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Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

The purpose of this paper is to evaluate the influence of culture in terms of individualism and collectivism (I-C), store image and customer satisfaction, on purchase intention at supermarkets.

In order to carry out the descriptive and quantitative research, the authors applied 443 questionnaires to consumers in two Brazilian supermarket chains. The authors used structured equation modeling for data analysis.

Customers’ satisfaction and the evaluation of store image positively affect purchase intention; however, I-C did not show any effect. The cultural dimension slightly influenced the assessment of store image by only 2 percent. Store image positively affects consumer satisfaction.

This study used convenience sampling and omission of vertical and horizontal variations of I-C in data analyses. The authors suggested the use of probabilistic sampling and the inclusion of vertical and horizontal variations of I-C. The relationships investigated herein can still be analyzed in other contexts.

In order to increase satisfaction and purchase intention, it is necessary to prioritize aspects related to layout, service and products’ variety and quality. Still, managers should not worry about I-C, since it does not affect purchase intention.

The authors observed the relevance of the study after carrying out a literature review on the subject. There is a lack of studies that investigate the relationship between I-C, evaluation of store image, consumer satisfaction and purchase intention, either together or in the context of supermarkets.

The consumption of organic food increases worldwide, which raises the need for studies that try to understand the variables that affect the consumption of this kind of food. The purpose of this paper is to analyze the influence of attributes, consumer trust and perceived value on purchase intention of organic food.

A descriptive quantitative research was conducted through a survey of 247 valid cases of organic food consumers. For the purposes of analysis, exploratory factorial and linear regression analyzes were chosen.

Exploratory factor analysis showed that all tested constructs were valid for the Brazilian context. Linear regression analyses showed that emotional value, consumer trust and the attribute “sensory appeal” affect the purchase intention of organic food. Emotional value had a stronger relationship and sensory appeal showed a negative relationship with purchase intention.

Research results can help managers increase the likelihood of buying organic foods through strategic marketing management focused on emotional value and sensory appeal factors.

The study offers value to the study of organic foods in view that this theme is not much explored in the Brazilian scenario even with the market in an increasing pattern, as well as the combination of perceived value, attributes, trust and purchase intention as variables in a single prediction model.