Search results

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.

Innovation is regarded as a crucial determinant of growth and development in South Africa, and small, medium and micro enterprises (SMMEs) have been earmarked as instruments for the achievement of the socio-economic goals and innovation as set out in the National Development Plan. The purpose of this study is to investigate the effect of innovation on SMME performance in South Africa.

The empirical analysis was conducted using the quantile regression technique to examine the effect of innovation on the performance of firms at different sales levels. Data from the World Bank's enterprise survey was used for the analysis.

The results of the empirical analysis showed that R & D expenditures have a positive and significant effect on performance for firms with higher sales (high growth or larger firms). There is evidence that the introduction of new products/services promotes performance for low growth/ smaller firms.

The empirical results imply that innovation is crucial for SMMEs’ development and growth. However, smaller/low growth firms are not able to spend on R & D due to a lack of funds which could be the reason for their low survival rate. More support needs to be provided to smaller firms with lower sales growth, given the large financial outlay required for R & D expenditures. Despite the lack of funding for R & D expenditure, smaller firms are encouraged to introduce new products and methods of production that do not require major financial outlays.

There is scant empirical evidence on the impact of innovation on firm performance in South Africa. Most studies investigate the challenges faced by SMMEs and the different types of innovation approaches used by firms. Furthermore, the study employs the quantile regression approach which highlights the effect of innovation on firms of different sizes.