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

Damianos P. Sakas () and Dimitrios P. Reklitis
Additional contact information
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

A chapter in Information and Communication Technologies for Agriculture—Theme IV: Actions, 2021, pp 269-283 from Springer

Abstract: 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.

Keywords: Agri-logistics; Marketing strategies; Prediction model; Statistical analysis (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84156-0_14

Ordering information: This item can be ordered from
http://www.springer.com/9783030841560

DOI: 10.1007/978-3-030-84156-0_14

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().