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Segooa, Mmatshuene Anna
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Computer Science & IT

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Journal : Journal of Information Systems and Informatics

 1 
Evaluating the Efficacy of AI Tools in Systematic Literature Reviews: A Comprehensive Analysis Mogoale, Phumzile Dorcus; Pretorius, Agnieta Beatrijs; Mogase, Refilwe Constance; Segooa, Mmatshuene Anna
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.v7i1.1035

Abstract

Artificial Intelligence (AI) tools can revolutionize literature review practices by transforming the research landscape towards more efficient and reliable review processes. While conducting literature can be challenging and time-consuming, there is a plethora of AI powered tools which uncover potential solutions to the challenge. AI tools may reduce the time spent on repetitive tasks, allowing scholars to focus more on critical analysis and interpretation. Due to the rising abundance of AI tools, it is difficult to decide which AI tools are best for individual research problems or projects. While concerns exist around the ethical and quality consequences of using AI. The study aims to explore the usage of AI tools on the systematics literature review process, specifically focusing on their effectiveness in various stages and ethical concerns. IEEE and MDPI Journal papers from 2020 to 2024 were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RobotReviewer, Covidence and EPPI-Reviewer are AI tools commonly used. These AI tools are designed to support different aspects of the systematic literature review process by offering capabilities such as problem formulation, literature search, inclusion screening and quality assessment. AI tools demonstrate improved effectiveness of literature searches, screening processes and data extraction. Language and content presentation, incorrect citation and plagiarism, grammar and spelling errors may be ren when utilizing AI. Concerns related to data quality, biases, and the need for human oversight were identified.
Robotic Process Automation Readiness Barriers and Enablers in South Africa’s Energy Supply Chain Motsoeneng, Mariah Thokozile; Segooa, Mmatshuene Anna; Motjolopane, Ignitia; Kgopa, Alfred Thaga
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1281

Abstract

South Africa’s energy industry faces ongoing challenges including power shortages, ageing infrastructure, and supply chain inefficiencies, while, limited empirical evidence exists on how organisations in this industry prepare for Robotics Process Automation (RPA) adoption. This study examines the RPA readiness barriers and enablers within the supply chain of South Africa’s energy industry. The research adopts a qualitative design grounded in the Technology-Organisation-Environment (TOE) framework and the Awareness, Desire, Knowledge, Ability, Reinforcement (ADKAR) change management model to connect technological capability with individual and organisational readiness for change. Data were gathered through semi-structured interviews with 18 professionals representing eight stakeholder groups, including supply chain managers, IT specialists, process improvement leads, and employees affected by automation. Four key readiness barriers emerged: readiness gaps (61 mentions), organisational misalignment (158), infrastructure strain (83), and job security and resistance (60). Corresponding enablers included leadership accountability, RPA governance and alignment frameworks, readiness checklists, structured communication protocols, KPI frameworks, capability audits, investment planning, psychological safety, and regulatory alignment mechanisms. The integration of TOE and ADKAR offers a novel dual-lens perspective that extends existing knowledge. The findings provide practical guidance for managers and policymakers seeking to strengthen organisational systems and structures with human readiness factors in emerging economies.
Integrating Diversity, Equity, and Inclusion into Systems Analysis and Design Education Segooa, Mmatshuene Anna; Barber, Connie S
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1277

Abstract

Diversity, Equity, and Inclusion (DEI) represent the involvement of different groups supported equally and fairly while their differences are acknowledged and recognized. Many organizations, including higher education institutions, have adopted the notion of DEI by introducing the phenomenon through their institutional strategies. However, there is limited evidence of the inclusion of DEI content in information systems (IS) education. The paper aimed to develop a framework to integrate DEI in Information Systems Analysis and Design education and projects. A survey to investigate how organizations actively include DEI content in the different stages of the System Development Lifecycle (SDLC) methodology was administered. Results identified that 23-40% of organizations represented address DEI at some stage of the SDLC. Practical implications for instructors include well-informed and prepared students as well as improved System Analysis & Design (SA & D) curriculum. The practical implications for information systems (IS) practitioners are resulting system designs that are culturally sensitive, end user perspective driven, and accessible for all users. Bridging IS education with industry practice through a framework for DEI in the system design space brings new insights into system design, better preparing students to face DEI content in their careers across all industries. Significantly raising awareness that problem solving and IS solutions should meet the needs of society with different backgrounds and cultures.
Factors Influencing Generative AI Adoption for Knowledge Management in South Africa’s Automotive Sector Ratsiku, Diana Maphefo; Segooa, Mmatshuene Anna; Kgoetiane, Cecil Hlopego
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1393

Abstract

South Africa’s automotive sector is under increasing pressure to sustain competitiveness amid Fourth Industrial Revolution (4IR) transitions, persistent operational inefficiencies, and workforce ageing. Generative AI (GenAI) presents a potential pathway to strengthen knowledge management (KM) by supporting faster knowledge capture, synthesis, retrieval, and decision support. This study identifies the determinants of GenAI adoption for improving KM practices in South Africa’s automotive context. A quantitative, hypothesis-driven design was employed, integrating constructs from the PPOA, TEOG, and IEO frameworks to provide a consolidated adoption perspective. Survey data were collected from 142 industry participants and analysed using SPSS (correlation and multiple regression). The model demonstrated strong explanatory power (Adjusted R² = 0.624, p < 0.001). Results indicate that GenAI adoption is significantly and positively influenced by FATAA ethical principles, KM practices, GenAI tool capability, perceived enjoyment, perceived usefulness, compatibility, competition intensity, organisational size, mimetic pressure, and normative pressure (p < 0.05). In contrast, perceived ease of use and coercive pressure were not statistically significant in this context (p > 0.05). The study contributes a context-specific, integrated adoption model for GenAI-enabled KM in an under-researched setting and offers actionable implications for managers and policymakers focused on responsible, effective GenAI deployment.
Co-Authors Barber, Connie S Kgoetiane, Cecil Hlopego Kgopa, Alfred Thaga Mogase, Refilwe Constance Mogoale, Phumzile Dorcus Motjolopane, Ignitia Motsoeneng, Mariah Thokozile Pretorius, Agnieta Beatrijs Ratsiku, Diana Maphefo