Acceptance of Rooftop Solar Technology in Kenya A Solar Adoption Model for the Main Electricity Grid
Article Sidebar
Main Article Content
Abstract
Amongst renewable technologies, solar power has the highest potential as a substitute energy generation option to fossil fuels. However, adoption of rooftop solar technology is still comparatively low. Thus, this paper examined acceptance of rooftop solar technology in Kenya using the Technology Acceptance Model 3 framework. A survey was conducted (N = 402) in two regions of the Kenyan coast, which receive more than 2,000 peak sunlight hours annually. Using Structural Equation Modelling, the analysis revealed self-efficacy, anxiety, occupational relevance, perception of external control, and perceived enjoyment positively influenced perceived ease of use and perceived usefulness. These factors also influenced behavioural intentions and indirectly influenced the actual adoption of rooftop solar technology. The study showed a significant impact of perceived ease of use and perceived usefulness on behavioural intentions towards adoption, which guarantees a reliable energy source and income generation. A majority of respondents (67%) planned to adopt the technology due to its perceived benefits. Solar companies could use these factors to target new niche markets.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: Asia-Pacific International University reserve exclusive rights to publish, reproduce and distribute the manuscript and all contents therein.
References
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. https://doi.org/10.2307/3250951
Ahmad, S., Mat Tahar, R., Cheng, J. K., & Yao, L. (2017). Public acceptance of residential solar photovoltaic technology in Malaysia. PSU Research Review, 1(3), 242–254. https://doi.org/10.1108/PRR-11-2016-0009
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour (Paperback ed.). Prentice-Hall.
Barranis, N. J. (2011). Altering user perceptions of applications: How system design can impact playfulness and anxiety [Master’s thesis, University of Illinois at Urbana-Champaign]. https://www.ideals.illinois.edu/ bitstream/handle/ 2142/24139/Barranis_Nanthida.pdf?sequence=1
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research. International Journal of Research in Marketing, 13(2), 139–161. https://doi.org/ 10.1016/0167-8116(95) 00038-0.
Bilgen, S., Keleş, S., Kaygusuz, A., Sarı, A., & Kaygusuz, K. (2008). Global warming and renewable energy sources for sustainable development: A case study in Turkey. Renewable and Sustainable Energy Reviews, 12(2), 372–396. https://doi.org/10.1016/j.rser.2006.07.016
Chin, W.W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22, 7–16. https://www. jstor.org/stable/pdf/249674.pdf?refreqid=excelsior%3Ac8b2bd4af35e730c294bf94a088d8290
Faizi, N., & Kazmi, S. (2017). Universal health coverage There is more to it than meets the eye. Journal of Family Medicine and Primary Care, 6(1), 169–70. http://www.jfmpc.com/article.asp?issn=2249-4863;year=2017; volume= 6; issue=1;spage=169;epage=170;aulast=Faizi.
Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–143. https://doi.org/10.1287/isre.6.2.118
Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1002. https://doi.org/10.1287/mnsc.35.8.982
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 357–369. https://doi.org/10.2307/249524
Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Kadner, S., Zwickel, T., & Matschoss, P. (Eds.). (2011). Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change. Cambridge University Press.
Elasmar, M. G., & Carter, M. E. (1996). Use of e-mail by college students and implications for curriculum. Journalism & Mass Communication Educator, 51(2), 46–54. https://doi.org/10.1177/107769589605100206
Gurtner, S., Reinhardt, R., & Soyez, K. (2014). Designing mobile business applications for different age groups. Technological Forecasting and Social Change, 88, 177–188. https://doi.org/10.1016/j.techfore. 2014.06.020
Hair, J. Jr., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis (5th ed.). Prentice-Hall.
Hair, J. F., Page, M., & Brunsveld, N. (2019). Essentials of business research methods (4th ed.). Routledge. https://doi.org/10.4324/9780429203374
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Prentice Hall.
Halilovic, S., & Cicic, M. (2015). Changes in beliefs, satisfaction and information system continuance intention of experienced users. International Journal of Business Information Systems, 20(4), 509–535. https://doi.org/10.1504/IJBIS.2015.072745
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage Publications.
Huang, C., & Kao, Y. (2012). The Fuzzy DNP based TAM3 for analyzing the factors influencing the acceptance of PadFones. 2012 International Conference on Fuzzy Theory and Its Applications (iFUZZY2012), 36–41. https://doi.org/10.1109/IFUZZY.2012.6409672
Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
International Energy Agency. (2013). Organisation for economic co-operation and development. World energy outlook 2013. Paris: OECD/IEA. https://www.iea.org/publications/freepublications/publication/2013_ AnnualReport.pdf
IPCC Intergovernmental Panel on Climate Change. (2011). Summary for policymakers – IPCC special report on renewable energy sources and climate change mitigation. Cambridge University Press.
Jackson, C. M., Chow, S., & Leitch, R. (1997). Toward an understanding of the behavioural intention to use an information system. Decision Science Journal, 28(2), 357–389. https://doi.org/10.1111/j.1540-5915.1997. tb01315.x
Jaradat, M. R., & Al-Mashaqba, A. M. (2014). Understanding the adoption and usage of mobile payment services by using TAM3. International Journal of Business Information Systems, 16(3), 271–296. https://doi.org/10.1504/IJBIS.2014.063768
Kiplagat, J. K., Wang, R. Z., & Li, T. X. (2011). Renewable energy in Kenya: Resource potential and status of exploitation. Renewable and Sustainable Energy Reviews, 15(6), 2960–2973. https://doi.org/10.1016/ J.RSER.2011.03.023
Lane, M. S., & Stagg, A. (2014). University staff adoption of iPads: An empirical study using an extended TAM model. Australasian Journal of Information Systems, 18(3), 53–74. https://doi.org/10.3127/ajis.v18i3.876
Lay, J., Ondraczek, J., & Stoever, J. (2013). Renewables in the energy transition: Evidence on solar home systems and lighting fuel choice in Kenya. Energy Economics, 40, 350–359. https://doi.org/10.1016/J.ENECO. 2013.07.024
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support System, 29(3), 269–282. https://doi.org/10.1016/S0167-9236(00)00076-2
MacCallum, R., Browne, M., & Sugawara, H. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.
Macias, E., & Ponce, A. (2006, May 7–12). Photovoltaic solar energy in developing countries [Paper presentation]. 2006 IEEE 4th World Conference on Photovoltaic Energy Conference, Waikoloa, HI, USA. https://doi.org/10.1109/ WCPEC.2006.279638
Mansour, I. H., Eljelly, A. M., & Abdullah, A. M., (2016). Consumers' attitude towards e- banking services in Islamic banks: The case of Sudan. Review of International Business and Strategy, 26(2), 244–260. https://doi.org/10.1108/RIBS-02-2014-0024
Mills, A., Tennant, V., & Chevers, D. (2011, December 3). Understanding mobile internet diffusion: The case of Jamaica [Paper presentation]. Proceedings of the Annual Workshop of the AIS Special Interest Group for ICT in Global Development, Shanghai, China. https://aisel.aisnet.org/globdev2011/6
Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6
Ondraczek, J. (2014). Are we there yet? Improving solar PV economics and power planning in developing countries: The case of Kenya. Renewable and Sustainable Energy Reviews, 30, 604–615. https://doi.org/10.1016/J.RSER. 2013.10. 010
Pai, F., & Huang, K. (2011). Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78, 650-660. https://doi.org/10.1016/ J.TECHFORE. 2010.11. 007
Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349–1365. https://doi.org/10.1108/02635570710834009
Saghafi, F., Moghaddam, E., & Aslani, A. (2016). Examining effective factors in initial acceptance of high-tech localized technologies: Xamin, Iranian localized operating system. Technological Forecasting and Social Change, 122. https://doi.org/10.1016/j.techfore.2016.04.010.
Savci, M., & Griffiths, M. (2019). The development of the Turkish Social Media Craving Scale (SMCS): A validation study. International Journal of Mental Health and Addiction, 19, 359–373. https://doi.org/10.1007/s11469-019-00062-9
Schierz, P., Schilke, O., & Wirtz, B. (2010). 'Understanding consumer acceptance of mobile payment services: An empirical analysis'. Electronic Commerce Research and Applications, 9(3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005
Shen, C. C., & Chiou, J. S. (2010). The impact of perceived ease of use on internet service adoption: The moderating effects of temporal distance and perceived risk. Computers in Human Behavior, 26(1), 42–50. https://doi.org/ 10.1016/j.chb.2009.07.003
Shin, D-H. (2010). Modeling the interaction of users and mobile payment system: Conceptual framework. International Journal of Human–Computer Interaction, 26(10), 917–940. https://doi.org/10.1080/10447318.2010.502098
Sun, T., Tai, Z., & Tsai, K. (2010). Perceived ease of use in prior e-commerce experiences: A hierarchical model for its motivational antecedents. Psychology and Market, 27(9), 874–886. https://doi.org/10.1002/ mar.20362
Teo, T., Lim, V., & Lai, R. (1999). Intrinsic and extrinsic motivation in internet usage. OMEGA International Journal of Management Science, 27(1), 25–37. https://doi.org/10.1016/S0305-0483(98)00028-0
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52(5), 463–479.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating perceived behavioural control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Von Borgstede, C., Andersson, M., & Johnsson, F. (2013). Public attitudes to climate change and carbon mitigation implications for energy-associated behaviours. Energy Policy, 57, 182–193. https://doi.org/10.1016/j.enpol. 2013.01.051
Williams, M. D., Slade, E. L., & Dwivedi, Y. K. (2014). Consumers’ intentions to use e-readers. Journal of Computer Information Systems, 54, 66–76. https://doi.org/10.1080/08874417.2014.11645687
Yang, K. (2010). The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. Journal of International Consumer Marketing, 22(2), 117–127. https://doi.org/10.1080/08961530903476147
Zaltman, G., & Burger, P. (1975). Marketing research: Fundamentals and dynamics (1st ed.). Dryden Press.