Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

Background: Digital and smart supply chains are reforming the food chain to help eliminate waste, improve food safety, and reduce the possibility of a global food catastrophe. The globe currently faces numerous food-related issues, ranging from a lack of biodiversity to excessive waste, and from ill health caused by excessive consumption to widespread food insecurity. It is time to look back at how technology has tackled food supply-chain challenges related to quality, safety, and sustainability over the last decade. Moreover, continuous transformations of the food supply chain into a more sustainable business model with utmost resilience is the need of the hour due to COVID-19 disruptions. Method: This study aimed to systematize literature (2010–2021) in the described context and propose a future research direction, with the assistance of a systematic review and bibliometric analysis on the research agenda proposed above. Results: The findings reveal that technological Industry 4.0 (IR 4.0) tools face specific barriers due to the scope and objective of the application. Conclusion: The Internet of Things has received more attention than any other IR 4.0 tool. More integration between the specialized tools is needed to address this issue. Furthermore, the authors have proposed a food supply chain-based operational framework on technological inclusion to facilitate the roadmap for food supply chain 4.0 for more resilience and food supply chain viability.

Recently, customer knowledge management has been widely recognized as a determinant of business performance. However, knowledge of customers that the organization gathers is useless unless it is shared internally. Sharing of this knowledge among organizational members will greatly strengthen the competitiveness of the organization, which makes it possible to take advantage of the competitive dynamics in the business environment. Although the importance of knowledge sharing especially the one that is related to customers has long been recognized by the community of researchers, investigation in this area is very limited. Therefore, the present study was conducted to investigate the relationship between Customer Knowledge Management dimensions, which consist of knowledge for customers, knowledge about customers, knowledge from the customer and knowledge sharing among insurance companies in Malaysia. A total of 180 managers of insurance companies in Malaysia participated in the survey. The results show that the three knowledge dimensions are positively and significantly related to knowledge sharing. Moreover, the results indicate that the insurance companies have implemented knowledge sharing practices especially in securing and managing their customer data to ensure the currency, accuracy, uniqueness and completeness of the customer data. Finally, research and practical implications are discussed.

Advertising is one of the main contributors to the rising number of online shoppers worldwide. In the current digital era, digital advertising has emerged as a crucial means of marketing, with a notable global trend in this direction. Amidst the rapid growth of digital advertising, a critical issue has surfaced regarding the prevalence and impact of false advertising on consumer purchasing behaviour online. Some businesses, in fact, incorporate deceptive advertising practices into their marketing strategies. This study aims to examine the variables influencing customers' online purchasing decisions. To achieve this, a survey was distributed to 588 individuals who have made at least one purchase from major e-commerce sites in Malaysia. After the questionnaire's validity and reliability were evaluated, SPSS software was used to collect and analyse the responses. The results show that Malaysian consumers' online buying habits can be significantly impacted by false advertising. Companies can affect consumer perceptions and ultimately influence their purchasing decisions by providing misleading or inaccurate information. This can lead to several negative consequences for consumers, including financial losses, disappointment with purchased products, and erosion of trust in online shopping.