Importance–Performance Map Analysis of the Drivers for the Acceptance of Genetically Modified Food with a Theory of Planned Behavior Groundwork

The revolution in biotechnology at the end of the 20th century has improved agricultural productivity, reduced environmental impact, and enhanced food nutrition. However, genetically modified food (GMF) consumption remains low, particularly in the European Union, including Spain. This study develops a GMF acceptance model based on the Theory of Planned Behavior, incorporating five key variables alongside gender and age as control factors. Attitude-related variables include perceived benefit (PB) and perceived risk (PR). Perceived control factors involve food neophobia (PHOB) and knowledge (KNOWL) about GMFs. Additionally, subjective norm (SN) is considered. Using a sample of 728 Spanish consumers, the model was validated, explaining 70% of the variance and demonstrating strong predictive capability. The results so PB, KNOWL, and SN positively influence GMF acceptance, whereas PR and PHOB have negative effects. PB and SN emerged as the most influential variables, which are also highlighted as priorities in the importance-performance map analysis (IPMA). Based on these findings, strategies have been proposed to enhance perceived benefits and influence subjective norms, potentially increasing GMF acceptance. This study provides valuable insights into consumer behavior and offers guidance for policymakers and industry stakeholders to promote the adoption of GMFs.

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Publication Details

Journal
Foods
Published
2025-03-09
DOI
https://doi.org/10.3390/foods14060932
Citations
6
Primary Topic
Genetically Modified Organisms Research
Type
article
Field-Weighted Citation Impact
10.50
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article

Importance–Performance Map Analysis of the Drivers for the Acceptance of Genetically Modified Food with a Theory of Planned Behavior Groundwork

Jorge de Andrés Sánchez, Mar Souto-Romero, Mario Arias-Oliva, María Puelles-Gallo
6 citations
Foods
Genetically Modified Organisms Research
10.50
article

Importance–Performance Map Analysis of the Drivers for the Acceptance of Genetically Modified Food with a Theory of Planned Behavior Groundwork

Jorge de Andrés Sánchez, Mar Souto-Romero, Mario Arias-Oliva, María Puelles-Gallo
article en
6 citations

Abstract

The revolution in biotechnology at the end of the 20th century has improved agricultural productivity, reduced environmental impact, and enhanced food nutrition. However, genetically modified food (GMF) consumption remains low, particularly in the European Union, including Spain. This study develops a GMF acceptance model based on the Theory of Planned Behavior, incorporating five key variables alongside gender and age as control factors. Attitude-related variables include perceived benefit (PB) and perceived risk (PR). Perceived control factors involve food neophobia (PHOB) and knowledge (KNOWL) about GMFs. Additionally, subjective norm (SN) is considered. Using a sample of 728 Spanish consumers, the model was validated, explaining 70% of the variance and demonstrating strong predictive capability. The results so PB, KNOWL, and SN positively influence GMF acceptance, whereas PR and PHOB have negative effects. PB and SN emerged as the most influential variables, which are also highlighted as priorities in the importance-performance map analysis (IPMA). Based on these findings, strategies have been proposed to enhance perceived benefits and influence subjective norms, potentially increasing GMF acceptance. This study provides valuable insights into consumer behavior and offers guidance for policymakers and industry stakeholders to promote the adoption of GMFs.

FoodsVol. 14(6)
Universidad Complutense de Madrid (ES), Universidad Rey Juan Carlos (ES), Universitat Rovira i Virgili (ES)
Zero hunger
Openalex Percentile: Top 2%
Genetically Modified Organisms Research
10.50
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Importance–Performance Map Analysis of the Drivers for the Acceptance of Genetically Modified Food with a Theory of Planned Behavior Groundwork — Jorge de Andrés Sánchez, Mar Souto-Romero, et al. · Foods (2025) | TGRS Research Map | TGRS