Perceived Organizational Readiness and Enabling Factors for Human Capital Management System Use in Ugandan Public Universities

Öz

With the emergence of technology as an asset in human resource management (HRM), institutions are increasingly adopting human capital management systems (HCMSs), defined as comprehensive digital systems designed to automate human resource management processes. In 2021, Uganda’s Ministry of Public Service launched its HCMS, but as of 2025, no Ugandan public university has fully adopted the system owing to challenges such as limited digital literacy, infrastructure, and management support. Additionally, HCMS adoption in African higher education remains underexplored, with the available research focusing on narrow theoretical frameworks. This study adapted the unified theory of acceptance and use of technology (UTAUT) to investigate the influence of perceived organiza7 tional readiness (facilitating conditions) on HCMS use while also identifying contextual factors that enable HCMS use in Ugandan public universities. Applying a mixed7methods sequential explanatory design, quantitative data were collected from 362 staff across three public universities. Quantitative findings revealed that perceived organizational readiness significantly influenced HCMS use, while age and experience did not serve as significant moderators. Of the 362 respondents, 16 were purposively sampled for interviews. The qualitative findings identified stakeholder policy enactment, end7user involvement, functional ICT gadgets, internet connectivity, sufficient technical assistance, training, system effectiveness, system user7friendliness, improved performance ability, time7saving benefits, expe7 rience using the HCMS and positive attitude as key enabling factors for HCMS adoption. An extension to the UTAUT framework is proposed, integrating predictors—policy enactment, experience, end7user involvement, and a positive attitude. This study presents practical insights for promoting HCMS adoption and enhancing digital transformation in higher education.

Anahtar Kelimeler

• Human capital management system
• perceived organizational readiness
• facilitating conditions
• unified theory of acceptance and use of technology

Uganda Kamu Üniversitelerinde İnsan Sermayesi Yönetim Sistemi Kullanımına Yönelik Algılanan Örgütsel Hazırlık ve Etkinleştirici Faktörler

Öz

Teknolojinin insan kaynakları yönetiminde (İKY) bir varlık olarak ortaya çıkmasıyla birlikte, kurumlar insan sermayesi yönetim sistemlerini (İSYS) giderek daha fazla benimsemektedir. İSYS, insan kaynakları yönetimi süreç7 lerini otomatikleştirmek üzere tasarlanmış kapsamlı dijital sistemler olarak tanımlanmaktadır. 2021 yılında Uganda Kamu Hizmeti Bakanlığı kendi İSYS’sini başlatmıştır, ancak 2025 itibarıyla sınırlı dijital okuryazarlık, altyapı eksikliği ve yönetim desteği gibi zorluklar nedeniyle hiçbir Uganda kamu üniversitesi sistemi tamamen benimsememiştir. Ayrıca, Afrika yükseköğretiminde İSYS’nin benimsenmesi hâlâ yeterince araştırılmamış olup mevcut çalışmalar dar teorik çerçevelere odaklanmaktadır. Bu çalışma, teknoloji kabulü ve kullanımına ilişkin birleşik teoriyi (TKKBT) uyarlayarak algılanan örgütsel hazırlığın (kolaylaştırıcı koşullar) İSYS kullanımına etkisini incelemiş ve Uganda kamu üniversitelerinde İSYS kullanımını mümkün kılan bağlamsal faktörleri belirlemiştir. Karma yöntemli sıralı açıklayıcı bir tasarım uygulanarak üç kamu üniversitesinden 362 personelden nicel veri toplanmıştır. Nicel bulgular, algılanan örgütsel hazırlığın İSYS kullanımını önemli ölçüde etkilediğini, yaş ve deneyimin ise anlamlı bir düzenleyici değişken olarak rol oynamadığını ortaya koymuştur. 362 katılımcıdan 16’sı amaçlı örnekleme ile görüşmeler için seçilmiştir. Nitel bulgular, paydaş politika uygulaması, son kullanıcı katılımı, işlevsel BT cihazları, internet bağlantısı, yeterli teknik destek, eğitim, sistemin etkinliği, kullanıcı dostu olması, performans artırma yeteneği, zaman tasarrufu, İSYS kul7 lanma deneyimi ve olumlu tutumun İSYS’nin benimsenmesinde temel kolaylaştırıcı faktörler olduğunu göstermiştir. Çalışmada politika uygulaması, deneyim, son kullanıcı katılımı ve olumlu tutum gibi yordayıcıları içeren genişletilmiş bir TKKBT çerçevesi önerilmektedir. Bu çalışma, İSYS’nin benimsenmesini teşvik etmek ve yükseköğretimde dijital dönüşümü geliştirmek için pratik içgörüler sunmaktadır.

Anahtar Kelimeler

• insan sermayesi yönetim sistemi
• algılanan örgütsel hazırlık
• kolaylaştırıcı koşullar
• teknoloji kabulü ve kullanımına ilişkin birleşik teori

Kaynakça

1. Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 7, 205-7224 https://doi.org/l0.1007/s10639-021-10573-5 google scholar
2. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211. https://doi. org/l0.1016/0749-5978(9l)90020-t google scholar
3. CNBC. (2020). More big employers are talking about permanent work-from-home positions. Retrieved January 3, 2023, from https:// www.cnbc.com/2020/05/01/major-companies-talking-about-permanent-work-from-home-positions.html google scholar
4. Akbar, F. (2013). What affects students’ acceptance and use of technology? A test of utaut in the context of a higher-education institution in qatar, [Senior honors thesis]. Carnegie Mellon University. google scholar
5. Al-Adwan, A. S., Yaseen, H., Alsoud, A., Abousweilem, F., & Al-Rahmi, W. M. (2021). Novel extension of the UTAUT model to understand continued usage intention of learning management systems: The role of learning tradition. Education and Information Technolo-gies, 27, 3567-3593. https://doi.org/l0.1007/s10639-021-10758-y google scholar
6. Ali, I., & Warraich, N. F. (2023). Use and acceptance of technology with academic and digital libraries context: A meta-analysis of UTAUT model and future direetion. Journal of Librarianship and Information Science, 56(4), 965-977. https://doi.org/10.1177/ 09610006231179716 google scholar
7. Alkhwaldi, A. F., Alobidyeen, B., Abdulmuhsin, A. A., & Al-Okaily, M. (2022). Investigating the antecedents of HRIS adoption in public sector organizations: Integration of UTAUT and TTF. International Journal of Organizational Analysis, 31(7), 3251-3274. https://doi. org/l0.1108/IIOA-04-2022-3228 google scholar
8. Almazroi, A. A. (2023). An empirical investigation of factors influencing the adoption of internet of things services by end-users. Arabian Journal for Science and Engineering, 48, 1641-1659. https://doi.org/l0.1007/s13369-022-06954-8 google scholar

9. Al-Mobaideen, H. O., Allahawiah, S., & Basioni, E. (2013). Factors influencing the successful adoption of human resource information system: The content of aqaba special economic zone authority. Intelligent Information Management, 05(01), 1-9. https://doi.org/ 10.4236/iim.2013.51001 google scholar
10. Altarawneh, I., & Al-Shqairat, Z. (2010). Human resource information systems in Jordanian universities. International Journal of Business and Management, 5(10), 113-127. https://doi.org/10.5539/ijbm.v5n10p113 google scholar
11. Arning, K., & Ziefle, M. (2007). Understanding age differences in PDA acceptance and performance. Computers in Human Behaviour, 23(6), 2904-2927. https://doi.Org/l0.1016/j.chb.2006.06.005 google scholar
12. Attuquayefio, S. N. (2019). Extending UTAUT to understand the adoption of ICT among Ghanaian university students. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 15(1), 116-131. http://ijedict. dec.uwi.edu/viewarticle.php?id=1895 google scholar
13. Augner, C. (2022). Digital divide in elderly: Self-rated computer skills are associated with higher education, better cognitive abilities and increased mental health. The European Journal of Psychiatry, 36(3), 176-181. https://doi.org/10.1016/j.ejpsy.2022.03.003 google scholar
14. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory, Prentice-Hall, Inc. google scholar
15. Bawack, R. E., & Kamdjoug, J. R. K. (2018). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109, 15-22. https://doi.Org/10.1016/j.ijmedinf.2017.10.016 google scholar
16. Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175-184. https://doi.org/10.1509/jmkr.44.2.175 google scholar
17. Bervell, B., & Umar, I. N. (2017. Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6471-6490. https://doi.org/10.12973/ejmste/78076 google scholar
18. Bhat, M. A., Tiwari, C. K., Bhaskar, P. & Khan, S. T. (2024), Examining ChatGPT adoption among educators in higher educational institutions using extended UTAUT model. Journal of Information, Communication and Ethics in Society, 22(3), 331-353. https://doi.org/10.1108/ IICES-03-2024-0033 google scholar
19. Cabellos, B., Siddiq, F., & Scherer, R. (2023). The moderating role of school facilitating conditions and attitudes towards ICT on teachers’ ICT use and emphasis on developing students’ digital skills. Computers in Human Behaviour, 150, 107994. https://doi.org/10.1016/ j.chb.2023.107994 google scholar
20. Chao, C. M. (2019). Factors Determining the Behavioural Intention to Use Mobile Learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(10), 1652. https://doi.org/10.3389/fpsyg.2019.01652 google scholar
21. Chaouali, W., & Souiden, N. (2019). The role of cognitive age in explaining mobile banking resistance among elderly people. Journal of Retailing and Consumer Services, 50, 342-350. https://doi.org/10.1016/j.jretconser.2018.07.009 google scholar
22. Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. https://doi.Org/l0.1016/j.im.2008.02.003 google scholar
23. Cohen, J. (1960) A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20, 37-46. http://dx.doi. org/10.1177/001316446002000104 google scholar
24. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688 google scholar
25. Creswell, J. W., & Creswell, J. D. (2018). Mixed methods procedures. In Research design: Qualitative, quantitative, and mixed methods approaches (5th ed., pp. 213-246). SAGE Publications, Inc. google scholar
26. Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21(2), 333-352. https://doi.org/10.1037/0882-7974.21.2.333 google scholar
27. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology a comparison of two theoretical models. Management Science, 35, 982-1003. https://www.scirp.org/reference/ReferencesPapers?ReferencelD=1470119 google scholar
28. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psycho-logical Inquiry, 11(4), 227-268. https://doi.org/10.1207/s15327965pli1104_01 google scholar
29. Del Valle, M. N., taba, K., & Mayer, C. (2024). Unlocking Technology acceptance among South African employees: A psychological perspective. SA Journal of Industrial Psychology, 50, a2220, https://doi.org/10.4102/sajip.v50i0.2177 google scholar
30. Du, L., & Lv, B. (2024). Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Education and Information Technologies, 29, 24715-24734. https://doi.org/10.1007/s10639-024-12835-4 google scholar
31. Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211-230. https://doi.org/10.1016/j.giq.2017.03.001 google scholar
32. Ennajeh, L., & Najjar, T. (2024). Blockchain technology adoption through the UTAUT model. Journal of Telecommunications and the Digital Economy, 12 (i), 328-355. https://doi.org/l0.18080/jtde.v12n1.873 google scholar
33. Fettermann, D., & Calegari, L. P. (2024). E-health Technology acceptance: A meta-analysis. Ciencia Da Informaçâo, 52(2), 112-140. https:// doi.org/l0.18225/ci.inf.v52i2.7089 google scholar
34. Fishbein, M. A., & Ajzen, l. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research, Addison-Wesley. google scholar
35. Garg, A. (2021). lnvestigating the moderating effects of age and gender on customers’ use of tablet menu in casual dining restaurants. Journal of Quality Assurance in Hospitality & Tourism, 23(6), 1509-1547. https://doi.org/l0.1080/l528008x.2021.2002786 google scholar
36. Granic, A. (2022). Educational Technology Adoption: A systematic review. Education and Information Technologies, 27(7), 9725-9744. https://doi.org/10.1007/s10639-022-10951-7 google scholar
37. Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? Field Methods, 18(i), 59-82. https://doi.org/10.1177/1525822 x05279903 google scholar
38. Halid, H., Yusoff, Y. M., & Somu, H. (2020). The relationship between digital human resource management and organizational perfor-mance. ln Proceedings of the First ASEAN Business, Environment, and Technology Symposium (ABEATS 2019), Atlantis Press. https:// doi.org/10.2991/aebmr.k.200514.022 google scholar
39. Hamzah, N., Othman, Y. H., & Cheumar, M. T. (2023). The influence of facilitating conditions and perceived usefulness on intention to use mobile Applications in Uganda. Malaysian Journal of Social Sciences and Humanities (MJSSH), 8(4), e002257. https://doi.org/ 10.47405/mjssh.v8i4.2257 google scholar
40. Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multi-categorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470. http://dx.doi.org/10.1111/bmsp.12028 google scholar
41. Hecklau, F., Galeitzke, M., Flachs, S., & Kohl, H. (2016). Holistic approach for human resource management in industry 4.0. Procedia CIRP, 54, 1-6. https://doi.org/10.1016/j.procir.2016.05.102 google scholar
42. Hennink, M., & Kaizer, B. N. (2022b). Sample sizes for saturation in qualitative research: A systematic review of empirical tests, Social Science & Medicine, 292, 114523. https://doi.Org/l0.1016/j.socscimed.2021.114523 google scholar
43. Hou, C. K. (2014). Exploring the user acceptance of business intelligence systems in Taiwan’s electronics industry: applying the UTAUT model. International Journal of Internet and Enterprise Management, 8(3), 195-226. https://doi.org/10.3389/fpsyg.2019.01652 google scholar
44. Hua, C., Cole, S., & Xu, N. (2021). Rethinking trust in tourism apps: the moderating effect of age. Journal of Hospitality and Tourism Technology, 12(3), 548-562. https://doi.org/l0.1108/jhtt-01-2020-0013. google scholar
45. Humes, L. L. (2007). Communities of practice for open source software. ln K. St. Amant & B. Still (Eds.), Handbook of research on open source software: Technological, economic, and social perspectives (pp. 610-623). lGl Global Scientific Publishing. https://doi.org/ 10.4018/978-1-59140-999-1.ch047 google scholar
46. Im, I., Hong, S., & Kang, M. (2011). An international comparison of technology adoption, testing the UTAUT Model. Information & Management, 48(1), 1-27. https://doi.Org/l0.1016/j.im.2010.09.001 google scholar
47. Isabel, R. (2018). Knowledge management paradigms, philosophical assumptions: An outlook on future research. American Journal of Management, 18(3), 61-74. https://www.proquest.com/scholarly-journals/knowledge-management-paradigms-philosophical/ docview/2138468152/se-2 google scholar
48. Islam, M., Mamun, A. A., Afrin, S., Quaosar, G. M. a. A., & Uddin, M. A. (2022). Technology adoption and human resource management prac-tices: The use of artificial intelligence for recruitment in Bangladesh. South Asian Journal of Human Resources Management, 9(2), 324-349. https://doi.org/l0.1177/23220937221122329 google scholar
49. Ivanaj, S. (2023). HRIS adoption in organizations: A meta-analytic test of competing theoretical models. @GRH, 46(i), 95-131. https:// doi.org/l0.3917/grh.046.0095 google scholar
50. Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18 (i), 3-20. https://doi.org/l0.1177/l525822X05282260 google scholar
51. Izkair, A. S., & Lakulu, M. M. (2021. Experience moderator effect on the variables that influence intention to use mobile learning. Bulletin of Electrical Engineering and Informatics, 10(5), 2875-2883. https://doi.org/10.11591/eei.v10i5.3109 google scholar
52. Jena, R. (2022). Examining the factors affecting the adoption of blockchain technology in the banking sector: An extended UTAUT model. International Journal of Financial Studies, 10(4), 90. https://doi.org/10.3390/ijfs10040090. google scholar
53. Jewer, J. (2018c). Patients’ intention to use online postings of ED wait times: A modified UTAUT model. International Journal of Medical Informatics, 112, 34-39. https://doi.Org/l0.1016/j.ijmedinf.2018.01.008 google scholar
54. Kaba, B., & Toure, B. (2014). Understanding Information and communication Technology behavioural intention to use: Applying the UTAUT model to social networking site adoption by young people in a least developed country. Journal of the Association for Information Science & Technology, 65(8), 1662-1674. https://doi.org/10.1002/asi.23069 google scholar
55. Kalvakolanu, S., Chendragiri, M., & Shaik, K. (2023). Exploring the adoption of human resource analytics among human resource professionals. International Journal of Organizational Leadership, 12(4), 425-441. 10.33844/ijol.2023.60387 google scholar
56. Kamaghe, J., Luhanga, E., & Kisangiri, M. (2020). The challenges of adopting m-learning assistive technologies for visually impaired learners in higher learning institutions in Tanzania. International Journal of Emerging Technologies in Learning (iJET), 15(1), 140-151. https://doi.org/l0.399l/ijet.v15i01.11453 google scholar
57. Kananu, K.M., & Nyakego, M.O. (2016). Challenges and strategies in the implementation of human resource information systems in Kenyan universities. Research on Humanities and Social Sciences, 6, 148-160. [suspicious link removed] google scholar
58. Keong, L. W., & Abdul Rahman, A. A. B. (2021). The relationship between UTAUT model and behavioural intention to use HRIS system among middle-level employees at a retail company. In Proceedings of the 7th International Conference on Research and Innovation in Information Systems (ICRIIS), 1-5. google scholar
59. Kim, T. B., & Ho, C. B. (2021). Validating the moderating role of age in multi-perspective acceptance model of wearable healthcare Technology. Telematics and Informatics, 61, 101603. https://doi.Org/10.1016/j.tele.2021.101603 google scholar
60. Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of generative ai system adoption and usage behaviour in Korean companies: Applying the UTAUT Model. Behavioural Sciences, 14(il), 1035. https://doi.org/l0.3390/bs14111035. google scholar
61. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development, Prentice Hall, http://academic.regis.edu/ ed205/Kolb.pdf google scholar
62. Kopplin, C. (2023). A configurational view on technology acceptance: The example of highly integrated collaboration platforms. Australasian Journal of Information Systems, 27. https://doi.org/10.3127/ajis.v27i0.4043 google scholar
63. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. google scholar
64. Kumar, V., Maheshwari, B., & Kumar, U. (2003). An investigation of critical management issues in ERP implementation: empirical evidence from Canadian organizations. Technovation, 23(10), 793-807. https://doi.org/l0.1016/s0166-4972(02)00015-9 google scholar
65. Lai, J. W. M., De Nobile, J., Bower, M., & Breyer, Y. (2022). Comprehensive evaluation of the use of technology in education - validation with a cohort of global open online learners. Education and Information Technologies, 27(7), 9877-9911. https://doi.org/10.1007/s 10639-022-10986-w google scholar
66. Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of internet and mobile banking. Journal of Business Research, 69(7), 2432-2439. https://doi.org/10.1016/j.jbusres.2016.01.013 google scholar
67. Lavrakas, P. J. (2008). Encyclopedia of survey research methods. SAGE Publications Ltd. doi: 10.4135/9781412963947 google scholar
68. Leso, B., & Cortimiglia, M. (2021). The influence of user involvement in information system adoption: An extension of TAM. Cognition, Technology & Work, 24, 215-231. https://doi.org/10.1007/s10111-021-00685-w. google scholar
69. Lopez-Rodrîguez, C. E., & Rodrîguez-Calderon, L. F. (2024). Acceptance and use of Technology: The influence on consumption in the Colombian banking sector. Revista Galega De Economia, 33(3), 9714. https://doi.Org/10.15304/rge.33.3.9714 google scholar
70. Mahardika, H., Thomas, D., Ewing, M., & Japutra, A. (2020). Comparing the temporal stability of behavioural expectation and behavioural intention in the prediction of consumers pro-environmental behaviour. Journal of Retaılıng and Consumer Servıces, 54, 101943. https://doi.Org/l0.1016/i.iretconser.2019.101943. google scholar
71. Manca, F., Sivakumar, A., & Polak, J. (2022). Capturing the effect of multiple social influence sources on the adoption of new transport Technologies and services. Journal of Choice Modelling, 42, 100344. https://doi.org/10.1016Zi.iocm.2022.100344. google scholar
72. Menant, L., Gilibert, D., & Sauvezon, C. (2021). The application of acceptance models to human resource information systems: A literature review. Frontiers in Psychology, 12, 659421. https:Z/doi.orgZ10.3389Zfpsyg.2021.659421 google scholar
73. Miah, M., Singh, J., & Rahman, M. (2023). Factors influencing technology adoption in online learning among private üniversity students in Bangladesh post COVID-19 pandemic. Sustainability, 15(4), 354. https://doi.orgZ10.3390/su15043543 google scholar
74. Michelotto, F., & Joia, L. A. (2024). organizational digital transformation readiness: An exploratory investigation. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3283-3304. https:ZZdoi.orgZ10.3390Zjtaer19040159 google scholar
75. Ministry of Public Service Uganda (2021, November 28). Human capital management system (hcm): Facts and implementation process. https:ZZpublicservice.go.ugZhuman-capital-management-system-hcm-facts-and-implementation-progressZ google scholar
76. Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adoption and information technology innovation. Information Systems Research, 2(3), 192-222. google scholar
77. Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing workforce. Personnel Psychology, 53(2), 375-403. DOI: 10.1111/i.1744-6570.2000.tb00206.x google scholar
78. Mtebe, J. (2015). Learning management system success: Increasing learning management system usage in higher education in sub-Saharan Africa. International Journal of Education and Development Using Information and Communication Tech-nology, 11(2), 51-64. https://www.researchgate.net/publication/288670218_Learning_Management_System_success_Increasing_ Learning_Management_System_usage_in_higher_education_in_sub-Saharan_Africa google scholar
79. Mtebe, J. S., & Raisamo, R. (2014). Investigating perceived barriers to the use of Open Educational Resources in higher education in Tanzania. International Review of Research in Open and Distance Learning, 15(2), 43-65. DOI: 10.19173/irrodl.v15i2.1803 google scholar
80. Naatu, F., Selormey, F. S., & Naatu, S. (2024). Determinants of digital technology adoption in sub-Sahara Africa: Ghana. International Journal of Emerging MarKets, 20(10), 4111-4133. https://doi.orgZ10.1108/iioem-09-2023-1503 google scholar
81. Neves, C., Oliveira, T., & Santini, F. (2022). Sustainable technologies adoption research: A weight and meta-analysis. Renewable and Sustainable Energy Reviews, 165, 112627. https://doi.org/10.1016/i.rser.2022.112627 google scholar
82. Noutsa, F. A., Kamdioug, J. R. K., & Wamba, S. F. (2017). Acceptance and use of HRIS and influence on organizational performance of SMEs in a developing economy: The case of Cameroon. İn Â. Rocha, A. M. Correia, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Recent advances in intelligent systems and computing (pp. 563-580). https://doi.org/10.1007/978-3-319-56535-4_57 google scholar
83. Nunes, A., Limpo, T., & Castro, S. L. (2019). Acceptance of mobile health applications: Examining key determinants and moderators. Frontiers in Psychology, 10(2791). https://doi.org/l0.3389/fpsyg.2019.02791 google scholar
84. Nysveen, H., & Pedersen, P. E. (2014). Consumer adoption of RFID-enabled services: Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293-314. https://doi.orgZ10.1007/s10796-014-9531-4 google scholar
85. Or, C. (2023). Revisiting unified theory of technology and use of technology using meta-analytic structural equation modelling. Inter-national Journal of Technology in Education and Science, 7(i), 83-103. https://doi.orgZ10.46328/iites.420 google scholar
86. Oye, N. D., Iahad, N. A., & Rahim, N. Z. A. (2012). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies, 19(i), 251-270. https://doi.orgZ10.1007/s10639-012-9189-9 google scholar
87. Paniaitan, E. H. H. (2023). Implementing human resource information system (HRIS) for efficient human resource management. Inter-national Journal of Science and Society, 5, 128-139. DOI: 10.54783/iisoc.v5i2.676 google scholar
88. Parra-Sânchez, D. T., Talero-Sarmiento, L. H., & Guerrero, C. D. (2021). Assessment of ICT policies for digital transformation in Colombia: Technology readiness for IoT adoption in SMEs in the trading sector. Digital Policy Regulation and Governance, 23(4), 412-431. https://doi.org/10.1108/dprg-09-2020-0120 google scholar
89. Peng, M. Y., Xu, C., Zheng, R., & He, Y. (2023). The impact of perceived organizational support on employees’ knowledge transfer and innovative behavior: comparisons between Taiwan and mainland China. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02242-4 google scholar
90. Pimmer, C., Mateescu, M., & Gröhbiel, U. (2016). Mobile and ubiquitous learning in higher education settings: A systematic review of empirical studies. Computers in Human Behaviour, 63, 490-501. https:Z/doi.orgZ10.1016Zj.chb.2016.05.057 google scholar
91. Rogers, E.M. (1995) Diffusion of innovations (4th ed.). The Free Press. google scholar
92. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon and Schuster. google scholar
93. Roberts, R., Millar, D., Corradi, L., & Flin, R. (2021). What use is technology if no one uses it? The psychological factors that influence technology adoption decisions in oil and gas. Technology Mind and Behavior, 2(1). https://doi.org/10.1037/tmb0000027 google scholar
94. Ruel, H., Bondarouk, T., & Looize, J. K. (2004). E-HRM: Innovation or irritation. An explorative empirical study in five large companies on web-based HRM. Management Revue, 15(3), 364-380. https://doi.org/l0.577l/0935-9915-2004-3-364 google scholar
95. Samuel, J., Nerur, S., Mahapatra, R., & White, B. (2025). Building Al talent in organizations - An experiential learning approach. Journal of Information Systems Education, 36(3), 277-286. https://doi.org/10.62273/NRQW1204 google scholar
96. Sari, N., Duong, M., Li, D., Nguyen, M., & Vuong, Q. (2024). Rethinking the effects of performance expectancy and effort expectancy on new Technology adoption: Evidence from Moroccan nursing students. Teaching and Learning in Nursing, 19(3), 557-565. https:// doi.org/l0.1016/j.teln.2024.04.002. google scholar
97. Satispi, E., Rajiani, I., Murod, M., & Andriansyah, A. (2023). Human resources information system (HRIS) to enhance civil servants’ innovation outcomes: Compulsory or complimentary? Administrative Sciences, 13(2), 32. https://doi.org/10.3390/admsci13020032 google scholar
98. Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2017). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality & Quantity, 52(4), 1893-1907. https://doi.org/10.1007/s11135-017-0574-8 google scholar
99. Schuler, R., & Jackson, S. (1987) Linking competitive strategies with human resource management practices. Academy of Management Executive, 1, 207-219. https://doi.org/l0.5465/AME.1987.4275740 google scholar
100. Sengkalit, D., Chair, U., & Abdullah, T. (2025). Factors influencing technology adoption among SMES: A case study of youtap and the role of performance expectancy and effort expectancy. Syntax Literate; Jurnal llmiah Indonesia, 10(i), 322-333. https://doi.org/10. 36418/syntax-literate.v10i1.17279. google scholar
101. Shaikh, A. A., & Karjaluoto, H. (2014). Mobile banking adoption: A literatüre review. Telematics and Informatics, 32(i), 129-142. https:// doi.org/l0.1016/j.tele.2014.05.003 google scholar
102. Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1), 48-58. https://www.researchgate.net/publication/ 279653519_The_theory_of_user_acceptance_and_use_of_technology_UTAUT_A_meta-analytic_review_of_empirical_findings google scholar
103. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. google scholar
104. The Observer - Uganda. (2023, October 27). MaKerere University lecturers reject biometric clocK-in system again. https://observer.ug/ news/headlines/79578-makerere-university-lecturers-biometric-clock-in-system-again#google vignette google scholar
105. Theres, C., & Strohmeier, S. (2023). Consolidating the theoretical foundations of digital human resource management acceptance and use research: a meta-analytic validation of UTAUT. Management Review Quarterly, 74, 2683-2715. https://doi.org/10.1007/s11301-023-00367-z google scholar
106. Thompson, R. L., Higgins, C. A. & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 151, 125-143. http://dx.doi.org/10.2307/249443 google scholar
107. Timbula, M. A., & Marvadi, C. (2023). Digital transformation: Acceptance and use of technology among microfinance institutions in developing countries—An application of the UTAUT2 model. International Journal of Information Technology, 15, 4459-4468. https://doi.org/l0.1007/s41870-023-01535-w google scholar
108. Tsai, L. L. (2021). Evaluating the effects of facilitating conditions and usage experience on mobile payment. International Journal of Information Systems in the Service Sector, 13(4), 88-106. https://doi.org/10.4018/ijisss.2021100105 google scholar
109. Tusubira, F. F., & Mulira, N. (2005). Integration of ICT in higher education institutions: Challenges and best practice recommendations based on experience of Makerere University & Other organizations. Computer Science, Business, Education https://www.s emanticscholar.org/paper/Integration-of-ICT-in-organisations%3A-challenges-and-Tusubira-Mulira/163ae94a5028c7c74267d2672 ced70bcde7c2059 google scholar
110. Vaithilingam, S., Nair, M., Macharia, M., & Venkatesh, V. (2022). Mobile communication and use behaviour of the urban poor in a developing country: A field study in Malaysia. International Journal of Information Management, 63, 102440. https://doi.org/10. 1016/j.ijinfomgt.2021.102440 google scholar
111. VanDerSchaaf, H., Daim, T., & Basoglu, N. (2023). Factors Influencing Student Information Technology Adoption. IEEE Transactions on Engineering Management, 70(2), 631-643. https://doi.org/l0.1109/TEM.2021.3053966. google scholar
112. Varadaraj, D. A. & Wadi, D. B. M. A. (2021). A study on contribution of digital human resource management towards organiza-tional performance. International Journal of Management Science and Business Administration, 7(5), 43-51. DOI: 10.18775/ ijmsba.1849-5664-5419.2014.75.1004 google scholar
113. Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioural intention, facilitating conditions, and behavioural expectation. MIS Quarterly, 32(3), 483-502. DOI: 10.2307/25148853 google scholar
114. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. DOI: 10.2307/30036540 google scholar
115. Wang, L., Ba, Z., & Wang, Y. (2024). How does adoption behavior towards ICT policies affect digital divides? Evidence from Chinese prefecture-level cities. Technology in Society, 102803. https://doi.org/10.1016/j.techsoc.2024.102803 google scholar
116. Wang, M., Huang, K., Li, X., Zhao, X., Downey, L., Hassounah, S., Liu, X., Jin, Y., & Ren, M. (2025). Health workers’ adoption of digital health technology in low- and middle-income countries: A systematic review and meta-analysis. Bulletin of the World Health Organization, 103(02), 126-135. https://doi.org/l0.247l/blt.24.292157 google scholar
117. Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488. DOI: 10.1108/JEIM-09-2014-0088 google scholar
118. Willis, G., & Artino, A. R. (2013). What do our respondents think we're asking? Using cognitive interviewing to improve medical education surveys. Journal of Graduate Medical Education, 5(3), 353-356. DOI: 10.4300/JGME-D-13-00154.1 google scholar
119. Wong, L. H., Chai, C. S., & Poh, A. G. (2017). seamless language learning: Second language learning with social media. Comunicar, 50, 9-21. https://doi.org/l0.3916/C50-2017-01 google scholar
120. Xu, J., Yoo, C., & Pan, Y. (2023). Prediction of user behavioural intentions based on structural equation modelling in AI painting cognitive conditions. International Journal of Communication NetworKs and Information Security, 15, 37-48. https://doi.org/10.17762/ijcnis. Vİ5İ1.5788. google scholar
121. Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT) in higher education: A systematic review. SAGE Open, 14(1). https://doi.org/10.1177/21582440241229570 google scholar
122. Yap, Y., Tan, S., & Choon, S. (2022). Elderly's intention to use Technologies: A systematic literatüre review. Heliyon, 8(i), e08765. https:// doi.org/l0.1016/j.heliyon.2022.e08765. google scholar
123. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behaviour, 28(5), 1902-1911. https://doi.Org/l0.1016/i.chb.2012.05.008 google scholar
124. zur Kammer, K., Hudelmayer, A., & Schütz, J. (2025). Measuring computer skills of older people in SHARE: Evaluation of the item IT003_PC_Skills (IT module). (SHARE Working Paper No: 98-2025.) SHARE-ERIC. DOI: 10.6103/wp.982025 google scholar