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Talafidaryani, M., Jalali, S. M. & Moro, S. (N/A). Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting. Journal of Information Science. N/A

M. Talafidaryani et al.,  "Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting", in Journal of Information Science, vol. N/A, N/A

@article{talafidaryaniN/A_1730780469895,
	author = "Talafidaryani, M. and Jalali, S. M. and Moro, S.",
	title = "Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting",
	journal = "Journal of Information Science",
	year = "N/A",
	volume = "N/A",
	number = "",
	doi = "10.1177/01655515221148365",
	url = "https://journals.sagepub.com/doi/10.1177/01655515221148365"
}

TY  - JOUR
TI  - Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting
T2  - Journal of Information Science
VL  - N/A
AU  - Talafidaryani, M.
AU  - Jalali, S. M.
AU  - Moro, S.
PY  - N/A
SN  - 0165-5515
DO  - 10.1177/01655515221148365
UR  - https://journals.sagepub.com/doi/10.1177/01655515221148365
AB  - Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline. 
ER  -