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Modelling and Analysis of Network Information Data for Product Purchasing Decisions

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Advances in Data Science and Information Engineering

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Abstract

Technology has enabled consumers to gain product information from different online platforms such as social networks, online product reviews and other digital media. Large manufacturers and retailers can make use of this network information to forecast accurately, to manage the demand and thereby to improve profit margin, efficiency, etc. This paper proposes a novel framework to model and analyse consumers’ purchase decision for product choices based on information obtained from two different information networks. The model has also taken into account variables such as socio-economic and demographic characteristics. We develop a utility-based discrete choice model (DCM) to quantify the effect of consumers’ attitudinal factors from two different information networks, namely, social network and product information network. The network information modelling and analysis are discussed in detail taking into account the model complexity, heterogeneity and asymmetry due to the dimension, layer and scale of information in each type of network. The likelihood function, parameter estimation and inference procedures of the full model are also derived for the model. Finally, extensive numeric investigations were carried out to establish the model framework.

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Author information

Authors and Affiliations

  1. Staffordshire University, Stoke on Trent, UK

    Md Asaduzzaman & Samanthika Gallage

  2. University of Nottingham, Nottingham, UK

    Uchitha Jayawickrama

Authors
  1. Md Asaduzzaman
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  2. Uchitha Jayawickrama
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  3. Samanthika Gallage
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Corresponding author

Correspondence to Md Asaduzzaman .

Editor information

Editors and Affiliations

  1. HBS – Hamburg Business School, Institute of Information Systems, University of Hamburg, Hamburg, Hamburg, Germany

    Robert Stahlbock

  2. Department of Computer & Information Science, Fordham University, New York, NY, USA

    Gary M. Weiss

  3. College of Engineering & Computer Science, University of Michigan-Dearborn, Dearborn, MI, USA

    Mahmoud Abou-Nasr

  4. Department of Computer Science, University of Taipei, Taipei City, Taiwan

    Cheng-Ying Yang

  5. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  6. School of Computing and Data Sciences, Wentworth Institute of Technology, Boston, MA, USA

    Leonidas Deligiannidis

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Asaduzzaman, M., Jayawickrama, U., Gallage, S. (2021). Modelling and Analysis of Network Information Data for Product Purchasing Decisions. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_5

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