Human Resource Management and Technology Development in Artificial Intelligence Adoption in the UAE Energy Sector

Akhund, M. A., Memon, A. H., Ali, T. H., Memon, N. A., & Kazi, S. (2019). Waste management in construction projects of pakistan. Pakistan Journal of Science, 71(4), 59. Search in Google Scholar

Al-Damen, R. A. (2017). The Impact of Total Quality Management on organizational performance Case of Jordan Oil Petroleum Company. International Journal of Business and Social Science, 8(1), 192-202 Search in Google Scholar

Al Mansoori, F. T., Rahman I. A. & Kasim, R. (2020). Deciding the Rank of Factors Affecting Abu Dhabi Oil & Gas Industry. International Journal of Sustainable Construction Engineering and Technology, 11(2), 133-13910.30880/ijscet.2020.11.02.015 Search in Google Scholar

Amirrudin, M., Nasution, K., & Supahar, S. (2020). Effect of Variability on Cronbach Alpha Reliability in Research Practice. Jurnal Matematika, Statistika Dan Komputasi, 17(2), 223–230. https://doi.org/10.20956/jmsk.v17i2.1165510.20956/jmsk.v17i2.11655 Search in Google Scholar

Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. In Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2020.10989910.1016/j.rser.2020.109899 Search in Google Scholar

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2020). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.12042010.1016/j.techfore.2020.120420 Search in Google Scholar

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications: Profound change is coming, but roles for humans remain. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap806210.1126/science.aap806229269459 Search in Google Scholar

Buell, R. W., Campbell, D., & Frei, F. X. (2010). Are self-service customers satisfied or stuck? Production and Operations Management, 19(6), 679–697. https://doi.org/10.1111/j.1937-5956.2010.01151.x.10.1111/j.1937-5956.2010.01151.x Search in Google Scholar

Čaplová, Z., & Švábová, P. (2020). IBM SPSS statistics. In Statistics and Probability in Forensic Anthropology, 343–352, https://doi.org/10.1016/b978-0-12-815764-0.00027-710.1016/B978-0-12-815764-0.00027-7 Search in Google Scholar

Cressey, P., Grint, K., & Woolgar, S. (1998). The Machine at Work: Technology, Work and Organisation. The British Journal of Sociology. https://doi.org/10.2307/59133110.2307/591331 Search in Google Scholar

Daleure, G. (2017). Economic Vision of the UAE. In Emiratization in the UAE Labor Market, pp. 27–37, Springer Singapore. https://doi.org/10.1007/978-981-10-2765-9_310.1007/978-981-10-2765-9_3 Search in Google Scholar

Daniel, M. (2010). Introduction to quantitative research 1.1. Doing Quantitative Research in Education with SPSS. Search in Google Scholar

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.01910.1016/j.techfore.2016.08.019 Search in Google Scholar

Soomro, N. U. I., Memon, A. H., Memon, N. A. & K. R. Memon. (2020). Contractor’s Selection Criteria in Construction Works in Pakistan. Engineering, Technology & Applied Science Research, 10(2), 5520-5523. https://doi.org/10.48084/etasr.333410.48084/etasr.3334 Search in Google Scholar

Kannan, K. N., Pamuru, V., & Rosokha, Y. (2019). Using Machine Learning for Modeling Human Behavior and Analyzing Friction in Generalized Second Price Auctions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.331577210.2139/ssrn.3315772 Search in Google Scholar

Lee, J. M., & Rha, J. Y. (2016). Personalization-privacy paradox and consumer conflict with the use of location-based mobile commerce. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.05.05610.1016/j.chb.2016.05.056 Search in Google Scholar

Lewis, S. (2015). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. In Health Promotion Practice. https://doi.org/10.1177/152483991558094110.1177/1524839915580941 Search in Google Scholar

Li, C., Xie, C., Zhang, B., Chen, C., & Han, J. (2018). Deep Fisher discriminant learning for mobile hand gesture recognition. Pattern Recognition, 77, 276–288. https://doi.org/10.1016/j.patcog.2017.12.02310.1016/j.patcog.2017.12.023 Search in Google Scholar

Mao, S., Wang, B., Tang, Y., & Qian, F. (2019). Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry. In Engineering. https://doi.org/10.1016/j.eng.2019.08.01310.1016/j.eng.2019.08.013 Search in Google Scholar

McCarthy, S. M., Vayanos, P., & Tambe, M. (2017). Staying ahead of the game: Adaptive robust optimization for dynamic allocation of threat screening resources. IJCAI International Joint Conference on Artificial Intelligence, 0, 3770–3776. https://doi.org/10.24963/ijcai.2017/52710.24963/ijcai.2017/527 Search in Google Scholar

Munir, M. A., Zaheer, M. A., Haider, M., Rafique, M. Z., Rasool, M. A. & Amjad, M. S. (2019). Problems and barriers affecting total productive maintenance implementation. Engineering, Technology & Applied Science Research, 9(5), 4818-482310.48084/etasr.3082 Search in Google Scholar

Nordrum, A. (2017). Govern by blockchain dubai wants one platform to rule them all, while Illinois will try anything. IEEE Spectrum. https://doi.org/10.1109/MSPEC.2017.804884110.1109/MSPEC.2017.8048841 Search in Google Scholar

Pazzaglia, A. M., Stafford, E. T., & Rodriguez, S. M. (2016). Survey methods for educators: Analysis and reporting of survey data (part 3 of 3). In Applied Resarch Methods. Search in Google Scholar

Pirani, S. I., & Arafat, H. A. (2016). Reduction of food waste generation in the hospitality industry. Journal of Cleaner Production, 132, 129–145. https://doi.org/10.1016/j.jclepro.2015.07.14610.1016/j.jclepro.2015.07.146 Search in Google Scholar

Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS. In Routledge. Search in Google Scholar

Poola, I. (2017). How Artificial Intelligence in Impacting Real Life Every day. International Journal of Advance Research and Development. Search in Google Scholar

Russel, S., & Norvig, P. (2012). Artificial intelligence—a modern approach 3rd Edition. In The Knowledge Engineering Review. https://doi.org/10.1017/S026988890000772410.1017/S0269888900007724 Search in Google Scholar

Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105–114. https://doi.org/10.1609/aimag.v36i4.257710.1609/aimag.v36i4.2577 Search in Google Scholar

Strydom, A., Musango, J. K., Currie, P. K. (2020). Connecting energy services, carriers and flows: rethinking household energy metabolism in Cape Town, South Africa, Energy Res. Social Sci., 60, Article 10131310.1016/j.erss.2019.101313 Search in Google Scholar

Stevens, J. P. (2012). Applied Multivariate Statistics for the Social Sciences. In Applied Multivariate Statistics for the Social Sciences. Routledge. https://doi.org/10.4324/978020384313010.4324/9780203843130 Search in Google Scholar

Souza, D. C., Wegner, F., Costa, L. C. M., Chiavegato, L. D., & Lunardi, A. C. (2017). Measurement properties of the Human Activity Profile questionnaire in hospitalized patients. Brazilian Journal of Physical Therapy. https://doi.org/10.1016/j.bjpt.2017.03.01110.1016/j.bjpt.2017.03.011553746628473282 Search in Google Scholar

Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). In New York: Harper and Row. Search in Google Scholar