Framework for the adoption of automation and robotic system in manufacturing industry in the kingdom of Saudi Arabia

Abstract

Without a doubt, the Automation and Robotics System (ARS) has a crucial role in industrial firms, and in turn, in the overall economic development. ARS can hold the key to the manufacturing industry’s progress, and as such, for the industry to be enhanced, new technologies have to be adopted for increased productivity. The limited understanding of ARS adoption factors seems to contribute to employees resisting such adoption rather than manufacturing industries do not know the appropriate strategies to take up the challenge. The manufacturing sector indubitably holds great significance to the development of the global economy, whereby innovative methods have been brought forward to implement new technologies, including ARS. However, regardless of the advantages of robots for productivity and management efficiency, the circumstances in which they are used as alternatives or labor assistance, the influence on the formation of new firms, their facilitation of effective and efficient management, and the formation of regional economies, the topic with all the related aspects still requires extensive insight. There is a lack of frameworks to be used in a successful and proper adoption. Hence, this study proposes a framework for adopting ARS to support productivity in the manufacturing industry using the Technology Acceptance Model (TAM) and Technology, Organization, Environment (TOE) Theory. Mixed methods were used to collect quantitative and qualitative data. The collected data was analyzed using SPSS 25. Structural Equation Modelling (SEM) and Smart PLS 3 software were used to propose the framework. Ten experts verify the proposed framework by means of interviews. Quantitative data reveals that all the fit indices satisfy the recommended range of values which assumed the developed framework is acceptable. The results revealed that the framework fitness is appropriate and indicates the theory's stability used in building the framework. The study revealed that most of the factors were significant and positively influenced the adoption of ARS (71.2% of variance) except for two factors: social norm and anxiety. The study also showed that ARS was important and had significant relation with productivity (49.2 of variance). In addition, the qualitative results confirmed the findings obtained from the theoretical study and contributed to enriching the understanding of the adoption of ARS in the manufacturing industry. This study could help shape both theoretical and empirical studies on ARS technology, specifically on adoption to enhance productivity in the manufacturing industry.