Evidence map›Paper›PMID 41243204›Full record

SynthesisComprehensive reviews in food science and food safety2025

Emerging Technologies for Investigating Food Consumer Behavior: A Systematic Review.

Kyriaki Kechri, Christina Kleisiari, Leonidas Sotirios Kyrgiakos, Marios Vasileiou, Dimitra Despoina Tosiliani, Vasileios Angelopoulos, George Kleftodimos, George Vlontzos

Abstract readSystematic Review
In one paragraph

Synthesis in Comprehensive reviews in food science and food safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Emerging Technologies for Investigating Food Consumer Behavior: A Systematic Review.Comprehensive reviews in food science and food safety · 2025
    Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kyriaki KechriDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.ORCID 0009-0000-1500-028X
Christina KleisiariDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.
Leonidas Sotirios KyrgiakosDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.
Marios VasileiouDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.
Dimitra Despoina TosilianiDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.
Vasileios AngelopoulosDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.
George KleftodimosMediterranean Agronomic Institute of Montpellier (CIHEAM-IAMM), Montpellier, France.
George VlontzosDepartment of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece.

Funding

HORIZON EUROPE
6 · The paper itself

Abstract

The evolving nature of food preferences and consumption patterns highlights the need for ongoing research in food consumer behavior. Most existing research relies on traditional methods, like questionnaires, which are often costly, time-consuming, and prone to bias. The increasing integration of emerging technologies, including Artificial Intelligence (AI), Virtual Reality (VR), Social Media Analytics (SMA), and Big Data, into the food sector presents novel opportunities to overcome these limitations. This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, identified 628 records, out of which 159 eligible articles were included. These examine how technological innovations contribute to understanding food trends and consumption preferences, as well as consumer attitude towards these technologies. The findings reveal the considerable potential of big data, SMA, and transactional analytics to provide large-scale, diverse, and real-time data into consumer behavior. Machine Learning (ML) techniques improve the analysis and interpretation of such complex datasets, enabling high predictive capability and a more precise market segmentation to provide consumers with personalized marketing content. Immersive technologies like VR offer realistic simulations of food purchasing behaviors and adapt to multiple research scenarios, overcoming limitations of traditional research methods. However, most technology-based studies remain primarily quantitative, limiting depth of understanding. Challenges in automated data interpretation, reduced sensory immersion in VR environments, users' unfamiliarity and data privacy concerns need also to be addressed. Thus, future research should focus on technological advancement, improving usability and establishing ethical frameworks to foster consumer trust, while also integrating qualitative methods too beyond only relying on technology-based outcomes.

Indexed as

Consumer BehaviorArtificial IntelligenceBig DataFood PreferencesHumansMachine LearningSocial MediaVirtual Realitybig dataconsumer insightsdigital analyticsfood sectorVirtual Reality

Identifiers

PMID41243204
PMCPMC12620339

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.