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 aimed to test the impact of digital supply chains on lean manufacturing, the digital supply chain was a multidimensional measurement composed of seven dimensions: Digital performance management, digital information technology and digital manufacturing, digital human resources, digital suppliers, digital logistics and inventory and digital clients. The electronic industries companies were targeted to represent the research population and collect the primary necessary data. According to the research budget and time constraints, a convenience sampling method was implemented in the data collection process. Structural equation modeling (SEM) was applied to test the research hypotheses through AMOS software. The results indicated that most of the digital supply chain dimensions had a positive impact on lean manufacturing, except digital suppliers and digital clients, which had no effect on lean manufacturing. Findings from this research help organizational managers make multiple decisions related to investing and allocating resources to increase profit and reduce expenses along digital supply chains.

The aim of the study is to determine the effectiveness of corporate governance on corporate social responsibility (CSR) performance and financial reporting quality in Saudi Arabia's manufacturing sector. The data is collected through the database of Thomson Reuters from 30 manufacturing companies of Saudi Arabia over the period 2014-2020. Descriptive statistics and the generalized least square (GLS) model were applied. The dependent variable was calculated through residuals and was found as discretionary accruals (DA). The findings reveal that there was a positive influence of corporate governance on CSR performance and financial reporting quality. It was found that sample size was one of the biggest limitations because only data from 2014 to 2020 were collected and to make the study more reliable and authentic, larger data is required.