Consumer acceptance of cultured meat: some hints from Italy

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Abstract

Purpose

The purpose of this paper is to contribute to the current literature on consumer acceptance of cultured meat and to investigate the main factors that might affect it.

Design/methodology/approach

Data were collected from a sample of 490 consumers in Italy, using a web-based survey. The empirical analysis follows an exploratory approach based on the training and checking of a random forest model.

Findings

An important finding of this study concerns the overall positive perception of cultured meat on the part of the interviewees in a country that is the fifth-largest meat producer at the European level. Age, environmental and ethical issues, and scepticism about new food technologies are the most important factors that guide consumer acceptance of cultured meat. The results suggest that in order to increase cultured meat acceptance it would be important to inform and educate consumers towards new food and new food production methods.

Research limitations/implications

The sample analysed in this study is not representative of the whole national population, as it happens in most papers dealing with new food.

Originality/value

Although the conclusions of this exploratory study cannot be over-generalized, the results provide interesting insights on how to increase cultured meat acceptance in view of the possible development of a new market for cultured meat.

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Most cited references 75

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Random Forests

Leo Breiman (2001)

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SMOTE: Synthetic Minority Over-sampling Technique

N. Chawla, K. W. Bowyer, L Hall … (2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.