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This paper investigates factors that influence Zambian higher education lecturer's attitude towards integrating ICTs in research and teaching. To determine these factors, we used online administered survey with a total of 163 respondents from public and private higher education institutions in Zambia. Quantitative data analysis based on descriptive and inferential statistics was used in this study. Inferential analysis was employed to investigate the relationships among variables using Pearson correlation analysis and completely randomized design. The results revealed that lack of adequate Internet bandwidth is among the major barrier or obstacle for assimilating ICTs in research and teaching for Zambian higher education lecturers. Additionally, training lecturers on the use of ICTs e.g. email, PowerPoint presentations, electronic boards and its advantages is the most important incentive to motivate lecturers to incorporate ICTs in research and teaching. Therefore, this study contributes to literature on factors that influence higher education lecturers' attitude towards integrating ICTs in research and teaching. This study also demonstrates a positive correlation between the important factors that influence lecturers in integrating ICTs in research and teaching.

COVID-19 is a highly infectious respiratory disease that belongs to the SARS group of viruses that has presented a global challenge to almost everyone world-wide. During the early stages of the pandemic in Zambia, a major challenge was the limited data and datasets for COVID-19. This challenge restricted research, especially in data mining. The challenge of data and datasets is currently improving. This paper presents the challenges of using data mining techniques and models to analyze and forecast the COVID-19 pandemic in Zambia. The analysis initially presents the methodology used for creating a dataset that focuses on the pandemic at provincial scope and uses the Zambia National Public Health Institute (ZNPHI) and Ministry of Health Zambia daily situation reports. The analysis of the pandemic at country level used the COVID-19 datasets from the Humanitarian Data Exchange (HDX) and the European Center for Disease Prevention and Control (ECDC). The study finally discusses the development and evaluation of the forecasting model. The forecasting model is based on the COVID_SEIRD Python package. To evaluate the forecasting model, the research utilized a combination of correlation and the max-function from basic statistics. The analysis focuses on finding the provincial area with the most COVID-19 cases in Zambia, while the forecasting process manages to forecast the trend of the pandemic for recoveries and fatalities. [ABSTRACT FROM AUTHOR]

Electricity consumption in developing countries such as Zambia continues to grow as the economy grows. As a result, it is important to study how the rate of electricity consumption affects the economy of a country. For this study, the economic variables that were used are the Gross Domestic Product and the Consumer Price index. The results from this study are that there is a unidirectional relationship between electricity consumption and the consumer price index where the rate of electricity consumption Granger causes the consumer price index. The study also showed that there is no causal relationship between electricity and GDP and that there was no causal relationship between electricity consumption and the Consumer Price Index. [ABSTRACT FROM AUTHOR]