Factors Influencing the Quality of Decision-Making Using Business Intelligence in a Metal Rolling Plant in KwaZulu-Natal

The current study sought to investigate the factors which influence the quality of decision-making using business intelligence (BI) in a metal rolling plant in KwaZulu-Natal. Specifically, the study was focused on information quality, system quality and BI service quality. The study used a self-administered survey sent out to participants having sufficient report runs which made up the population of the study. The collected data came from different levels of employees, namely; managers (47%) and non-managers (53%) with varying levels of BI experience, and the data was imported into SPSS for analysis. The results showed that information quality had a positive significant impact on the quality of decision-making; system quality had a positive significant impact on the quality of decision-making; and BI service quality had a positive significant impact on the quality of decision-making. Multiple linear regression analysis was conducted to determine the strength of these variances in influencing decision-making. It was found that the three variables explained 65.7% of the variance in the quality of decision-making. Overall, the study found that high quality information, coupled with a high-quality system and good BI service, leads to a higher quality of decision-making, and that the impact of BI on decision-making is positive. The study recommends that the company implement data quality management focusing on data cleansing, it should also implement more sophisticated analysis techniques to get insights and have strategies to upskill both technical and business workers

The function of competitive intelligence in South African insurance post-COVID-19 outbreak

Background: Competitive intelligence (CI) involves monitoring competitors and providing organisations with actionable and meaningful intelligence. Some studies have focussed on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers’ competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms’ operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area

Competitive intelligence and strategy implementation: Critical examination of present literature review

Background: Competitive intelligence (CI) involves recognising that intelligence is the basis of firms engaging in strategic activities in a competitive marketplace. Numerous studies have demonstrated the benefits of CI in strategic planning processes, but the impact of CI on strategy execution has received less academic attention. Objectives: This study aims to critically examine the current state of the CI and the strategy implementation literature. It also identifies gaps and limitations in the existing literature. Method: This study employed systematic literature with the selection criterion to identify 33 publications published between 2008 and 2022. Thematic content analysis provided a range of analytic options. Academic Search Complete, EBSCOhost, and Google Scholar databases were used to locate peer-reviewed journal articles. The publications were grouped by journal names, year of publication, and article count. Results: The study found that CI played a vital role in developing company strategies and practices. Our study also showed that a plethora of peer-reviewed academic articles on CI were published in developed countries such as North America and Europe. Only a few CI-related studies have been published in developing countries. Conclusion: Most authors have not examined how CI leads to strategy implementation, even though CI research has gained traction internationally over the years. Competitive intelligence was not found to have a relationship with implementing, evaluating, and monitoring strategies pointing out gaps in the literature and suggesting future research. Contribution: This study examines the current state of the literature on CI and strategy implementation, and identifies gaps in the existing literature. Furthermore, the study contributes to academic knowledge by emphasising the significance of a well-defined and structured CI process in achieving strategy implementation outcomes

Original Research By Young Twinkle Students (ORBYTS): Ephemeris Refinement of Transiting Exoplanets III

We report photometric follow-up observations of thirteen exoplanets (HATS-1 b, HATS-2 b, HATS-3 b, HAT-P-18 b, HAT-P-27 b, HAT-P-30 b, HAT-P-55 b, KELT-4A b, WASP-25 b, WASP-42 b, WASP-57 b, WASP-61 b and WASP-123 b), as part of the Original Research By Young Twinkle Students (ORBYTS) programme. All these planets are potentially viable targets for atmospheric characterisation and our data, which were taken using the LCOGT network of ground-based telescopes, will be combined with observations from other users of ExoClock to ensure that the transit times of these planets continue to be well-known, far into the future