Cloud computing (CC) is a recently developed computing paradigm that can be utilized to deliver everything-as-a-service to various businesses. In higher education institutions (HEIs), CC is rapidly being deployed and becoming an integral part of institution experience. CC adoption in HEIs is accompanied by numerous scientific contributions that address the topic from different perspectives. A systematic review of these heterogeneous contributions, which provide a coherent taxonomy, can be considered interesting for HEIs to identify opportunities to use CC in its own context. Therefore, this systematic literature review aims to analyze existing research on adopting and using CC in HEIs, review background research to develop a coherent taxonomy and provide a landscape for future research on CC in HEIs. The outcomes of this paper include a coherent taxonomy and an overview of the basic characteristics of this emerging field in terms of motivation and barriers of adopting CC in HEIs, existing individual and organizational theoretical models to understand the future requirements for extensively adopting and using CC in HEIs, and factors that influence the adoption of CC in HEIs at individual and organizational levels. Considerable information is available in relation to adopting and using CC in HEIs. This review will enhance this information by offering an in-depth analysis of the existing data to bridge any gap and expand on existing literature.

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Safeguarding the high quality of halal food products is a new realm to explore with the advent of new technologies. The efficiency of food industry management has boosted the applicability of product traceability system with the aid of the internet of things (IoT). Traceability system with the use of IoT has facilitated food industry players in managing their product information along the supply chain. As one of the halal food industry key players, halal agro-food small and medium enterprises (SMEs) are reportedly yet to embrace the adoption of IoT. With IoT, halal agro-food SMEs supply chain has undoubtedly provided a trusted platform. However, halal related issues and scandals in the market are recurring persistently. Besides, the emergence of IoT in the agriculture sector requires active involvement by halal agro-food SMEs. Thus, the objective of this study was to investigate the adoption of IoT among Malaysian halal agro-food SMEs and its challenges. A self-administered questionnaire was employed to gather data from selected 158 halal agro-food SMEs. Descriptive analysis, mean score analysis, and Pearson correlation analysis were carried out to analyze the data. The results showed a lack of IoT adoption among halal agro-food SMEs in managing their business activities. The SMEs were also found to be low tech-savvy users of IoT in managing their halal products. Therefore, a vast improvement is needed in implementing IoT among Malaysian halal agro-food SMEs.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015-2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly. INDEX TERMS Healthcare, big data, big data management, big data analytics.

Big data analytics (BDA) readiness factors have been widely researched; nevertheless, few have investigated the impact and success factors of BDA implementation in the organizational context. The relevance and quality of BDA outcomes have been a significant concern to the organizational leaders in supporting them for strategic decision-making. To that end, the objective of this study is twofold. Firstly, it investigates the factors that influencing the success of BDA implementation for effective decision-making. Secondly, this study adds to the body of knowledge in the information system (IS) domain, especially with its focus on BDA implementation packages. Based on 18 selected papers, this review has established 10 influencing factors that may influence the success of BDA implementation, therefore, contribute to the practice and research of BDA domain and its effectiveness towards the organizational performance enhancement.

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Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions.