Predicting consumer healthy choices regarding type 1 wheat flour

Mariantonietta Fiore, Crescenzio Gallo, Evangelos Tsoukatos, Piermichele La Sala

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Purpose: Healthy and safety food issues are more and more becoming the purchasing process core of conscious consumer. “Type 1” wheat flour means higher protein and ash content. The purpose of this paper is to investigate the attributes usually referred to the characteristics of wheat flour known to consumers and at implementing a predictive model of purchasing that allows to make correct decisions without the necessary experience of a real human expert. Design/methodology/approach: In order to investigate the research aims of the paper, an online survey was carried out and conducted by means of the Google Forms in the detection time January-April 2016. The online survey collected responses from 467 Italian respondents asked to give feedback about their buying habits of various types of flour. The responses were analyzed through a data mining approach. This paper implements predictive analytics to create a statistical model of future behavior by means of a machine learning algorithms. Findings: In line with recent healthy and dynamic trends in the food industry, conscious consumer seems to be willing to pay a price for “type 1” wheat flour that is four times higher than the price related to the basic types of wheat flour. Social implications: Consumer seems not to know well the “type 1” wheat flour and its healthy characteristics; then, it should be crucial to implement promotional strategies and marketing hand in hand. Promotion can be a key element in putting across the health benefits of special kinds of wheat flour. Originality/value: Highlighting health issues about the “type 1” wheat flour gives insights and sheds some light on the crucial need of changing eating and purchasing behavior. Then, originality of this paper can be found in the used predictive algorithm of the artificial intelligence.

Original languageEnglish
Pages (from-to)2388-2405
Number of pages18
JournalBritish Food Journal
Volume119
Issue number11
DOIs
Publication statusPublished - 2017

Keywords

  • Basis types of wheat flour
  • Consumer behaviour
  • Healthy food
  • Machine learning algorithms

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    Fiore, M., Gallo, C., Tsoukatos, E., & La Sala, P. (2017). Predicting consumer healthy choices regarding type 1 wheat flour. British Food Journal, 119(11), 2388-2405. https://doi.org/10.1108/BFJ-04-2017-0200