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A text mining and topic modelling perspective of ethnic marketing research

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  • Moro, Sérgio
  • Pires, Guilherme
  • Rita, Paulo
  • Cortez, Paulo

Abstract

This study presents an enhanced automated approach based on literature analysis and synthesis for establishing the dimensions of the ethnic marketing literature, covering a set of 239 journal articles published by nine major publishers. The approach reported is enhanced by two novel procedures to address previously identified limitations, namely: definition of a relevant dictionary based on both a sufficient lexicon extracted from a definition of the core theme and a conditional dictionary, with related but non-core terms; and a visually appealing pictorial representation to summarize the discovered topics. The application of the method to ethnic marketing indicates that ethnic marketing research is characterized by high conceptual heterogeneity, although a clear definition of “ethnic marketing” is imperative for research development. Overall, the paper advances an approach with considerable scalability advantages when compared with extant approaches, an important issue to consider when textual sources become big data.

Suggested Citation

  • Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2019. "A text mining and topic modelling perspective of ethnic marketing research," Journal of Business Research, Elsevier, vol. 103(C), pages 275-285.
    • Handle: RePEc:eee:jbrese:v:103:y:2019:i:c:p:275-285
    DOI: 10.1016/j.jbusres.2019.01.053
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    References listed on IDEAS

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    Cited by:

    1. Hugo S. Gonçalves & Sérgio Moro, 2023. "On the economic impacts of COVID‐19: A text mining literature analysis," Review of Development Economics, Wiley Blackwell, vol. 27(1), pages 375-394, February.
    2. Julio Cesar Alonso Cifuentes, 2020. "Una introducción a la construcción de Word Clouds (para economistas) en R," Icesi Economics Lecture Notes 18187, Universidad Icesi.
    3. Rahul Kumar & Rahul Thakurta, 2021. "Exfoliating decision support system: a synthesis of themes using text mining," Information Systems and e-Business Management, Springer, vol. 19(1), pages 247-279, March.
    4. Ozcan Saritas & Pavel Bakhtin & Ilya Kuzminov & Elena Khabirova, 2021. "Big data augmentated business trend identification: the case of mobile commerce," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1553-1579, February.
    5. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    6. Despina S. Giakomidou & Athanasios Kriemadis & Dimitrios K. Nasiopoulos & Dimitrios Mastrakoulis, 2022. "Re-Engineering of Marketing for SMEs in Energy Market through Modeling Customers’ Strategic Behavior," Energies, MDPI, vol. 15(21), pages 1-20, November.
    7. Barkemeyer, Ralf & Faugère, Christophe & Gergaud, Olivier & Preuss, Lutz, 2020. "Media attention to large-scale corporate scandals: Hype and boredom in the age of social media," Journal of Business Research, Elsevier, vol. 109(C), pages 385-398.
    8. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    9. Marco Guerzoni & Massimiliano Nuccio & Federico Tamagni, 2022. "Discovering pre-entry knowledge complexity with patent topic modeling and the post-entry growth of Italian firms," LEM Papers Series 2022/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Chatzopoulou, Elena & Navazhylava, Kseniya, 2022. "Ethnic brand identity work: Responding to authenticity tensions through celebrity endorsement in brand digital self-presentation," Journal of Business Research, Elsevier, vol. 142(C), pages 974-987.
    11. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    12. Duan, Dinglin & Gao, Zhifeng & Uddin, Md Azhar & Nian, Yefan & Nguyen, Ly, 2022. "Tracing the Trends in Consumer Preferences for Eco-labeled Food: A Text Mining and Topic Modeling Approach," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322419, Agricultural and Applied Economics Association.

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