A Measurement of Non-linear Relationship between IS Qualities and User Satisfaction

Transcription

1 Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2010 Proceedings Americas Conference on Information Systems (AMCIS) A Measurement of Non-linear Relationship between IS Qualities and User Satisfaction Heun Dong Pak Seoul National University, phd516@hanmail.net Soo Min Pak Seoul National University, sm0213@snu.ac.kr Ik Hoon Jang Seoul National University, iks0404@snu.ac.kr Young Chan Choe Seoul National University, aggi@snu.ac.kr Follow this and additional works at: Recommended Citation Pak, Heun Dong; Pak, Soo Min; Jang, Ik Hoon; and Choe, Young Chan, "A Measurement of Non-linear Relationship between IS Qualities and User Satisfaction" (2010). AMCIS 2010 Proceedings This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in AMCIS 2010 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org.

2 A Measurement of Non-linear Relationship between IS Qualities and User Satisfaction Heun Dong Park Seoul National University Ik Hoon Jang Seoul National University Soo Min Park Seoul National University Young Chan Choe Seoul National University ABSTRACT This empirical study attempts to verify non-linearity relationships among three quality dimensions and user satisfaction from the DeLone and McLean IS Success Model. We use survey data from Agriculture integrated excellence systems (AgriX) users and a multiple regression analysis with dummy variables to analyze separately high and low performance groups. Results from the analysis suggest that influences of qualities on satisfaction are non-linear. The impact of qualities is not symmetric and grouped into three distinct types- basic factors, performance factors, and excitement factors. Keywords DeLone and McLean IS Success Model, Three-Factor theory, Non-Linear INTRODUCTION Agriculture integrated information excellent system (AgriX) is a web based system to simplify work processes and to standardize registration procedures for agriculture businesses, leading to efficient management for investment and loan business in agriculture. In AgriX, agriculture workers get quick access to agriculture business data and register for a business with simpler procedures. AgriX makes it better for officials to process businesses and to figure out how a business is going. In general, benefit for agriculture workers increases and inefficiency of procedures decreases. Like AgriX, most agricultural IS businesses in Korea are initiated by government (Park, 2010). However, support from government is executed annually or multi-annually. Therefore, it is important to have strategic managerial implications for the support. Studies of such strategic support have been based on linear relationships between independent variables and dependent variables (Hong et al., 2007; Yoo, et al., 2009). However, research based on linearity have problems with clarification, such as the fact that boosting a specific quality performance does not make users satisfied any more or the fact that users would be indifferent to lack of some qualities rather than complaining that i.e. qualities affect user satisfaction in non-linear ways (Kano, Seraku, Takahashi, and Tsuji, 1984). Park (2010) investigates the difference of qualities attributes impact on user satisfaction in an initial stage and in assimilation stage. System quality dimension is the last attribute to be considered in assimilation stage while the dimension has the most prior impact on user satisfaction in an early stage. Such difference means that supports from government, yearly or biannually, need to be executed in a strategic way. Moreover, research for the strategic support, based on linear relationship, is qualified to explain what attributes is supported. Therefore, this study attempts to suggest alternative to compensate the limitation of linearity. Kano model has been used frequently to understand customer satisfaction (Arbore and Busacca, 2009; Füller and Matzler, 2008; Matzler and Renzl, 2007; Matzler, Sauerwein, and Heischmidt, 2003). The model was later refined as the three-factor theory. Research using this theory has raised a question on previous studies of linear effect on customer satisfaction (Arbore and Busacca, 2009; Matzler et al., 2003). Such doubt is also applied to IS research using PLS based on linearity assumption (Chin, 1998; Duncan, 1966; Tenenhaus, Vinzi, Chatelin, and Lauro, 2005). Managerial recommendations from the research should be changed in terms of the three-factor theory (Lee and Chung, 2009; Seddon and Kiew, 1994). Furthermore, it is needed to categorize attributes into the three factors with non-linearity (Matzler et al., 2004). This categorization helps to make decisions for appropriate strategies in business (Matzler et al., 2004). This objective of this empirical study is to verify nonlinearity of relationships between three quality dimensions and user satisfaction from the DeLone and McLean IS Success Model. In addition, we will categorize the three quality dimensions into three different factors based on the three-factor theory. Finally, results from the analysis are explained, and implications will be followed. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

3 BACKGROUND TO THE STUDY DeLone and McLean IS Success Model DeLone and McLean (1992) proposed a IS success model for IS measurement by seeking a dependent variable in IS discipline. Ten years later, DeLone and Mclean refined their model and presented an updated DeLone and Mclean IS Success Model with three major quality dimensions- IQ, SQ, and servq as independent variables and with use, user satisfaction, and net benefit as dependent variables to measure IS Success (Delone and McLean, 2003). Based on the productivity model, SQ measures information processing system itself (Delone et al. 2003). In studies implying DeLone and McLean IS Success model, SQ is measured for adaptability, reliability, and usability (DeLone et al. 1992; Delone et al. 2003; Iivari 2005).Also based on the productivity model, IQ measures information system output (DeLone et al. 1992; Delone et al. 2003). In IS research, IQ is represented by completeness, ease of understanding, and relevance (DeLone et al. 1992; Delone et al. 2003). ServQ comes from SERVQUAL in marketing and is the overall support delivered by the service provider (Delone et al. 2003). ServQ can be measured with assurance, empathy, and responsiveness, (Delone et al. 2003; Delone et al. 2004; Iivari 2005). Applying the DeLone and Mclean IS Success Model, studies in the IS discipline have evaluated IS success based on relationships between independent variables and dependents variables (Iivari, 2005; Lee and Chung, 2009; Seddon and Kiew, 1994). Among numerous methods, PLS is popular method in IS research for testing models. PLS assumes that each variable is independent from other variables, and that relationships between independents variables and dependent variables are linear and symmetric (Chin, 1998, Gefen, Straub, and Boudreau, 2000). Therefore, studies which apply the DeLone and Mclean IS Success Model are based on linear relationships between three quality dimensions and satisfaction when using PLS (Iivari 2005; Lee et al. 2009; Wixom et al. 2005). For example, Lee and Chung (2009) have used PLS in their research to understand factors affecting satisfaction. In this research it is contended that, in causal sense, more trust leads to greater satisfaction in a linear fashion (Lee and Chung, 2009). However, research on customer satisfaction in marketing has demonstrated that quality attributes may have a non-linear effect on overall satisfaction (Kano et al., 1984; Matzler and Sauerwein, 2002; Oliver, Rust, and Varki, 1997). Kano et al. (1984) verified non-linear effects of quality attributes on customer satisfaction and categorized the attributes into three distinct types. The non-linearity is robust across context, services, and business relationships and should be considered in studies of satisfaction (Matzler et al., 2004). The Three-factor Theory of Customer Satisfaction In the 70s and 80s, research of the effect on customer satisfaction was based on the two-factor theory of customer satisfaction introduced by Herzberg (Cadotte and Turgeon, 1988; Maddox, 1981). In this theory, customers are satisfied with a product depending on a limited set of attributes (Cadotte and Turgeon, 1988). In other words, some attributes of the product relatively play an important role to satisfy customers while other attributes might lead to dissatisfaction (Maddox, 1981). Herzberg made a big contribution to intuition of non-linear effect between quality attributes and satisfaction (Maddox 1981). However, Herzberg s research has been criticized because of limited reproduction and of no context consideration (Maddox 1981; Matzler et al. 2004b). Kano et al. (1984) refined Herzberg s theory and proposed the Kano Model, formulizing their insights. Kano model has been widely accepted for understanding customer needs (Berman, 2005; Evans and Lindsay, 2002). The Kano Model was later improved by other researchers and has been presented as the three-factor theory (Anderson et al. 2000; Matzler et al. 2004; Oliver et al. 1997). In the three factor theory, quality attributes are grouped into the following three distinctive factors (Matzler et al. 2004): Basic Factors (dissatisfiers) are attributes taken for granted by customers. It does not affect satisfaction even if a performance of products or services is fulfilled sufficiently, but customer becomes very dissatisfied if performance does not meet their needs (e.g. hardware stability of laptop is a must-be quality). When there is no problem, users are indifferent to stability. However, if a defect like hardware reset occurs, user satisfaction on a laptop can rapidly decrease. Excitement factors (satisfiers) are attributes that lacking performance does not lead to dissatisfaction. However, it is a cause of high level satisfaction if a performance is fulfilled more than the expectation of the customer (e.g. nice color and good design of a laptop is not an essential element for performance, but users feel considerable satisfaction when acquiring the appearance that they hoped for. Actually, a well designed laptop is usually selected on condition that the performance is the same. Performance factors (hybrid) have symmetrical characteristics. A fulfillment of performance affects a satisfaction and insufficient performance generates a dissatisfaction (e.g. users want to buy a laptop with high processing speed. Conversely, users don t want a slow laptop. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

4 Figure 1. Three-Factor Theory (Matzler et al. 2004) In the three-factor theory, basic factors play a role as a market entry threshold (Matzler et al. 2004). If they reach a satisfactory level, increasing their performance does not lead to an increase in customer satisfaction (Füller et al. 2008; Matzler et al. 2004). Typically, performance factors are directly connected to customers explicit expectations. Thus, a company should be competitive with regard to performance factors (Matzler et al. 2004). Excitement factors are unexpected, surprising customers (Füller et al. 2008). As they generate delight, a company should try to stand out from the competition with regard to these attributes (Füller et al. 2008). The three-factor theory suggests implications especially for business (Matzler et al. 2002). For example, Matzler et al. (2004) have classified customer care, a service attribute in the auto industry, as an excitement factor, and verified that the attribute has a higher impact on overall satisfaction when performance is high. Moreover, they suggested that managers in business should not decide to lower performance of customer care when unsatisfied customers do not care for the attribute, because the attribute has more affect on satisfied customers (Matzler et al. 2004). Therefore, it is important to divide quality attributes into the three distinct types for research on user satisfaction, (Füller et al. 2008; Matzler et al. 2004; Matzler et al. 2007). DATA AND METHOD We collected data from online survey to users of Agriculture integrated information exellent system (AgriX) over a period of 2 weeks in AgriX is the integrated online systems for the efficient management of loans and investments in agriculture. Three quality dimensions and user satisfaction were measured on a 1 to 7 Likert scale ranging from Strongly Disagree through Neutral to Strongly Agree. Of the 2,000 questionnaires distributed, 842 were returned and 798 questionnaires were completed and usable for data analysis, thus showing an effective response rate of 40 %. The questionnaire include four LVs (System Quality, Information Quality, Service Quality, User satisfaction), and each LV has specific attributions as Table 1. Constructs System Quality (SQ) Information Quality (IQ) Service Quality (ServQ) User satisfaction (US) Specific Attributions Ease of use, Ease of learning, Stability, Security Accuracy, Reliability, Usefulness, Correlation with task Kindness, Acceptability, Ease to use FAQ/Q&A menu, Support to usage training Overall satisfaction, Hope of spread usage, Intention to recommend Table 1. Questionnaire Construction PLS is first applied to fit the model for US with 3 quality variables. Then the linearity of relationship between the quality dimensions and US is tested based on three-factor theory. A dichotomized type of regression analysis with dummy variables is often applied to identify the non-linear impacts of attribute performances on overall satisfaction, categorizing the performances according to its underlying features of being a basic, performance or excitement factor (Matzler et al., 2004; Mittal et al., 1998). In this study, one set of dummy variables is created and used to quantify excitement factors, while another set is created to quantify basic factors. Following Matzler et al. (2004), this study recoded performance ratings to form the dummy variables, based on percentile, such that high performance is coded (1,0), low performance (0,1), and average performance (i.e. the rest of part out of high and of low performance) (0,0). Y = α + β 1 SQD1 + β 2 SQD2 + β 3 IQD1 + β 4 IQD2 + β 5 ServQD1 + β 6 ServQD2 + β 7 Year (Equation 1) Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

5 D1 with each of the qualities (SQD1, IQD1, ServQD1) designates high performance dummy set derived from code (1,0), and suffix D2 with each of the qualities(sqd2, IQD2, ServQD2) indicates low performance dummy set derived from code (0,1). A variable Year is an age of survey respondents, which is used for eliminating bias caused by an age. Using dummy variables, two regression coefficients come from each dimensions, two coefficients indicating impacts of high performance and of low performance on US. If dimensions are categorized as a basic factor or as an excitement, differences between two groups are significant, leading to the following hypothesis (Matzler and Renzl, 2007). H1. In SQ dimension, the difference between high performance and low performance is significant. H2. In IQ dimension, the difference between high performance and low performance is significant. H3. In ServQ dimension, the difference between high performance and low performance is significant. ANALYSIS AND RESULTS This study attempts to identify nonlinear relationship between three quality dimensions and US in the IS discipline. In studies on the IS Success model using Structural Equation Modeling (SEM), each dimension SQ, IQ, ServQ, and USare measured with indicators (Delone and McLean, 2003; Lee and Chung, 2009; Seddon and Kiew, 1994). Table2 shows reliability for each of the dimensions. SQ IQ ServQ US Composite Reliability AVE SQ1.900 **.613 **.569 **.726 ** SQ2.871 **.611 **.568 **.633 ** SQ3.856 **.604 **.541 **.671 ** SQ4.847 **.627 **.578 **.612 ** IQ1.626 **.873 **.517 **.592 ** IQ2.646 **.920 **.574 **.616 ** IQ3.598 **.908 **.539 **.567 ** IQ4.645 **.869 **.587 **.629 ** SerQ1.563 **.555 **.852 **.612 ** SerQ2.583 **.539 **.879 **.628 ** SerQ3.581 **.559 **.918 **.631 ** SerQ4.537 **.524 **.868 **.563 ** SerQ5.528 **.500 **.786 **.541 ** CS1.745 **.644 **.681 **.923 ** CS2.688 **.620 **.627 **.952 ** CS3.725 **.647 **.651 **.960 ** Table 2. Composite Reliability, AVE, and Cronbach α Cronbach α All composite reliability values are over and all AVE are over High AVE assures convergent validity of measures, and LVs are explained. Figure 2. Data Analysis Result by PLS-graph Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

6 Figure2 shows PLS results between quality dimensions and satisfaction relationships. SQ has first priority to improve US with a bigger coefficient than other quality dimensions. ServQ and IQ follow in order. T-values (SQ= , IQ=4.9104, and ServQ=8.5362) show that all three quality variables have significant impact on US at level of In order to find non-linear effects for each quality dimension, a dichotomized type of regression analysis with dummy variables is used based on equation 1. Two regression coefficients from each dimensions are obtained as shown in Table 3 one (D1 ending) to measure the impact when the performance is high, and the other one (D2 ending) to measure when the performance is low. Model Unstandardized Coefficients B Std. Error Standardize d Coefficients Beta t Sig. Zeroorder Correlations Partial Part Collinearity Statistics Toleranc e (Constant ) SQD SQD IQD IQD ServQD ServQD Year Table 3. Coefficients of Regression with Dummy Variables There are differences between high performance groups and low performance groups for each IS quality. In the dimensions of IQ and ServQ, the differences are not extensive, but do exist. The difference in SQ dimension is significant. The standardized coefficient of high performance is and the value of low performance coefficient is From these coefficient differences, the relationship between IS quality dimensions and user satisfaction is nonlinear. T-test, using Equation2, is applied to identify differences of parameters between high performance group and low performance group, and the hypothesis test result from the t-test shown as Table 4. The t-statistics for SQ are much higher than critical value (t 0.05 (338)=1.96) at 1 % level of significant. Thus, we reject parameter stability and assure nonlinearity, validating that there is difference between high performance group and low performance group in SQ dimensions. However, t-statistics of IQ and ServQ are lower than critical value (t 0.1 (338)=1.645) at 10% level of significant, showing that there is no difference between the two groups. VIF (Equation 2) Dimension t-statistic t 0.05 < t-statistic System Quality Yes (H1 is not rejected) Information Quality No (H2 is rejected) Service Quality 0 No (H3 is rejected) Table 4. t-test Results In addition to hypothesis test concerning parameters difference between high and low performance group, we derive beta ratio to clarify quality dimensions in terms of the three-factor theory (Matzler and Renzl, 2007). Beta Ratio = (Equation 3) According to Matzler and Renzl (2007), a ratio below 1 indicates that the factor is classified as a basic while a ratio above 1 indicates that the factor is classified as excitement. If a ratio is close by 1, the factor is classified as performance. Below 1, an attribute is classified as a basic factor, and vice versa. Based on Matzler and Renzl (2007), the three quality dimensions in this study are categorized as follow, - System quality (SQ) : basic factor Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

7 - Information quality (IQ) : performance-basic factor - Service quality (ServQ) : performance-excitement factor Classified into a basic factor, SQ has a significantly stronger impact on overall satisfaction when users are dissatisfied than when satisfied. Also, IQ is a basic factor as well as SQ is, but the ratio is close to 1. Thus, the dimension is categorized as a performance-basic factor. ServQ is a performance-excitement factor, which has a slightly stronger impact on satisfaction when satisfaction is high. However, the ratio is close to 1, thus the factor is also close to the performance factor. For more supplementary verification, curve estimation - SPSS version 17.0 functions is executed. The curve estimation function calculates regression statistics with 11 different regression models such as the linear, logarithmic, and quadratic models. Table 5 is the regression statistics for curve estimation. For each quality dimensions, this study compares the linear model with a nonlinear model which has the highest coefficient of determination (R 2 ). In cases of SQ and IQ, R 2 of the cubic model is slightly higher than that of the linear model. In addition, the R 2 of the quadratic model is slightly higher than the linear model in ServQ. This result illustrates that a non-linear model is more fit to US than a linear model, although a comparison of R 2 has no rigor statistic validity in curve estimation and the difference of R 2 is very small. In addition, a trend of curve gives a hint whether each quality attributes belongs to the excitement or basic factor. Figure 3 explains that SQ and IQ appear to be basic factors, and ServQ appears to be an excitement factor. Dependent Variable : User Satisfaction Quality attribute System Quality Information Quality Service Quality Regression Model Summary Parameter Estimates Model R 2 F df1 df2 Sig. Constant β 1 β 2 β 3 Linear Cubic Linear Cubic Linear Quadratic Table 5. Results of Curve Estimation CONCLUSION Figure 3. Graphs of Curve Estimation Research using PLS in the IS discipline assumes that a relationship between variables is linear (Chin, 1998). In this case, the research finds how strong a quality attribute impacts user satisfaction. But, it is difficult to understand whether a quality attribute has more influence on satisfied or dissatisfied users. This is the limitation for a linear relationship, leading to inappropriate managerial strategy (Matzler et al., 2004). Therefore, it is important to find out whether a relationship is or is not non-linear (Matzler et al., 2004). This study identified a nonlinear relationship between three qualities and user satisfaction, which are derived from the DeLone and McLean IS Success Model. Moreover, it is confirmed that the three-factor theory developed is able to be applied to the three IS quality dimensions. In this study, system quality is a basic factor, while information quality closes to a performance. Service quality is grouped in a performance-excitement factor. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

8 Considering a strategic point of view, Matzler et al. (2004) contended that different attributes have different priorities to set on practice. He said, fulfill all basic factors, be competitive with regard to performance factors, and stand out from competition regarding excitement factors. In other words, management should put a first priority to basic factor since basic factors have much influence on dissatisfied users. The second concern is performance factors which correlate with user satisfaction linearly. Thus, it is important to maintain above a certain level not only for making system users have no complaint but also being competitive with rivals. As excitement factors can achieve competitive advantages against rivals, management can save resources from basic factors over-performed, reinforce excitement factors to obtain more user satisfaction. Such a strategic approach may be applied to this study. Primarily, the management should focus on dissatisfied user with SQ, removing the dissatisfied factors. Moreover, it will be a good strategy for the management to support an excitement factor as a distinct service to satisfied users, based on the fact that ServQ, presented as an excitement factor, has more impact on satisfied users rather than on dissatisfied users. DISCUSSION AND LIMITATION Although this study contributes to managerial strategy, there are limitations which should be considered. First of all, there is no big difference in this study between the results based on linearity and on non-linearity. The results both from linearity and from non-linearity method imply that SQ is the most prior factor to be supported. However, SQ is basic factor, which has no big impact on satisfied users, so it should be considered extent of which the factor is support. Such implication cannot come from research based on linearity. Second, factor classifications depend on situational factors, especially demographic characteristics, such as education and experience (Matzler et al., 2004). Considering the situational factors, this study has better implications and improved managerial strategy. Third, this study tests nonlinearity with LVs, which have several item variables. Implications would be more detailed if this study tests in terms of each item variables. Therefore, test with item variables needs to be added to further study. REFERENCES 1. Anderson, E., & Mittal, V. (2000) Strengthening the satisfaction-profit chain. Journal of Service Research, 3, 2, Arbore, A., & Busacca, B. (2009) Customer satisfaction and dissatisfaction in retail banking: Exploring the asymmetric impact of attribute performances. Journal of Retailing and Consumer Services, 16, 4, Berman, B How to delight your customers. California Management Review, 48, 1, Cadotte, E., & Turgeon, N. (1988) Dissatisfiers and satisfiers: suggestions from consumer complaints and compliments. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 1, 1, Chin, W. (1998) The partial least squares approach to structural equation modeling. Modern methods for business research, 295, Daskalkis, S., & Mantas, J. (2008) Evaluating the impact of a service-oriented framework for healthcare interoperability. EHealth Beyond the Horizon: Get It There, DeLone, W., & McLean, E. (1992) Information systems success: the quest for the dependent variable. Information Systems Research, 3, 1, Delone, W., & McLean, E. (2003) The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19, 4, Duncan, O. (1966) Path analysis: Sociological examples. American Journal of Sociology, 72, 1, Evans, J., & Lindsay, W. (2002) The management and control of quality. South-Western, Cincinnati. 11. Füller, J., & Matzler, K. (2008) Customer delight and market segmentation: An application of the three-factor theory of customer satisfaction on life style groups. Tourism Management, 29, 1, Gefen, D., Straub, D., & Boudreau, M. (2000) Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4, 7, Hong, H.Y., Moon, J.H., Yoo, C.W., & Choe, Y.C. (2008) An Analysis of Social-Psychological Factors that Influence the Intention to Use the Agricultural Information System LFcenter System. Journal of Agricultural Extension & Community Development, 15, 4, Iivari, J. (2005) An empirical test of the DeLone-McLean model of information system success. ACM SIGMIS Database, 36, 2, 27. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,

9 15. Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984) Attractive quality and must-be quality. The Journal of the Japanese Society for Quality Control, 14, 2, Lee, K. C., & Chung, N. (2009) Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean's model perspective. Interacting with Computers, 21, 5-6, Maddox, R. (1981) Two-factor theory and consumer satisfaction: Replication and extension. The Journal of Consumer Research, 8, 1, Matzler, K., Bailom, F., Hinterhuber, H., Renzl, B., & Pichler, J. (2004) The asymmetric relationship between attribute-level performance and overall customer satisfaction: a reconsideration of the importance performance analysis. Industrial Marketing Management, 33, 4, Matzler, K., & Renzl, B. (2007) Assessing asymmetric effects in the formation of employee satisfaction. Tourism Management, 28, 4, Matzler, K., & Sauerwein, E. (2002) The factor structure of customer satisfaction. International Journal of Service Industry Management, 13, 4, Matzler, K., Sauerwein, E., & Heischmidt, K. (2003) Importance-performance analysis revisited: the role of the factor structure of customer satisfaction. The Service Industries Journal, 23, 2, Mikuli, J., & Prebežac, D. (2008) Prioritizing improvement of service attributes using impact rangeperformance analysis and impact-asymmetry analysis. Managing Service Quality, 18, 6, Mittal, V., Ross, W. T., Jr., & Baldasare, P. M. (1998) The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions. The Journal of Marketing, 62, 1, Oliver, R., Rust, R., & Varki, S. (1997) Customer delight: foundations, findings, and managerial insight. Journal of Retailing, 73, 3, Park, H.D. & Choe, Y.C. (2010) A study on Factors and Moderation Effects Influencing Information Quality Between Early Adopters and Continuous Users in Public Business Systems. Working Paper. 26. Seddon, P., & Kiew, M. (1994) A partial test and development of the DeLone and McLean model of IS success. Society for Information Management, 4, 1, Tenenhaus, M., Vinzi, V., Chatelin, Y., & Lauro, C. (2005) PLS path modeling. Computational statistics and data analysis, 48, 1, Wixom, B., & Todd, P. (2005) A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16, 1, Yoo, C.W., Park, S.M., Choe, Y.C., & Shim, G.S. (2009) Success factors for web-based agricultural information systems. Journal of Korean Society of Rural, 15, 4, Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, August 12-15,