Replication and comparative analysis of instrument of manufacturing capabilities in a different context: the Brazilian case

• Autor(a) principal: Calderaro,Douglas Rhoden
• Data de Publicação: 2021
• Outros Autores: Lacerda,Daniel Pacheco, Teixeira,Rafael, Soares,Priscila Ferraz, Piran,Fabio Sartori
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Gestão & Produção
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000400206
• topic: Manufacturing strategy; Replication; Back-translation; Exploratory factor analysis; Multiple regression analysis; Evaluation of manufacturing

Abstract: Managers of organizations have few tools to evaluate manufacturing capabilities. Such scarcity implies in a greater difficulty to generate or maintain sustainable competitive advantages over competitors. This study aims to replicate the instrument developed by Jain et al. (2014) for the evaluation of manufacturing capabilities and analyze the results in the Brazilian business and cultural contexts. The instrument was translated into Portuguese using back-translation. After this, was performed a pre-test to verify understanding and clarity, then the instrument was distributed electronically. For data analysis, reliability analysis, face validation method, content validation, multiple regression analysis and factor analysis were performed. Subsequently, the results were compared with those of the work by Jain et al. (2014). Additionally, an exploratory factor analysis was performed to verify the convergent validity of the work. Statistical results were not adequate to validate the instrument in its current format, which requires improvements for it to be applied as a manufacturing capabilities assessment method in Brazil. The reliability index was adequate in approximately half of the instrument questions. As for multiple regression analysis, the results were not satisfactory. In addition, this research performed an exploratory factor analysis. Inconsistencies were identified. From ten expected factors, only four were obtained and had a low reliability index. These results contributed to the improvement of the instrument developed by Jain et al. (2014). It will be possible to take into account the results obtained in this study for implementations of statistical improvements and to observe questions that need to be changed in order to actually represent the 10 manufacturing decision areas of Hayes et al. (1988). Thus, it is necessary to conduct further studies and make improvements to make it a valuable tool for manufacturing.