A Study on consumers continuing to use online group-buying platforms: The impact of price performance expectations

Transcription

1 December 2010, Volume 9, No.12 (Serial No.90) Chinese Business Review, ISSN , USA A Study on consumers continuing to use online group-buying platforms: The impact of price performance expectations Yi-Wen Fan 1, Mei-Hsia Chiang 1, 2, Jhih-Yuan Wang 1, Eric T. G. Wang 1 (1. Department of Information Management, National Central University, Taoyuan 32001, Taiwan; 2. Department of Information Management, Hsing Wu Institute of Technology, Taipei 244, Taiwan) Abstract: In this study, we aim to understand the characteristics of online group-buying consumers and to investigate salient factors which influence the continuance intention of online group-buying platforms (OGBP) to bridge this knowledge gap. An expectation-confirmation model of information systems (IS) continuance is adapted to construct a research model in online group-buying contexts. A total of 289 complete and valid responses were collected. Our findings contribute to academics and practitioners in two ways: Firstly, our respondents show that they are young (93% of the respondents ages range between 19 and 28 years old), female (88% of the respondents), and thrifty (82% of the respondents transaction amounts are below US$16). Secondly, based on our results, price performance expectations have a direct impact on confirmation. In addition, in contrast to the IS continuance model (Bhattacherjee, 2001), the effect of perceived usefulness on satisfaction is not supported. Thus, in online group-buying settings, confirmation is the key antecedent of satisfaction. Satisfaction and perceived usefulness are significantly associated with OGBP continuance intention. Consequently, in addition to offering a wide assortment of merchandise and a convenient online shopping experience to enhance customers perceived usefulness of OGBP, OGBP managers should aim low-price marketing strategies at this female, young, thrifty and price-sensitive segment to transcend consumers price expectations and attract consumers continued intention to visit OGBP. Key words: online group-buying platforms; expectation-confirmation model of IS continuance; expectation-confirmation theory; technology acceptance model; price performance expectations 1. Introduction Online group-buying platforms (OGBP) have recently been a prevailing medium of innovative Internet-based commercial transaction. The main idea of online group-buying is to recruit online consumers to leverage their collective bargaining power to negotiate for a better price of products. At the same time, suppliers can also create Yi-Wen Fan, Ph.D., associate professor of information management, Department of Information Management, National Central University; research fields: electronic commerce and customer relationship management. Mei-Hsia Chiang, Ph.D. candidate, Department of Information Management, National Central University, and an instructor in Department of Information Management, Hsing Wu Institute of Technology; research fields: electronic commerce, service marketing and management. Jhih-Yuan Wang, master of information management, Department of Information Management, National Central University; research field: electronic commerce. Eric T. G. Wang, Ph.D., professor of information management, Department of Information Management, National Central University; research fields: electronic commerce, supply chain management and organizational effects of information technology. 44

2 leverage to diminish the cost of recruiting customers (Kauffman, Lai & Ho, 2010). Demand aggregation and volume discount are at the core of group buying (Anand & Aron, 2003). Both suppliers and consumers can benefit from online group-buying. Despite the tremendous growth of the online group-buying market, relatively few studies have been conducted to date. Most previous online group-buying studies focus mainly on the pricing mechanisms (Kauffman, 2001; Kauffman, 2002; Anand & Aron, 2003; Chen, Chen & Song, 2002; Chen, Chen & Song, 2007), coalition formation (Li, Chawla, Rajan & Sycara, 2004; Li, Sycara & Scheller-Wolf, 2010), the benefits of bidder cooperation (Chen, Chen, Kauffman & Song, 2009), uncertain demand (Chen, Kauffman, Liu & Song, 2010), incentive mechanisms (Kauffman, Lai & Ho, 2010), and consumer adoption (Kauffman, Lai & Lin, 2010). These studies reflect the blooming trend of online group-buying and have attracted more and more attention from academics. Bhattacherjee (2001) theorizes an expectation-confirmation model of information systems (IS) continuance and argues that IS users continuance decisions show three characteristics: (1) they follow an initial acceptance, (2) they are influenced by the initial use experience, and (3) they can potentially lead to ex post reversal of the initial decision. Although the initial acceptance of an IS is an important first step toward realizing the IS s success, long-term success relies both on the initial acceptance and continued intention to use it. The initial acceptance of an IS does not guarantee the usage continuance afterward, because users can reevaluate their earlier acceptance decision or may experience psychological motivation changes after their initial acceptance (Rogers, 1995; Bhattacherjee, 2001; Bhattacherjee & Premkumar, 2004). In internet-based group buying settings, information systems provide a critical tool for OGBP. Thus, consumers initial acceptance of OGBP does not guarantee that they will continue to use them thereafter. The long-term success depends on both their initial acceptance and their continued intention to use them. The importance of examining the continued intention to use OGBP is augmented by the growing online group-buying market. Accordingly, in this study we aim to understand the profile of online group-buying consumers and to investigate those salient factors influencing the continued intention of OGBP to bridge this knowledge gap. This paper proceeds as follows. In section 2 we propose a theoretical model and six hypotheses in online group-buying contexts, in section 3 we describe the research methods used for testing the above model, and in section 4 we analyze data and present the results. In the final section, we delineate conclusions and managerial implications. 2. Conceptual background and research hypotheses An expectation-confirmation model of IS continuance (Bhattacherjee, 2001) plays a prevailing role in explaining continuation of IS use. In online group-buying contexts, IS provides a critical tool for OGBP. Consequently, it is legitimate to utilize the expectation-confirmation model of IS continuance to propose our research model. The research model and its hypotheses are shown in Fig An expectation-confirmation model of IS continuance Bhattacherjee (2001) adapts expectation-confirmation theory (ECT) (Oliver, 1980) from the consumer behavior literature to IS field and integrates the technology acceptance model (TAM) (Davis, Bagozzi & Warshaw, 1989) to theorize a post-acceptance model of IS continuance. Bhattacherjee (2001) indicates that the IS continuance intention is determined by users satisfaction with initial IS use and their perceived usefulness of IS use. Users satisfaction is influenced by both confirmation of expectation between previous IS use and following 45

3 actual use as well as users perceived usefulness of IS use. Moreover, the perceived usefulness of IS use is determined by the users confirmation level. In addition, Bhattacherjee and Premkumar (2004) propose a two-stage model of belief and attitude change by drawing on ECT and the extant information technology (IT) usage literature. They show that user beliefs and attitudes are key perceptions driving IT usage. These perceptions may change with time as users follow actual IT usage and in turn alter their subsequent IT usage behavior. Emergent factors such as disconfirmation and satisfaction are critical to understanding changes in IT users beliefs and attitudes. Perceived usefulness H5 H2 Satisfaction H3 H1 OGBP continuance intention H4 Price performance expectations H6 Confirmation Fig. 1 Research model 2.2 Satisfaction Satisfaction refers to the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with the consumer s prior feelings about the consumption experience (Oliver, 1981). Oliver (1980) shows that satisfaction has both direct and indirect connections with future intention through its impact on attitude. IS satisfaction has directly influence on both the intention to use and the actual use (Davis, 1989; Davis, et al., 1989; Karahanna, et al., 1999). According to the expectation-confirmation model of IS continuance (Bhattacherjee, 2001), users IS continuance intention is determined primarily by their satisfaction with prior IS use. Thus, the continuance intention is positively associated with satisfaction. In online group-buying settings, the connection between satisfaction and the intention to continue using OGBP is proposed as the following hypothesis. H1: Users level of satisfaction with initial use of OGBP is positively associated with their continuance intention of OGBP. 2.3 Perceived usefulness Perceived usefulness is defined as the prospective user s subjective probability that using a specific application system will increase his or her job performance within an organizational context (Davis, et al., 1989). In IS continuance contexts, perceived usefulness is expected to be the most salient ex post expectation influencing users satisfaction (Bhattacherjee, 2001). Therefore, this argument leads to the following hypothesis in online group-buying contexts. H2: Users perceived usefulness of OGBP is positively associated with their satisfaction with OGBP. Although the usefulness-intention association is originally derived in an acceptance context, it is likely to hold true in continuance contexts, because human tendencies for subconsciously pursuing instrumental behaviors or striving for rewards are independent of the timing or stage of such behaviors. The effect of perceived 46

4 usefulness on users intention in continuance contexts attests to this association across temporal stages of IS use (Bhattacherjee, 2001). Accordingly, in the online group-buying context, the association between online group-buying continuance intention and the user s perceived usefulness is hypothesized as follows: H3: Users perceived usefulness of OGBP is positively associated with their continuance intention of OGBP. 2.4 Confirmation Confirmation corresponds to the comparison of consumers actual experience or perceived performance with their initial expectations. Consumers assess their perceived performance vis-a-vis their original expectations to determine the extent to which their expectation is confirmed. Confirmation is positively related to satisfaction with IS use because it implies realization of the expected benefits of IS use (Bhattacherjee, 2001). Therefore, the following hypothesis is proposed in online group-buying settings. H4: Users extent of confirmation is positively associated with their satisfaction with OGBP. According to the post-acceptance model of IS continuance (Bhattacherjee, 2001), confirmation will tend to elevate users perceived usefulness and disconfirmation will reduce such perceptions. This association comes from cognitive dissonance theory (Festinger, 1957), which indicates that users may experience cognitive dissonance or psychological tension if their pre-acceptance usefulness perceptions are disconfirmed during actual use. Rational users may try to remedy this dissonance by distorting or modifying their usefulness perceptions to be more consistent with reality. Therefore, in online group-buying settings, the following hypothesis is proposed. H5: Customers extent of confirmation is positively associated with their perceived usefulness of OGBP. 2.5 Price performance expectations Price performance expectations are influenced by price perceptions of former purchase experience. Price perceptions refer to the translation of the objective price into cognitions that are meaningful and relevant to the consumer (Voss, Parasuraman & Grewal, 1998). Voss, et al (1998) indicate that prepurchase performance expectations are influenced by prepurchase price perceptions and postpurchase price perceptions affect postpurchase performance perceptions. Thus, the extent of confirmation of the consumer s expectations is determined by the comparison between prepurchase and postpurchase price perceptions. Based on the above, we propose that price performance expectations have a direct impact on confirmation. Consequently, the following hypothesis is proposed in online group-buying contexts. H6: Price performance expectations are positively associated with confirmation. 3. Methods 3.1 Measurement development Wherever possible, measurement items from the literature were adapted to online group-buying contexts. Six face-to-face personal interviews with experienced online group-buying consumers were conducted. Their comments were gathered to more fully understand the online group-buying phenomenon. In addition, a pilot study was conducted with two scholars and eight PhD students with experience using OGBP to rephrase and modify the wording of the measurement. There are five constructs measured in this research. Items related to confirmation and continuance intention of OGBP were adapted from Bhattacherjee (2001). Items for measuring perceived usefulness were adapted from Davis, et al (1989). Satisfaction was assessed with items adapted from Szymanski and Hise (2000). Price performance 47

5 expectations were measured with items adapted from Voss, et al (1998). Thus, a 14-item instrument was developed using seven-point Likert scale anchored from strongly disagree (1) to strongly agree (7) (see Appendix). 3.2 Data collection The research model was tested with data collected during a cross-sectional field survey of experienced online group-buying consumers during the period of March 20 to April 10, The questionnaires were distributed by two methods: online field surveys and self-administered questionnaires. Online field surveys have several advantages: (1) lower costs, (2) faster responses, and (3) geographically unrestricted sample (Bhattacherjee, 2001). The my3q website ( was utilized to create the online survey. A message inviting recipients to fill in the questionnaire, with a hyperlink to the my3q website, was posted on buy-together board of several university bulletin board systems (BBS) and on the discussion board of the ihergo website ( To encourage participation, twenty randomly-drawn respondents were offered convenience store coupons with a value of US$3 as an incentive. In addition, self-administered questionnaires were distributed to a sample of consumers with experience using OGBP. Respondents who finished the questionnaire were offered a gift reward worth US$1. 4. Data analysis and results 4.1 Profile of respondents A total of 289 complete and valid responses were collected. Of these, 250 returns came from the web survey and 39 from the self-administered questionnaires. The collected data were analyzed using the SPSS statistical software package. According to our respondents, the demographic profile was as follows: 88% of the respondents were female, 93% of the respondents ages ranged between 19 and 28 years old, and 74% of the respondents had online group-buying experiences one to six times within the last six months. 82% of the respondents transaction amounts were below US$16 (see Fig. 2) Number of 40 respondents Amount (NTD) per transaction Fig. 2 Online group-buying amount per transaction Model fitness The validity of the measurement model depends on (1) finding specific evidence of construct validity, and (2) establishing acceptable levels of goodness-of-fit for the measurement model (Hair, et al., 2010). 48

6 Reliability is one of the most critical elements in assessing the quality of the construct measures (Churchill, 1979). A statistically reliable scale provides consistent and stable measures of a construct. This study uses Cronbach s coefficient alpha to evaluate the construct reliability. The reliability coefficients of all of the five constructs were greater than 0.7 (see Table 1) and were considered acceptable (Nunnally & Bernstein, 1994; Hair, Black, Babin & Anderson, 2010). Fornell and Larcker (1981) suggest that the convergent validity of scales be validated by two criteria: (1) all indicator loadings should be significant at the p = 0.01 level and exceed 0.7, and (2) the average variance extracted (AVE) should exceed 0.5. Confirmatory factor analysis (CFA) was applied to test the AVE and goodness-of-fit for the measurement model with Amos All factor loadings were above 0.7 and all of the values of AVE were greater than 0.5 (see Table1). Table 1 Descriptive statistics of constructs Constructs Price performance expectations (PP) Satisfaction (S) Perceived usefulness (PU) Confirmation (C) OGBP continuance intention (CI) Items Factor loadings PP PP S S PU PU PU PU C C C CI CI CI Cronbach s alpha AVE Correlation matrix PP SA PU CO CI Note: Boldface characters on the diagonal of correlation matrix represent the square root of the AVE. Fornell and Larcker (1981) also suggest that to test discriminate validity, the square root of the AVE for each construct should be greater than the correlation between this construct and any other construct in the model. Table 1 shows that all the diagonal values in the correlation matrix exceed the inter-construct correlations and the discriminate validity was verified. To examine the model fit, the following six fit indices (Hair, et al., 2010) were adopted in our study: (1) The chi-square value normalized by degrees of freedom (χ 2 /df) should be less than 3, (2) Goodness-of-fit (GFI) values of greater than 0.9 typically were considered good, (3) Adjusted goodness-of-fit (AGFI) values should exceed 0.8, (4) The normed fit index (NFI) should exceed 0.9, (5) Comparative fit index (CFI) values above 0.9 are usually related to a model that fits well, and (6) The Root mean square error of approximation (RMSEA) value should be between 0.03 and For the current measurement model, χ 2 /df ratio was 1.653, GFI equals 0.946, AGFI was 0.921, NFI equals 0.946, and CFI, RMSEA were and respectively. The above values indicate an adequate model fit. Five of the six standard path coefficients demonstrated a p value of less than (see Fig. 3). Accordingly, hypotheses H1, H3, H4, H5 and H6 were supported. The p value of path coefficient of hypothesis H2 was not 49

7 significant at the 0.05 level of significance. Thus, hypothesis H2 was not supported. There was no significantly positive association between perceived usefulness and satisfaction. Based on the results of Amos analysis, confirmation explained 78% variance of satisfaction and was the strongest predictor of satisfaction (β = 0.65, p< 0.001). Continuance intention of OGBP had a strong association with satisfaction (β = 0.53, p< 0.001) and a significant association with perceived usefulness (β = 0.28, p< 0.001). Both satisfaction and perceived usefulness together accounted for 60% variance of continuance intention of OGBP. Confirmation explained 56% variance of perceived usefulness and had a strong positive impact on perceived usefulness with standard path coefficient β of 0.61 (p< 0.001). Price performance expectations had significant influence on confirmation (β = 0.51, p< 0.001) and accounted for 56% variance of confirmation (see Fig. 3). Perceived usefulness (R 2 = 0.56) 0.61*** Satisfaction (R 2 = 0.78) 0.28*** 0.53*** OGBP continuance intention (R 2 =0.60) Price performance expectations 0.51*** Confirmation (R 2 = 0.56) 0.65*** Fig. 3 Results of Amos analysis Notes: + p <0.1, *** p < Conclusions and managerial implications There are two unique contributions to academics and practitioners in this study. Firstly, our respondents profile demonstrates that 88% of the respondents are female and 93% of the respondents ages range between 19 and 28 years old. In addition, 82% of the respondents transaction amounts are below US$16. This shows that the online group-buying market is not suitable for sale of high-priced products or luxury goods and most online group-buying consumers are very price-sensitive consumers. Thus, OGBP managers should position their products and aim low-price marketing strategies at this female, young, thrifty, and price-sensitive segment to transcend consumers price expectations and attract consumers continued intentions to visit OGBP. Secondly, price performance expectations have a direct impact on confirmation. If postpurchase price perceptions are better than or meet prepurchase price perceptions, price performance expectations are positively confirmed. Confirmed price performance expectations, through confirmation, have a positive association with consumer satisfaction. In addition, in contrast to the IS continuance model (Bhattacherjee, 2001), the effect of perceived usefulness on satisfaction is not supported. Thus, in online group-buying settings, confirmation is the key antecedent of satisfaction. Satisfaction and perceived usefulness are significantly associated with OGBP continuance intention. The findings imply that online group-buying consumers are very price-sensitive consumers. OGBP managers may provide real time price information of various products e.g., historical price, competitors price etc. to 50

8 online group-buying consumers. In addition to offering a wide assortment of merchandise and a convenient online shopping experience to enhance customers perceived usefulness of OGBP, OGBP managers should develop low-price strategies to transcend customers expectations. It is only when consumers price performance expectations are confirmed that customers are kept satisfied. Satisfied customers will continue visiting and revisiting OGBP. References: Anand, K. & Aron, R.. (2003). Group buying on the web: A comparison of price-discovery mechanisms. Management Science, 49(11), Bhattacherjee, A.. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), Bhattacherjee, A. & Premkumar, G.. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), Chen, J., Chen, X., Kauffman, R. J. & Song, X.. (2009). Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions. Electronic Commerce Research and Applications, 8(4), Chen, J., Chen, X. & Song, X.. (2002). Bidder s strategy under group-buying auction on the internet. IEEE Transaction on Systems, Man and Cybernetics: Part A, 32(6), Chen, J., Chen, X. & Song, X.. (2007). Comparison of the group-buying auction and the fixed pricing mechanism. Decision Support Systems, 43(2), Chen, J., Kauffman, R. J., Liu, Y. & Song, X.. (2010). Segmenting uncertain demand in group-buying auctions. Electronic Commerce Research and Applications, 9(2), Churchill, G. A.. (1979). A paradigm for developing better measures of marketing construct. Journal of Marketing Research, 16(3), Davis, F. D.. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), Davis, F. D., Bagozzi, R. P. & Warshaw, P. R.. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), Festinger, L. A.. (1957). A theory of cognitive dissonance, Row and Peterson. Evanston, IL: Row, Peterson and Company. Fornell, C. & Larcker, D. F.. (1981) Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 18(1), Hair, J. F., Black, B., Babin, B. & Anderson, R. E.. (2010). Multivariate data analysis: A global perspective (7th ed.). New Jersey: Prentice-Hall. Karahanna, E., Straub D. W. & Chervany, M. L.. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), Kauffman, R. J., Lai, H. & Ho, C. T.. (2010). Incentive mechanisms, fairness and participation in online group-buying auctions. Electronic Commerce Research and Applications, forthcoming. Kauffman, R. J., Lai, H. & Lin, H. C.. (2010, April 2). Consumer adoption of group-buying auctions: An experimental study. Information Technology and Management, Published online. Kauffman, R. J. & Wang, B.. (2001). New buyers arrival under dynamic pricing market microstructure: The case of group-buying discounts on the internet. Journal of Management Information Systems, 18(2), Kauffman, R. J. & Wang, B.. (2002). Bid together, buy together: On the efficacy of group-buying business models in internet-based selling. In: P. B. Lowry, J. O. Cherrington & R. R. Watson (Eds.), Handbook of electronic commerce in business and society, Boca Raton, FL.: CRC Press. Li, C., Chawla, S., Rajan, U. & Sycara, K.. (2004). Mechanism design for coalition formation and cost sharing in group-buying markets. Electronic Commerce Research and Applications, 3(4), Li, C., Sycara, K. & Scheller-Wolf, A.. (2010). Combinatorial coalition formation for multi-item group-buying with heterogeneous customers. Decision Support Systems, 49, Nunnally, J. C. & Bernstein, I. H.. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Oliver, R. L.. (1980). A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research, 17(3), 51

9 Oliver, R. L.. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57(3), Szymanski, D. M. & Hise, R. T.. (2000). E-satisfaction: An initial examination. Journal of Retailing, 76(3), Voss, G. B., Parasuraman, A. & Grewal, D.. (1998). The roles of price, performance, and expectations in determining satisfaction in service exchanges. Journal of Marketing, 62, (Edited by Ruby and Chris) Appendix Measures of the five constructs Construct OGBP continuance intention Satisfaction Perceived usefulness Confirmation Price performance expectations Scale item CI1: I intend to continue using the OGBP rather than discontinue its use. CI2: My intentions are to continue using the OGBP than use any alternative means. CI3: If I could, I would like to discontinue my use of the OGBP. Overall, how do you feel about your OGBP experience? S1: Very satisfied; S2: Very pleased. PU1: Using the OGBP improves my performance in finding out group-buying products. PU2: Using the OGBP increases my productivity in group-buying. PU3: Using the OGBP provides me with more diverse channels to enhance my effectiveness in group-buying products. PU4: As for overall platform design, I find the OGBP useful. C1: My experience with using the OGBP was better than what I expected. C2: The service level and information provided by the OGBP was better than what I expected. C3: Overall, most of my expectations from using the OGBP were confirmed. PP1: I feel the online group-buying price more reasonable. PP2: I think the online group-buying price more satisfying. Note: Platform includes website and any other internet medium, e.g., BBS or blog, etc. 52