Consumer Behaviour Toward ’Smart’ Labels: A Systematic Literature Review Using the Technology Acceptance Model Framework
(with Bazzani Claudia) – manuscript in preparation
(with Scarpa Riccardo and Bazzani Claudia) – manuscript in preparation.
29/08/2023 – 01/01/2023
Accepted Poster Presentation for XVII EAAE Congress 2023
Rennes, France
Title of the contribution
Investigating the optimal number of preference classes: A comparative study.
Paper abstract/description of the contribution
The increasing popularity of Choice Experiments has positively influenced the number of software packages available today for Discrete Choice Models (DCM) estimation. In the present work, we focus on Latent Class Models (LCM) and investigate whether and how the selection of software packages can impact LCM estimates. We tested five software packages (Stata, Nlogit, LG Choice, Xlogit and Apollo) on four datasets. Results from this work show that the software selection for LCM estimation affects model performance, especially in determining the optimal number of classes. This latter issue is of extreme importance since the selection of the software package may influence the definition of market segments. This might significantly impact the development of marketing strategies for the targeting and positioning of food products.
Authors
Claudia Bazzani, Marta Bonioli, Riccardo Scarpa.
26/06/20234 – 28/06/2026 Accepted Presentation for AISSA#under40, Firenze
Title of the contribution
Consumer perception and preferences towards upcycled foods
Paper abstract/description of the contribution
This study explores consumer perceptions and preferences for upcycled foods—products made from ingredients that would otherwise be wasted, procured, and produced sustainably. Despite their environmental benefits, these products face challenges, such as consumer resistance to higher prices and scepticism, which hinder market growth. The research involves a systematic review of 37 articles from 2019-2023, employing sentiment analysis to examine consumer sentiments from scholarly articles, online content, and Twitter data. Findings reveal regional differences in awareness and willingness to buy upcycled foods, with environmental benefits as the main motivator. Consumers link upcycled foods to reducing food loss and waste, suggesting that marketers should emphasize these benefits. The study highlights consumer behaviour, acknowledges scepticism, and suggests strategies to promote sustainable food consumption, crucial for expanding the upcycled food market and encouraging environmentally conscious choices.
Authors
Marta Bonioli, Claudia Bazzani, Diego Begalli.
26/06/20234 – 28/06/2026 Accepted Poster Presentation for AISSA#under40, Firenze
Title of the contribution
Consumer perception and preferences for ’smart’ labels
Paper abstract/description of the contribution
This study examines consumer perceptions and preferences for smart labels (e-labels) in food production, which provide real-time data to enhance food safety, traceability, and environmental sustainability. Despite their benefits, consumer adoption is low due to mistrust and lack of knowledge. Using a systematic review and the Technology Acceptance Model (TAM), the study analyzes factors influencing acceptance, such as perceived usefulness, ease of use, and attitudes. Consumers prefer detailed product information and recognize benefits like waste reduction and improved supply chain communication. However, awareness gaps highlight the need for effective communication strategies. User-friendly designs, especially for QR code scanning, are crucial for perceived ease of use. Factors like environmental sensitivity and demographics affect perceptions, particularly among younger consumers. To boost smart label adoption, consumer education and accessibility for non-digital natives are essential. The study calls for in-depth case studies to understand consumer behaviour and develop adoption strategies, emphasizing a holistic approach to integrating smart labels into everyday life.
Authors
Marta Bonioli, Claudia Bazzani.