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Foods
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Market Research of Food Systems and Supply Chains
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Market Research of Food Systems and Supply Chains

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Systems".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 16166

Special Issue Editors

Dr. Attila Gere

E-Mail Website
Guest Editor
Faculty of Food Science, Institute of Food Technology, Department of Postharvest Science, Trade and Sensory Evaluation, Szent István University, H-1118 Budapest, Hungary
Interests: eye tracking; sensometrics; sensory analysis; virtual reality; chemometrics; consumer sensory analysis; food product development; entomophagy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nick Bellissimo

E-Mail Website
Guest Editor
School of Nutrition, Faculty of Community Services, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
Interests: food intake regulation; eating behaviours; pediatrics; exercise physiology; cognitive performance; nutritional physiology; sugars; dietary proteins; exercise- and diet-related energy expenditure; energy imbalances; overweight/obesity; glycemic control; insulin resistance; inflammation
Prof. Dr. Howard Moskowitz

E-Mail Website
Guest Editor
1. MindCart AI, Inc., White Plains, NY 10605, USA
2. Department of Postharvest and Sensory Evaluation, Institute of Food Technology, Faculty of Food Sciences, Szent Istvan University, H-1118 Budapest, Hungary
Interests: mind genomics; horizontal segmentation; product development; consumer behavior; mindsets; regression analysis

Special Issue Information

Dear Colleagues,

The ever-growing product range and ever-changing consumer needs provide a continuous challenge for researchers. New methods and procedures are needed to determine rapid responses to consumer needs. With this information in hand, stakeholders of the food system and supply chain are ready to adapt their products and/or services to meet new consumer needs. As a result of the COVID-19 pandemic, a series of online tools have been brought to the forefront, providing a fast flow of information. This Special Issue aims to cover all aspects related to market research of food systems and supply chains, including but not limited to development of new methods and/or techniques, consumer product development, service development, emotion analysis, definition of consumer mindsets, consumer reactions to food products, and application of big data for the evaluation of market research data.

Dr. Attila Gere
Prof. Dr. Nick Bellissimo
Prof. Dr. Howard Moskowitz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • method development
  • consumer behavior
  • product development
  • service development
  • big data

Published Papers (4 papers)

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Research

15 pages, 2875 KiB  
Article
Positioning Phytosanitary Food Treatments: Exploring the Role of Business-to-Consumer Stakeholder Literacy as an Information Gatekeeper in New Zealand
by Denise M. Conroy, Jennifer Young, Amy Errmann and Tracey Phelps
Foods 2022, 11(14), 2108; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11142108 - 15 Jul 2022
Viewed by 1165
Abstract
Various phytosanitary treatments are used globally to ensure biosecurity for borders, whilst maintaining public health and safety in the consumption of fruits and vegetables. However, public health literacy of phytosanitary treatments is still low. Furthermore, little is known of the literacy on important [...] Read more.
Various phytosanitary treatments are used globally to ensure biosecurity for borders, whilst maintaining public health and safety in the consumption of fruits and vegetables. However, public health literacy of phytosanitary treatments is still low. Furthermore, little is known of the literacy on important information gatekeepers, such as business-to-consumer (B2C) stakeholders. This study investigates the health literacy of phytosanitary treatments by B2C stakeholders, and the subsequent positioning marketing narratives as an outcome of such literacy. We use health literacy as a theoretical lens for classifying different strategies that B2C stakeholders may use when positioning phytosanitary food treatments. Data were collected using in-depth interviews with 12 purposefully recruited New Zealand B2C retailers, based on the criteria of making and/or influencing decisions about the supply of fresh fruits and vegetables to consumers. Thematic analysis was used to analyze the qualitative data. The study advances research in food marketing by showing how different literacy levels may influence marketing narratives in the global food system. It makes a valuable contribution to literature by unveiling how appraisals of invasiveness, familiarity, naturalness, and sustainability lead to different applications of positioning narratives: the purist approach, maintaining the romance, and full transparency. Full article
(This article belongs to the Special Issue Market Research of Food Systems and Supply Chains)
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20 pages, 3994 KiB  
Article
Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique
by Anirban Adak, Biswajeet Pradhan, Nagesh Shukla and Abdullah Alamri
Foods 2022, 11(14), 2019; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11142019 - 08 Jul 2022
Cited by 11 | Viewed by 4519
Abstract
The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs [...] Read more.
The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model. Full article
(This article belongs to the Special Issue Market Research of Food Systems and Supply Chains)
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16 pages, 908 KiB  
Article
What Intentions and Interesting Information Can Attract Consumers to Scan QR Code While Buying Eggs?
by Shang-Ho Yang, Huong Thi Thu Phan, Chi-Ming Hsieh and Tzu-Ning Li
Foods 2022, 11(9), 1259; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11091259 - 27 Apr 2022
Cited by 5 | Viewed by 2076
Abstract
A Quick Response Code (QR Code) aims to provide accurate and traceable information to consumers wanting to verify the quality of agri-food products. This study aimed to investigate the experiences and intentions of scanning QR Code in traditional markets and supermarkets. Furthermore, the [...] Read more.
A Quick Response Code (QR Code) aims to provide accurate and traceable information to consumers wanting to verify the quality of agri-food products. This study aimed to investigate the experiences and intentions of scanning QR Code in traditional markets and supermarkets. Furthermore, the types of egg information in the QR Code were explored to identify consumer interests when purchasing eggs. The empirical data were collected from 1112 valid responses throughout Taiwan from July to September, 2020. The Logit, Probit models, and the Bivariate Probit model were used to examine the data. Results showed that shoppers’ propensity to scan QR Code revealed a significant difference between traditional markets and supermarkets, i.e., supermarket shoppers having higher a propensity to scan a QR Code. Of the 10 types of potential egg information in the QR Code, over half of respondents said that the production certificate label and inspection information were the top reasons that they would be interested in scanning a QR Code. This was particularly the case for homemakers aged between 51 and 60 years old and those who had scanned QR Code before and would like to pursue more egg information. Since the egg producers have resisted joining the traceability system, the implication of this study provides very practical strategies for government, policy makers, and producers in Taiwan. Full article
(This article belongs to the Special Issue Market Research of Food Systems and Supply Chains)
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19 pages, 2083 KiB  
Article
Development of a Predictive Model for Agave Prices Employing Environmental, Economic, and Social Factors: Towards a Planned Supply Chain for Agave-Tequila Industry
by Walter M. Warren-Vega, David E. Aguilar-Hernández, Ana I. Zárate-Guzmán, Armando Campos-Rodríguez and Luis A. Romero-Cano
Foods 2022, 11(8), 1138; https://0-doi-org.brum.beds.ac.uk/10.3390/foods11081138 - 14 Apr 2022
Cited by 8 | Viewed by 7320
Abstract
The interest of consumers to acquire Tequila has caused an increase in its sales. As demand increases, the Tequila industry must obtain its raw material at a constant rate and agave farmers must be prepared to satisfy this supply chain. Because of this, [...] Read more.
The interest of consumers to acquire Tequila has caused an increase in its sales. As demand increases, the Tequila industry must obtain its raw material at a constant rate and agave farmers must be prepared to satisfy this supply chain. Because of this, modernization of the strategies used to ensure a planned, scheduled, timely, and predictable production will allow farmers to maintain the current demand for Tequila. This has been evidenced in official historical records from 1999 to 2020 where there is a fluctuation in the price of agave due to supply and demand. Given this scenario, this research shows the development of a multivariable predictive mathematical model that will permit the agave–Tequila production chain to work based on a smart implementation of planned actions to guarantee the agave supply to the Tequila industry. The proposed model has a goodness of fit (R = 0.8676; R¯2 = 0.8609; F(1,20) = 131.01 > F0.01 (1,20) = 8.10) and demonstrates the impact on agave prices is due to several factors: Tequila exports (α = 0.50) > agave plants harvested “jima” (α = 0.44) > dollar exchange (α = 0.43) > Tequila production (α = 0.06) > annual accumulated precipitation (α = 0.05). Nevertheless, the price forecast can be influenced by climate change or economic crises that affect the supply chain. In conclusion, a prediction of agave price stabilization for five years is shown where authorized producers can evaluate future scenarios so that the agave supply chain can be guaranteed for Tequila production, facilitating the decision making regarding its raw material. Full article
(This article belongs to the Special Issue Market Research of Food Systems and Supply Chains)
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