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ISSN : 1738-3110

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Vol.19 No.7

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A qualitative comparison study of information search behavior in online distribution

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2021, v.19 no.7, pp.61-73
https://doi.org/https://doi.org/10.15722/jds.19.7.202107.61
MIAO, Miao
MIAO,, M. (2021). A qualitative comparison study of information search behavior in online distribution. The Journal of Distribution Science, 19(7), 61-73, https://doi.org/https://doi.org/10.15722/jds.19.7.202107.61

Abstract

Purpose: This study offers suggestions to e-commerce companies for increasing shoppers' repurchase intention by considering the effect of distribution information in online shopping. It applies complexity theory to incorporate habitual information search behavior and shopper characteristics into the Stimulus-Organism-Response model and indicates how these complex factors work together in online shopping. Research design, data, and methodology: This study used an interview survey of 158 Vietnamese consumers with an experience of online shopping. A fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to examine the relationship between antecedents and outcomes depending on complex conditions in the given contexts. Results: The results (1) indicate the importance of observing information search patterns and investigating their influence on online distribution, and (2) clarify what kind of configurations, under what conditions, predict a high or low outcome; this provides evidence and hints for the development of frameworks for future studies. Conclusions: The findings suggest that shoppers' unconscious, habitual behavior can work with conscious attitude factors, such as satisfaction, to increase their repurchase intention. Hence, e-commerce companies should consider how to present useful distribution information and create functions that allow shoppers to engage with a variety of information while increasing their repurchase intention on the site.

keywords
online shopping, repurchase intention, habitual information search behavior, complexity theory, distribution science

Reference

1.

Amoroso, D. & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702.

2.

Anderson, R. E. & Srinivasan, S. S. (2003). E-satisfaction and eloyalty: A contingency framework. Psychology & Marketing, 20(2), 123-138.

3.

Ansari, A., Mela, C.F., & Neslin, S.A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60-76.

4.

Anshari, M., Alas, Y., Hardaker, G., Jaidin, J.H., Smith, M., & Ahad, A.D. (2016), Smartphone habit and behavior in Brunei:Personalization, gender, and generation gap. Computers in Human Behavior, 64, 719-727.

5.

Ashraf, A.R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68-93.

6.

Ashraf, A. R., Thongpapanl, N., Menguc, B., & Northey, G. (2017). The role of m-commerce readiness in emerging and developed markets. Journal of International Marketing, 25, 25-51.

7.

Basu, R., K. Guin, K., & Sengupta, K. (2014). Do apparel store formats matter to Indian shoppers? International Journal of Retail & Distribution Management, 42(8), 698-716.

8.

Bhatnagar, A. & Ghose, S. (2004). An analysis of frequency and duration of search on the Internet. Journal of Business, 77(2), 311-330.

9.

Bhatnagar, A. & Papatla, P. (2019). Do habits influence the types of information that smartphone shoppers seek? Journal of Business Research, 94, 89-98.

10.

Brenes, E. R., Ciravegna, L., & Woodside, A. G. (2017). Constructing useful models of firms’ heterogeneities in implemented strategies and performance outcomes. Industrial Marketing Management, 62, 17-35.

11.

Brunner-Sperdin, A., Scholl-Grissemann, U.S., & StokburgerSauer, N.E. (2014). The relevance of holistic website perception. How sense-making and exploration cues guide consumers’ emotions and behaviors. Journal of Business Research, 67, 2515-2522.

12.

Carpenter, J. M. & Balija, V. (2010). Retail format choice in the US consumer electronics market. International Journal of Retail & Distribution Management, 38(4), 258-274.

13.

Chiu, C.-M., Hsu, M.-H., Lai, H., & Chang, C.-M. (2012). Reexamining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision Support Systems, 53, 835-845.

14.

Dutta, C.B. & Das, D.K. (2017). What drives consumers’ online information search behavior? Evidence from England. Journal of Retailing and Consumer Services, 35, 36-45.

15.

Eroglu, S.A., Machleit, K.A., & Davis, L.M. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology & Marketing, 20(2), 139-150.

16.

Fang, J., Shao, Y. & Wen, C. (2016). Transactional quality, relational quality, and consumer e-loyalty: Evidence from SEM and fsQCA. International Journal of Information Management, 36(6), 1205-1217.

17.

Fleenor, C.P. & Raven, P. (2011). Barriers to effective e-business in developing countries. International Business & Economics Research Journal, 1(4), 39-48.

18.

Fuentes, C. & Svingstedt, A. (2017). Mobile phones and the practice of shopping: A study of how young adults use smartphones to shop. Journal of Retailing and Consumer Services, 38, 137-146.

19.

Gao, Y. & Koufaris, M. (2006). Perceptual antecedents of user attitude in electronic commerce. ACM SIGMIS Database, 37(2-3), 42-50.

20.

Gallino, S. & Moreno, A. (2014). Integration of online and offline channels in retail: The impact of sharing reliable. Inventory Availability Information. Management Science, 60(6), 1434-1451.

21.

Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: An empirical investigation among smartphone users, The International Review of Retail, Distribution and Consumer Research, 25, 215-235.

22.

Hair, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet, Journal of Marketing Theory and Practice, 19(2), 139-151.

23.

Hamilton, M., Kaltcheva, V.D., & Rohm, A.J. (2016). Social media and value creation: The role of interaction satisfaction and interaction immersion. Journal of Interactive Marketing, 36, 121-133.

24.

Hasan, B. (2016). Perceived irritation in online shopping: The impact of website design characteristics. Computers in Human Behavior, 54, 224-230.

25.

Ho, T. H. L. & Chen, Y. (2014). Vietnamese consumers’ intention to use online shopping: The role of trust. International Journal of Business and Management, 9(5), 145-159.

26.

Hsu, M.-H., Chang, C.-M., & Chuang, L.-W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35, 45-56.

27.

Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.

28.

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), 75-194.

29.

Khalifa, M. & Liu, V. (2007). Online consumer retention:Contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, 16 (6), 780-92.

30.

Kim, J., Fiore, A. M., & Lee, H. (2007). Influences of online store perception, shopping enjoyment, and shopping involvement on consumer patronage behavior towards an online retailer. Journal of Retailing and Consumer Services, 14(2), 95-107.

31.

Kim, J., Kim, M., Choi, J., & Trivedi, M. (2017). Offline social interactions and online shopping demand: Does the degree of social interactions matter? Journal of Business Research, Available online 1 November.

32.

Kim, S. S. & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying post-adoption phenomena. Management Science, 51(5), 741-755.

33.

Kim, S.Y. & Lim, Y.J. (2010). Consumers’ perceived importance of and satisfaction with internet shopping. Electronic Marketing, 11(3), 148-154.

34.

Kim, Y.A. & Srivastava, J. (2007). Impact of social influence in e-commerce decision making, in ICEC ‘07 Proceedings of the ninth international conference on Electronic commerce in Minneapolis, Minnesota, USA, 2007, pp. 293-302.

35.

Kotzé, T., North, E., Stols, M., & Venter, L. (2012). Gender differences in sources of shopping enjoyment. International Journal of Consumer Studies, 36(4), 416-424.

36.

Kshetri, N. (2007). Barriers to e-commerce and competitive business models in developing counties: A case study. Electronic Commerce Research and Applications, 6, 443-452.

37.

Kumar, A. & Kim, Y. K. (2014). The store-as-a-brand strategy:The effect of store environment on customer responses. Journal of Retailing and Consumer Services, 21, 685-695.

38.

Kuo, Y., Hu, T., & Yang, S. (2013). Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention: The moderating roles of word-of-mouth and alternative attraction. Managing Service Quality: An International Journal, 23(3), 168-187.

39.

Lai, I. K. W. & Hitchcock, M. (2017). Sources of satisfaction with luxury hotels for new, repeat, and frequent travelers: A PLS impact-asymmetry analysis. Tourism Management, 60, 107-129.

40.

Lee, J.Y. & Bell, D.R. (2013). Neighborhood social capital and social learning for experience attributes of products. Marketing Science, 32(6), 960-976.

41.

Limayem, M., Hirt, S.G., & Cheung, C.M.K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737.

42.

Lin, C. & Lekhawipat, W. (2014). Factors affecting online repurchase intention. Industrial Management & Data Systems, 114(4), 597 – 611.

43.

Liu, C. & Forsythe, S. (2010). Sustaining online shopping:Moderating role of online shopping motives. Journal of Internet Commerce, 9(2) 83-103.

44.

Liu, H., Chu, H., Huang, Q., & Chen, X. (2016). Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce. Computers in Human Behavior, 58, 306-314.

45.

Loureiro, S. M. C. & Roschk, H. (2014). Differential effects of atmospheric cues on emotions and loyalty intention with respect to age under online/offline environment. Journal of Retailing and Consumer Services, 21(2), 211-219.

46.

MacKenzie, S. B. & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542-555.

47.

Moriuchi, E. & Takahashi, I. (2016). Satisfaction trust and loyalty of repeat online consumer within the Japanese online supermarket trade. Australasian Marketing Journal, 24(2), 146-156.

48.

Pappas, I. O. (2018). User experience in personalized online shopping: A fuzzy-set analysis. European Journal of Marketing, 52(7/8), 1679-1703.

49.

Pappas, I. O. & Woodside, A. G. (2021). Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in information systems and marketing, International Journal of Information Management, 58, 102310.

50.

Pereira, H.G., Salgueiro, M. de F., & Rita, P. (2016). Online purchase determinants of loyalty: The mediating effect of satisfaction in tourism. Journal of Retailing and Consumer Services, 30, 279-291.

51.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y. ,& Podsakoff, N. P.(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

52.

Ragin, C. C. (2008). Redesigning social inquire: Fuzzy sets and beyond. Chicago: University of Chicago Press.

53.

Ren, L., Qiu, H., Wang, P., & Lin, P. M. (2016). Exploring customer experience with budget hotels: Dimensionality and satisfaction. International Journal of Hospitality Management, 52, 13-23.

54.

Sinha, R. & Swearingen, K. (2001). Comparing recommendations made by online systems and friends. Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.

55.

Sohn, S. (2017). A contextual perspective on consumers'perceived usefulness: The case of mobile online shopping. Journal of Retailing and Consumer Services, 38, 22-33.

56.

Srinivasan, S.S., Anderson, R. & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retail, 78(1), 41-50.

57.

Trafimow, D., Hyman, M. R., Kostyk, A., Wang, C. & Wang, T.(2021). The harmful effect of null hypothesis significance testing on marketing research: An example. Journal of Business Research, 125, 39-44.

58.

Van Slyke, C., Lou, H., Belanger, F., & Sridhar, V. (2010). The influence of culture on consumer-oriented electronic commerce adoption. Journal of Electronic Commerce Research, 11(1), 30-40.

59.

Wang, J., Yang, Z., & Brocato, E.D. (2018). An investigation into the antecedents of prepurchase online search. Information &Management, 55, 285-293.

60.

Wood, W., Quinn, J. M., & Kashy, D. A. (2002). Habits in everyday life: Thought, emotion, and action. Journal of Personality and Social Psychology, 83(6), 1281-1297.

61.

Woodside, A. G. (2014). Embrace•perform•model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67(12), 2495-2503.

62.

Woodside, A. G. (2019). Accurate case-outcome modeling in economics, psychology, and marketing. Psychology &Marketing, 36(11), 1046-1061.

63.

Woodside, A. G., Nagy, G. & Megehee, C. M. (2018). Applying complexity theory: A primer for identifying and modeling firm anomalies. Journal of Innovation & Knowledge, 3(1), 9-25.

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