The drivers of product innovation performance in Malaysian electrical and electronic industry

• Main Author: Tan, Kar Hooi
• Format: Thesis
• Language: English; English; English
• Published: 2019
• Subjects: HD28-70 Management. Industrial Management; HF5001-6182 Business; T Technology (General)
• Online Access: https://etd.uum.edu.my/9616/1/s901298_01.pdf; https://etd.uum.edu.my/9616/2/s901298_02.pdf; https://etd.uum.edu.my/9616/3/s901298_references.docx; https://etd.uum.edu.my/9616/

In today's dynamic business environment, the flipside to Malaysia's Electrical and electronics (E&E) industry is that it is still very much focused on manufacturing and not expanded to higher value-chain activities such as R&D. The challenge is to leverage emerging technologies to accelerate the growth of the E&E sector with emphasis on developing indigenous capacity and capability To explain the concerns, this study examined the impact of key enablers namely, technology scanning capability, innovation type, big data analytics capability and organizational learning on product innovation performance. This study also examines the successive impact of organizational learning on product innovation performance and investigates whether organizational learning mediates key capabilities and performance relationship under study. In order to achieve the research objectives, quantitative research approach is necessitated. 538 survey questionnaires were distributed to respondents in Malaysian electrical and electronics organizations. 281 usable responses representing 52% response rate were empirically tested through structural equation modeling by using SPSS and SmartPLS. Research findings revealed that technology scanning capability, innovation type, big data analytics capability and organizational learning contributed to firm's product innovation performance, whereas, technology scanning capability and innovation type was insignificant towards organizational learning. The findings also revealed a significant mediation effect of organizational learning in the model under study. Limitations and recommendations for future study are discussed.