Antecedents of online purchase intention and behaviour: uncovering unobserved heterogeneity
The paper aims at exploring the antecedents of customers’ online purchase intention and behaviour, and at uncovering sources of heterogeneity. A sample of customers was surveyed to measure perceived risk and benefits, trust, online purchase intention and behaviour. The study confirmed the causal chain of perceived risks-trust-perceived benefits-online purchase intention-actual purchase. A Finite Mixture Partial Least Squares (FIMIX-PLS) was performed to uncover sources of heterogeneity. It found that the level of security of the payment methods is relevant to understand the relationship between purchase intention and behaviour, while the level of previous experience with the online medium clarifies the relationship between perceived risk and trust. The study contributes to understanding the antecedents of online purchase intention and their relationship with actual purchase behaviour. Additionally, it offers evidence of heterogeneity in the proposed causal relations, particularly, concerning the level of trust in the payment methods and the level of Internet experience.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping. Internet Research, 25(5), 707-733. https://doi.org/10.1108/IntR-05-2014-0146
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
Arce-Urriza, M., Cebollada, J., & Tarira, M. F. (2017). The effect of price promotions on consumer shopping behavior across online and offline channels: differences between frequent and non-frequent shoppers. Information Systems and e-Business Management, 15(1), 69-87. https://doi.org/10.1007/s10257-016-0310-2
Bagozzi, R. P., & Youjae, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1177/009207038801600107
Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: an integrated model of trust, user experience and branding. Computers in Human Behavior, 61, 103-113. https://doi.org/10.1016/j.chb.2016.03.014
Büttner, O. B., & Göritz, A. S. (2008). Perceived trustworthiness of online shops. Journal of Consumer Behaviour, 7(1), 35-50. https://doi.org/10.1002/cb.235
Chang, H. H., & Chen, S. W. (2008). The impact of online store environment cues on purchase intention. Online Information Review, 32(6), 818-841. https://doi.org/10.1108/14684520810923953
Chang, M. K., Cheung, W., & Lai, V. S. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42(4), 543-559. https://doi.org/10.1016/j.im.2004.02.006
Chen, Y., Yan, X., Fan, W., & Gordon, M. (2015). The joint moderating role of trust propensity and gender on consumers’ online shopping behavior. Computers in Human Behavior, 43, 272-283. https://doi.org/10.1016/j.chb.2014.10.020
Chien, S.-H., Chen, Y.-H., & Hsu, C.-Y. (2012). Exploring the impact of trust and relational embeddedness in e-marketplaces: an empirical study in Taiwan. Industrial Marketing Management, 41(3), 460-468. https://doi.org/10.1016/j.indmarman.2011.05.001
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535. https://doi.org/10.1016/S0022-4359(01)00056-2
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). New York, NY: Psychology Press.
Clemons, E. K., Wilson, J., Matt, C., Hess, T., Ren, F., Jin, F., & Koh, N. S. (2016). Global differences in online shopping behavior: understanding factors leading to trust. Journal of Management Information Systems, 33(4), 1117-1148. https://doi.org/10.1080/07421222.2016.1267531
Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons.
Comegys, C., Hannula, M., & Váisánen, J. (2009). Effects of consumer trust and risk on online purchase decision-making: a comparison of Finnish and United States students. International Journal of Management, 26(2), 295.
Constantinides, E., LorenzoRomero, C., & Gómez, M. A. (2010). Effects of web experience on consumer choice: a multicultural approach. Internet Research, 20(2), 188-209. https://doi.org/10.1108/10662241011032245
Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003). On-line trust: concepts, evolving themes, a model. International Journal of Human-Computer Studies, 58(6), 737-758. https://doi.org/10.1016/S1071-5819(03)00041-7
Díaz, A., Gómez, M., & Molina, A. (2017). A comparison of online and offline consumer behaviour: an empirical study on a cinema shopping context. Journal of Retailing and Consumer Services, 38, 44-50. https://doi.org/10.1016/j.jretconser.2017.05.003
Dodds, W. B., & Monroe, K. B. (1985). The effect of brand and price information on subjective product evaluations. In E. C. H. & M. B. Holbrook (Eds.), NA – Advances in Consumer Research (pp. 85-90). Provo, UT: Association for Consumer Research.
Douglas, S. P., & Nijssen, E. J. (2003). On the use of “borrowed” scales in cross-national research: a cautionary note. International marketing review, 20(6), 621-642. https://doi.org/10.1108/02651330310505222
Escobar-Rodríguez, T., & Bonsón-Fernández, R. (2017). Analysing online purchase intention in Spain: fashion e-commerce. Information Systems and e-Business Management, 15(3), 599-622. https://doi.org/10.1007/s10257-016-0319-6
Fang, J., Wen, C., George, B., & Prybutok, V. R. (2016). Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: the role of gender, age, and shopping motives. Journal of Electronic Commerce Research, 17(2), 116-131.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.2307/3150980
Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75. https://doi.org/10.1002/dir.20061
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185-214. https://doi.org/10.1080/07421222.2001.11045669
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016a). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications.
Hair Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016b). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method. European Business Review, 28(1), 63-76. https://doi.org/10.1108/EBR-09-2015-0094
Harridge‐March, S. (2006). Can the building of trust overcome consumer perceived risk online. Marketing Intelligence & Planning, 24(7), 746-761. https://doi.org/10.1108/02634500610711897
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hong, I. B. (2015). Understanding the consumer’s online merchant selection process: the roles of product involvement, perceived risk, and trust expectation. International Journal of Information Management, 35(3), 322-336. https://doi.org/10.1016/j.ijinfomgt.2015.01.003
Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939. https://doi.org/10.1016/j.ijinfomgt.2013.08.007
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone-based mobile shopping: mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychology & Marketing, 34(2), 175-194. https://doi.org/10.1002/mar.20982
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1/2), 45-71. https://doi.org/10.1023/A:1019104520776
Jasper, C. R., & Ouellette, S. J. (1994). Consumers’ perception of risk and the purchase of apparel from catalogs. Journal of Direct Marketing, 8(2), 23-36. https://doi.org/10.1002/dir.4000080205
Jeske, D., & van Schaik, P. (2017). Familiarity with Internet threats: beyond awareness. Computers & Security, 66, 129-141. https://doi.org/10.1016/j.cose.2017.01.010
Kim, C., Tao, W., Shin, N., & Kim, K.-S. (2010). An empirical study of customers’ perceptions of security and trust in e-payment systems. Electronic Commerce Research and Applications, 9(1), 84-95. https://doi.org/10.1016/j.elerap.2009.04.014
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
Kim, H.-W., Xu, Y., & Gupta, S. (2012). Which is more important in Internet shopping, perceived price or trust. Electronic Commerce Research and Applications, 11(3), 241-252. https://doi.org/10.1016/j.elerap.2011.06.003
Kim, M.-J., Chung, N., & Lee, C.-K. (2011). The effect of perceived trust on electronic commerce: shopping online for tourism products and services in South Korea. Tourism Management, 32(2), 256-265. https://doi.org/10.1016/j.tourman.2010.01.011
Kline, R. B. (2011). Principles and practice of structural equation modeling 2011 (3rd ed.). New York, NY: NY Guilford Press.
Latan, H., & Noonan, R. (Eds.). (2017). Partial least squares path modeling: basic concepts, methodological issues and applications. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3
Li, H., Kuo, C., & Rusell, M. G. (2006). The impact of perceived channel utilities, shopping orientations, and demographics on the consumer’s online buying behavior. Journal of Computer-Mediated Communication, 5(2), 0-0. https://doi.org/10.1111/j.1083-6101.1999.tb00336.x
Lin, H.-F. (2007). Predicting consumer intentions to shop online: an empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442. https://doi.org/10.1016/j.elerap.2007.02.002
Molinillo, S., Gómez-Ortiz, B., Pérez-Aranda, J., & Navarro-García, A. (2017). Building customer loyalty: the effect of experiential state, the value of shopping, and trust and perceived value of service on online clothes shopping. Clothing and Textiles Research Journal, 35(3), 156-171. https://doi.org/10.1177/0887302X17694270
Mou, J., Shin, D.-H., & Cohen, J. F. (2017). Trust and risk in consumer acceptance of e-services. Electronic Commerce Research, 17(2), 255-288. https://doi.org/10.1007/s10660-015-9205-4
Nepomuceno, M. V., Laroche, M., & Richard, M.-O. (2014). How to reduce perceived risk when buying online: the interactions between intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services, 21(4), 619-629. https://doi.org/10.1016/j.jretconser.2013.11.006
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
OECD. (2018). ICT access and usage by individuals. Retrieved from https://stats.oecd.org
Pappas, N. (2017a). Effect of marketing activities, benefits, risks, confusion due to over-choice, price, quality and consumer trust on online tourism purchasing. Journal of Marketing Communications, 23(2), 195-218. https://doi.org/10.1080/13527266.2015.1061037
Pappas, N. (2017b). Risks and marketing in online transactions: a qualitative comparative analysis. Current Issues in Tourism, 20(8), 852-868. https://doi.org/10.1080/13683500.2016.1187586
Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: integrating the effects of assurance on trust antecedents. Tourism Management, 47, 286-302. https://doi.org/10.1016/j.tourman.2014.10.009
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS3. Hamburg: SmartPLS. Retrieved from http//www.smartpls.com
Ringle, C. M., Sarstedt, M., & Mooi, E. A. (2010). Response-based segmentation using finite mixture partial least squares. In Annals of Information Systems: Data Mining (pp. 19-49). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4419-1280-0_2
Roghanizad, M. M., & Neufeld, D. J. (2015). Intuition, risk, and the formation of online trust. Computers in Human Behavior, 50, 489-498. https://doi.org/10.1016/j.chb.2015.04.025
Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011). Uncovering and treating unobserved heterogeneity with FIMIX-PLS: which model selection criterion provides an appropriate number of segments. Schmalenbach Business Review, 63(1), 34-62. https://doi.org/10.1007/BF03396886
Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, 37(8), 1299-1318. https://doi.org/10.1080/02664760903030213
Shainesh, G. (2012). Effects of trustworthiness and trust on loyalty intentions. International Journal of Bank Marketing, 30(4), 267-279. https://doi.org/10.1108/02652321211236905
Shang, R.-A., Chen, Y.-C., & Shen, L. (2005). Extrinsic versus intrinsic motivations for consumers to shop on-line. Information & Management, 42(3), 401-413. https://doi.org/10.1016/j.im.2004.01.009
Sharma, S., Menard, P., & Mutchler, L. A. (2017). Who to trust? Applying trust to social commerce. Journal of Computer Information Systems, 1-11. https://doi.org/10.1080/08874417.2017.1289356
Sheeran, P., & Webb, T. L. (2016). The intention-behavior gap. Social and Personality Psychology Compass, 10(9), 503-518. https://doi.org/10.1111/spc3.12265
SIBS. (2017). SIBS Market report: digital commerce. Lisbon. Retrieved from https://www.sibs.pt/wp-con-tent/uploads/sites/5/2017/02/SIBS-MARKET-REPORT_2016_PDF-INTERACTIVO_20170222.pdf
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39-50. https://doi.org/10.1108/03090569310026637
Sun, B., & Morwitz, V. G. (2010). Stated intentions and purchase behavior: a unified model. International Journal of Research in Marketing, 27(4), 356-366. https://doi.org/10.1016/j.ijresmar.2010.06.001
Teo, T. S. H., & Liu, J. (2007). Consumer trust in e-commerce in the United States, Singapore and China. Omega, 35(1), 22-38. https://doi.org/10.1016/j.omega.2005.02.001
Van Slyke, C., Bélanger, F., Johnson, R. D., & Hightower, R. (2010). Gender-based differences in consumer e-commerce adoption. Communications of the Association for Information Systems, 26(1), Article 2.
Verhagen, T., & van Dolen, W. (2009). Online purchase intentions: a multi-channel store image perspec-tive. Information & Management, 46(2), 77-82. https://doi.org/10.1016/j.im.2008.12.001
Wu, L.-Y., Chen, K.-Y., Chen, P.-Y., & Cheng, S.-L. (2014). Perceived value, transaction cost, and repurchase-intention in online shopping: a relational exchange perspective. Journal of Business Research, 67(1), 2768-2776. https://doi.org/10.1016/j.jbusres.2012.09.007
Yang, Q., Pang, C., Liu, L., Yen, D. C., & Michael Tarn, J. (2015). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior, 50, 9-24. https://doi.org/10.1016/j.chb.2015.03.058
Zhang, W.-G., Zhang, Q., Mizgier, K. J., & Zhang, Y. (2017). Integrating the customers’ perceived risks and benefits into the triple-channel retailing. International Journal of Production Research, 55(22), 6676-6690. https://doi.org/10.1080/00207543.2017.1336679
Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model – a critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41-62.
Zhou, Z., Jin, X.-L., & Fang, Y. (2014). Moderating role of gender in the relationships between perceived benefits and satisfaction in social virtual world continuance. Decision Support Systems, 65, 69-79. https://doi.org/10.1016/j.dss.2014.05.004