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Background: Big data analytics in education is a new concept that has the potential to change the decision-making landscape in South African Colleges. Higher institutions of learning, including Technical and Vocation Education Training (TVET) colleges like all other organisations, rely on data for their decision-making. These decisions affect the w...
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... post-apartheid South Africa, the former Department of Education was split into two different departments: Department of Basic Education and Department of Higher Education and Training (DHET) was then established in 2018. Higher Institutions of Learning in South Africa are classified into four divisions as illustrated in Figure 1. ...
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... A revisão sistemática da literatura executada na Parte I destaca em maior detalhe os estudos, lacunas e oportunidades apresentadas pelos últimos dez anos que possuam potencial conexão com o escopo desta pesquisa. Isto posto, é fundamental o estudo da IA nas organizações -sobretudo porque este tipo de algoritmo está fortemente relacionado à obtenção da vantagem competitiva em relação aos seus concorrentes (Akhtar et al., 2019;Selowa;Ilorah;Mokwena, 2022) e na expansão e descoberta de oportunidades de negócio (Akhtar et al., 2019). A adoção de IA nas organizações não é somente uma inovação tecnológica, mas também uma inovação em modelos de negócio e estratégias das organizações (Buhalis et al., 2019;Armour;Sako, 2020;Raisch;Krakowski, 2021;Jimenez, 2021). ...
... A revisão sistemática da literatura executada na Parte I destaca em maior detalhe os estudos, lacunas e oportunidades apresentadas pelos últimos dez anos que possuam potencial conexão com o escopo desta pesquisa. Isto posto, é fundamental o estudo da IA nas organizações -sobretudo porque este tipo de algoritmo está fortemente relacionado à obtenção da vantagem competitiva em relação aos seus concorrentes (Akhtar et al., 2019;Selowa;Ilorah;Mokwena, 2022) e na expansão e descoberta de oportunidades de negócio (Akhtar et al., 2019). A adoção de IA nas organizações não é somente uma inovação tecnológica, mas também uma inovação em modelos de negócio e estratégias das organizações (Buhalis et al., 2019;Armour;Sako, 2020;Raisch;Krakowski, 2021;Jimenez, 2021). ...
... C.4.1.1 IA nas organizações O uso de soluções de big data e inteligência analítica (como ML e IA) em uma organização está fortemente relacionado à obtenção da vantagem competitiva em relação aos seus concorrentes (Akhtar et al., 2019;Selowa;Ilorah;Mokwena, 2022). No caso, estas tecnologias possibilitam a expansão das operações de uma organização e a descoberta de novas oportunidades de negócio (Akhtar et al., 2019). ...
The existing literature on management and organizational strategy often portrays organizations as primarily composed of individuals working in various departments and teams. However, this perspective fails to consider the significant impact of algorithms and artificial intelligence (AI) systems on modern organizations. The caricatured image of the effective executive, taught in management courses and found in textbooks in the field, needs to be updated and compatible with the era of automation, data, and digitization. While executives are often portrayed as experienced decision-makers who rely on their instincts, the reality is that algorithms and AI have become indispensable for organizational success. Unlike executives or physical infrastructure, the failure of AI systems can cause significant disruptions in business operations and profitability. Thus, this study is based on Actor-Network Theory (ANT) with the socio-technical participant observation approach, where the researcher positions themselves as a data scientist responsible for developing and maintaining AI algorithms within an organization. From this perspective, it is proposed to analyze how humans apprehend AI algorithms in organizations by formulating the articulations between AI algorithms and human actors. The study describes the interactions between human and non-human actors, interpreting the role of AI algorithms as actors and examining the extent of their associations with Information Technology and AI managers, Data Science professionals, business analysts, and managers from outside these areas. The results offer a significant contribution to the field of Management, proposing a management method that takes advantage of the symbiotic interaction between humans and AI, highlighting the importance of considering the complexity of socio-technical relationships when formulating AI-centric organizational strategies.
This study designed a big data analytics artefact for the prediction of outcome-based funding (OBF) in South African public universities. Universities in South Africa (SA) are subsidized based on their performance known as OBF that is measured depending on the outputs from teaching, research, and engagements. OBF metrics are well documented; however, public universities fail to achieve the targets for higher scores. These failures are attributed to poor decision-making resulting from limited analysis of the voluminous data generated. This study used design science methodology to develop a big data analytics artefact for prediction of OBF outcomes. The artefact was evaluated for prediction using machine learning training and tested with data collected from South African universities. Findings indicated that for better prediction using big data analytics, system characteristics, size, structure, top management support, market, infrastructure, and government regulations factors play a significant role.
BACKGROUND: Social media comprise technologies that facilitate learning in higher education institutions. However, many first-year students at tertiary education institutions are not taking advantage of social media for their learning because of environmental and personal factors related to the digital divide (DD OBJECTIVES: The objective of this research study was to investigate the impact of the DD factors on first-year students in using social media for learning in tertiary education institutions METHOD: A survey method was used to conduct the study. Social cognitive theory was employed as a theory underpinning this research. A questionnaire technique was used to collect data from 600 first-year students of a multi-campus university. Three hundred students came from each of the two campuses. Regression analysis was performed with the purpose of testing the hypotheses of the study RESULTS: The result of the analysis revealed a low computer access and usage but a high percentage of mobile devices usage by students from disadvantaged backgrounds. Personal factors were found to have an impact on the behaviour of students in adopting social media for their studies. The study also found that the prevalence of social media nullifies the lack of computer resources and connection to the Internet in disadvantaged areas CONCLUSION: This study demonstrated that the DD was more complex than hitherto envisaged. The study recommends that ownership of computers and devices connected to the Internet needs to be promoted, especially in disadvantaged areas
