No abstract

Business Intelligence (BI) ideally provides organizations that embrace them with immerse impact. Very few researches have empirically assessed these claims. There are limited studies of its impacts on organizational performance in the context of higher education institutions (HEIs) in developing countries. The study aims to develop a conceptual framework for the impacts of BI adoption on organizational performance among higher education institutions. A conceptual framework was developed using the Kaplan and Norton's balanced scorecard (BSC).

In the era of big data, many organizations are aiming to become more data-driven and increase their decision-making efficiency. Nevertheless, there is an insufficient investigation that explores the antecedents of data-driven decision making (DDDM) in the context of Malaysian Higher Education Institutions (HEIs). Therefore, the study examines existing literature and utilises the Big Data Analytics Technology Capability (BDATC) dimensions such as connectivity, compatibility and modularity to conceptualize a DDDM framework. The study utilises Resource-Based Theory (RBT) to highlight the key dimensions for BDATC and how these dimensions are being integrate in order to establish effective DDDM for excellent performance. The antecedents of DDDM is a relatively new approach that is documented in literature, where literature seems to be diversified in terms of offering theoretical and conceptual frameworks, together with a model that Higher Education Institutions (HEIs) can utilize. The study chooses HEIs that undergo Malaysian Research Assessment Instrument (MyRA) as the focus of investigation. The data was collected from the key informants of Malaysian HEIs. In conclusion, the contribution of the study is to highlight the influence of BDATC for DDDM to attain better performance of HEIs in Malaysia.

In every sector of the economy, artificial intelligence (AI) is becoming more feasible, and higher education service is no exception. At an unparalleled pace, both within and outside the classroom, AI opens the possibility for institutions of higher learning (IHLs) to become scalable. This paper proposed a research framework for AI adoption of in IHLs. The research aims to examine the determinants that significantly affected AI adoption among IHLs. This study presents an interpretation of the Technology-Organisation-Environment (TOE) theory for the adoption of AI. The research framework derived from the TOE theory, where the context of technological, organisational, and environmental are vital for IT adoption. It discussed the development of hypotheses that consisted the determinants for the adoption of AI from the context of technological (relative advantage and compatibility), organisational (resources availability, top management support and organisation size) and environmental (government regulation and competitive pressure).

Human capabilities play an essential role in decision making regarding sustainable procurement practice (SPP). As tools in helping Higher Education Institutions (HEIs) to achieve Sustainable Development Goals (SDGs), SPP requires supports from top management in ensuring the proper practice. Additionally, the leaders' sustainability experience has the potential to influence the decision regarding SPP. Therefore, a decision support system is needed to help the HEIs' top management in making a proper decision regarding the SPP and can have a high impact on overall SDGs achievement. The study utilizes the Resource-Based Theory (RBT) in explaining the importance of human capabilities in practicing sustainable procurement.