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The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana

Received: 10 March 2014    Accepted: 8 April 2014    Published: 10 April 2014
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Abstract

The paper empirically assesses the impact of Partial Least Squares Structural Equation Modelling (PLS-SEM) training on academic staff’s intentions to adopt PLS-SEM software in their future research work. Our original contribution to knowledge is the application of the Technology Adoption Model (TAM) to study faculty intention to adopt SEM data analysis software in an under-researched context of developing country Higher Institution of Learning (HIL). Building upon the TAM, we developed a research model that conceptualises PLS-SEM training as an external variable that affects the technology adoption process. The research model was tested using data from 34 faculty members who fully participated in a PLS-SEM training workshop at the College of Technology Education, Kumasi (COLTEK) of University of Education, Winneba. The data was analysed using SmartPLS 2.0 for PLS-SEM analysis. The findings indicate that PLS-SEM training has a positive impact on faculty members’ intentions to use the PLS-SEM software in future research. Moreover, the findings confirm the applicability and efficacy of the TAM framework that it can predict about 86% of faculty members’ intention to adopt data analysis software. This paper is one of the initial studies into the adoption of SEM data analysis software by the research community in developing countries HIL context. Despite its limitations, the paper offers important theoretical and managerial contributions. It contributes to the literature in the area of adoption of SEM data analysis software in the information systems literature.

Published in International Journal of Business and Economics Research (Volume 3, Issue 2)
DOI 10.11648/j.ijber.20140302.11
Page(s) 42-49
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Developing Countries, Partial Least Squares, Data Analysis Training, Structural Equation Modelling, Technology Adoption

References
[1] Nimako, S.G., Danso, H. and Donkor, F. (2013). Using Research Workshop to Assist Senior Members Develop Competence in Academic Writing in a Public University. Journal of Studies in Social Sciences, 5 (2), 301-327.
[2] Lee, M. K. O., Cheung, C. M. K. and Chen, Z. (2005). Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42, pp. 1095–1104.
[3] Chin, W. (2010) How to write up and report PLS analyses. In: EspositoVinzi V, Chin W.W, Henseler J, Wang H (eds) Handbook of partial least squares: concepts, methods and applications. Springer Heidelberg, pp. 655 – 690.
[4] Esposito Vinzi, V., Chin, W. W., Henseler, J. and Wang, H. (2010) Handbook of partial least squares: Concepts, methods and applications, pp. 2 - 35.
[5] Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011) PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory and Practice 19 (2), pp. 139-151.
[6] Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989) User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982- 1003.
[7] Bagozzi R.P., (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift, Journal of the Association for Information Systems, 8(4): pp. 244–254.
[8] Legris, P., Ingham, J. and Collerette, P. (2004) Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191–204.
[9] Temme, D., Kreis, H. and Hildebrandt, L. (2010) A comparison of current PLS path modelling software: features, ease-of-use, and performance. In Handbook of Partial Least Squares (pp. 737-756). Springer Berlin Heidelberg.
[10] Ringle, C., Sarstedt M. and Straub, D. (2012) A critical look at the use of pls-sem in mis quarterly. MISQ 36(1): iii–xiv
[11] Ajzen, I. and Fishbein, M. (1980), Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice Hall.
[12] Lin, J. C., and Lu, H. (2000) Towards an understanding of the behavioral intention to use a Web Site. International Journal of Information Management, 20, pp. 197–208.
[13] Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology & Society, 12 (3), 150–162.
[14] Pituch, K.A, & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers Education, 47, 222–244.
[15] Saks, A. M., Tamkin, P. and Lewis, P. (2011) Management training and development. International Journal of Training and Development, 15(3), 179-183.
[16] Liu, S., Liao, H. and Peng, C. (2005) Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. Issues in Information Systems, 6(2), pp. 175-181.
[17] Saadé, R. G. (2003). Web-based education information system for enhanced learning, EISL: Student assessment. Journal of Information Technology Education, 2, 267–277.
[18] Ringle, C.M., Wende, S. and Will, S. (2005). SmartPLS 2.0 (M3) Beta. Hamburg, http://www.smartpls.de.
[19] Anderson, J.C. and Gerbing, D.W. (1988) Structural equation modelling in practice: A review and recommended two-step approach, Psychol. Bull, 103 (3), pp. 411-423.
[20] Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010) Multivariate Data Analysis, Englewood Cliffs, NJ: Prentice Hall.
[21] Fornell, C. and Larcker, D. F. (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
[22] Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences. HillsDale, NJ: Lawrence Erlbaum.
[23] Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, pp.179–211.
[24] Bandura, A. (1994) Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behaviour (pp. 71–81). New York, NY: Academic Press.
[25] Grandon, E., Alshare, O., & Kwan, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46–56.
[26] Ajzen, I. and Fishbein, M. (2000) Attitudes and the Attitude Behavior Relation: Reasoned and Automatic Processes, European Review of Social Psychology 11 (1), pp. 1-33.
[27] Ajzen, I. and Fishbein, M. (2005) The Influence of Attitudes on Behavior, in The Handbook of Attitudes, D. Albarracín, B. T. Johnson, and M. P. Zanna (eds.), Mahwah, NJ: Erlbaum, pp. 173-221.
[28] Reisdorph, N., Stearman, R., Kechris, K., Phang, T. L., Reisdorph, R., Prenni, J., ... & Geraci, M. (2013). Hands-on Workshops as An Effective Means of Learning Advanced Technologies Including Genomics, Proteomics and Bioinformatics. Genomics, proteomics & bioinformatics, 11(6), 368-377.
[29] Svensson, G. (2005) Ethnocentricity in top marketing journals, Marketing Intelligence & Planning, Vol. 23 Iss: 5, pp.422 - 434
[30] Svensson, G., Tronvoll, B., Helgesson, T. and Slåtten, T. (2009) The ‘geographical affiliations’ in ‘top’ research journals of general marketing. Australasian Marketing Journal (AMJ), 17(3), 154-159. DOI.10.1108/02634500510612618
[31] Jöreskog, K.G. and Sörbom, D. (1986) LISREL VI: Analysis of linear structural relationships by maximum likelihood and least squares methods. Scientific Software, Mooresville, IN
Cite This Article
  • APA Style

    Simon Gyasi Nimako, Francis Bondinuba Kwesi, Emmanuel Kofi Owusu. (2014). The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana. International Journal of Business and Economics Research, 3(2), 42-49. https://doi.org/10.11648/j.ijber.20140302.11

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    ACS Style

    Simon Gyasi Nimako; Francis Bondinuba Kwesi; Emmanuel Kofi Owusu. The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana. Int. J. Bus. Econ. Res. 2014, 3(2), 42-49. doi: 10.11648/j.ijber.20140302.11

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    AMA Style

    Simon Gyasi Nimako, Francis Bondinuba Kwesi, Emmanuel Kofi Owusu. The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana. Int J Bus Econ Res. 2014;3(2):42-49. doi: 10.11648/j.ijber.20140302.11

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  • @article{10.11648/j.ijber.20140302.11,
      author = {Simon Gyasi Nimako and Francis Bondinuba Kwesi and Emmanuel Kofi Owusu},
      title = {The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana},
      journal = {International Journal of Business and Economics Research},
      volume = {3},
      number = {2},
      pages = {42-49},
      doi = {10.11648/j.ijber.20140302.11},
      url = {https://doi.org/10.11648/j.ijber.20140302.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20140302.11},
      abstract = {The paper empirically assesses the impact of Partial Least Squares Structural Equation Modelling (PLS-SEM) training on academic staff’s intentions to adopt PLS-SEM software in their future research work. Our original contribution to knowledge is the application of the Technology Adoption Model (TAM) to study faculty intention to adopt SEM data analysis software in an under-researched context of developing country Higher Institution of Learning (HIL). Building upon the TAM, we developed a research model that conceptualises PLS-SEM training as an external variable that affects the technology adoption process. The research model was tested using data from 34 faculty members who fully participated in a PLS-SEM training workshop at the College of Technology Education, Kumasi (COLTEK) of University of Education, Winneba. The data was analysed using SmartPLS 2.0 for PLS-SEM analysis. The findings indicate that PLS-SEM training has a positive impact on faculty members’ intentions to use the PLS-SEM software in future research. Moreover, the findings confirm the applicability and efficacy of the TAM framework that it can predict about 86% of faculty members’ intention to adopt data analysis software. This paper is one of the initial studies into the adoption of SEM data analysis software by the research community in developing countries HIL context. Despite its limitations, the paper offers important theoretical and managerial contributions. It contributes to the literature in the area of adoption of SEM data analysis software in the information systems literature.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - The Impact of PLS-SEM Training on Faculty Staff Intention to Use PLS Software in a public university in Ghana
    AU  - Simon Gyasi Nimako
    AU  - Francis Bondinuba Kwesi
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    Y1  - 2014/04/10
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    JO  - International Journal of Business and Economics Research
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijber.20140302.11
    AB  - The paper empirically assesses the impact of Partial Least Squares Structural Equation Modelling (PLS-SEM) training on academic staff’s intentions to adopt PLS-SEM software in their future research work. Our original contribution to knowledge is the application of the Technology Adoption Model (TAM) to study faculty intention to adopt SEM data analysis software in an under-researched context of developing country Higher Institution of Learning (HIL). Building upon the TAM, we developed a research model that conceptualises PLS-SEM training as an external variable that affects the technology adoption process. The research model was tested using data from 34 faculty members who fully participated in a PLS-SEM training workshop at the College of Technology Education, Kumasi (COLTEK) of University of Education, Winneba. The data was analysed using SmartPLS 2.0 for PLS-SEM analysis. The findings indicate that PLS-SEM training has a positive impact on faculty members’ intentions to use the PLS-SEM software in future research. Moreover, the findings confirm the applicability and efficacy of the TAM framework that it can predict about 86% of faculty members’ intention to adopt data analysis software. This paper is one of the initial studies into the adoption of SEM data analysis software by the research community in developing countries HIL context. Despite its limitations, the paper offers important theoretical and managerial contributions. It contributes to the literature in the area of adoption of SEM data analysis software in the information systems literature.
    VL  - 3
    IS  - 2
    ER  - 

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Author Information
  • Department of Management Studies Education, University of Education, Winneba, Box 1277, Kumasi Campus, Ghana, Accra Institute of Technology, Accra, Ghana

  • Kumasi Polytechnic, P.O Box 854, Kumasi-Ghana, School of the Built Environment Institute for Housing and Urban Research (IHURER), Heriot-Watt University, Edinburgh, UK

  • Department of Accounting Studies Education, University of Education, Winneba, Box 1277, Kumasi Campus, Ghana

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