Business Intelligence Readiness for Higher Learning Institution (IHL): Preliminary Study and Research Model

Rahmat, Abdul Razak; Ahmad, Azizah; Ta’a, Azman

doi:10.9734/bjast/2016/24383

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…HEIs are an essential institution for the production of the workforce in all countries (Ong, 2016). Typically, these institutions are complicated and involve massive data sources across departmental boundaries to achieve academic excellence (Rahmat, Ahmad, & Ta'a, 2016;Wong, 2017;Wong et al, 2018). In general, the HEIs comprises five business areas: (1) student affairs;…”

Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

“…(2) academic staff affairs; (3) finance affairs; (4) research and development affairs; and (5) development of infrastructure (Rahmat et al, 2016). Each business area uses different system for their respective needs and this needs to be integrated.…”

Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

“…Each business area uses different system for their respective needs and this needs to be integrated. This is because data generated from each application contain useful information that should be accessible by multiple departments such as HEI senior management, faculty, university administrators, scientists and relevant decision makers (Rahmat et al, 2016;Wong, 2017;Wong et al, 2018).…”

Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

“…Literature on BI adoption has largely focused on its applications in the business sector, (Boonsiritomachai et al, 2016;Qushem, 2017;Ramamurthy, Sen, & Sinha, 2008;Shen, Hsu, & Peng, 2012) while studies for the HEI sector have been scarce. Although the trend in decision-making support tools continues to increase, HEIs have been lagged behind in the adoption of BI (Rahmat et al, 2016). In most cases, some HEIs still use spreadsheets to analyse data.…”

Section: Limitations In Bi Adoptionmentioning

confidence: 99%

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Technology Adoption Among Higher Education Institutions in Malaysia: An Analysis of Business Intelligence Adoption

Iskandar

Nazri

2021

IJSSR

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Purpose: The study strives to determine the outcomes of business intelligence (BI) adoption among Malaysian Higher Education Institutions and factors that affect its adoption. Research methodology: The research design is formulated in the context of a quantitative research strategy for this study. The study used the e-mail survey questionnaire as a method for collecting data. 769 public and private HEIs listed by the Malaysia Qualification Agency participated in the analysis. Structural Equation Mode (SEM) is utilized to collect data.Findings: Overall findings show that HEIs in Malaysia are at the moderate level of BI adoption. Technology: Complexity Environment: Competitive Pressure describes the significant factors of BI adoption among HEIs in Malaysia.Practical implications: The researcher also hopes that the model of this study can be used for analysing other IT adoptions in the context of the HEIs. Also, the researcher expects that the empirical finding in this research from the validated template will provide further knowledge of the benefits of HEIs adoption of BI in Malaysia.Originality/Value: There is insufficient research in the area of BI adoption in HEIs. The objective of this analysis is therefore to assess the factors and results of BI adoption by Malaysian HEIs. The researcher also expects that the model for this study may be used in the review of other IT adoptions in the HEI sense.

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Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

Section: The Relevance Of Bi To Heis In Malaysiamentioning

confidence: 99%

Section: Limitations In Bi Adoptionmentioning

confidence: 99%

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Technology Adoption Among Higher Education Institutions in Malaysia: An Analysis of Business Intelligence Adoption

Iskandar

Nazri

2021

IJSSR

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Big Data Analytics Technology Capability and Data-Driven Decision Making in Malaysian Higher Education Institutions: A Conceptual Framework

Ashaari

Amran

Ahmad

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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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.

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Business Intelligence Readiness for Higher Learning Institution (IHL): Preliminary Study and Research Model

Cited by 2 publications

References 19 publications

Technology Adoption Among Higher Education Institutions in Malaysia: An Analysis of Business Intelligence Adoption

Technology Adoption Among Higher Education Institutions in Malaysia: An Analysis of Business Intelligence Adoption

Big Data Analytics Technology Capability and Data-Driven Decision Making in Malaysian Higher Education Institutions: A Conceptual Framework

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