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      Research trends of sustainability and marketing research, 2010–2020: Topic modeling analysis

      research-article
      a , b ,
      Heliyon
      Elsevier
      Sustainability, Marketing, Topic modeling, Latent dirichlet allocation (LDA), Research trend

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          Abstract

          In recent decades, rapid growth has been observed in the incorporation of sustainability into marketing. Accordingly, the contrasting relationships between them have been carefully studied to assess the relevance of one idea to the other and vice versa. In response to this change, scholars and practitioners have been tasked with exploring the trends in sustainability and marketing. Therefore, the purpose of this study is to investigate existing literatures on both sustainability and all levels of marketing, determine the research trends and provide implications of applying the trends for future research and practices. This research has investigated only the title, abstract, and keywords of 2147 articles that were published between 2010 and 2020 in SSCI or SCIE indexed journals by applying the topic modeling based on the Latent Dirichlet Allocation (LDA) model. The results show that the research trend has shifted from general sustainable concept to more environmental and industrial technology based on the empirical evidence of 14 latent topics of sustainability and marketing. This article aids in understanding the recent research trend in sustainability and marketing, and the findings will be a valuable resource for future scholars and practitioners. It contributes to both existing and future literatures by providing valuable insights from recent research trend in sustainability and marketing and by providing recommendations for future research avenue. Among other bibliometric review articles, this is the most up-to-date comprehensive and empirical article, providing overview of the research trend.

          Highlights

          • 14 research topics of sustainability and marketing over 2010–2020 were obtained from the Latent Dirichlet Allocation.

          • Two hot topics and one cold topic were identified.

          • The research trend has shifted from general sustainability concept to environmental and industrial technology.

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          Most cited references185

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          Finding scientific topics.

          A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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            Circular Economy: The Concept and its Limitations

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              Industry 4.0: state of the art and future trends

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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                05 March 2023
                March 2023
                05 March 2023
                : 9
                : 3
                : e14208
                Affiliations
                [a ]Center for Entrepreneurship Studies, Dong-A University, 225 Gudeok-ro, Seo-gu, Busan, Republic of Korea
                [b ]Da Vinci College of General Education, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea
                Author notes
                []Corresponding author. e010120302@ 123456cau.ac.kr
                Article
                S2405-8440(23)01415-9 e14208
                10.1016/j.heliyon.2023.e14208
                10025026
                36950617
                231b5d48-3895-45cb-8930-2ebd83a7c5b2
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 June 2022
                : 22 February 2023
                : 23 February 2023
                Categories
                Research Article

                sustainability,marketing,topic modeling,latent dirichlet allocation (lda),research trend

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