Search Results (4)

This study aimed to investigate the relationships between knowledge, attitudes, and values in promoting zero-waste, pro-environmental behaviour among the university campus community and how technology plays its role. A survey instrument was developed from the findings of earlier focus group discussions and in-depth interviews, which were based on the adaptation and adoption of previous studies, especially Kollmuss and Agyeman’s 2002 Model of Pro-Environmental Behaviour and a modified Knowledge-Attitude-Practises Model. Three hundred ninety-three respondent samples from Universiti Kebangsaan Malaysia, Universiti Putra Malaysia, and Universiti Sains Malaysia were analysed for their insights regarding their vision of a Zero-Waste Campus using the Structural Equation Modelling approach. The results show positive relationships between the five studied variables and the proposed hypotheses. Knowledge and attitudes serve as moderating variables, enhancing the strength of the causal effects of the related relationships. As the results show good significance, proper planning for integrating the studied variables and the proposed zero-waste campus framework can be used by university management, other institutions, or national-level agencies to develop an environment-specific plan to promote zero-waste, pro-environmental behaviour. Full article

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. Full article

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. Full article

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. Full article