Management Factors Across Entrepreneurial Business Sectors in Emerging Economies of Nigeria and South Africa
Charles Temitope Jegede,
Folashade Oyeyemi Akinyemi,
Temitope Favour Jiboye,
Michael Olufemi Akinyosoye
Issue:
Volume 4, Issue 3, May 2019
Pages:
39-48
Received:
18 April 2019
Accepted:
11 June 2019
Published:
10 July 2019
Abstract: The paper examined strategic management factors that promote entrepreneurial activities across some selected business sectors in the emerging economies of Nigeria and South Africa. Specifically, it identified the most prominent management factor in each economy, and the most crucial in each sector. We proposed that by examining these factors across sectors, in two emerging economies, management factors that enhance entrepreneurial activities would be industry specific and dissimilar. The sample size consisted of a total of 1200 entrepreneurs that aged between 18 to 64 years in Lagos and Johannesburg. The data were cleaned and analyzed using STATA Purposive sampling technique. Principal Component Analysis (PCA) was used to identify the most crucial policy factor (s) in each sector, Also, Chi-square test was used to show the association between the variables, and Cronbach’s Alpha was employed to test the internal consistency and reliability of some of the critical indicators. Research findings suggested that the management factors that enhanced entrepreneurial activities differ across business sectors. Some management factors are more crucial in some sectors than others. In Nigeria, marketing strategy was the principal component in all except service sector whereas in South Africa, recruitment policy was the principal component in all the seven sectors. For the pooled data, however, marketing strategy was the principal component in four out of seven sectors. Two factors, namely, Organizational Structure and recruitment policy were equally crucial in the telecommunication sector while accounting policy and recruitment policy were the principal components in metal and service sectors, respectively. The study suggests that entrepreneurial firms in Lagos needs to concentrate more on their marketing strategies while those in South Africa needs to focus more on their recruitment procedures for better performance.
Abstract: The paper examined strategic management factors that promote entrepreneurial activities across some selected business sectors in the emerging economies of Nigeria and South Africa. Specifically, it identified the most prominent management factor in each economy, and the most crucial in each sector. We proposed that by examining these factors across...
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Social Media Text Data Visualization Modeling: A Timely Topic Score Technique
Issue:
Volume 4, Issue 3, May 2019
Pages:
49-55
Received:
7 April 2019
Accepted:
5 June 2019
Published:
26 July 2019
Abstract: Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.
Abstract: Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision...
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