PurposeThe purpose of this study is to explore the role individual innovativeness along with e-learning self-efficacy play in predicting the e-learning readiness of first- and second-year students of an open and distance education institutions in an African context.Design/methodology/approachTherefore, building on previous related research in this area, a quantitative approach was adopted to address the research questions and to establish whether a statistically significant relationship existed between individual innovativeness, e-learning self-efficacy, the independent variables; and e-learning readiness, the dependent variable. In total, 476 first- and second-years students of the university participated in the four-Likert-type scale survey. The research instrument which comprises 74 survey items was completed by 217 of the students. Statistical tools used for analysing data included both Pearson Product Moment Correlation coefficients and t-tests.FindingsIt was discovered that a strong positive and significant relationship was observed between individual innovativeness and e-learning readiness of first- and second-year students of the Yenagoa Study Centre of the National Open University of Nigeria (NOUN); a statistically significant relationship was also found between e-learning self-efficacy scores and the e-learning readiness of the first- and second-year students of the Yenagoa Study Centre of NOUN; there was a statistically significant joint relationship between the three variables under investigation; findings equally revealed that male respondents had higher e-learning readiness than their female counterparts.Research limitations/implicationsLike every other study of this nature, this one also suffers some limitations. First, NOUN is a very large university with over half a million students spread across almost 78 study centres. This means that observation from just one study centre amounts to a very small sample size. This according to Schweighofer, Weitlaner, Ebner and Rothe (2019) jeopardises the generalisability and validity of study results. The authors also maintain that empirical data generated from surveys that usually rely participants' abilities to read and select responses without further interpretation by the researchers suffer from cognitive biases like social desirability. To address the above limitations, detailed studies involving all studies centres of NOUN be undertaken and other qualitative and or mixed research methodologies be adopted in the future.Practical implicationsThe implications for this study are that people who are innately innovative will willingly accept technology and by extension, learning in technology-rich environments like those found in like NOUN whose mode of study is blended learning inherently found in open and distance learning (ODL) institution. Therefore, this study is significant as it will provide relevant information to the management and administrators of NOUN, policymakers and regulatory institutions for the development, deployment and implementation of e-learning strategies. Findings will also benefit e-learning initiatives undertaken by similar institutions that adopt the ODL mode of education in Nigeria and other developing countries.Originality/valueEven though, studies on the antecedents of e-learning readiness have been widely conducted across diverse contexts, studies exploring the associations between individual innovativeness, e-learning self-efficacy and e-learning readiness are relatively hard to come by. The above two variables as predicting the e-learning readiness in the study context are comparatively new. This study thus focuses on the relationships between the individual innovativeness levels, e-learning self-efficacy beliefs of students and their e-learning readiness which ultimately determines their ability to sustain studies in an ODL institution.
There is an observed dearth of technological entrepreneurship across Africa, worse still, the Niger Delta region (NDR) of Nigeria. Entrepreneurship generally and technological entrepreneurship in particular are now considered as the engine for economic development. Technology-business incubators (TBI) are seen as a means of tackling developmental challenges. Most developed and emerging economies and developing countries have adopted TBI to fast-track the creation of new technology-based enterprises because of its more than 80% success rate of new venture creation, and have consequently benefited from its multiplier effects such as technology/knowledge transfer, employment generation and wealth creation. In the light of lack of data on incubation activities in Africa, more so for Nigeria, the paper highlights the contributions of TBIs to regional development. This to draw attention of stakeholders in the Yenagoa Technology Incubation Centre which will contribute to the socioeconomic and technological development of the State and NDR, by promoting technological entrepreneurship, eradicating poverty, enhancing Nigeria's technological capability, thereby ultimately reducing her reliance on petroleum resources.
The study evaluated the queuing system in Blue Meadows restaurant with a view to determining its operating characteristics and to improve customers' satisfaction during waiting time using the lens of queuing theory. Data was obtained from a fast food restaurant in the University of Benin. The data collected was tested to show if it follows a Poisson and exponential distribution of arrival and service rate using chi square goodness of fit. A 95% confidence interval level was used to show the range of customers that come into the system within a time frame of one hour and the range of customers served within that time frame. Using the M/M/s model, the arrival rate, service rate, utilization rate, waiting time in the queue and the probability of customers likely balking from the restaurant was 442 INDEPENDENT JOURNAL OF MANAGEMENT & PRODUCTION (IJM&P)http://www. ijmp.jor.br v. 8, n. 2, April -June 2017 ISSN: 2236 The number of servers present in the system was two. The average number of customers in the system in an hour window was 40 customers with a utilization rate of 0.909. The paper concludes with a discussion on the benefits of performing queuing analysis to a restaurant.
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