Modelling of Instructors Publication Factors in Ethiopia Public Universities: Advanced Count Regression Models

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Alebachew Abebe


Instructors’ publication (IP) is one of the major activity in higher education institutes. Currently, IP faced problem both high prevalence and severity in Ethiopia public universities. Even if the problem is common to both developed and developing countries, about 352 (73.9 %) of the instructors employed by public universities in Ethiopia have been affected by a lack of scholarly publications. Since the outcomes from IP factors are mostly discrete variable; they are often modelled using advanced count regression models. The purpose of this study was to model the appropriate count regression model that efficiently fit the IP data and further to identify the key risk factors contributing significantly to IP in public Universities in Ethiopia. The data were collected between November 2015 through November 2016 from selected thirteen (13) public universities in Ethiopia through both questionnaires and interview. The cross-sectional study design was employed using IP data. A simple random sampling technique was applied to the population of Ethiopia public universities to obtain a sample of 13 universities or 476 individual instructors were selected. The average age of the 476 participants was found to be 30 years with 31(6.5%) being females and 445(93.5%) being males. The count outcomes obtained were modelled using count regression models which included Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP) and Poisson Hurdle regression models. To compare the performance and the efficiency of the listed count regression models concerning the IP data, the various model selection methods such as the Vuong Statistic (V) and Akaike’s Information Criterion (AIC) were used. The ZINB count regression model concerning the values of the Vuong Statistic and AIC was selected as the most appropriate and efficient count regression model for modelling IP data. Based on the ZINB model the variables age, experience, average work-load, association member and motivation to work were statistically significant risk factors contributing to IP in Ethiopia public universities.

Instructors’ publication, zero-inflated negative binomial, zero-inflated poisson, poisson hurdle, vuong statistic

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How to Cite
Abebe, A. (2019). Modelling of Instructors Publication Factors in Ethiopia Public Universities: Advanced Count Regression Models. Journal of Scientific Research and Reports, 24(6), 1-11.
Original Research Article


Dent D. Innovation and growth-the role of R & D. Dent Associates White Papers 11-02; 2011.

Rosenberg N. Innovation and economic growth; 2004.

Cassiolato JE, Lastres HM, Maciel ML. Systems of innovation and development. UK: Edward Elgar Publishing; 2003.

Philips M, Coy P. Look Who‘s Driving R & D Now: Companies spend to boost productivity, while government cuts back on research. Bloomberg; 2015.

Mensah IA, Alhassan EA, Affi PO, Baah A, Sarfo BKO. Modelling the occurrence of dental carries in adult population in Ghana; A comparison of competing count regression models. J Biostat Biometric App. 2018;3(2):201.

Garomssa HD. The state of entrepreneurialism in a public university in Ethiopia: Status, challenges and opportunities (Master's thesis); 2016.

Arnaut D. Towards an entrepreneurial University. International Journal of Euro- Mediterranean Studies. 2010;3(1):135-152. Available:

Etzkowitz H. Innovation in innovation: The Triple Helix of University-Industry-Government Relations. Social Science Information. 2003;42(3):293-337.
DOI: 10.1177/05390184030423002

Cowan K. Higher Education's Higher Accountability. American council on education leadership and advocacy; 2013.

Gibb A, Haskins G, Hannon P, Robertson I. Leading the Entrepreneurial University: Meeting the entrepreneurial development needs of higher education institutions. University of Oxford; 2012.
Available: content/uploads/2014/05/EULP-LEADERS-PAPER.pdf

Clark BR. Creating entrepreneurial universities: Organizational pathways of transformation. Oxford: IAU PRESS; 1998.

Rothaermel F, Agung S, Jiang L. University entrepreneurship: A taxonomy of the literature. Industrial and Corporate Change. 2007;16(4):691-791.
DOI: 10.1093/icc/dtm023

Lee Y, Gaertner R. Technology transfer from University to industry. A large-scale experiment with technology development and commercialization. Policy Studies Journal. 1994;22(2):384-399.
DOI: 10.1111/j.1541-0072.1994.tb01476.x

Siegel DS, Waldman DA, Atwater LE, Link AN. Commercial knowledge transfers from universities to firms: Improving the effectiveness of University-industry collaboration. The Journal of High Technology Management Research. 2003; 14(1):111-133.
DOI: 10.1016/s1047-8310(03)00007-5

De Coster R, Butler C. Assessment of proposals for new technology ventures in the UK: Characteristics of University Spin-off Companies. Technovation. 2005;25(5): 535-543.
DOI: 10.1016/j.technovation.2003.10.002

Lee SS, Osteryoung JS. A comparison of critical success factors for effective operations of University business incubators in the United States and Korea. Journal of Small Business Management. 2004;42(4):418-426.
DOI: 10.1111/j.1540-627x.2004.00120.x

Lee AA, Xiang L, Fung WK. Sensitivity of score tests for Zero inflation in count data. Stat Med. 2004;23:2757-69.

Mullahy J. Specification and testing of some Modified count data models. J Econometrics. 1986;33:341-65.

Lambert D. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34: 1-14.

Akaike H. Information theory and an extension of the maximum likelihood principle. Proc. 2nd Inter Symposium Information Theory. 1973;267-81.