INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY https://rajpub.com/index.php/ijmit KHALSA PUBLICATIONS en-US INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 2278-5612 <p><a href="http://creativecommons.org/licenses/by/4.0/" rel="license"><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="Creative Commons License" /></a> All articles published in <em>Journal of Advances in Linguistics</em> are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> Why Do Data Scientists Want to Change Jobs: Using Machine Learning Techniques to Analyze Employees’ Intentions in Switching Jobs https://rajpub.com/index.php/ijmit/article/view/9058 <p>Data scientists are among the highest-paid and most in-demand employees in the 21<sup>st</sup> century. This gives them opportunities to switch jobs quite easily. In this paper, we follow the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and the data science life cycle process to analyze factors which predict whether a data scientist is looking for a new job or not. Specifically, we use machine learning techniques to analyze data from Kaggle.com. We find that features that have the highest impact on whether a data scientist wants to change his/her job include the city development index, company size, and company type. When we examine the city development index more carefully, we find evidence suggesting that employees move from cities with lower to higher development indexes, as they become more experienced. The predictive analysis system we use is able to predict with average accuracy rates of higher than 78%.</p> Sumali J. Conlon Copyright (c) 2021 Sumali J. Conlon https://creativecommons.org/licenses/by/4.0 2021-06-07 2021-06-07 16 59 71 10.24297/ijmit.v16i.9058 Predicting Tech Employee Job Satisfaction Using Machine Learning Techniques Sumali J. Conlon1 Lakisha L. Simmons2 Feng Liu3 https://rajpub.com/index.php/ijmit/article/view/9072 <p>High-tech industry employees are among the most talented groups of people in the workforce, and are therefore difficult to recruit and retain. We analyze employee reviews submitted by employees from five technology companies. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the data science life cycle process, we use machine learning techniques to analyze employees’ reviews. Our goal is to predict an overall measure of whether employees are satisfied or not, using other information from the reviews, such as employer attitudes towards upper management. We also use predictive analysis to determine which features are more helpful in determining an employee’s overall job satisfaction. Finally, we analyze which prediction algorithm provides the most accurate predictions. We find the percentage of true positives we correctly identify in the holdout sample is 97.4%, while the percentage of true negatives correctly identified is 72.5%.</p> Sumali Conlon Lakisha Simmons Feng Liu Copyright (c) 2021 Sumali Conlon, Lakisha L. Simmons, Feng Liu https://creativecommons.org/licenses/by/4.0 2021-06-29 2021-06-29 16 72 88 10.24297/ijmit.v16i.9072 Effect of economic sectors on employment in Sub-Saharan Africa. https://rajpub.com/index.php/ijmit/article/view/8941 <p>The motif of this study was to determine the effect of economic sectors on employment in Sub-Saharan Africa, given that the Sub-Saharan African region had had about two decades of sustained economic growth. Thirty Sub-Saharan African countries were used in this study, their data that was obtained and used spanning from the year 1990 to the year 2015. The study made use of the traditional neo-classical aggregate production function in the estimation of the regression results. The software program that was used in data analysis was STATA. Hausman test was undertaken and it determined that fixed effects estimations were preferred to random effect and as a result fixed effects were utilized in the study in carrying out regression analysis. On effect of economic sectors on employment, foreign direct investment was found to negatively influence employment though the influence was not statistically significant. The export and agriculture variables negatively and statistically significantly influenced employment. All the other variables in the study were found to positively and statistically significantly influence employment. Empirical results established that the gender gap in employment was maintained in the whole period of the study with more men being employed than women.</p> KAMAU NDUNGU Copyright (c) 2021 KAMAU NDUNGU https://creativecommons.org/licenses/by/4.0 2021-01-09 2021-01-09 16 42 58 10.24297/ijmit.v16i.8941 Effect of economic growth on employment in Sub-Saharan Africa. https://rajpub.com/index.php/ijmit/article/view/8940 <p>The study aimed at investigating the effect of economic growth on employment in Sub-Saharan African. The study employed secondary data that was sourced from the World Bank, World development indicators and FAOSTAT covering 30 Sub Saharan African Countries for the period 1990 to 2015. The study employed the traditional neo-classical aggregate production function in its estimation of the regression results. The panel data obtained was analysed using the STATA software program. Hausman test was used and it determined that fixed effects estimation was preferred to random effects estimation and therefore fixed effects regression was used during the analysis. Empirical results on effect of economic growth on employment established that total employment, women in employment and men in employment statistically and significantly influenced economic growth and on the other hand economic sectors which comprised of domestic capital, imports, exports and services sectors statistically and significantly influenced economic growth.</p> Francis Kamau Ndung’u Professor Niu Xiongying Copyright (c) 2021 Francis Kamau Ndung’u, Professor Niu Xiongying https://creativecommons.org/licenses/by/4.0 2021-01-09 2021-01-09 16 28 41 10.24297/ijmit.v16i.8940 The effect of demographic characteristics on employment in Sub- Saharan Africa. https://rajpub.com/index.php/ijmit/article/view/8939 <p>This study aimed at establishing the effect of demographic characteristics on employment in Sub Saharan Africa. The study used data ranging from the year 1990 to the year 2015 that was obtained from the data banks of World Bank and FAOSTAT. The panel data that was obtained and used was for 30 Sub-Saharan African Countries. The traditional Neo classical production function was utilized in this study in estimating the regression results. Hausman test was carried out and it determined that fixed effects estimations were preferred to random effects and as a consequence, random effects estimations were made use of during the analysis of data. In establishing the relationship between demographic characteristics and employment, demographic characteristics, imports and services sectors variables were found to statistically and significantly influence employment. However, domestic capital was found to negatively influence employment though this was not statistically significant, while exports was found negatively and statistically significantly influencing employment.</p> Francis Kamau Ndung’u Copyright (c) 2021 Francis Kamau Ndung’u https://creativecommons.org/licenses/by/4.0 2021-01-09 2021-01-09 16 12 27 10.24297/ijmit.v16i.8939 Motives and Consequences of Social Network Sites: Teachers in Greece A Case Study https://rajpub.com/index.php/ijmit/article/view/8943 <p>the last fifteen years and especially during the pandemic of the COVID-19 virus, there have been an intense In use of Social Network Sites (SNS) by all age, social and professional groups. Logically, that teacher could not escape from this trend. The purpose of this work is to evaluate the hedonic use, utilitarian use, socializing, procrastination, job escapism, work productivity of the specific professional team from the use of SNS. For this reason, a questionnaire consisting of 30 questions was created which was given to a sample of 351 teachers (N=351, Cronbach’s alpha=0.90) in Greece. The results showed that the strongest motivation for the use of SNS is job escapism. It became apparent that the negative effects are associated with escapism and socializing, while hedonic use seems to be more linked with procrastination.</p> Vasileios Gougas Lucia Malinova Copyright (c) 2021 Vasileios Gougas, Lucia Malinova https://creativecommons.org/licenses/by/4.0 2021-01-09 2021-01-09 16 1 11 10.24297/ijmit.v16i.8943