Abstract Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

RESUMO Empresas que têm o cliente como foco, por meio do uso da Gestão de Relacionamento com Clientes ou Customer Relationship Management (CRM), obtêm melhores resultados e conquistam a lealdade dos clientes. Torna-se atrativo estudar o relacionamento entre clientes e companhias aéreas, uma vez que o setor de aviação é de alta concorrência e relativamente comoditizado. Destarte, o objetivo desse trabalho foi desenvolver e validar uma escala para medir a percepção dos clientes quanto ao seu relacionamento com as companhias aéreas. A escala ficou composta por quatro fatores de primeira ordem - lealdade de clientes, experiência de compra, experiência de voo e atendimento - e um fator de segunda ordem - relacionamento com clientes, todos com bons índices psicométricos. O desenvolvimento e validação de escala em um setor pouco explorado pela literatura de CRM foi a principal contribuição do trabalho, pois poderá ser utilizada como diagnóstico para os gerentes de companhias aéreas incrementarem seu relacionamento com clientes estratégicos e obterem resultados mais efetivos.

ABSTRACT Businesses with focus on customers through the use of Customer Relationship Management (CRM) perform better and win customer loyalty. Therefore, it is relevant to study the relationship between customers and airline companies, given that the aviation industry is highly competitive and relatively commoditized. Thus the aim of this study was to develop and validate a scale to measure customer’s perception of relationship with airline companies. The scale was composed of four primary factors (customer loyalty, purchase experience, flight experience, and service) and a second order factor (customer relationship), all with good psychometric indices. The development and validation of a scale in an industry little explored by the CRM literature was the main contribution of this research, as it may be used as a diagnostic tool for airline companies to scale up their relationship with strategic customers and achieve more effective results.

Given the strategic relevance of Human Resources Management (HRM) in organizations and the lack of scientific instruments to measure employees' perceptions about policies and practices of HRM, this study aimed to validate the Human Resources Management Policies and Practices Scale (HRMPPS) through exploratory and confirmatory factor analysis using the maximum likelihood method. The study has a quantitative design, but also included qualitative analysis required for the development of a scale. Employees from various organizations composed a sample of 632 people. Scale reliability was assessed by Cronbach's alpha and Jöreskog's rho. A six-factor model was generated showing high-reliability and good fit. Construct validity was provided through convergent and discriminant analyses. The factors were consistent with the literature review and explained about 58% of the construct's total variance. This study contributes to the scientific production in the area of Human Resources Management since HRMPPS can be used not only in relational studies but also as an evaluation instrument by managers who wish to improve their employees' well-being as well as organizational outcomes.