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Review

Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach

by
Claudiu Cicea
,
Corina Marinescu
* and
Cristian Silviu Banacu
Faculty of Management, Bucharest University of Economic Studies, 010374 Bucharest, Romania
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2023, 18(1), 19-36; https://doi.org/10.3390/jtaer18010002
Submission received: 30 October 2022 / Revised: 11 December 2022 / Accepted: 15 December 2022 / Published: 21 December 2022
(This article belongs to the Special Issue Multi-Channel Retail and Its Applications in the Future of E-Commerce)

Abstract

Electronic commerce appeared as a new way of managing businesses in the digital era. However, it has also been accelerated by the recent pandemic situation. Retailers had to find new strategies of reaching customers in the online environment. Thus, concepts such as multi-channel and omni-channel retailing have gained the attention of both retailers and researchers in this field. This paper aims at using a text-mining approach in order to reveal the researchers focus on this theme in a period that also precedes and covers the COVID-19 pandemic. The research methodology follows five steps that are necessary in order to obtain a relevant collection of documents that will further provide the content to be analyzed. These steps refer to: (1) Creating the database of documents for analysis purposes; (2) identifying geographic areas for separating the collection’s documents; (3) framing a thematic dictionary of descriptors; (4) exploring the text using text mining approach; and (5) correspondence analysis. The discussion of the main findings is constructed starting with the geographic and the temporal distribution of documents and the design of a thematic dictionary of descriptors. Then, exploring the content of the documents provides information on the frequency of descriptors and reveals clusters of descriptors along with a link analysis. All of them are presented separately on geographic regions. Finally, the correspondence analysis of descriptors versus years provides the proximity maps and reveals the preferred topics and less approached themes. Among the main findings, one can highlight: (1) The greatest contributor in terms of documents related to the theme of interest is the United States; (2) a higher number of connections (and stronger) among descriptors for America as compared to the other two regions; (3) some categories of descriptors are specific to a particular year, which means that there are different themes under the researchers lens depending on the period; (4) the most frequently used descriptors are included in the following categories from the dictionary: Online retail environment and Consumer behavior, regardless of the region. In the end of this paper, research limitations and guidelines for future research are elaborated.
Keywords: electronic commerce; multi-channel retail; omni-channel retail; online retailing; online shopping electronic commerce; multi-channel retail; omni-channel retail; online retailing; online shopping

Share and Cite

MDPI and ACS Style

Cicea, C.; Marinescu, C.; Banacu, C.S. Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 19-36. https://doi.org/10.3390/jtaer18010002

AMA Style

Cicea C, Marinescu C, Banacu CS. Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach. Journal of Theoretical and Applied Electronic Commerce Research. 2023; 18(1):19-36. https://doi.org/10.3390/jtaer18010002

Chicago/Turabian Style

Cicea, Claudiu, Corina Marinescu, and Cristian Silviu Banacu. 2023. "Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach" Journal of Theoretical and Applied Electronic Commerce Research 18, no. 1: 19-36. https://doi.org/10.3390/jtaer18010002

APA Style

Cicea, C., Marinescu, C., & Banacu, C. S. (2023). Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 19-36. https://doi.org/10.3390/jtaer18010002

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