A Model for Service Quality and Customer Satisfaction of Mobile Commerce

Transcription

1 A Model for Service Quality and Customer Satisfaction of Mobile Commerce Dr. Yu-Chung Hung, National Chung Cheng University, Taiwan ABSTRACT The purposes of this study are to investigate what factors may affect the quality of mobile commerce (QMC) and to explore a model, which can depict the relationship between customer satisfaction and QMC. This study integrated both IS Success Model and PZB Model to induct 21 service metrics for mobile commerce. Then, 4 critical factors, which may affect service quality of mobile commerce, were extracted from 21 service metrics by factor analysis. Of these 4 critical factors, the results of regression analysis showed that information quality, responsiveness and assurance and reliability had significant effect on customer satisfaction. Consequently, this study derived a regression model to delineate the relationship among customer satisfaction of mobile commerce and the above three factors. INTRODUCTION While Internet service is reaching its mature stage, mobile commerce (MC), which is emerging as a new arena for the IT (Information Technology) industry increasingly seeks bigger footprint in business and daily life. According to the statistics of IDC (2005), there were over 14 billion cellular phones users in The research of Morgan Stanley (2005) also indicates that many internet users, who are used to log on the Web by desktop computers, have gradually turned to leverage mobile devices. These facts highlight that mobile commerce has become the next wave and an unavoidable business trend. In order to gain more competitive edges in the mobile wave, mobile service providers have to offer quality service to achieve customer satisfaction (CS). Eugene, Forenell and Lehma (1994) pointed out that CS is increased with improved service quality and lead next consumption. CS and repeating consumption will result in profit for service providers (Johnson and Fornell, 1992). However, MC, which is composed of both personal service and IT systems, is different from the service provided by the traditional service industry. The conventional service quality metrics and models, such as SERVQUAL and the PZB model (Parasuraman, Zeithamland and Berry, 1998), may no longer be strong enough to measure the quality of mobile commerce (QMC) due to disregarding the effects of IT. Similarly, although many researches have explored the relationship between CS and service quality (Eugene, Forenell and Lehma, 1994; Bolton and Drew, 1991; Bolton and Drew, 1991; Churchill and Suprenaut, 1982; Chow, Huang and Kuo, 1999; Johnson and Forenell, 1992), few researches take the effect of IT into account. Therefore, investigating what factors may affect QMC and establishing a conceptual model, which can fully delineate the relationship between customer satisfaction and QMC, has received the spotlight of government, industry, and academic institutions. In order to help mobile service providers understand what factors may affect QMC and how to improve service quality and customer satisfaction, this study integrated both IS Success Model (Delone and McLean, 1992) and PZB Model to extract affecting factors of QMC and to construct a conceptual model which can fully delineate the relationship between customer satisfaction and QMC. Thus, the research objectives of this study are the following: (1) Investigate factors that influence QMC; (2) Construct a conceptual model to depict the relationship among customer satisfaction of mobile service and affecting factors of QMC. MOBILE COMMERCE AND APPLICATIONS OF MOBILE COMMERCE The recent union of e-commerce and wireless telecommunication technology has led to emergence of MC. While it

2 has been described using numerous definition, generally, it is recognized as the use of wireless devices, like cell phones and personal digital assistants (PDAs), to connect to the Internet for the purposes of communicating and/or conducting business without location restriction. (Bedford, 2005) In contrast to e-commerce, mobile commerce not only extends the benefits of the Web, but also allows unique services and additional benefit. (Tsalgatidou & Pitoura, 2001) Although some differences exist between e-commerce and mobile commerce, the applications of mobile commerce are very wide just like e-commerce. In order to better understand the applications of mobile commerce, this study will discuss mobile commerce from the following viewpoints: (1). The viewpoint of service and/or content providers: Applications of MC include mobile entertainment services, mobile information services, mobile communication services and mobile trading services. Mobile entertainment services provide users on-line game, music, videos and on-line horoscopes via mobile devices. Mobile information services offer browsers real-time news, weather forecasting, on-line magazines, on-line yellow pages and digital maps. Mobile communication services contain SMS (short message services), MMS (multimedia messaging services), , personal information management and mobile conferences. Mobile trading services allow users conduct transactions with business and/or organizations, such as mobile shopping, mobile banking and mobile ticketing. (2). The viewpoint of the telecommunications industry:owing to the popularization of 3G (UMTS, Universal Mobile Telecommunications System), the telecommunication industry explores MC from technologies of 3G and GPRS. At present, 3G and can offer Mobile Internet Access, Mobile Internet/Extranet Access, Mobile portal, SMS, MMS, customized information, Location-based Service, mobile guiding, digital map and multiple voice services. SERVICE QUALITY, PZB MODEL AND IS SUCCESS MODEL The definition of service quality in this study is a comparison of customer expectation with actual service performance. Parasuraman, et al. (1985) based on the conceptual model of service quality for developing measures of marketing constructs. They started by making extensive use of focus groups, and identified 10 potentially overlapping dimensions of service quality. These determinants included:(1) Reliability, (2) Responsiveness, (3) Competence, (4) Access, (5) Courtesy, (6) Communication, (7) Credibility, (8) Security, (9) Understanding and (10) Tangibles. Pitt et al. (1995) adapted the DeLone and McLean model to customer service settings by adding service quality components and considered that service quality, system quality, and information quality had an impact on the relationship between use and user satisfaction. The augmented IS success model are shown in Figure 1. Figure 1. Augmented IS Success Model (Source: Pit et al. (1995) When we evaluate the quality of information system, besides service quality, system quality and information quality should also be included. Thus, we need to augment system quality and information quality as a measure of MC. The factors may influence on mobile valued-added service are shown in Table 1.

3 Table 1. Factors may influence on mobile service Factor Related Researches Information quality Delone and McLean (1992), Pitt et al. (1995) System quality Delone and McLean (1992), Pitt et al. (1995) Tangibles Parasurman et al. (1988), Pitt et al. (1995) Reliability Parasurman et al. (1988), Pitt et al. (1995) Responsiveness Brown et al. (1993), Parasurman et al. (1988), Pitt et al. (1995) Assurance Brown et al. (1993), Parasurman et al. (1988), Pitt et al. (1995) RESEARCH FRAMEWORK AND HYPOTHESES The conceptualized framework of this research is the PZB service quality model, and adjusted and revised with the service items provided by present mobile value-added providers. Figure2. Research Framework The independent variable is acquired from the use of IS Success Model revised by Pitt et al. (1995) and three scholars of PZB (1988) addressed SERVOUAL perspective. In which the System quality and Information quality are from IS Success Model, whereas Tangible, Reliability, Responsiveness and Assurance are referred to SERVOUAL perspective and cooperate and revised with the service items provided by mobile value-added providers. We found that service quality dependent on the comparison of expected service with perceived service. The following hypothesis is based on these findings: H1 :Perceived service is positively associated with perceived service quality; H2 : Expected service is significant effect on customer satisfaction; H3 : Perceived service is significant effect on customer satisfaction; H4 : Perceived service quality is significant effect on customer satisfaction. DATA COLLECTION The sampling targets are undergraduate students, which come from 15 universities located in Taipei City and have used mobile value-added services, as shown in Table 2. This study obtains stratified convenience samples from these 15 universities. Within each layer, samples are allocated proportionally. This research collects data by face-to-face survey by questionnaires. This questionnaire used a 5-point Likert scale ranging from very strongly disagree (1) to very strongly agree (5). The respondents were asked to choose number from 1 to 5, which best represented their attitudes towards each question.

4 Table 2. Number of Students in Universities School Name Number of Students (%) School Name Number of Students (%) 1. National Taiwan University Tatung University National Cheng-chi University Shih Hsin University National Yang-Ming University Ming Chuan University National Taipei University Shih Chien University National Taiwan Normal Univ Chinese Culture Univ National Taiwan University of Science and Technology Taipei Medical University 7. National Taipei University of Taipei National Technology University of the Arts 8. Soochow University Sum Source: Statistics provided by Department of Statistics in Ministry of Education, Taiwan (2006) The period of data collection is from October 1 to 31, A total of 750 questionnaires are conducted, and 624 questionnaires are returned at response rate of 83.2%; 582 questionnaires, which are valid after deleting the incompleteness, yield the valid response rate of 77.6%. DATA ANALYSIS The methods of data analysis used in this study include descriptive statistics, reliability analysis, factory analysis, relativity analysis and multiple regression analysis. The results of descriptive statistics are shown in Table 3. The factors affecting consumers to adopt Mobile Value-added Service are shown in Table 4. Table 3. Demographic Data for Respondents Count (%) Count (%) Gender Male Female Usage experience Under 3 months months months 1 years years years Over 3 years Usage frequency 1-5 times a week times a week times a week Over 20 times a week Table 4. Statistics of Using the Mobile Value-added Service s Consideration Factors Factors % Factors % Cheap monthly rental 15.6 Easy calculating measures 8.8 Attracting promotion 9.0 Reasonable value-added price 8.7 Handy operation 15.1 Popular, fresh, and abundant contents 11.9 Prompt value-added contents 7.4 Unobstructed channel to get the value-added 8.1 Rapid transmission speed 15.4 Sum services 100

5 Table 5 shows that all the perception mean scores in relation to the service quality items are lower than the expectation mean scores, and the paired t-tests between the respective expectation means and perception means of all the 21 items shows that they are significantly different (t < 0.01). The research findings reveal that there is a gap between customer expectations and perceptions in terms of the quality of the mobile valued-added service. The results shows that customers had relatively high expectation mean scores of Information content provided by mobile value-added providers could amuse or attract users (E4), Was quickly to find what I was searching for (E18), and I feel safe when making transactions (E21). The findings suggest that customers consider some using factors such as entertainments, convenience, and security in mobile valued-added service as more important than other items. There are relatively low perception mean scores for Has fast download times (P7), Service capability is clear (P8), Mobile value-added providers could transact accurate and reliable information (P15). However, the largest gap mean scores were found for Has fast download times (Q7), Service capability is clear (Q8), and Was simple to use (Q11). It appears that mobile valued-added service companies should make more efforts to improve their service quality among these items. The stepwise multiple regression is employed to predict CS (Y) with the expected service of information quality (X 1 ), responsiveness and assurance (X 2 ), reliability (X 3 ), and system quality (X 4 ). Results are shown in Table 6. Based on the literature, we expected that expected service is significant effect on CS. H2-1 : Expected information quality has a significant effect on customer satisfaction. H2-2 : Expected responsiveness and assurance has a significant effect on customer satisfaction. H2-3 : Expected reliability has a significant effect on customer satisfaction. H2-4 : Expected system quality has a significant effect on customer satisfaction. No VIF value exceeds 10.0, and the tolerance values show that collinearity does not explain more than 10 percent of any independent variable s variance. There is no evidence of significant collinearity. The results of regression analysis show that only 3 independent variables enter into the stepwise regression, i.e. information quality (X 1 ), system quality (X 4 ), responsiveness and assurance (X 2 ). The coefficient of adjusted R Square (R 2 ) is indicated that 83.9 % of the variance in the customer satisfaction was explained by the three expected service quality factor. The variable, which predicts most of the customer satisfaction, is information quality (X 1 ) and explains 83% of the variance of customer satisfaction. The results of hypotheses are shown in Table 7. Table 5. Distribution of Service Quality Values between Customers Expectations and Perceptions Expectations Perceptions Gap Mean Mean (E) Mean (P) (Q=P-E) t-value E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q **

6 Expectations Perceptions Gap Mean t-value Mean (E) Mean (P) (Q=P-E) E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** E P Q ** Notes: 1. *t-test two-tail with probability < Gap mean is defined as perception mean expectation mean Table 6. Results of Regression Analysis of Expected Service to Customer Satisfaction Significant Predictor Variables Standardized Coefficients Beta Adjusted R Square Collinearity Statistics Tolerance Information Quality.882** % System Quality.106** Responsiveness and Assurance.048* Note: P<0.01(**);P<0.05(*) Table 7. Results of Hypothesis of Expected Service to Customer Satisfaction Hypothesis Result H2-1 Expected information quality has a significant effect on customer satisfaction. Yes H2-2 Expected responsiveness and assurance has a significant effect on customer satisfaction. Yes H2-3 Expected reliability has a significant effect on customer satisfaction. No H2-4 Expected system quality has a significant effect on customer satisfaction Yes VIF Then, we can derives the following regression equation: Y= 0.882X X X 4 where Y:Customer Satisfaction; X 1 :Information Quality; X 2 :Responsiveness and Assurance; X 3 :Reliability;X 4 : System Quality. We ran stepwise multiple regression models predicting CS (Y) with the perceived service of information quality (X 1 ), responsiveness and assurance (X 2 ), reliability (X 3 ), and system quality (X 4 ). Results are shown in Table 8.Based on the literature, we expected that perceived service is significant effect on CS, and the results of hypotheses are shown in Table 9. Table 8. Results of Regression Analysis of Perceived Service to Customer Satisfaction Significant Predictor Variables Standardized Coefficients Beta Adjusted R Square Collinearity Statistics Tolerance Information Quality -.480** System Quality -.231** 38.5% Reliability -.081* Note: P<0.01(**);P<0.05(*) VIF

7 Table 9. Results of Hypothesis of Perceived Service to Customer Satisfaction Hypothesis Result H3-1 Perceived information quality has a significant effect on customer satisfaction Yes H3-2 Perceived responsiveness and assurance has a significant effect on customer satisfaction. No H3-3 Perceived reliability has a significant effect on customer satisfaction Yes H3-4 Perceived system quality has a significant effect on customer satisfaction Yes Then, we can derives the following regression equation: Y= -0.48X X X 4 where Y:Customer Satisfaction; X 1 :Information Quality; X 2 :Responsiveness and Assurance; X 3 :Reliability;X 4 : System Quality. Similarly, we ran stepwise multiple regression models predicting CS (Y) with the perceived service quality of information quality (X 1 ), responsiveness and assurance (X 2 ), reliability (X 3 ), and system quality (X 4 ). Results are shown in Table 10. Based on the literature, we expected that perceived service quality is significant effect on customer satisfaction, and the results of hypotheses are shown in Table 11. Table 10. Results of Regression Analysis of Perceived Service Quality to Customer Satisfaction Significant Predictor Variables Standardized Coefficients Beta Adjusted R Square Collinearity Statistics Tolerance Information Quality -.878** Responsiveness and Assurance -.081** 70.4% Reliability.055* Note: P<0.01(**);P<0.05(*) Table 11. Results of Hypothesis of Perceived Service Quality to Customer Satisfaction Hypothesis Result H4-1 Perceived service quality of information quality has a significant effect on CS. Yes H4-2 Perceived service quality of responsiveness and assurance has a significant effect on Yes CS. H4-3 Perceived service quality of reliability has a significant effect on CS. Yes H4-4 Perceived service quality of d system quality has a significant effect on CS. No VIF Then, we can derives the following regression equation: Y= X X X 3 where Y:Customer Satisfaction; X 1 :Information Quality; X 2 :Responsiveness and Assurance; X 3 :Reliability;X 4 : System Quality. CONCLUSIONS AND SUGGESTIONS According to the results of data analysis, we found that perceived service is positively associated with perceived service quality. When Perceived service > Expected service, perceived service quality is more than satisfactory and tend toward ideal quality, with increased discrepancy between Expected service and Perceived service. When Expected service > Perceived service, perceived service quality is less than satisfactory and tend toward total unacceptable quality, with increased discrepancy between expected service and perceived service.

8 Table 5 shows that all the perception mean scores in relation to the service quality 21-item in this study were lower than the expectation mean scores, and the gap means were negative, meaning that the perceived service quality provided by mobile valued-added providers did not meet customers expectations. Referring to the conceptual model of service quality, we can know that the different between customer expectations and perceptions (Gap5), which was influenced by Gaps 1-4. The largest gap mean scores were found for Has fast download times (Q7), Service capability is clear (Q8), and Was simple to use (Q11). Therefore, value-added providers should avoid over-commitment. When providers advertisement information largely differs from mobile phone s actual functions, users are likely to have bad feeling because they expect to use their cellular to do what they have done in PC, but present expectation could not be satisfied. Due to time limit, this study is unable to cover all users in all ages, and only draws subjects from 15 universities in Taipei City. The parametric representation is limited. The suggestion is that future research can cover larger samples, so that this study results can be general. Also, in order to simplify analysis process of this research, we assumed that the weighting of each factor analyzed in the questionnaire is the same, and represent by each factor question s mean value; as for the other weighting factors that influence each factor s evaluation question in this research are not discussed here, however, this portion is worth for further research. REFERENCES Bedford, D. W. (2005). Empirical Investigation of Acceptance and Intended Use of Mobile Commerce: Location, Personal Privacy and Trust. Ph. D. Dissertation. College of Business and Industry, Mississippi state University Bolton, R. N. and Drew, J. H. (1991) A Multistage Model of Customers Assessments of Service Quality and Value. Journal of Consumer Research, 17(4), Brown, T.J., Churchill, C.A.J., and Peter, J.P. (1993). Improving the measurement of Service Quality. Journal of Retailing. 169(1), Churchill, G.A., and Suprenaut, C. (1982) An Investigation into the Determinants of Customer Satisfaction. Journal of Marketing Research, 19, Chow, T.H., Huang, J.Y. and Kuo, T.P. (1999) The study of Service Quality and Customer Satisfaction Measurement Model. FU-JEN Management commentary, 1(1), 61 DeLone, W. H. and McLean, E. R.. (1992). Information System Success: The Quest for the Dependent Variable. Information Systems Research,.6(2), Eugene, A.W., Formell, C. and Lehma D.R. (1994). Customer Satisfaction, Market Share, and Profitability:Findings Form Sewden. Journal of Marketing, 58(3), IDC. (2005). Johnson, M. D. and Fornell, C. (1992) A Framework for Comparing Customer Satisfaction Across Individuals and Product Categories. Journal of EcoNomic Psychology, 12(2), Ministry of Education of Taiwan, Department of Statistics. (2006). Morgan Stanley. (2005). Parasuraman, A., Zeithamland, V. A., and Berry, L. L. (1985) A Conpectual Model of Service Quality and Its Implication for Future Research. Journal of Marketing,.49, Fall, Parasuraman, A., Zethaml, V. A., and Berry, L. L. (1988) SERVQUAL: A Multiple Consumer Perceptions of Service Quality. Journal of Retailing,.64(1), Pitt, L. F., Watson, R. T., and Kavan, C. B. (1995). Service Quality: A Measure of Information System Effectiveness. MIS Quarterly, 19(2), Tsalgatidou, A., and Pitoura, E. (2001). Business Models and Transactions in Mobile Electronic Commerce: Requirements and Properties. Computer Networks, 37,