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Predicting Consumers’ Decision-Making Styles by Analyzing Digital Footprints on Facebook

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Listed:
  • Yuh-Jen Chen

    (Department of Accounting and Information Systems, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, R. O. C.)

  • Yuh-Min Chen

    (Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, R. O. C.)

  • Yu-Jen Hsu

    (Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, R. O. C.)

  • Jyun-Han Wu

    (Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, R. O. C.)

Abstract

In the past, enterprises used time-consuming questionnaire surveys and statistical analysis to formulate consumer profiles. However, explosive growth in social media had produced enormous quantities of texts, images, and videos, which is sometimes referred to as a digital footprint. This provides an alternative channel for enterprises seeking to gain an objective understanding of their target consumers. Facilitating the analysis of data used in the formulation of a marketing strategy based on digital footprints from online social media is crucial for enterprises seeking to enhance their competitive advantage in today’s markets. This study develops an approach for predicting consumer decision-making styles by analyzing digital footprints on Facebook to assist enterprises in rapidly and correctly mastering the consumption profile of consumers, thereby reducing marketing costs and promoting customer satisfaction. This objective can be achieved by performing the following tasks: (i) designing a process for predicting consumer decision-making styles based on the analysis of digital footprints on Facebook, (ii) developing techniques related to consumer decision-making style prediction, and (iii) implementing and evaluating a consumer decision-making style prediction mechanism. In the practical experiment, we obtained questionnaires and various digital footprint contents (including “Likes,” “Status,” and “Photo/Video”) from 3304 participants in 2018, 2644 of which were randomly selected as a training dataset, with the remaining 660 participants forming a testing dataset. The experimental results indicated that the accuracy increased to 75.88% and proved that the approach proposed in this study can effectively predict consumers’ decision-making styles.

Suggested Citation

  • Yuh-Jen Chen & Yuh-Min Chen & Yu-Jen Hsu & Jyun-Han Wu, 2019. "Predicting Consumers’ Decision-Making Styles by Analyzing Digital Footprints on Facebook," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 601-627, March.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:02:n:s0219622019500019
    DOI: 10.1142/S0219622019500019
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    References listed on IDEAS

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    1. Huosong Xia & Zhe Hou, 2016. "Consumer use intention of online financial products: the Yuebao example," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-12, December.
    2. Yuh-Jen Chen & Yuh-Min Chen & Chien-Wei Fu, 2017. "Identifying Desirable Product Specifications from Target Customers’ Chinese eWOM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 545-572, March.
    3. Daniel Preoţiuc-Pietro & Svitlana Volkova & Vasileios Lampos & Yoram Bachrach & Nikolaos Aletras, 2015. "Studying User Income through Language, Behaviour and Affect in Social Media," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
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