This study focused on the factors affecting the quality / outcome of online learning, which would eventually affect accounting students' perceptions of online learning in universities in Ho Chi Minh City in the age of Industry 4.0. The study used convenience-sampling method with sample size of 992 to collect information from accounting students who were taking online classes at Universities in Ho Chi Minh City on an irregular basis; then, the information was tested using Structural Equa-tion Modelling (SEM). The findings confirmed the appropriateness of the research model. All the information collected, analytical methods, and the results showed the direct relationship between service quality, information quality, system quality, IT skills, students' perceptions and students' awareness of Industry 4.0 with quality of online learning system. Based on the findings, accounting students' perceptions of online learning at universities in Ho Chi Minh City can be improved. Uni-versities in Ho Chi Minh City can use this result and other management methods to implement ef-fective online learning programs that are appropriate and effective for students in the Age of Indus-try 4.0.

The objective of this study was to examine the role of Industry 4.0 in the sustainable supply chain (SSC). The relationship between Industry 4.0, data storage, data assessment, performance evaluation, data inquiry and SSC were examined. To achieve the study objective, the mediation effect of data storage, data assessment, performance evaluation and data inquiry between Industry 4.0 and SSC was examined. Employees of textile companies were selected for data collection in Indonesia. Results of the study highlighted that Industry 4.0 had a significant positive effect on SSC. Better implementation of Industry 4.0 showed a positive effect on SSC. In addition to this, Industry 4.0 had a positive effect on data storage, data assessment, performance evaluation and data inquiry. Finally, data storage, data assessment, performance evaluation and data inquiry had a positive effect on SSC.

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.

Innovation on business model is not only describing the bundle of products and services but also it is integrated with the input of customer experience. Customer experience should become a key role in developing business model innovation in driving transformational performance. This paper argues that the transformational performance is derived from business model innovation and focuses on customer experience. We use telecommunication firms as our unit analysis with sample of 195 Indonesian ICT firms out of 542 units. The analytical approach and solution technique that is used for analysis is Partial Least Square (PLS). The findings demonstrate that business model played significant role on supporting contribution of customer experience in driving transformational performance. The finding has implication that by synergizing the value proposition of customer experience in a business model innovation, the transformational performance can be maintained through focus on customer experience driven business model innovation. Further study can be explored through additional variable, sample and further study on longitudinal on digital transformation firms.

Millennials are the generation that lived in the era of information technology revolution with the emergence of the internet and smart phones. Because of this experience, millennial has become an important element for human resources to meet industry 4.0. However, it is not clear how older leadership behaves on this generation and what impact they have on organizational performance. Thus, this paper focuses on investigating the effect of openness on millennial employee style on leadership effectiveness and its impact on 4.0 organizational performance. Survey is conducted on 224 millennial employees working in various companies in Indonesia. Data are analyzed using Smart-PLS 3.0 based on structural equation modeling (SEM). Our results showed that openness to millennial employee style had a positive effect on 4.0 organizational performance partially mediat-ed by 4.0 leadership effectiveness in industrial settings. These results confirm the role of millennial in shaping organizational performance and how managers need to implement appropriate measures to manage millennials and be open to various characteristics of this generation.

The objective of the current study is to examine the role of Industry 4.0 in supply chain sustainability (SCS). To examine the effect of Industry 4.0 on SCS, the big data technology is considered. As the supply chain process requires a significant data handling system among the companies, however, companies are lacking in this area. Therefore, the relationship between data storage, data transformation, data utilization, order management and SCS were examined. Data was collected from the rubber industry. For the purpose of data collection, questionnaires were used, and data were collected from the rubber companies of Indonesia. Results of this study shows that Industry 4.0 has a vital role in SCS. Implementation of Industry 4.0 among the rubber companies shows a positive effect on SCS. Particularly, the applications of big data technology have a vital role in order management and SCS. Big data technology has a significant positive effect on SCS. Big data technology has a positive role to promote order management which further influences positively on SCS.

The study aim is to examine the impact of industry 4.0 on sustainability of industrial organizations in Jordan. The population of the study consists of employees at various administrative levels of industrial organizations in Jordan. Due to the large population and the spatial and temporal limitations of the research, it was difficult to collect data using the comprehensive method. Therefore, the random sampling method was applied to collect data from the research population. The structural equation modeling (SEM) technique was used to test the impact of industry 4.0 dimensions on sustainability. The study results confirmed that all dimensions of industry 4.0 had an impact on sustainability. The greatest effect was for cyber-physical systems. Based on this result, researchers recommend the management of industrial organizations to invest in information technology to provide a large variety of data in a very short time and providing appropriate programs for analyzing big data and producing accurate and reliable information that can be used by employees.

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies to enhance efficiency and sustainability. However, the transition to sustainable green manufacturing presents numerous challenges. This paper analyzes these challenges using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). By incorporating expert opinions and fuzzy logic, various obstacles are evaluated and prioritized in the implementation of green manufacturing practices in the context of Industry 4.0. The analysis reveals that market uncertainty in the economic landscape ranks as the top challenge, followed by high costs of implementation, maintenance, security, and integration. Uncertain benefits and trade-offs are also found as significant barriers. Key factors include the need for substantial investments, cybersecurity concerns, integration difficulties, and the complexities of predicting returns on investment. From the study, it is also evident that the impact of Industry 4.0 on supply chains and emissions from Electronics manufacturing is also a critical issue. The study provides actionable insights and strategic recommendations for policymakers and industry leaders to facilitate the adoption of sustainable green manufacturing practices in the era of Industry 4.0.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.