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Study of Online Shopping Behavior Differences Between Goal-Directed Search and Exploratory Browsing in Intermediate Choice List on E-Commerce: How Do People Shop With Purpose and Without Purpose in E-Commerce and What Role ICL Be in the Online Shopping Process

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Published:09 July 2022Publication History

ABSTRACT

With the development of the internet and shipping, and also the appearance of COVID-19, the growth of E-commerce is rising in a fast way. People change their shopping behavior from offline to online. It means there is a significant change in customers’ shopping process. Lots of scholars did research about it, such as from AIDMA [1] to AISAS [2], and also from Buyer Decision Process [3] to Dynamic Buyer Decision Process [4]. Both of them talk about how customers change their behavior and process of shopping in the digital generation. So it's necessary to know clearly how customers do and think to make the serve, user interface, and user experience in websites better. But the study found that most of those existing models are for “goal-directed search” shopping (shopping with purpose). But in fact, “exploratory browsing” shopping is rising these days, it's better to know more about it, so this study focuses on the process of how people do online shopping in “goal-directed search” shopping and “exploratory browsing” shopping. Besides, this study also found because there is too much information on the internet now, people will make the two phases of the decision in the process of online shopping [5], and they need to mark and record some product information in the middle of these two phases. Hence, they need a space to save this information is called “Intermediate Choice List(ICL)” [6]. Therefore, this study also explored how ICL takes a hand in the whole online shopping process. At the end of the study built a complete shopping process model for future use in E-commerce website design.

References

  1. Hall, S.R., Retail advertising and selling;: Advertising, merchandise display, sales-planning, salesmanship, turnover and profit-figuring in modern retailing, ... typography as applied to retail advertising". 1924, New York: McGraw-Hill book company.Google ScholarGoogle Scholar
  2. Dentsu. Buying Behavior Data, Models. 2008.Google ScholarGoogle Scholar
  3. Kotler, P., Marketing Management, Analysis, Planning, Implementation, and Control. 1988: Prentice Hall.Google ScholarGoogle Scholar
  4. Zellweger, P., Web-Based sales: Defining The Cognitive Buyer. Electronic Markets, 1997. 7(3): p. 10-16.Google ScholarGoogle Scholar
  5. Ha¨ubl, G. and V. Trifts, Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 2000. 19(1): p. 4–21.Google ScholarGoogle Scholar
  6. Popovicha, D. and R. Hamilton, Intermediate Choice Lists: How Product Attributes Influence PurchaseLikelihood in a Self-Imposed Delay. Journal of Retailing, 2021: p. 251–266.Google ScholarGoogle Scholar
  7. Department of Statistics, M.o.E.A., ROC. Online Sales Statistical Survey of Retail Industry. 2021; Available from: https://dmz26.moea.gov.tw/GMWeb/investigate/InvestigateEA05.aspx.Google ScholarGoogle Scholar
  8. eMarketer. Global Ecommerce Forecast 2021:Digital Leads the Way, Building on 2020’s Growth. 2021; Available from: https://www.emarketer.com/content/global-ecommerce-forecast-2021.Google ScholarGoogle Scholar
  9. Kim, C., , Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications, 2012. 11: p. 374–387.Google ScholarGoogle Scholar
  10. Batra, R. and O.T. Ahtola, Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes. Marketing Letters, 1990. 2(2): p. 159-170.Google ScholarGoogle Scholar
  11. Moe, W.W., Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream. JOURNAL OF CONSUMER PSYCHOLOGY, 2003. 14(1&2): p. 29-39.Google ScholarGoogle Scholar
  12. Babin, B.J., W.R. Darden, and M. Griffin, Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value JOURNAL OF CONSUMER RESEARCH, 1994. 20.Google ScholarGoogle Scholar
  13. Chou, S.-C., The Relationship between Consumers’ Intention to Revisit and Repurchase in Online Shopping. 2014, National Taiwan University. p. 64.Google ScholarGoogle Scholar
  14. Childers, T.L., , Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 2001. 77: p. 511–535.Google ScholarGoogle Scholar
  15. To, P.-L., C. Liao, and T.-H. Lin, Shopping motivations on Internet: A study based on utilitarian and hedonic value. Technovation, 2007. 27: p. 774–787.Google ScholarGoogle Scholar
  16. Hirschman, E.C. and M.B. Holbrook, Hedonic consumption: emerging concepts, methods and propositions. Journal of Marketing Management, 1982. 46: p. 92-101.Google ScholarGoogle Scholar
  17. Pace, S., A grounded theory of the flow experiences of Web users. Human-Computer Studies, 2004. 60: p. 327–363.Google ScholarGoogle Scholar
  18. Hoffman, D.L. and T.P. Novak, A New Marketing Paradigm for Electronic Commerce. The Information Society, 1997. 13: p. 43-54.Google ScholarGoogle Scholar

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  1. Study of Online Shopping Behavior Differences Between Goal-Directed Search and Exploratory Browsing in Intermediate Choice List on E-Commerce: How Do People Shop With Purpose and Without Purpose in E-Commerce and What Role ICL Be in the Online Shopping Process

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    • Published in

      cover image ACM Other conferences
      ICEEG '22: Proceedings of the 6th International Conference on E-Commerce, E-Business and E-Government
      April 2022
      439 pages
      ISBN:9781450396523
      DOI:10.1145/3537693

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      Publication History

      • Published: 9 July 2022

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