The study aimed at exploring the impact of three smart innovative solutions, i.e., Internet-of-Things, Big Data Analytics, and Cloud Computing on supply chain performance. Collecting data by questionnaires administered to a sample consists of supply chain managers of industrial firms. The results pointed out that these three smart solutions significantly and positively lift firms’ supply chain performance. That is, the hypotheses that Internet-of-Things, Big Data Analytics, and Cloud Computing have significant impacts on supply chain performance were supported. Therefore, the study concluded that for industrial firms to improve supply chain performance, such smart solutions should be assessed and applied. The study contributes to both academics and practitioners through providing empirical results on concurrent impacts of three advanced technologies in supply chain management context.

This research aims at exploring barriers of adopting Industry 4.0 in manufacturing supply chains. Data were collected based on a review of extant literature on barriers Industry 4.0 adoption, individual interviews with a panel consisted of academic and industry experts. Following numerous previous studies, interpretive structural modeling (ISM) and matrix multiplication applied to classification (MICMAC) analysis were conducted to order 10 barriers based on their importance and impacts. The results excluded one barrier “cyber security challenges”, categorized another one as a dependent barrier “lack of digital strategy”, and eight barriers as linkage barriers “lack of infrastructure”, “personnel resistance to adopt new technologies”, “high investment requirements”, “data management and quality challenges”, “uncertainty of economic benefits”, “low maturity level of technology”, “lack of adequate skills”, and “job disruptions”. Henceforward, it was concluded that mitigating these eight barriers is very critical to ensure a successful adoption of Industry 4.0 technologies in supply chains. Further studies are required to categorize these eight barriers based on their importance and relationships.

This study aims at investigating the effect of digital HRM practices on employee motivation and hence employee job performance, or in other words, the mediating role of employee motivation between digital HRM practices and employee job performance. Two digital HRM practices were used in this study: digital training and digital performance appraisal. Collecting data using a valid and reliable questionnaire from employees at industrial companies, the results show that digital training had significant effects on both employee motivation and job performance, digital performance appraisal had significant effects on employee motivation and performance appraisal, and employee motivation exerted a significant effect on job performance. Consequently, it was approved that employee motivation partially mediated the effect of digital HRM practices on job performance. It was concluded that skilled employees who are aware of their performance level are motivated to show higher levels of job performance. Theoretically, the study called scholars to carry out further results to examine the effects of other HRM practices on job performance through employee motivation. Empirically, organizations are requested to conduct training sessions and assess employee performance using digital means.