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Predictive Model for Estimating the Impact of Technical Issues on Consumers Interaction in Agri-Logistics Websites

In: Information and Communication Technologies for Agriculture—Theme IV: Actions

Author

Listed:
  • Damianos P. Sakas

    (School of Applied Economics and Social Sciences, Agricultural University of Athens)

  • Dimitrios P. Reklitis

    (School of Applied Economics and Social Sciences, Agricultural University of Athens)

Abstract

Today’s evolving digital marketing tactics have brought about a considerable change in marketing strategies. As a consequence, it is a growing challenge to refine the approach used within logistics sites to appeal to potential customers. Marketers, designers and developers have to take into consideration all the complex and interconnected behavioral factors of users, in order to be more efficient in their decision-making process. Taking this into account, a three-stage methodological process has been developed by the authors in order to prognosticate and optimize the potential customers’ lead generation into these websites. Firstly, numerous web analytics have been extracted from different world-leading agri-logistics websites. Following the data gathering process, the authors used statistical analysis to examine the possible inter-correlations between the harvested web analytics metrics. The findings informed the creation of a Fuzzy Cognitive Map (FCM), as an essential step to build the predictive model. Finally, the authors created a process and agent-based simulation model which is based on the analysis of the predictive model structure. Numerous research topics within the agri-logistics industry are answered by the findings of this research which presents data and results that add to the necessity to prognosticate effective digital marketing strategies for complicated situations.

Suggested Citation

  • Damianos P. Sakas & Dimitrios P. Reklitis, 2021. "Predictive Model for Estimating the Impact of Technical Issues on Consumers Interaction in Agri-Logistics Websites," Springer Optimization and Its Applications, in: Dionysis D. Bochtis & Simon Pearson & Maria Lampridi & Vasso Marinoudi & Panos M. Pardalos (ed.), Information and Communication Technologies for Agriculture—Theme IV: Actions, pages 269-283, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84156-0_14
    DOI: 10.1007/978-3-030-84156-0_14
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