This study investigates which attributes drive Italian customers while choosing a restaurant, how many of these attributes correspond to intrinsic and extrinsic characteristics of restaurants and which are the main segments of customers. A structured online questionnaire was used to reach 513 respondents through the snowball sampling technique (valid response rate of 97%). Descriptive statistics and exploratory factor analysis were applied to infer information. A distance-based ordination technique (Principal Component Analysis) was implemented to display patterns in multivariate data. The reliability of the model was evaluated through the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. Six components were extracted, namely: ‘geographic proximity and accessibility’, ‘aesthetic-based requisites’, ‘fine dining and renowned eating places’, ‘average standard requirements’, ‘traditional cuisine’, ‘feedbacks and personal experience’. A cluster analysis was performed and four different profiles of restaurant customers were found, with specific socio-demographic characteristics and attitudes towards intrinsic and extrinsic attributes of restaurants. The homogenous features customers have within each segment can be used by foodservice operators as an information to orientate their strategies.

Italians’ behavior when dining out: Main drivers for restaurant selection and customers segmentation

D'Amico M.;Di Vita G.
Ultimo
2022-01-01

Abstract

This study investigates which attributes drive Italian customers while choosing a restaurant, how many of these attributes correspond to intrinsic and extrinsic characteristics of restaurants and which are the main segments of customers. A structured online questionnaire was used to reach 513 respondents through the snowball sampling technique (valid response rate of 97%). Descriptive statistics and exploratory factor analysis were applied to infer information. A distance-based ordination technique (Principal Component Analysis) was implemented to display patterns in multivariate data. The reliability of the model was evaluated through the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. Six components were extracted, namely: ‘geographic proximity and accessibility’, ‘aesthetic-based requisites’, ‘fine dining and renowned eating places’, ‘average standard requirements’, ‘traditional cuisine’, ‘feedbacks and personal experience’. A cluster analysis was performed and four different profiles of restaurant customers were found, with specific socio-demographic characteristics and attitudes towards intrinsic and extrinsic attributes of restaurants. The homogenous features customers have within each segment can be used by foodservice operators as an information to orientate their strategies.
2022
Cluster analysis
Customers' segmentation
Intrinsic and extrinsic attributes
Restaurant
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/586675
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