Research on Software Continuous Usage Based on Expectation-confirmation Theory

Research on Software Continuous Usage Based on Expectation-confirmation Theory Daqing Zheng 1, Jincheng Wang 1, Jia Wang 2 (1. School of Information Management & Engineering, Shanghai University of Finance & Economics, Shanghai, China, 200433 2. H. John Heinz III College, Carnegie Mellon University, Pittsburgh, USA, 15213-3890) Abstract: The article explained the phenomenon of software package continuous usage, based on expectation-confirmation theory of the survey method. The results of the research showed that expectation-confirmation theory can well explain software continuous usage phenomenon, and then the mechanism of intention between attitude and satisfaction was discussed, and the authors offered some theoretical and practical suggestions. Key words: Technology acceptance model (TAM), Information system continuous usage, Expectation-confirmation theory (ECT) 1. Introduction In the past twenty years, information systems acceptance, continuous usage and related researches have been paid more and more attention in information systems research field. According to the chronological order of individual usage of information system, information systems usage can be divided into two phases: pre-adoption and post-adoption. Users initial adoption of information systems is one of the short-term goals of a success, whereas in the long-run, the ultimate success of information system depends on user continuous usage. So, the information systems continuous usage is becoming an important research issue. 2. Literature review and theoretical foundation ECT derived from marketing academic field and explained the consumers repurchasing behaviors was first used to illustrate the phenomenon of individual continuous usage of information systems by Bhattacherjee. Based on the ECT, user expectation and the confirmation of the expectation after usage are crucial factors determining the user satisfaction. User satisfaction will further influence continuance usage (Bhattacherjee 2001). In 1970 s, scholars in marketing filed first proposed ECT, and constructed a theoretical framework with key variable of satisfaction to illustrate consumer repurchasing behavior (GILBERT A. CHURCHILL and SURPRENANT 1982). According to ECT, satisfaction is affected by two main variables: consumers expectation towards service or product, and perceived performance of usage. ECT explicates consumers repurchasing behavior as below: First of all, before purchasing, the user will have an expectation towards future product or service; later on, after using the product or experiencing the service, the user will form a perception of the product or service based on consumption experience; finally, the user will form corresponding satisfaction according to consumption experience and initial expectation before making a decision, stopping or continuous using. If consumption experience outstrips expected expectations, the user will form positive confirmation, thus may have repurchasing intention; vice versa, if expected expectation exceeds the consumption experience, then the user will form negative confirmation, thus may stop repurchasing behavior. Overall, the higher users consumption experience exceeding expectation is, the higher user satisfaction is, which leads to stronger 1.Supporting fundation: Shanghai university of finance and economics 211 Project leading academic discipline project of phase Ⅲ; supported by the Chinese education social science project Chinese electronic government acceptance research (09YJC630147). Authors introduction: Daqing Zheng (1978- ), Male, assistant professor of Shanghai university of finance and economics, engaged in the research of information systems, the electronic government; Jincheng Wang (1988- ), male, graduate student of Shanghai university of finance and economics, engaged in the research of information systems; Jia Wang (1990- ), graduate student of H. John Heinz III College, Carnegie Mellon University, engaged in the research of the Information Systems. 1

repurchasing intention. Bhattacherjee compared the similarities between continuous usage and users repurchasing behavior, and considered it is proper to use ECT to analyse the information systems continuous usage. Bhattacherjee and other researchers applied ECT into the research field and identified that perceived usefulness, expectation before usage as well as experience confirmation influence user satisfaction, thus determining user repurchasing intention (Bhattacherjee 2001; BHATTACHERJEE, PEROLS et al. 2008). At operational level, some variables of ECT are measured in different time periods. For example, we divide the usage process into two phases: pre-adoption (T1) and post-adoption (T2), therefore, users expectation should be measure in the period of T1, and the perceived performance should be measured in T2, which evolves into confirmation degree through comparison with expectation formed in T1, thus further affecting satisfaction. Users comparison of expectation and confirmation happen in T2, here, the expectation originates from T2 may vary from the one from T1; while the expectation from T2 is the real factor that influence the users decision-making process. Based on aforementioned analyses, we will revise ECT appropriately to validate the impact user expectation on elements such as confirmation and satisfaction, etc. 3. Research model and hypotheses ECT, proposed by Oliver, explains the mechanism which affects user repurchasing of goods or service via satisfaction, and it also illustrates that user expectation, confirmation of expectation after usage together determine satisfaction, namely the expectation and confirmation affect satisfaction, which then affects repurchasing behavior finally. Drawing from Myers classification of information system evaluation, we divide information system quality evaluation into system quality, information quality and support service quality (Myers, Kappelman et al. 1997). On account of the fact that QQ instant messaging software belongs to standardized software, its main function is to support the information communication, thereby, we only measure two variables information system quality expectation and information system quality performance. The core of ECT is the relationship between expectation, perceived performance and confirmation. In information system field, it is expressed as information system expectation, perceived confirmation and final confirmation. The concept of information system perceived performance describes the practical perception and experience the user have towards software usage. The perceived information system quality performance is a remarkably vital content in measuring satisfaction. Existing studies have already demonstrated a significant relationship between user perceived information system performance and satisfaction (Guinea and Markus 2009; Venkatesh and Goyal 2010). The higher user perceived information system performance is, the higher user satisfaction is. Hence, our theoretical hypothesis is as followings: H1. Information quality performance has a positive effect on confirmation degree; User expectation is a set of belief users hold towards information system ultimate effect, and the significant relationship between user expectation and satisfaction has verified by some researchers (Mahmood 2000). As for the relationship between expectation and satisfaction, the adaptation level theory gives more powerful explanations. User perceptions towards external influence are all built on a reference, which is decided by external influence, users psychological characteristics and others (Oliver 1980; Edwards and Harrison 1993). According to the definition of expectation given by Oliver, high expectation means the users expecting 2

event will happen, and non-expecting event will not happen. Low expectation means the user expecting event will not happen, and non-expecting event will happen (Edwards and Harrison 1993). That also means, high expectation will improve users satisfaction; whereas low expectation will reduce user satisfaction(bhattacherjee 2001). Hereby, our hypothesis is expressed as follows: H2. Information quality has a negative effect on confirmation degree; One of the key point in satisfaction research field is the discussion of confirmation degree, which considers satisfaction to be the function of confirmation degree(kopalle and LEHMANN 2001). Prior to this, Oliver also demonstrated that expectation and confirmation degree are important antecedent factors determining satisfaction (Oliver, 1980). Therefore, we make an assumption as follows: H3. Confirmation degree has a positive effect on satisfaction; From the time sequence point view, satisfaction is considered as one of the pre-determined factor (Oliver 1980; HONG, KIM et al. 2008), thus, we have hypotheses as follows: H4. Satisfaction has a positive effect on attitude; According to the assumptions of the theory of reasoned action (TRA), and the basic assumption of ECT (Oliver 1980; Ajzen 1991; Bhattacherjee 2001), there are positive impact between attitude and continuous usage intention, and satisfaction and continuous usage intention. Thus: H5. Attitude has a positive effect on continuance-use intention; H6. Satisfaction has a positive effect on continuance-use intention; Finally, out research model is shown as Figure 1. Information quality expectation H1 (ϒ1) Confirmation degree H3(ϒ3) Satisfaction degree H6 (ϒ6) Continuance-use intention H 2(ϒ2) H4 (ϒ4) H5 (ϒ5) Information 信息质量绩效 quality performance Attitude Figure 1 Research model 4. Research method Conducting the survey method, the research chose university students as the sampling. From December, 2010 to January, 2011, about six hundred questionnaires are sent out. In the end, 418 questionnaires were returned, and 275 samplings are valid, accounting for about 65.8% returned ratio. We refused some invalid samples based on the opposite questions setting in the questionnaires. As a consequence, the demographic indicators are shown in table 1. Table1. Demographic indicators of the sample Sample features Sample Sample Proportion (%) Sample features size size Proportion (%) sex 275 100 Education level 275 100 male 124 45.1 Junior high school or below 0 0 female 151 54.9 Technical secondary school or senior high school 9 3.3 Junior college or graduate 232 84.4 Post graduate 32 11.6 3

Doctors and above 2 0.7 Internet-usage duration 275 100 age 275 100 Less than 1 year 1 0.4 18 years old or below 2 0.7 1-3 years 23 8.4 19-23 years old 268 97.5 3-5 years 76 27.6 24-30 years old 5 1.8 5-10 years 141 51.3 31-35 years old 0 0 More than 10 years 28 10.2 36-40 years old 0 0 Value missed 6 2.3 41 years old or above 0 0 5. Data Analysis 5.1 Reliability and validity analysis Software package of SPSS 19 and LISREL 8.2 are used to analyze the data. The reliabilities of latent variables are reflected by Cronbach, showing in table2. The Cronbach of all latent variables are above criterion, which is 0.7(Bagozzi and Yi 1988). Therefore, the reliability of the latent variables is in accordance with the requirements. Table2. Reliability of the variables Variable name 4 Variable numbers Cronbach Satisfaction(SA) 4 0.945 Continuous usage intention(cui) 3 0.865 Behavior attitude(ba) 4 0.910 Information quality expectation (IQE) 3 0.827 Information quality performance (IQP) 3 0.783 Information system confirmation(con) 3 0.884 The validity of variables includes surface validity, convergent validity and discriminant validity. In order to improve surface validity, the questions in our questionnaire are extracted from the most influential research literatures and adopted or discarded on the basis of the study s specific background. Through 4 academic researchers translation and comparison, the final questionnaire was generated finally. The convergent validity and discriminant validity of variables are measured via composite reliability (CR) and average variance extraction (AVE), the acceptance criteria of CR and AVE are greater than 0.70 and 0.50 respectively (FORNELL and LARCKER 1981; Bearden, Netemeyer et al. 1993). Square root of AVE also satisfies the requirement. Therefore, all the variables in the study meet the requirement of convergent validity and discriminant validity, as shown in Table3. The correlation matrix in Table3 indicates that there is a signification correlation between multiple potential variables. The correlation coefficients range from 0.366 to 0.682, thus, they won t affect the subsequent analysis. Table3. Correlation matrix No. Variables Mean SD Composite reliability (AVE) 1 2 3 4 5 6 1 CUI 5.098 1.967 0.88 0.70 0.84 2 SA 4.5145 1.980 0.95 0.81 0.641** 0.9 3 BA 4.4027 1.627 0.91 0.72 0.573** 0.682** 0.85 4 IQE 3.8352 1.473 0.83 0.62 0.388** 0.579** 0.593* 0.79 5 IQP 4.3661 1.591 0.78 0.55 0.366** 0.482** 0.560** 0.676** 0.74 6 Con 4.0048 1.598 0.83 0.63 0.436** 0.678** 0.606** 0.644** 0.464** 0.79 * Significant correlation on 0.05 level (bilateral); ** significant correlation on 0.01 level (bilateral); The gray background figures on the diagonal is the square root of the average variation extraction. Note: CUI means Continuous usage intention; SA means satisfaction; BA means behavior attitude; IQE means information quality expectation; IQP means information quality performance; Con means information systems confirmation.

5.2 Fitting analysis of the research model According to the results of structural equation model analysis, the research hypotheses are totally proved, which can be shown in Table 4. The research hypothesis H1 suggests that information quality performance has a significantly positive impact on confirmation, and the data analysis of the structural equation model also confirm the presence of the assumed relationship, whose influence coefficient is 1.28 (T-value is 5.17, P <0.001). Research hypothesis H2 indicates that information quality expectation has significantly negative impact on confirmation degree, and this relationship has been proved through the analysis results of the structural equation model, whose influence coefficient is -0.56 (T-value equals to -2.29, P <0.05). Research hypothesis H3 shows that confirmation degree significantly affects satisfaction, from the results of structural equation model analysis, and this relationship has been confirmed base on the influence coefficient, which is 0.72 (T value is 11.62, P <0.001). Research hypothesis H4 assumes that the satisfaction has significant influence on the behavior attitude, and this relationship is proved in the structural equation model analysis results based on the influence coefficient, which is 0.74 (T value equals to 12.46 P <0.001). Research hypothesis H5 assumes that attitude has a significant effect on usage intention, and this assumption has been proved via the analysis of structural equation model of the influence coefficient, which is 0.29 (T-value equals to 3.51, P <0.001). The hypothesis H6 suggests satisfaction has a significant effect on the sustained utilization intention, and it proved by the influence coefficient of 0.45 (T-value equal to 5.40, P <0.001). Table 4 Calibration results of research hypothesis Variable relationship Path coefficient T-value Hypothesis Results IQP Con 1.28 5.17 H1 Supported IQE Con - 0.56-2.29 H2 Supported Con SA 0.72 11.62 H3 Supported SA BA 0.74 12.46 H4 Supported BA CUI 0.29 3.51 H5 Supported SA CUI 0.45 5.40 H6 Supported Note: CUI means Continuous usage intention; SA means satisfaction; BA means behavior attitude; IQE means information quality expectation; IQP means information quality performance; Con means information systems confirmation. 6. Conclusion and discussion First of all, based on the analysis results of the hypothesis of H1, H2 and H3, we can see that ECT is suitable to explain the continuous usage of software. Moreover, the negative coefficient between information quality expectation and confirmation is a good testification of the famous remark the higher expectation is, the more disappointment is. Under the circumstance in which user perceived practical performance is fixed, the greater user expectation towards information system is, the greater degree of negative confirmation is. The proving of Hypothesis H2 indicates that expectation in phase 2 (post-adoption) indeed has an impact on confirmation degree. This conclusion is an excellent supplement for current ECT. In the second place, this study proves the relationship between attitude and satisfaction. Both Venkatech s and Lee have confirmed that satisfaction is the most important factor affecting continuance-usage intention (Lee 2010; Venkatesh and Goyal 2010), which has also been testified in this study. Thirdly, the study also discusses the relationship between attitude and satisfaction, which has been seldom discussed in IS continuance-use researches. As early as 1980, Olive has already explained the mechanism of the relationship between satisfaction, attitude and continuous usage intention under the conditions of considering the two different time periods, and he also proved satisfaction has significant influence on attitude(oliver 1980). However, this relationship lacks of discussion in information system studies. Results of this study have 5

confirmed the correctness of Olive s assumption in the context of information system. Both attitude and satisfaction have significant influence on continuous usage intention. Satisfaction has a direct effect on intention (0.45), and influence continuance-use intention indirectly through attitude (0.21), eventually it will generate stronger total effect (0.66), while attitude has a direct effect on continuance-use intention (0.29). As the results, we can make a conclusion that the satisfaction is more influential than attitude to intention in our research. In the end, from the practices view, reducing users expectations appropriately will increase customers satisfaction, which is a useful management strategy for practitioners. Reference Ajzen, I. (1991). "The Theory of Planned Behavior." Organization Behavior and Human Decision Processes 50(2): 179-211. Bagozzi, R. P. and Y. Yi (1988). "On the Evaluation of Structural Equation Models." Journal of Academy of Marketing Science 16(1): 74-94. Bearden, W. O., R. G. Netemeyer, et al. (1993). Handbook of Marketing Scales: Multi-Item Measures for Marketing and Consumer Behavior Research (Association for Consumer Research). Newbury Park, CA, Sage Publications. Bhattacherjee, A. (2001). "UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION-CONFIRMATION MODEL." MIS Quarterly 25(3): 351-370. BHATTACHERJEE, A., J. PEROLS, et al. (2008). "Information Technology Continuance: a Theoretic Extension and Empirical Test." Journal of Computer Information Systems(fall): 17-27. Edwards, J. R. and R. V. Harrison (1993). "Job Demands and Worker Health: Three-Dimensional Reexamination of the Relationship Between Person-Environment Fit and Strain." Journal of Applied Psychology 78(4): 628-648. FORNELL, C. and D. F. LARCKER (1981). "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error." Joural of Marketing Research 18(1): 39-50. GILBERT A. CHURCHILL, J. and C. SURPRENANT (1982). "An Investigation into the Determinants of Customer Satisfaction." Journal of Marketing Research 19(4): 491-504. Guinea, A. O. d. and M. L. Markus (2009). "Why Break the Habit of a Lifetime? Rethinking the Roles of Intention, Habit, and Emotion in Continuing Information Technology Use." MIS Quarterly 33(3): 433-444. HONG, S., J. KIM, et al. (2008). "ANTECEDENTS OF USE-CONTINUANCE IN INFORMATION SYSTEMS: TOWARD AN INEGRATIVE VIEW." Joural of Computer Information Systems 48(3): 61-74. KOPALLE, P. K. and D. R. LEHMANN (2001). "Strategic Management of Expectations: The Role of Disconfirmation Sensitivity and Perfectionism." Journal of marketing Research 38(3): 386-394. Lee, M.-C. (2010). "Explaining and predicting users' continuance intension toward e-learning: An extension of the expectation-confirmation model." Computers & Education 54: 506-516. Mahmood, M. A. (2000). "Variables Affecting Information Technology End-user Satisfaction: A Meat-analysis of the Empirical Literature." International Journal of human-computer Studies 52(4): 751-771. Myers, B. L., L. A. Kappelman, et al. (1997). "A comprehensive Model for Assessing the Quality and Productivity of the Information Systems Functions: Toward a Theory for Information Systems Assessment." Information Resourece Management Journal 10(1): 6-25. Oliver, R. L. (1980). "A Congnitive Model of the Antecedents and Consequences of Satisfaction Decisions." Journal of marketing Research XVII(November): 460-470. Venkatesh, V. and S. Goyal (2010). "Expectation Disconfirmation and Technology Adoption: Polynomial Modeling and Response Surface Analysis." MIS Quarterly 34(2): 281-303. 6