Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 32, Pages: 1-11
Ulises Mendoza, Lázaro Rico, Salvador Noriega, Andrés Hernández-Gómez and Vianey Torres-Argüelles*
Department of Industrial and Manufacturing Engineering, Industrial and Technology Insitute, Mexico; [email protected], [email protected], [email protected], [email protected], [email protected]
*Author for correspondence
Department of Industrial and Manufacturing Engineering, Industrial and Technology Insitute, Mexico; [email protected]
Objectives: To determine the relationships between quality improvement and train upon process innovation and also to find its effect on operational performance. Methods/Statistical Analysis: By a literature review of innovation, constructs are established for the determination and evaluation of the mentioned relationships and the improvement of operational performance. The main underlying proposal is the four constructs developed to establish the relationships. A questionnaire for the evaluation of the constructs is validated and applied to gather data to test four hypotheses with confirmatory factorial analysis. Several non-parametric tests are applied and explained their use. Findings: The questionnaire developed was validated and adequate for the measurements of the constructs. Personnel from product and process engineering of 27multinational plans replied with a response size of 236. Data is suitable, indicates the Kaiser-Meyer-Olkin test and the sphericity test exhibits the rationality of the constructs under the predictor type relationships of the factors influencing the variables. The four hypotheses cannot be rejected. Training has a positive impact on process innovation and on quality improvement projects. Process innovation is an important predictor of operational performance. Quality improvement and training are key factors in the development of process innovation and these innovations positively affect performance, as evidenced by the structural model [χ2=177.38; df=98; [χ2/df=1.87; p<.01; CFI=0.97; RMSA=0.59], it is advisable that the manufacturing industry takes this as reference for the improvement of operational performance. Application/Improvements: This model enhances the explanation power of this theory, also advices the companies about some of the organizational factors to consider for the increase of operational performance by process innovations.
Keywords: Factor Analysis, Predictor Model, Process Innovations, Production Performance, Quality Strategies
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