One of the challenges for implementing Sustainable Development Goals (SDGs) is the measurement of indicators that represent progress towards such goals. Measuring such progress enables data-driven decision-making and management of SDG-relevant projects and strategies. The premise of this research is that measuring such indicators depends on measuring so-called means of implementation, i.e. activities that directly contribute to the achievement of SDGs. Building on this premise, this article studies how the measurement of digital government (DG) can contribute to the measurement of SDGs. In particular, how the indicators originating in three DG measurement instruments can inform the SDG indicators. The main finding is an alignment matrix, showing how the DG indicators contribute with varying level of specificity to the measurement of 10 SDG indicators.
PurposeThis study illustrates the experience of scholar–practitioner collaboration for data-driven decision-making through the problematic of optimizing facility locations and minimizing logistics costs for La Palette Rouge (LPR) of Portugal.Design/methodology/approachThe authors used a mixed mixed-method approach involving (1) a quantitative exploratory analysis of big data, which applied analytics and mathematical modeling to optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner collaboration for data-driven decision-making.FindingsThe quantitative analysis compared more than 20 million possible configurations and proposed the optimal logistics structures. The proposed optimization model minimizes the logistics costs by 22%. Another optimal configuration revealed that LPR can minimize logistics costs by 12% through closing one of its facilities. The illustrative description demonstrates that well-established resource-rich multinational enterprises do not necessarily have the in-house capabilities and competencies to handle and analyze big data.Practical implicationsThe mathematical modeling for optimizing logistics networks demonstrates that outcomes are readily actionable for practitioners and can be extended to other country and industry contexts with logistics operations. The case illustrates that synergistic relationships can be created, and the opportunities exist between scholars and practitioners in the field of Logistics 4.0 and that scientific researcher is necessary for solving problems and issues that arise in practice while advancing knowledge.Originality/valueThe study illustrates that several Logistics 4.0 challenges highlighted in the literature can be collectively addressed through scholar–practitioner collaborations. The authors discuss the implications of such collaborations for adopting virtual and augmented reality (AR) technologies and to develop the capabilities for maximizing their benefits in mature low-medium technology industries, such as the food logistics industry.
The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion.
This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process.
Research supervision is one of the important aspect in academic quality assurance and the sustainbility of the science itself. However, there is lack of attention based on research literature and evidence of good practice on research supervision from the context of academic integrity in higher education. This study aims to develop a data-driven decision making strategy in supervisor selection for post-graduate program based using research projects data. Apart of that, the researchers reviewed the indicator of academic integrity in research supervisory from program standards in masters and doctoral degree by Malaysia Qualification Agency (MQA), international recommendation by UNESCO and Islamic principles according to the roles of the supervisor, administrator and student in the context of research supervisory. This study adopted data analytics and visualization technique using cloud-based collaborative platform as a research method for data acqusition, processing and analyzing the data. The researchers acquired the research projects profile data registered in the institutional database in Universiti Malaysia Pahang from Department of Research and Innovation as a case study. We categorized and mapped the research profile according to Malaysian Research and Development Classification System (MRDCS) code. The combined data was been analyzed and visualized to specific online dashboard to indicate the research experience in fraction of years as a metric. The researchers evaluate the characteristics of the dashboard based on the academic integrity indicators from MQA, UNESCO and Islamic principles as our measures. The result shows that there is a potential usefulness of the proposed strategy in assuring academic integrity for supervisor selection in post-graduate programmes. This novel approach has a potential impact on academic integrity in higher education which can be adopted at larger scale by higher education institution in Malaysia.
Contract-based Data-Driven Decision Making in Federated Data Ecosystems
