The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Consumer decision‐making in omnichannel retailing: Literature review and future research agenda
Abstract
The emergence of omnichannel retailing has revolutionized the way traditional e‐commerce business operates, subsequently bringing fundamental changes to consumer expectations and decision‐making processes. Despite the unquestionable relevance of this area of inquiry, the focal literature concerning omnichannel retailing remains sporadic and fragmented. With this in mind, the purpose of the current paper is to provide a comprehensive and concise state of the art literature review on omnichannel retailing. More specifically, we engage and draw upon the cognitive‐affective‐conative model to understand consumer behaviour within the omnichannel retailing context. The current study is built based on a review of total 131 research papers that were identified following a comprehensive search of the Web of Science database, capturing the time period between January 2011 and April 2020. This set of work was reviewed thoroughly to explore the aims, methodology and key contributions. In addition to engaging a systematic assessment and rigorous evaluation of the studies, we also extend literature by studying the relationship between omnichannel retailing and consumer decision making, with specific attention to consumer motivation, attitude and behaviour towards omnichannel retailing. Previous studies suggest consumer behaviour in omnichannel retailing to be a promising yet underexplored area with several potential avenues for future research. Among these, particularly lucrative directions include theory‐driven research, comparative cross‐cultural studies and qualitative approaches that capture rich first‐hand accounts of consumer decision‐making encounters. The current paper is timely and advantageous because it offers a holistic picture of omnichannel retailing research and provides literature‐driven evidence about a range of relevant consumer behavioural dimensions. It also integrates consumer responses using the cognitive‐affective‐conative model to advance our understanding of consumer decision‐making in the omnichannel customer journey. Recommendations for future research are provided using the Theory, Methodology and Context (TMC) framework. We conclude the paper by discussing implications for academics and practitioners.
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Convergent and discriminant validation by the multitrait-multimethod matrix.
Campbell, D. T., & Fiske, D. W. (1959) · Psychological Bulletin · doi:10.1037/h0046016 · Cited by 17,116
Mixed Methods Research: A Research Paradigm Whose Time Has Come
Johnson, R. B., & Onwuegbuzie, A. J. (2004) · Educational Researcher · doi:10.3102/0013189x033007014 · Cited by 11,170
Predicting green product consumption using theory of planned behavior and reasoned action
Paul, J., Modi, A., & Patel, J. (2016) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2015.11.006 · Cited by 2,399
The impact of electronic word-of-mouth communication: A literature analysis and integrative model
Cheung, C. M. K., & Thadani, D. R. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.06.008 · Cited by 2,005
Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future
Meesala, A., & Paul, J. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.10.011 · Cited by 668
‘Masstige’ marketing: A review, synthesis and research agenda
Kumar, A., Paul, J., & Unnithan, A. B. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.09.030 · Cited by 437
Five decades of research on foreign direct investment by MNEs: An overview and research agenda
Paul, J., & Feliciano-Cestero, M. M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.04.017 · Cited by 401
International franchising: A literature review and research agenda
Rosado-Serrano, A., Paul, J., & Dikova, D. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2017.12.049 · Cited by 401
Zhang, M., Ren, C., Wang, G. A., & He, Z. (2018) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2018.02.002 · Cited by 370
Dahl, A. J., D’Alessandro, A. M., Peltier, J. W., & Swan, E. L. (2018) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-04-2018-0051 · Cited by 39
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Enhancing Consumer Behavior and Experience Through AI-Driven Insights Optimization
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Metadata
- Authors
- Mishra, R., Singh, R. K., & Koles, B.
- Venue
- International Journal of Consumer Studies
- Publisher
- Wiley
- Published
- 2020-10-21
- Volume
- 45
- Issue
- 2
- Pages
- 147-174
- ISSN
- 1470-6423 (ISSN-L)
- 1470-6431
- OpenAlex
- W3089165149
- Cited by
- 401
- References
- 10
- FWCI
- 57.01
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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