Quality management practices in high-technology based companies: a partial least square analysis

Due to the changes in the relationship and interdependence between markets and different business segments, there has been growing interest toward innovation and globalization. In order to keep abreast of these changes, companies need to change the way they manage their existing (conventional) opera...

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Bibliographic Details
Main Authors: Mustafar, M., Rasli, A. M., Teong, L. K., Arshad, A. S.
Format: Article
Published: American Scientific Publishers 2017
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Online Access:http://eprints.utm.my/id/eprint/75158/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032985616&doi=10.1166%2fasl.2017.9887&partnerID=40&md5=4a64530cbabcbed917ee2cd3d8794739
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Summary:Due to the changes in the relationship and interdependence between markets and different business segments, there has been growing interest toward innovation and globalization. In order to keep abreast of these changes, companies need to change the way they manage their existing (conventional) operations management. Quality management practice (QMP) is a modern operational management practice that has been proven to promote effectiveness of company performance by many researchers. This exploratory study examined how QMP affects company performance through research and development (R&D). A total of 138 high-technology based companies in Malaysia that engaged in R&D activities took part in this study, which adopted the Partial Least Squares (PLS) method, based on Structural Equation Modeling (SEM). The findings of the study supported the importance of QMP, R&D resources, and company performance. This study also supported the QMP as a mediator between the R&D resources and company performance. Besides contributing to increasing knowledge and understanding of the relationship between QMP, R&D resources, and performance of high-technology based companies, the findings of this research coupld also allow the practitioner to obtain a deeper knowledge concerning the effects QMP in R&D when making related decisions.