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Gorejena, Karikoga Norman
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Computer Science & IT

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A Multi-Theory Framework for Assessing IoT Adoption in Botswana SMEs Mphale, Ofaletse; Gorejena, Karikoga Norman; Nojila, Olebogeng
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.737

Abstract

The rapid surge of technological innovation is reshaping industries, and Small and Medium Enterprises (SMEs) are key players in this digital transformation. However, despite their prominence, SMEs often struggle to adopt cutting-edge technologies like the Internet of Things (IoT). While IoT offers the potential to revolutionise SME operations, integrating these interconnected devices presents significant hurdles. Thus, to bridge this research gap, a robust conceptual framework is necessary to understand the factors influencing IoT adoption in SMEs. This study addresses a critical gap by proposing a novel multi-theoretical framework, integrating established technology adoption theories and models like, Diffusion of Innovation (DOI), Technology-Organisation-Environment (TOE) framework, Technology Acceptance Model (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT). This framework will assist researchers, policymakers, and practitioners in crafting strategies to improve SMEs IoT adoption in Botswana and other developing economies. Future studies will validate this framework through survey research.
The Future of Things: A Comprehensive Overview of Internet of Things History, Definitions, Technologies, Architectures, Communication and beyond Mphale, Ofaletse; Gorejena, Karikoga Norman; Nojila, Olebogeng
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.738

Abstract

This paper explores the multifaceted world of IoT, exploring its historical evolution, core definitions, enabling technologies, architectural considerations, communication models and future predictions. Moreover, the paper critically scrutinises the potential benefits and challenges associated with IoT adoption. This study employed a systematic literature review (SLR) methodology to review existing literature between January 2020 and January 2024 on IoT technology. 20 relevant studies out of an initial pool of 45,312 studies were reviewed. The study followed the established Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework to ensure a rigorous and unbiased selection process. Findings revealed the lack of a universally accepted definition for IoT and significant variations in architectural models across scholarly works. Moreover, the study acknowledges the complex and ever-evolving nature of IoT technology, recognising its early stages of development. This dynamic technological landscape calls for continuous exploration of future research. Findings will provide valuable insights for academics, researchers, and industry professionals interested in the understanding the intricate technical landscape of IoT and its development. Moreover, by providing insights into growth predictions, benefits, and existing challenges, this will empower stakeholders to navigate the evolving IoT landscape and contribute to its future progress.
Understanding IoT Adoption in Botswana’s SMEs: A Research Onion Approach Mphale, Ofaletse; Gorejena, Karikoga Norman; Nojila, Olebogeng Hellen
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.880

Abstract

The advent of the Internet of Things (IoT) presents a transformative opportunity for Small and Medium-sized Enterprises (SMEs), unlocking their potential for enhanced operational efficiency, productivity, and data-driven decision-making. However, harnessing these benefits necessitates a rigorous and structured methodological approach. On the next hand, selecting an appropriate research methodology can be problematic, as it demands consideration of context-specific factors. This study addresses a significant gap by theoretically evaluating and proposing a suitable "research onion" methodological approach, which can be employed to explain IoT adoption in Botswana's SMEs. This structured approach provides a comprehensive analytical lens comprising the research philosophies, research strategy, approaches, choices, time horizons, techniques and procedures. By carefully applying and justifying each element within the research onion’s distinct layers, the study empowers Information Systems (IS) researchers to effectively explain their methodological decisions. Hence, findings will inform policymakers and decision-makers in Botswana, enabling them to design targeted interventions that promote widespread IoT adoption in SMEs. Future research will empirically test this framework in Botswana's SME sector using surveys, thus furthering our understanding of the IoT adoption factors in SMEs.
IoT Adoption in Botswana's SMEs: Technological Readiness and Government Initiatives Mphale, Ofaletse; Gorejena, Karikoga Norman; Nojila, Olebogeng Hellen
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.885

Abstract

As Botswana seeks a more diversified and developed economy, fostering a robust Small and Medium-sized Enterprises (SME) sector is paramount. Consequently, integrating Industry 4.0 technologies, particularly the Internet of Things (IoT), presents a promising possibility for stimulating SME growth. Nonetheless, successful IoT adoption hinges on understanding Botswana's technological landscape. This study employed a Systematic Reviews and Meta-Analyses (PRISMA) guideline to survey the existing literature (academic and grey literature) published between 2015 and 2024 on Botswana's digital readiness. The review examined several areas of technological readiness, including e-commerce legislation, Information and Communication Technology (ICT) education, infrastructure investment, government online services, e-participation initiatives, internet costs, policy framework, cybercrime threats, and overall technology adoption. Results showed that while Botswana has made progress in digital integration, its digital maturity is still evolving. Strengths were found in areas including; e-commerce legislation, ICT education and infrastructure investment. However, weaknesses persist in areas including; limited government online services, limited e-participation initiatives, high internet subscription costs, inadequate policy framework, cybercrime threats, infrastructure limitations, and overall low technology adoption. These findings will equip SME decision-makers and policy makers with valuable insights to assess their digital preparedness for IoT adoption, while also considering Botswana's digital environment.
Hybrid Unsupervised Machine Learning for Insurance Fraud Detection: PCA-XGBoost-LOF and Isolation Forest Chapwanya, Natsai; Gorejena, Karikoga Norman
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.958

Abstract

Insurance fraud poses a significant threat to the financial stability of insurance companies, resulting in substantial economic losses. To combat this issue, this study proposes a novel unsupervised machine learning hybrid algorithm, integrating Principal Component Analysis (PCA), Extreme Gradient Boosting (XGBoost), Local Outlier Factor (LOF), and Isolation Forest. This hybrid approach aims to improve the detection accuracy of insurance fraud by combining the strengths of each individual algorithm. Experimental results a real-world insurance dataset demonstrate a detection accuracy of 92%, precision of 92% and recall of 96%. Our experimental results demonstrate that the proposed hybrid algorithm outperforms existing state-of-the-art methods, achieving a higher detection rate and reducing false positives. This research contributes to the development of effective insurance fraud detection systems, ultimately helping insurance companies to minimize financial losses and improve their overall profitability.
Co-Authors Chapwanya, Natsai Mphale, Ofaletse Nojila, Olebogeng Nojila, Olebogeng Hellen