Janelle Barlow & Claus Moller, 2001). However, as some researchers have concluded, the ratio of complaining customers to dissatisfaction customers is very low, which does not make for the development of company and society (Hirschman, Albert O., 1970). Firstly, company will lost many customers, more terribly, and miss a chance to close the gap (Richins, Marsha, 1983). Secondly, the bad word-of-mouth will deprive of good repute of company (Fornell, Claes & Birger Wernerfelt, 1987). Finally, company will not improve the products quality effectively without this negative information about products or services. Furthermore, when most individuals of a certain industry are in the above circumstance, industry development will be hold back with high society cost (Andreasen, Alan R. & Jean Manning, 1990). So identifying customer complaint behaviors accurately and managing them effectively will accelerate the development of industry. From 1970s, consumer complaint behaviors have become the hotspot in marketing research. The study of consumer complaint behaviors in west counties can be concluded in three aspects: First, intension of customer complaint; Second, customer complaint behaviors, CCB, include classifying, reasoning and affect factors analysis. Third, customer complaint management includes what the companies did and what they should do. The purpose of customer complaint research is to help company identify complaint behaviors, obtain availability information about improving the function and quality of products or services, and then increase customer satisfaction and loyalty through complaint management. So, it is important to realize the influence of customer complaint on customer satisfaction and loyalty. Fornell, Claes and Birger Wernerfelt discuss the relationship between customer complaint and loyalty and report encouraging customer to complain could increase customer loyalty (Fornell, Claes & Birger Wernerfelt, 1987). However, relevant literatures are limited, especially regarding China’s market. In China, ZHAO Ping and MOYa-lin discuss the consumer complaint behavior of durable goods (ZHAO Ping & MO Ya-lin, 2002); FAN Xiu-cheng, ZHAO Xian-de & ZHUANG He-jun empirically demonstrate the effect of values on complaining intentions to poor service (FAN Xiu-cheng, ZHAO Xian-de & ZHUANG He-jun, 2002) This problem will be further discussed from the angle of the relationship of different CCB, customer satisfaction and loyalty in this paper. Most researches on customer complaints consider customer dissatisfaction as the antecedent of customer complaint. For example, in ACSI, the CSI is a centrepiece in a chain of relationships running from the antecedents of customer satisfaction-expectations, perceived quality and value to its consequences-voice (complaints) and loyalty (Eugene W. Anderson & Claes Fornell, 2000). This design places complaints between CSI and loyalty, the immediate consequences of increased customer satisfaction are decreased customer complaints and increased customer loyalty, as shown in Fig. 1a. On the other hand, Voss, et al (2004) found that the use of systematic procedures for capturing customer feedback and complaints had a direct and positive influence on satisfaction. Debbie and Robert (2003) said that both the content and process of consumer complaint management create opportunities for strengthening organizational performance and increasing customer satisfaction. In this paper, we hold on this idea and take customer complaint as the reason, loyalty as the result, and satisfaction as the media-variable of the causal chain, shown in Fig. 1b. Furthermore, another feather of our work is combining the research of classification of customer complaint behaviors (Singh & Jagdip, 1988; Day, R., 1980; Dominique Crie, 2003) and finding out the relationship between customer complaint and customer satisfaction (Singh, 1991; Claes Fornell & Birger Wernerfelt, 1987) in 23


discussing the relationship of customer complaint, customer satisfaction and customer loyalty.

Fig. 1 Comparison with traditional study models

In addition, our study for the first time introduces the “complaint dealing with” into the measurement model. As an influential factor of consumer complaint, “complaint dealing with” represents the complaint management capacity of an enterprise. The complaint management depends on organizational factors (e.g., corporation culture, complaint dealing with ability), although most research focused on the complaint consumers’ characteristics (Andreasen, 1988; Gilly, et al., 1991). Finally, most study on CCB is conducted in west countries, but the findings are not necessarily suitable for other cultures, especially for China (Liu, Raymond R. & McClure, Peter, 2001). Considering the difference of CCB in different cultures, we collect data from mobile market with Chinese special culture, it helps to complement related studies in both academic and practical spheres, and helps to test relevant findings as reported in the literature, which adds valuable insights to the development of the theory. Therefore, this study makes several contributions to relevant studies as follows. First, the authors explore the impact of customer complaint behaviors on satisfaction and loyalty, which help firm recognize the relationships of them and effects of managing customer complaint. Second, the authors study the differences come from different CCB affecting on customer satisfaction and loyalty, which help firm treat complaint behaviors discriminatingly and avoid encourage or exclude complaining blindly. Third, the authors discuss the moderate function of “complaint dealing with” ability to the relationships.

2. Theoretical background 2.1 Customer complaint behaviors Customer complaint is a complex combination of psychology and behavior, involving many aspects such as reason, motive and way of act. So there are many different interpretations about its intension. Jacoby and Jaccard report that customer complaint is individual behavior to convey negative information about products or services to the enterprises or third-entities, which indicates that the characteristic of customer complaint behavior is to convey negative information (Jacoby, Jacob & James J. Jaccard, 1981). Fornell and Wernerfelt propose that customer complaint is a kind of customer efforts in order to change the dissatisfaction situation in their purchase or consumption, which emphasizes the purpose of customer complaint (Fornell, Claes & Birger Wernerfelt, 1987). Singh, in summarizing an extensive review of the literature on complaint, finds several common grounds of customer complaint. First, customer complaint is driven by their dissatisfied feeling and emotion (Day, Ralph L, 1984). Second, customer complaint can be classified into behavioral responses and non-behavioral ones. Third, various customer complaint behaviors are not antagonistic mutually, but may occur simultaneously. Then an acceptable 24


definition is presented by Singh: When customer dissatisfied with the goods (or services) they consumed, driven by the dissatisfaction emotion, they would take series of (single or not) behavioral or non-behavioral responses (Singh, Jagdip, 1988). Not all dissatisfied customers express their dissatisfaction directly toward sellers. Some consumers may take no action; some ones may complain to their friends and relatives or even third parties (e.g., consumers’ association or courts). According to complaint objects, Singh (Davidow M. & Dacin P A., 1997) classified CCB into three types (Singh, Jagdip, 1988): direct complaint, private complaint and the third complaint. Direct complaint means that consumer complains to individuals or organizations involved in the dissatisfying consumption and external of his social circle such as shopkeepers; Private complaint represents consumer complains to individuals or organizations that not directly involved in the dissatisfying exchange and are internal to the consumer’s social circle such as friends and relations; Third-party complaint represents consumer complains to individuals or organizations that are external to the consumer’s social circle and not directly involved in the dissatisfying exchange such as law institution, media. Some researchers think that third-party complaints are not at the same level with the other two types of complaints, and generally consumers will not firstly appeal to third-party. The number of consumers who take third-party actions is relatively small (Tipper, 1997). Singh and Wilkes (1996) concluded that if consumers believe that redress is possible by complaining directly to the selling organization, they are less likely to voice complaints to others. Debbie and Robert (2003) thought less inquiry has focused on third party complaints, where the customer takes a concern to a government agency, consumer protection group, Better Business Bureau, or some formal party. So our study focuses on direct and private complaint. In order to make comparison, private is named indirect in our study. 2.2 Customer satisfaction As a quantitative index, customer satisfaction describes the difference between expectation and perceived quality, and measures the degree of satisfaction. If expectation is lower than perceived quality, satisfaction will be high and customers will recognize the product; contrarily, satisfaction will be low and customers will complain the products. In general, high customer satisfaction should indicate increased loyalty for current customers, reduced price elasticity, insulation of current customers from competitive efforts; lower costs of future transactions reduced failure costs, lower coasts of attracting new customers and an enhanced reputation for the firm (Claes Fornell & Donald R. Lehmann, 1994). Cardozo (1975) points out the customer satisfaction can boost repeat purchase and cross purchase. Some other scholars such as Churchill and Surprenant (1982), Engel, Blackwell and Miniard (1984) all report the customer satisfaction is a kind of evaluation about purchase and using, and it is produced by the buyer anticipated result reward and the investment cost. Anderson, Fornell and Lehman (1994), in summarizing existing literatures, distinguish from specific transaction and cumulative transaction, two viewpoints explained the customer satisfaction. Specific transaction viewpoint suggested that customer satisfaction was the customer evaluated after buying behavior at some specific purchasing place or time, and it may provide diagnosis information to the specific commodity or service performance. Cumulative transaction viewpoint was that the customer satisfaction evaluated all purchase commodity or service experience, and it may provide the enterprise some important operational performance indicators in future (Anderson E. W., Claes Fornell & Donald R. Lehman, 1994). 2.3 Customer loyalty Jones and Sasser (1995) point out that customer loyalty is customer repeating purchase intention to some 25


specific products or services in the future. Oliver, Rust and Varki (1997) believe that the customer loyalty means that the customer may come under the environmental effect or marketing technique, which induce their possibly latent transformation behavior, but they wouldn’t change their repeat purchase commitment and further purchase intention with preference commodity or service. Bowen and Shoemaker (1998) think the customer loyalty is the customer once more visiting possibility and wants to participate in this enterprise. Loyal customers not only are satisfied with the product but have continual preference. So they can be “the firm fender” facing the attractive opportunity from competitors. Customer loyalty has become a perfect objective, which firms pursue for.

3. Hypotheses 3.1 Customer complaint and satisfaction At present, many firms pay great attention to “customer complaint”, and some ones even take the number of customer complaints as an important measure way about satisfaction. They try to increase customer loyalty by reducing customer complaints, but this approach is not satisfactory. Some statistics indicate that encouraging dissatisfied customers to complain directly is a cost efficient way to improve satisfaction and loyalty (Claes Fornell & Birger Wernerfelt, 1987). One reason for the fact is that these firms confuse the types of complaint behaviors. Customers, complaining directly with high expectations over a firm, are still in the hope of changing and unwilling to abandon the firm immediately. If their complaints could be dealt with well, some dissatisfied customers will be turned into satisfied ones and have a favorable view of the firms, then the overall customer satisfaction will increase. The complains contain a lot of important information such as product design, quality control and improvement of management, which are helpful to the firms for providing more satisfactory products and services (Claes Fornell, 1992). Gilly, et al (1991) also found that if the company can disseminate consumer direct complaint information in the organization and create remedies to preserve, customer satisfaction will be elevated. On the contrary, if consumers are not given organizational channels and opportunities to complain, they will voice concerns to others outside the company and their satisfaction degree will decreased. Debbie and Robert (2003) thought that the indirect complaint behavior normally indicates a degree of consumer dissatisfaction, company unresponsiveness or related factors, which can severely threaten marketing relationships and effectiveness. According to the viewpoint of Bart and Dirk (2005), if customers who do not complain to the firm when dissatisfied, the company will lose the opportunity to rectify the problem (Fornell & Wernerfelt, 1988; Levesque & McDougall, 1996) and to restore the customer’s satisfaction level (Smith, Bolton & Wagner, 1999). In addition, some dissatisfied customers may even tell their unpleasant experiences to others forming bad word of mouth, so the performance of advertisements and promotion would be abated as well as overall customer satisfaction. The first hypothesis of the model can be stated as follows: Hypothesis 1: Direct complaints have a positive effect on satisfaction, and indirect complaints have a negative effect on satisfaction. However, it is far from enough for the firms to only concern the significance of customer complaints. In order to improve satisfaction and retain customers effectively, they must deal with the complaints in a right way. The customers, complaining directly, will be disappointed if encounter blenching, delay, negligence or

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pretermission, furthermore abandon the firms. In more serious cases, they will seek protection from a third party, which will do harm to the firms. If taking a positive attitude to handle the voice effectively, such as sincere apologies for the error, investigating the causes of events, and making generous compensations to customers, the firms can considerably increase their customer satisfaction, retain customers, win good public praises and unpredictable economic returns. As a result, we have hypothesis 2. Hypothesis 2: In order to increase customer satisfaction, dissatisfied customers shall be encouraged to complain directly, but the contribution of complaints to satisfaction is largely subject to how to deal with complaints. 3.2 Customer complaint and loyalty Whenever customers dissatisfied with a brand, their loyalty to this brand will decrease correspondingly. But the possibility of repurchasing will not go down to zero immediately because dissatisfaction shows various degrees. In fact, there are two stages during purchasing process: First, if customer feel satisfied with the products, they may repurchase it; If feel dissatisfied, they will pass on to the second stage—make choices among all brands except the present one in the market according to consumption experiences and expectations. If the firm can retain the dissatisfied customers at this moment, change its image from the heart of customers, upgrade the expectations of customers, especially those voicing customers, and compensate them, customers will tend to repurchase products of the same firm. Otherwise, “high dissatisfaction” will lead consumers to make choices among all brands except recently purchased brand. Empirical studies indicate that handling customer direct complaints well is good for company. Fornell and Wernerfelt (1987) suggest that marketers maximize complaints from consumers in order to reduce customer turnover and other negative effects. Customers who complain and receive a proper response to firms’ failure services are more likely to stay (Conlon & Murray, 1996), to buy new products (Maxham III & Netemeyer, 2003), to pay price premiums (Zeithaml, Berry & Parasuraman, 1996), to engage in favorable word-of-mouth and to recommend the company’s services to others (Maxham III, 2001; Maxham III & Netemeyer, 2002). Bougie, et al (2003) found customers are less vulnerable to switch and less likely to spread negative word-of-mouth to friends when their direct complaint are treated with well. Surveys indicate that in the event of dissatisfactory purchases, 70% of complaining directly consumers will repeat purchasing (ZHAO Ping & MO Ya-lin, 2002). If the results of complaint processing satisfy them, this proportion will rise to 95%. In sum, there is overwhelming evidence from previous researches that successful complaint handling results in customer loyalty. However those dissatisfied customers without voicing inclined to abandon the firms, and turn to the competitors. for instance, Whiteley (1991) reported survey result which suggests that fewer than 10% of dissatisfied non-complainers buy again from current supplier, whereas more than 80% of disgruntled customers who do direct complain, and whose problems are quickly resolved, will probably repeat their purchases. As a result, we have hypothesis 3 and 4. Hypothesis 3: Customers complaining directly are more likely to repurchase than complaining indirectly ones. Hypothesis 4: Customers complaining directly are more likely to become loyal customers than complaining indirectly ones.

4. Model and methodology

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4.1 Modeling Since customer satisfaction, customer complaint and customer loyalty are all latent variables that are not directly observable, it is not easy to measure them by traditional statistical methods or study the relationship of them. Therefore this paper applies the structural equation model, choosing multiple measurable variables to measure each latent variable, and build a model according to casual-effect relationship of the latent variables. In the model, customer complaints are divided into two parts: direct complaint and indirect complaint. The model is shown in Fig. 2.

Fig. 2

The model of casual relations of direct complaint, indirect complaint, satisfaction and customer loyalty

According to Fig. 2, three structural equations are derived as follows:

Z 2 = β 21 Z1 + β 24 Z 4 + e 2

Z 3 = β 31Z1 + β 32 Z 2 + β 34 Z 4 + e3

Z 4 = β 41 + e4

(1) (2)

(3) In which: Z1—direct complaint; Z2—customer satisfaction; Z3—customer loyalty; Z4—indirect complaint; βij—path coefficient between latent variable j and latent variable i (i, j＝1, 2, 3, 4); ei—residual error (i＝1, 2, 3, 4). 4.2 Data collection The authors collect the data from mobile phone industry in China. The reason for us to choose this market is as follows. First, this is a relatively high voicing industry. According to the statistics of China Consumers’ Association, complaints on mobile phones in 2004 are 36.5% higher than that of last year, occupying the No. 1 among all kinds of complaints. Second, China’s mobile phone market has been the most active and has attracted much attention all over the world among so many markets in China’s telecommunication industry. Up to now, there are nearly 3400 billion users of mobile phone in China making the largest mobile communication network in the world. Therefore, our study is beneficial to explore the mobile phone market in China, and also makes it possible for researchers to compare our findings with the relevant ones of extant studies in mobile communication market of other countries. In order to collect enough data of high quality to test our hypotheses, internet and call surveys were conducted by adopting the sampling technique based on the measurement refinement results of the pilot study with customers of Motorola, Nokia, Sony-Ericsson, Siemens, TCL and so on. These brands are representative because they accounts for over 80% of the total market share. Moreover in all investigated cities, the market share of Motorola, Nokia and Samsung are listed in top three and they belong to the first camp; SonyEricsson, Siemens

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and Bird belong to the second camp; TCL, Alcatel and Philips belong to the third camp (IMI Consumer Behaviors & Life Styles Yearbook, 2004). This situation indicates that mobile market structures in objective cities are basically homological. Therefore the differences owing to diversity cities and brands are neglected in our study. According to China economic region division, our data comes from east China such as Beijing, Tianjin, Shanghai and Shandong, middle China such as Heilongjiang, Shanxi, Inner Mongolia etc, and west China such as Sichuan, Chongqing, etc. The sample is representative, covering economical developed and undeveloped areas. The authors take back 500 questionnaires in total and 404 of them are valid, the validity rate is 80.8%. Among them, net survey mainly aims at the students, office staffs and so on, accounting for 84%; While telephone investigation mainly aims at the community which is senior citizen or get net inconveniently, accounting for 16%. In order to avoid the difference between network and telephone investigation, we read the topics and record answers no explanation about items in telephone survey. In survey, we random draw out 20 respondents on net, and ask them second time by telephone; 10 respondents by telephone, and we survey them second time on net. Comparing these two results, we find little difference between them. The respondents are from 19 to 52 years old; 52.9% are male and 47.1% are female. The proportion is reasonable. In education background distribution, master and above, bachelor, junior college, high school, junior middle school and below are separately 36.3%, 16.1%, 12.5% and 6.5%. Most respondents have good education background because they would more like to collect the information, complain to firms and not to the third party (Moyer, 1984). In occupation distribution, the respondents concentrate on the officers, government members, individuals, students and so on, 7 parts in all, because they are main users of mobile phones at present. 4.3 Variables Before the formal investigation, we design the questionnaire according to scale development process which suggested by Churchill, based on the relevant literatures and customer group interview. The final questionnaire covers all constructs (customer satisfaction, customer loyalty, direct complaint and indirect complaint), used in our study and employs Likert 10 point scale. There are several periods in developing the questionnaire: first, we extract the experimental items from existing literatures; second, analysis these items by focus group discussions; third, refined the questionnaire by a pilot study of 40 customers in China. Customer satisfaction and customer loyalty are each measured by related research with a little word changes. Direct complaint is created, using two items from focus group comments, by asking customers to recall the times they had complained and whether were satisfied with “complaint dealing with”. As for indirect complaint, two items are used to evaluate the complaint degree based on our preliminary pilot study and literature review. All items and relevant literatures are shown in Table 1. Direct complaint means consumer complains to individuals or organizations involved in the dissatisfying consumption and external of his social circle such as shopkeepers. Direct complaint ratio and “complaint dealing with” are used to measure direct complaint. Direct complaint ratio is the number of voicing customers for their dissatisfactions about products or services to that of all dissatisfied ones given a certain time, and “complaint dealing with” lies on customer percipience about what extent the firm resolved the problems. Indirect complaint represents consumer complains to individuals or organizations not directly involved in the dissatisfying exchange and internal of consumer’s social circle such as friends and relations. Indirect complaint can be measured by indirect complaint ratio and complaint degree. The former item refers ratio of customers complaining to relatives or friends and maintaining silent to all dissatisfied customers given a certain time, and the later one can be measured by number of complaining to friends or relations. 29


Table 1 Latent variables

Latent variables and observed variables in the model

Observed variables Overall satisfaction (w1)

Satisfaction (Z1)

Distance from expectation (w2) Distance from ideal (w3) Direct complaint ratio (w4)

Direct complaint (Z2)

Complaint dealing with (w5) Indirect complaint ratio (w6)

Indirect complaint (Z3)

Loyalty (Z4)

Complaint degree (w7) Repurchase likelihood (w8) Price tolerance (w9)

References Claes Fornell, Michael D. Johnson, et al (1996); Claes Fornell (1992); Yi, Y.. (1990). A critical review of consumer satisfaction. In: Zeithmal, V. A. (Ed.), Review of Marketing. Chicago: American Marketing Association, 68-123. Claes Fornell, Michael D. Johnson, et al (1996); Bradford, Adelina Broadbridge and Julie Marshall (1995); Santa Barbara (2000). Claes Fornell, Michael D. Johnson, et al (1996); Bradford, Adelina Broadbridge, Julie Marshall (1995); Nancy Stephens, Kevin P. Gwinner (1998); Santa Barbara (2000). Claes Fornell, Michael D. Johnson, et al (1996); Claes Fornell (1992); Gould (1995); Dick and Basu (1994); Magnus (1998); Zeithaml, Berry and Parasuraman (1996).

4.4 Confidence and convergent validity testing Following the two-step approach recommended by Anderson and Gerbing (1988), the adequacy of each multi-item scale in capturing its construct was assessed using the measurement model of all constructs, by checking internal consistency reliability, convergent validity and discriminate validity, before testing the hypotheses via the causal model. Firstly, the composite reliability for internal consistency is demonstrated, since values for all constructs are above the suggested threshold of 0.70, with a minimum of 0.823 (see Table 2). Secondly, the standardized factor loadings for all items distribute from 0.415-0.995, and all are significant (P < 0.005), showing evidence of convergent validity. At the same time, as shown in Table 3, the average variance extracted (AVE) of each construct in our model was more than 0.60, 0.664-0.880, which meets the criterion of a construct’s AVE; i.e. it should be, at least, higher than 50% to guarantee more valid variance explained than error in its measurement (Fornell, et al., 1994; Fornell & Larcker, 1981). Thirdly, apart from the above-mentioned convergent validity, the constructs should also show high discriminate validity. According to Fornell and Cha (1994) and Fornell and Larcker (1981), this can be evidenced by the fact that the square root of the AVE of each construct is generally higher than the correlations between it and any other constructs in the model, which is presented in Table 3. That is, the constructs are both conceptually and empirically distinct from each other. Table 2

Confirmatory factor analysis results and relevant composite reliability

Constructs Customer satisfaction (Z1) 1 Overall satisfaction (w1) 2 Distance from expectation (w2) 3 Distance from ideal (w3) Direct complaint (Z2) 4 Direct complaint ratio (w4) 5 Complaint dealing with (w5) Indirect complaint (Z3) 6 Indirect complaint ratio (w6) 7 Complaint degree (w7) Loyalty (Z4) 8 Repurchase likelihood (w8) 9 Price tolerance (w9)

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Loading

t-value

0.904 0.909 0.802

Composite reliability 0.839

0.773 0.995

19.271 92.641

0.908

0.931 0.415

81.623 8

0.847

0.940 0.902

81.63 79.915

0.823


Table 3 Latent variables

Correlation coefficients and square root of AVE for all constructs

Customer satisfaction

Direct complaint

Indirect complaint

Customer satisfaction

0.815

Direct complaint

0.721

Indirect complaint

0.693

0.657

0.727

Customer loyalty

0.662

0.558

0.667

Customer loyalty

0.808 0.716

5. Empirical results 5.1 Methodology This paper employs the PLS Graph software to analysis data. Comparing with covariance-based structural equation models, the PLS-based structural equation model can provide a way to avoid problems of improper solutions and factor indeterminacy as well as the violations of distributional assumptions (Michel Tenenhaus, Vinacenzo Esposito Vinzi, Yves-Marie Chatelin & Carlo Lauro, 2005; Claes Fornell & Cha, 1994). Considering the limited sample size and satisfaction data is skew distribution (Claes Fornell & David F. Larcker, 1981), the PLS Graph software is adopted so as to achieve a better effect. The following several indicators are mainly adopted for validity testing of model and goodness of fit: communality, cv-communality, redundancy, cv-redundancy and GoF. 5.2 Validity testing The validity of PLS-based structural equation model can be tested by the validity of measurement model and structural model, respectively. The validity of measurement model can be tested by the accountability of each observed variable for its corresponding latent variable using communality and cv-communality. The model is acceptable when communality value situated between 0 and 1. High communality indicates high accountability of an observed variable for the latent variable. The communality of each latent variable in our study distribute from 0.698 to 0.839, with an average of 0.784, indicating that each observed variable has good accountability for the latent variable. Cv-communality means cross-validity testing between each observed variable and its corresponding latent variable. The model is not acceptable unless all the cv-communality of each latent variables are larger than 0. The cv-communalities in this model are 0.229, 0.063, 0.502 and 0.454, indicating that the model is acceptable. The validity of structural model can be tested by redundancy and cv-redundancy. For an endogenous latent variable, these indicators can test the predictability of model on each observed variable from indirect latent variables. The model is acceptable, when redundancy is larger than 0, and the maximum value is 1. High redundancy indicates strong predictability. The redundancy of each latent variable in this model is distributed between 0.024 and 0.389, with an average of 0.127, indicating that the model is acceptable. Cv-redundancy cross-validity testing between an observed variable of a latent variable with all observed variables relates with this latent variable, and when all cv-redundancy values of all latent variables are larger than 0, the model is acceptable. The cv-redundancy values in this model are distributed in 0.182, 0.337, 0.286, and all are larger than 0. So the model is acceptable. Furthermore, the magnitude of R2 value for final latent endogenous variables-customer loyalty is 0.479, showing that our model has strong predictive power. The goodness of fit (GoF) of model can be tested by GoF indicator, which is an absolute indicator and can be

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compared with the GoF indicator, derived by other methods to estimate the goodness of fit of this model. GoF =

communalit y × R 2

(4)

In which: communalit y —average communality of all endogenous latent variables in the model;

R 2 —average variance of all latent variables in the model. The GoF of this model is 0.4062 which is similar to that of European Customer Satisfaction Index (ECSI) measurement model “0.4645” (Michel Tenenhaus, Vinacenzo Esposito Vinzi, Yves-Marie Chatelin & Carlo Lauror, 2005), indicating the GoF of this model is acceptable. 5.3 Results and hypotheses testing The final results of structural equation model are shown in Fig. 3.

Fig. 3

Structural equation model of direct complaint, indirect complaint, satisfaction and customer loyalty

From the results we deduce the influence of direct complaint on satisfaction and loyalty, and the influence of indirect complaint on satisfaction and loyalty, as shown in Table 4 and Table 5. Table 4

Influence coefficient of direct complaint on customer satisfaction and loyalty

Direct complaint Direct effect Indirect effect Total effect Table 5

Satisfaction 0.123 0.096 0.219

Direct effect Total effect

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Indirect complaint -0.225 -0.225

Influence coefficient of indirect complaint on customer satisfaction and loyalty

Indirect complaint Indirect effect

Loyalty 0.147 0.165 0.312

Satisfaction -0.428 -0.428

Loyalty -0.300 -0.190 -0.490


From Table 4, and Table 5, it could be clearly found that the direct effect of direct complaint on satisfaction is 0.123, and the total effect is 0.219; while the direct effect of indirect complaint on satisfaction is -0.428, and the total effect is -0.428. These data indicate that there is a positive correlation between direct complaint and satisfaction, and direct complaint has certain influence on satisfaction.1 Because every 1% increase of direct complaints, satisfaction will increase by 0.219%. While there is a negative correlation between indirect complaint and satisfaction, and indirect complaint has large influence on satisfaction, for every 1% increase of indirect complaints, satisfaction will decrease by 0.428%. According to the analysis of results, Hypothesis 1 gets verified. In the measurement of direct complaint, the weight of number of direct complaints and complaint dealing with degree are -0.173 and 1.139 respectively. These indicate that the number of complaints has a negative effect on direct complaints. Increasing the number of complaints will reduce the score of direct complaints, while the complaint dealing with degree has a positive effect on direct complaints. Dealing with complaint effectively is helpful for increasing the score of direct complaints. Therefore whether increase or decrease direct complaints score depends on the absolute value of A (A=1.139 × “complaint dealing with degree”) and B (B=-0.173×number of direct complaints). If A > B , the score of direct complaints will increase, and leads to an increase of satisfaction because there exists an positive correlation between direct complaint and satisfaction (as proved in Hypothesis 1); If A > B , the score of direct complaints will decrease, and leads to a decrease of satisfaction. As a result, if the number of direct complaints is overlarge, “complaint dealing with” will be powerless and satisfaction will decrease. This result indicates that the firm should not encourage customers to make direct complaints unconditionally, but control the number of direct complaints within a certain range which is under the firm’s ability of “complaint dealing with”. Meanwhile, 1.139 is almost seven times of 0.173, therefore the complaint dealing with degree has more significance on direct complaints than the number of complaints. This result suggests that the positive effect of direct complaint on satisfaction largely depends on the complaint dealing with degree. According to above analysis, the latter part of Hypothesis 2 gets verified, the contribution of direct complaint to satisfaction is subject to the influence of the complaint dealing with degree, while the former part of the hypothesis 2 does not hold, the firm should not encourage customers to make direct complaints unconditionally, which is of no good for satisfaction if falling beyond a certain range. In loyalty measurement, the path coefficient between the repurchases likelihood and loyalty is 0.94. The total influence coefficient between direct complaint and loyalty is 0.312 (every 1% increase of direct complaints, loyalty will increase by 0.312%), and thus the authors find the coefficient between direct complaint and the repurchases likelihood is 0.293 (0.94×0.312=0.293), and every 1% increase of direct complaints, the possibility of repetitive purchases will increase by 0.293%. The total influence coefficient between indirect complaint and loyalty is -0.490 (every 1% increase of indirect complaints, loyalty will decrease by 0.49%). Similarly, the authors find the coefficient between indirect complaint and repurchases likelihood is -0.461 (094×-0.490=-0.461), and every 1% increase of indirect complaints, the repurchases likelihood will decrease by 0.461%. As a result, hypothesis 3 gets verified, directly complaining customers are more inclined to repeat purchasing than indirectly complaining ones. At the same time, hypothesis 4 also gets verified, comparing with indirectly complaining customers, directly complaining customers are more likely to become loyal customers. 1

“Great influence” means that every 1% change of factor A will lead to a change of factor B by 0.8% or above; “Significant influence” means that every 1% change of factor A will lead to a change of factor B by 0.6-0.8% (including 0.6%); “Large influence” means that every 1% change of factor A will lead to a change of factor B by 0.4-0.6% (including 0.4%); “Certain influence” means that every 1% change of factor A will lead to a change of factor B by 0.2-0.4% (including 0.2%); And “little influence” means that every 1% of factor A will lead to a change of factor B by 0-0.2% ( DINA Market Research Institute, 2003).

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6. Conclusions and implications This paper puts forward four hypotheses about the relationship of different customer complaint behaviors, customer satisfaction and customer loyalty, and conducts an empirical study. It mainly contributes to the theory as following five aspects. First, According to our study, there appears a positive correlation between direct complaint and satisfaction, and a negative correlation between indirect complaint and satisfaction. Furthermore, directly complaining customers are more likely to repurchase than indirectly complaining ones. Second, our study, for the first time, introduces the “complaint dealing with”, as an influential factor, representing complaint management capacity of the enterprise. The authors find the contribution of direct complaint to satisfaction is subject to the influence of the “complaint dealing with” degree. However, the number of direct complaints will have a negative effect on satisfaction if falling beyond the enterprise’s ability of “complaint dealing with”. Third, we in-depth study the viewpoint of encouraging customer complaint can enhance customer loyalty (Claes Fornell & Birger Wernerfelt, 1987; Anonymous, 1998). We find that, comparing with indirectly complaining customers, directly complaining customers are more likely to become loyal ones. For every 1% increase of direct complaints, loyalty will increase by 0.312%, while for every 1% increase of indirect complaints, loyalty will decrease by 0.490%. The purpose of customer complaint management is to turn dissatisfied customers into loyal ones, and this conclusion just gives an evidence for the feasibility of this opinion. Finally, it should be noted that this paper bases on developing countries under economic transformation—China. Currently, most relevant studies base on western developed countries and the applicability of these research results in developing countries is questionable. So, our findings have great significance to verify and widen international relevant theory and practice. As for contributes to relevant practice, our research enables firms to recognize difference between different CCB. Direct complaint has certain positive influence on satisfaction, and for every 1% increase of direct complaints, satisfaction will increase by 0.219%. Indirect complaint has large negative influence on satisfaction, and for every 1% increase of indirect complaints, satisfaction will decrease by 0.428%. So, firms should distinguish the types of complaint first but not blindly seek for minimizing the number of complaints, because direct complaint is beneficial for the firms at some extent, which will help them avoid mistakes in complaint management and management customer complaint effectively. On one hand, firms should not encourage customers to voice unconditionally or seek for customer complaint maximization blindly but consider its own ability of complaint processing first of all, and find a balance between them. If the number of complaints is overlarge, the firm’s ability of complaint processing will be expanded over much, which will make firm not to resolve the voice correctly and effectively. Furthermore, if complaints are accumulated up in a long time, the probability of the firm’s responding to the complaints will decrease. A “vicious circle of customer complaints” in the end will lead to operational disorders of the enterprise. On the other hand, the firm can make a judgment and predication on the possibility of repurchases according to different types of customer complaints. For every 1% increase of direct complaints, the possibility of repurchases will increase by 0.293%, while for every 1% increase of indirect complaints, the possibility of repurchases will decrease by 0.461%.

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(Edited by Ruby and Gracie)

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The relationship of customer complaints, satisfaction and loyalty_ Evidence from China’s mobile phone industry

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