The DeLone and McLean (D&M) information systems (IS) success model seeks to provide a comprehensive understanding of IS success by identifying and explaining the relationships among their most critical dimensions of success. Many studies have offered important insights into this model. Nevertheless, regarding the e-learning body of knowledge, D&M research remains to be systematically reviewed comprehensively. E-learning refers to the use of information technology to disseminate knowledge for education and training. With today’s e-learning boom necessitated for the most part by COVID-19, its adoption has become increasingly mandatory. Thus, a systematic review should help focus and accelerate future research. This study systematically reviews, compiles, analyzes, and synthesizes the D&M model studies in the e-learning context. This review adopts automatic and manual search methods to collect related studies from 2010 to 2020. After applying inclusion and exclusion criteria, 92 primary studies are identified. The findings show that most of reviewed studies were conducted in the education field. The success category was the most highly investigated area from among the selected studies. Most studies developed a hybrid/extended model. Additionally, this study also identified gaps in the literature and recommends seven potential future areas requiring further investigation, such as conducting studies to examine the net benefits at the organizational level of analysis. To that end, the findings of this systematic review study provide an impactful contribution through offering a holistic review of the current state of D&M model studies in an e-learning context, providing an essential reference for scholars in this field.

Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic.

Data is everywhere, organizations, governments, clicks, web server, business partner, and even our body. Big Data comes to life with insights to improve the technology fashion and lifestyle with calling for readiness to change. Big Data has become a significant factor that could be a material asset for both business and government organizations. As such, it is important to understand the definition of Big Data, their key characteristics, and the challenges surrounding the concept and characteristics. The implementation of Big Data brings new critical challenges that need to be addressed before starting the Big Data journey. The main challenges that are commonly faced by organizations are the implementation of Big Data alongside the critical challenges that involve the challenges related to technology, organization, process, data management, and skills. The challenges are difficult and the growth in terms of Big Data is increasing exponentially which calls for further investigation. This paper reviewed the existing literature on Big Data to achieve three objectives: first, to highlight the definitions and characteristics of Big Data and to summarize the most common definitions of existing works; second, to identify the impacts and the opportunities for the Big Data; third, to present and identify the main critical challenges related to Big Data and categorize these challenges as (People, Technology, Organization, Process, and Data management) challenges.

Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model.

This paper will explore the university campus community’s perspective towards the realization of a zero-waste campus. A qualitative approach is employed to identify the factors influencing the campus community’s awareness and participation in solid waste management (SWM). Perceptions, attitudes, beliefs, opinions, and ideas of Universiti Sains Malaysia (USM) campus community members are explored in focus group discussions (FGD) to determine the main factors influencing their zero-waste pro-environmental behaviour (PEB). The in-depth interview (IDI) is used to learn more about the university campus community’s perspectives on the themes defined by the FGD. The instruments were developed based on the adaption and adoption of previous studies, especially in reference to Kollmuss and Agyeman’s 2002 Model of Pro-Environmental Behaviour and a modified Knowledge-Attitude-Practices model. The findings show that environmental goals, environmental education, personal waste-related experience, environmental policy, environmental self-awareness, reinforcement contingencies, community engagement, social responsibilities, exemplary leadership, and social media influence the campus community’s zero-waste PEB. A framework based on an adaptation of Kollmuss and Agyeman’s model of pro-environmental behaviour is proposed to promote sustainable pro-environmental behaviour towards a Zero-Waste Campus.