Public Complaint Behaviour and Satisfaction with Complaint Handling in the Malaysian Mobile Phone Services Industry

Abstract

The number of mobile phone services subscribers in Malaysia has been increasing tremendously. Ironically the number of complaints received with regard to mobile phone services is insignificant although complaining provides significant impact to organizations as well as to complainers or consumers. This study aims to investigate the relationship between public complaint and satisfaction with complaint handling. A total of 285 complainers of mobile phone user were selected as respondents. The values for GOF, AVE, CR and convergent validity confirmed the measurement model prior proceeding to structural model. The structural model revealed mixed results which provide the indicator of consumer satisfaction with complaint handling.

Keywords: Mobile Phone ServicesConsumer Complaint BehaviourPublic Complaint Soft ActionPublic Complaint Extreme ActionSatisfaction with Complaint Handling

Introduction

Above and beyond complaining directly to the service provider, mobile phone users in Malaysia are also free to complain to third parties such as the Malaysian Communication and Multimedia Commission (MCMC), National Consumer Complaints Centre (NCCC), any Consumer Associations in Malaysia, Ministry of Domestic Trade Co-operatives and Consumerism, political leaders or the mass media. In spite of various complaint channels provided, the number of complaints reported in 2014 was only 5,868 (NCCC, 2014). Ironically, the number of mobile phone services subscribers in 2014 was 44,929,000 (MCMC, 2015). The small percentage (0.0001%) of complainers indicates that there are issues need to be resolved with regard to public complaint and one of the important issues is satisfaction with complaint handling. Therefore, this study was conducted to investigate the effect of public complaint on satisfaction with complaint handling in the Malaysian mobile phone services.

Literature Review

The literature implies consumer complaint behaviour (CCB) as a set of multiple responses that are triggered by perceived dissatisfaction towards the service provider. Essentially, the two types of behavioural responses due to dissatisfaction can be divided into two entities; namely, public and private actions. Public action denotes that consumers may complaint, take legal action, and return the item or request for repair to the sellers, manufacturers, service providers, official organizations and associations (Heung & Lam, 2003; Bearden & Oliver, 1985) while word-of-mouth, boycott or leaving are examples of private action targeting family, friends and relations (Day & Landon, 1975; Crie, 2003; Ndubisi & Ling, 2006). Researchers have also characterised private actions as switching brands and firms, boycotting a firm’s products, ceasing to patronize an establishment and negative word-of-mouth communications to friends and relatives (Broadbridge & Marshall, 1995; Kim et al., 2003; Tronvoll, 2011). Adopting Crie’s (2003) taxonomy on the response of dissatisfaction, previous study found that CCB consists of public complaint soft action (PCSA), public complaint extreme action (PCEA), private complaint soft action (PVSA) and private complaint extreme action (PVEA) (Rahman et al., 2015). In this study, we decided to investigate specifically the relationships between PCSA and PCEA, and satisfaction with complaint handling (SATCOM). PVSA and PVEA were excluded as these two types of complaints do not involve directly with the service provider.

Generally, satisfaction with complaint handling is the satisfaction of a complainer with a company’s response to his or her complaint. There are several synonyms with regard to satisfaction with complaint handling such as secondary satisfaction (Etzel & Silverman, 1981; Oliver, 1997), complaint response satisfaction (Blodgett & Granbois, 1992), service recovery satisfaction (Boshoff, 1999), satisfaction with complaint resolution (Andreassen, 1999) satisfaction with service recovery (Maxham & Netemeyer, 2002), overall complaint satisfaction (Stauss, 2002), and satisfaction with the remedy (Harris et al., 2006) or recovery disconfirmation (McCollough et al., 2000). Despite the linguistic differences, the general framework behind the definitions is the confirmation or disconfirmation of the complaint response (Oliver, 1980) and in all cases, the meaning is the same. This means customers compare their perceptions of the actual performance of the complaint handling procedures with their expectations towards that performance. In the study, SATCOM was adopted from Varela-Neira et al. 2010 to indicate complaint satisfaction.

Literary study on the relationship between public complaint and SATCOM is scant. Heung and Lam (2003) have conducted a study to identify the effect of public complaint action on the resolution satisfaction. Resolution satisfaction can be construed as complaint satisfaction as it includes “satisfaction with complaint resolution” (Andreassen, 1999). According to Heung and Lam (2003) resolution satisfaction was positively related to public complaining and negatively related to private complaining. In addition, complaint handling is also defined as service recovery, which can be construed as remedial measure taken by the service provider on customer’s complaint when service failure occurs (Gronroos, 1988; Lu et al., 2010). Essentially, it is a process that organizations do to eliminate customers’ dissatisfaction towards the service failure. Clearly, service recovery involves public complaints where the customers meet the service provider to report their dissatisfactions. In practise, only consumers who perform public complaining will be able to assess the performance of the complaint resolution and the result is either satisfaction or dissatisfaction. From this review it was hypothesized that: H₁ - PCSA has significant effect on SATCOM, and H₂ - PCEA has significant effect on SATCOM.

Methodology and Analysis

The questionnaire used in this study provides a question that requires a monosyllabic answer “Yes” or “No” to categorise respondents into complainers or non-complainers. Questions seeking the respondent’s demographic information were placed in Part I. Part II consisted of three (3) statements meant to measure PCSA, four (4) for PCEA, six (6) for PVSA and three (3) for PVEA. All items were adapted from previous studies (Liu & McClure, 2001; Ndubisi & Ling, 2006; Malhotra et al., 2008; Rahman et al., 2015). Finally, Part III which consisted of five (5) statements meant to measure SATCOM adopted from Varela-Neira et al. (2010). All items were rated on a five-point Likert scale, which ranges from 1=strongly disagree to 5=strongly agree.

The population for the study was the consumers of mobile phone services from all service providers in the state of Selangor and Wilayah Persekutuan (Kuala Lumpur and Putrajaya) which represented 28.6% (1,945,143) of the total subscribers in Malaysia. A total of 285 mobile phone services users were involved and identified as complainers. Using mall-intercept approach, twelve shopping malls were selected as centres for data collection. The validity of the models was assessed via confirmatory factory analysis (CFA) by using AMOS version 21 in order to verify the factor structure of observed variables. The unidimensionality assessment was performed prior to testing the reliability and validity of each construct (Anderson & Gerbing, 1988; Hair et al., 2010) as well as to test the convergent and discriminant validity of factor measurement (Hair et al., 2010).

In establishing model fit, the respective cut-off points of the indices have to be satisfied: RMSEA ≤ 0.08 (Hair et al., 2010), χ2/df ≤ 5.0 (Fornel & Larcker, 1981) and TLI, NFI, CFI ≥ 0.90 (Tseng et al., 2006). The results of CFA show a good fit between the data and the model with χ² = 261.521, df = 111, χ ²/df = 2.356, TLI = 0.939, CFI = 0.951, PNFI = 0.749 and RMSEA = 0.074. The results allow the testing of the structural model to be performed. The results show that the standardized factor loadings for all the items were in the reign of 0.60 to 0.99 which exceed the recommended value of 0.5 (Hair et al., 2010). The Cronbach’s alpha values were in the reign of 0.72 to 0.99 which exceed the recommended value of 0.70 (Hair et al., 2010). Composite reliability values, which depict the degree to which the construct indicators reflect the latent construct, are in the range of 0.71 to 0.91 and exceed the recommended value of 0.7 (Hair et al., 2010), 0.6 (Fornell & Larcker, 1981; Sekaran & Bougie, 2010).

Using formula introduced by (Fornell & Larcker, 1981) the average variance extracted (AVE) and construct reliability (CR) were calculated to confirm the reliability of the construct. Construct validity testifies how well the results obtained from the use of the measure fit the theories (Sekaran & Bougie, 2010) and can be examined through convergent and discriminant validity. Discriminant validity can be tested by comparing the correlations between constructs and the square root of the AVE for a given construct. As shown in Table 1 , the correlations for each construct were less than the square root of the AVE by the indicator measuring that construct, indicating adequate discriminant validity. The AVE value, which reflects the overall amount of variance in the indicators as accounted for by the latent construct, are in the range of 0.58 to 0.77 exceed the recommended value of 0.5 (Tseng et al., 2006; Hair et al., 2010).

Results and discussions

On the demographic, the results show that 141 male respondents (49.5 percent) and 144 female respondents (50.5 percent). The majority of the respondents aged between 21 to 30 years old (49.6%) and in terms of marital status, married respondents were slightly higher (53.0%). Most of the respondents (74%) were Malay, followed by 16.5% Chinese, 6.0% Indian and 3.5% from other races. In terms of education, 35.8% of the respondents hold tertiary level of education, 33.0% possess the diploma/higher school certificate, 29.5% with secondary school certificate and 1.8% said that they have other types of academic qualifications. On gross monthly income, 28.1% of the respondents earned less than RM2000, 19.3% with no income, 18.6% in the reign of RM2001 to RM3000, 14.7% in the reign of RM3001 to RM4000, 7.4% in the reign of RM4001 to RM5000, 5.6% in the reign of RM5001 to RM6000 and the rest earned more than RM6001. Evidently, education level and age are found to have consistent impact on complaints. Complainers are found to be relatively younger and more educated (Warland et al., 1975; Singh, 1989). One possible reason for the younger consumers to complain more could be the channel provided for complaining nowadays are more technologically advanced and suitable for the technology savvy generation. In terms of race, the result is consistent with the survey conducted by the Consumer Forum of Malaysia where the Malay subscribers complain more compared to Chinese and Indian (CFM, 2015) however it is inconsistent with the suggestion by Malhotra et al. (2008) that organisation in Malaysia should acknowledge complaint as race-neutral. Other variable such as monthly income, the result is inconsistent with the national scenario because of different scale of income used (MCMC, 2015).

The results of the final structural model show a good fit between the data and the model with x² = 280.597, df = 113, x²/df = 2.483, CFI = 0.945, TLI = 0.934, PNFI = 0.758 and RMSEA = 0.077. Thus, the result indicates that CCB (PCSA) has positive significant relationship with SATCOM. Conversely, CCB (PCEA) does not have significant relationship with SATCOM. The results were confirmed by β = 0.246, p = 0.001 for PCSA and β = -0.100, p = 0.154 for PCEA respectively as shown in Table 2 .

This study has revealed that consumer complaint behaviour has significant effect on SATCOM. However, only public complaint soft action (PCSA) has positive significant effect on SATCOM, whereas public complaint extreme action (PCEA) on the other hand resulted in negative non-significant effect on SATCOM. The possible reason for this is that, extreme actions are reflected by customers’ dissatisfaction with the complaints handling by the service provider.

Conclusion

The study has proven the effect of public complaint on SATCOM although not all actions in public complaint have significant effect. The negative effect of PCEA on SATCOM denotes that negative actions in PCEA such as write a letter to a local newspaper or mass media, report the problem to a consumer agency, complain to a government agency or politician and take legal action against the service provider have negative effects on the relationship between consumers and service providers. It conveys a message that the relationship between the consumer and the service provider is no longer necessary. This is an important note to the service providers to improve the quality of their complaint resolution process. The results of this study provide significant implication to service providers to improve their services for long term business sustainability. As the mobile phone services industry involves a huge number of Malaysian population this issue should become the focal point to the parties accountable in protecting the consumers.

References

1. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
2. Andreassen, T.W. (1999). What drives customer loyalty with complaint resolution?, Journal of Service Research , 1(4), 324-32.
3. Bearden, W. O., & Oliver, R. L. (1985). The role of public and private complaining in satisfaction with problem resolution. Journal of Consumer Affairs, 19(2), 222-240.
4. Blodgett, J. G., & Granbois, D. H. (1992). Toward an integrated conceptual model of consumer complaining behaviour. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behaviour, 5(1), 93-103.
5. Boshoff, C. (1999). An instrument to measure satisfaction with transaction-specific service recovery. Journal of Service Research, 1 (3), 236-49.
6. Broadbridge, A., & Marshall, J. (1995). Consumer complaint behavior: The case of electrical goods. International Journal of Retail & Distribution Management, 23(9), 8-18.
7. Communication and Multimedia Consumer Forum of Malaysia, (2015). CFM Communication and Multimedia Survey Report 2014. (Unpublished Report). CFM, Kuala Lumpur.
8. Crie, D. (2003). Consumers' complaint behavior. Taxonomy, typology and determinants: Towards a unified ontology. The Journal of Database Marketing & Customer Strategy Management, 11(1), 60-79.
9. Day, R. L., & Landon, E. L. (1977). Toward a theory of consumer complaining behavior. Consumer and Industrial Buying Behavior, 95, 425-437.0.
10. Etzel, M. J., & Silverman, B. I. (1981). A managerial perspective on directions for retail customer dissatisfaction research. Journal of Retailing, 50(3), 124-136
11. Fornell, C., & Larcker, D. F. (1981). Evaluating tructural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(2), 382-388.
12. Grönroos, C. (1988). Service quality: the six criteria of good perceived quality. Review of Business, 9: 10-13.
13. Hair Jr, J. F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis. Upper-Saddle River, New Jersey: Prentice Hall.
14. Harris, K. E., Grewal, D., Mohr, L. A., & Bernhardt, K. L. (2006). Consumer responses to service recovery strategies: the moderating role of online versus offline environment. Journal of Business Research, 59(4), 425-431.
15. Heung, V. C., & Lam, T. (2003). Customer complaint behavior towards hotel restaurant services. International Journal of Contemporary Hospitality Management, 15(5), 283-289.
16. Kim, C., Kim, S., Im, S., & Shin, C. (2003). The effect of attitude and perception on consumer complaint intentions. Journal of Consumer Marketing, 20(4), 352-371.
17. Liu, R. R., & McClure, P. (2001). Recognizing cross-cultural differences in consumer complaint behavior and intentions: An empirical examination. Journal of Consumer Marketing, 18(1), 54-75.
18. Lu, X., Yang, Y., & Qin, H. (2010). The Research on Personal Internet Banking Service Quality and Customer Loyalty Based on Complaints Handling. Management and Service Science (MASS), 2010 International Conference on (pp. 1-4). IEEE.
19. Malaysian Communication and Multimedia Commission. (2015). Annual Report 2014. MCMC, Cyberjaya. Retrieved from: http://www.skmm.gov.my/skmmgovmy/media/General/pdf/MCMC_RET523-667P_ENGLISH_COMPLETE.pdf
20. Malaysian Communication and Multimedia Commission. (2015) Hand Phone Users Survey 2014. Retrieved from: http://www.skmm.gov.my/skmmgovmy/media/General/pdf/MCMC-Hand-Phone User19112015.pdf
21. Malhotra, N. K., Oly-Ndubisi, N., & Agarwal, J. (2008). Public versus private complaint behavior and customer defection in Malaysia: Appraising the role of moderating factors. EsicMarket (September–December), 27-59.
22. Maxham J. G., & Netemeyer, R. G. (2002). Modeling customer perceptions of complaint handling over time: the effects of perceived justice on satisfaction and intent. Journal of Retailing, 78 (2002) 239 –252.
23. McCollough, M. A., Berry, L. L., & Yadav, M. S. (2000). An empirical investigation of customer satisfaction after service failure and recovery. Journal of Service Research, 3(2), 121-137.
24. NCCC, (2015). Annual NCCC Consumer Complaints Report, 2014. National Consumer Complaints Council, Petaling Jaya.
25. Ndubisi, N.O. & Ling, T.Y. (2006). Complaint behavior of Malaysian consumers. Management Research News, 29(1), 65-76.
26. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(November), 460-469.
27. Oliver, R. L. (1997). Satisfaction: A Behavioural Perspective on the Consumer. Irwin McGraw-Hill, Boston.
28. Rahman, M.K.B., Haron, S. A., Paim, L., Othman, M., Osman, S., & Othman, A. K. (2015). Construct validation of consumer complaint behaviour scale in the Malaysian Mobile Phone Services Industry. Asian Social Science, 11(24),6-17.
29. Sekaran U., Bougie R., (2010). Research methods for business: a skill building approach. Wiley, UK.
30. Singh, J. (1989). Determinants of consumers’ decisions to seek third party redress: An empirical study of dissatisfied patients. Journal of Consumer Affairs, 23(2), 329-363.
31. Stauss, B. (2002). The dimensions of complaint satisfaction: Process and outcome complaint satisfaction versus cold fact and warm act complaint satisfaction. Managing Service Quality, 12(3), 173-183.
32. Tronvoll, B. (2011). Negative emotions and their effect on customer complaint behavior. Journal of Service Management, 22(1), 111-134.
33. Tseng, W. T., Dörnyei, Z., & Schmitt, N. (2006). A new approach to assessing strategic learning: The case of self-regulation in vocabulary acquisition. Applied Linguistics, 27(1), 78-102.
34. Varela-Neira, C., Vázquez-Casielles, R., & Iglesias, V. (2010). Explaining customer satisfaction with complaint handling. International Journal of Bank Marketing, 28(2), 88-112.
35. Warland, R. H., Herrmann, R. O., & Willits, J. (1975). Dissatisfied consumers: Who gets upset and who takes action. Journal of Consumer Affairs, 9(2), 148-163.