Organizational Readiness to Adopt Artificial Intelligence in the Library and Information Sector of Pakistan


  • Saeed Ullah Jan Khushal Khan Khattak University Karak, Karak, Khyber Pakhtunkhwa, Pakistan
  • Muhammad Sajjad Ali Khan Khushal Khan Khattak University Karak, Karak, Khyber Pakhtunkhwa, Pakistan
  • Ali Saeed Khan University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan



Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres.

Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data.

Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns.

Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.


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Aboelmaged, M., Mouakket, S., & Ali, I. (in press). A scientometric analysis of digital library adoption over the past 30 years: Models, trends, and research directions. Journal of Librarianship and Information Science.

Aboelmaged, M. G. (2014). Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. International Journal of Information Management, 34(5), 639–651.

Ahmad, H., Butt, A. H., Khan, A., Shafique, M. N., & Nawaz, Z. (2020). Reluctance to acceptance: Factors affecting e-payment adoption in Pakistan (The integration of TRI and TAM). SMART Journal of Business Management Studies, 16(2), 49-59.

Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), Article 102437.

Asemi, A., & Asemi, A. (2018). Artificial intelligence (AI) application in library systems in Iran: A taxonomy study. Library Philosophy and Practice.

Asemi, A., Ko, A., & Nowkarizi, M. (2021). Intelligent libraries: A review on expert systems, artificial intelligence, and robot. Library Hi Tech, 39(2), 412–434.

Baharuddin, M. F., Izhar, T. A. T., & Shoid, M. S. M. (2018). Adoption of open source software (OSS) and organization performance in the library. International Journal of Academic Research in Business and Social Sciences, 8(9), 285–297.

Bello, O., & Zeadally, S. (2019). Toward efficient smartification of the Internet of Things (IoT) services. Future Generation Computer Systems, 92, 663–673.

Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. Journal of Computer Information Systems, 48(4), 69-76. https://doi/abs/10.1080/08874417.2008.11646036

Bolt, N. (2014). Libraries from now on: Imagining the future of libraries: ALA Summit on the Future of Libraries – Report to ALA membership. American Library Association.

Bourg, C. (2017, March 16). What happens to libraries and librarians when machines can read all the books? Feral Librarian.

Braun, V., & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern‐based qualitative analytic approaches. Counselling and Psychotherapy Research, 21(1), 37–47.

Brown, T. (2021). The future of events will be hybrid. In V. Ziakas, V. Antchak, & D. Getz (Eds.), Crisis management and recovery for events: Impacts and strategies (pp. 142–164). Goodfellow Publishers.

Chaubey, A., & Sahoo, C. K. (2021). Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis. International Journal of Information Management, 59, 102344.

Chen, J., Smadi, D., Zhang, X., & Xie, Z. (2018, March 25–28). An investigation of cyberinfrastructure adoption in university libraries [Poster presentation]. iConference 2018, Sheffield, England, UK.

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, Article 102383.

Cox, A. (2023). How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions. Journal of the Association for Information Science and Technology, 74(3), 367–380.

Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders' views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 37(3), 418–435.

Crawford, S., & Syme, F. (2018). Enhancing collection development with big data analytics. Public Library Quarterly, 37(4), 387–393.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42.

Dewi, M. A. A., Hidayanto, A. N., Purwandari, B., Kosandi, M., & Budi, N. F. A. (2018). Smart city readiness model based on technology-organization-environment (TOE) framework and its effect on adoption decision. In M. Tanabu & D. Senoo (Eds.), Pacific Asia Conference on Information Systems (PACIS) 2018 proceedings. Association for Information Systems.

Elman, C., Gerring, J., & Mahoney, J. (Eds.). (2020). The production of knowledge: Enhancing progress in social science. Cambridge University Press.

Eze, S. C., Chinedu-Eze, V. C., Bello, A. O., Inegbedion, H., Nwanji, T., & Asamu, F. (2019). Mobile marketing technology adoption in service SMEs: A multi-perspective framework. Journal of Science and Technology Policy Management, 10(3), 569–596.

Gholami, Z., Abdekhoda, M., & Gavgani, V. Z. (2018). Determinant factors in adopting mobile technology-based services by academic librarians. DESIDOC Journal of Library & Information Technology, 38(4), 271–277.

Gitonga, F. N., Gichohi, P. M., & Gichoya, D. (2023). Technology responsive measures adopted by university libraries in Meru County, Kenya in addressing disruptive forces. International Journal of Professional Practice, 11(4), 52–62.

Harisanty, D., Anna, N. E. V., Putri, T. E., Firdaus, A. A., & Noor Azizi, N. A. (in press). Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: A pilot study. Library Hi Tech.

Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, Article 102497.

Hoopes, A. J., Gilmore, K., Cady, J., Akers, A. Y., & Ahrens, K. R. (2016). A qualitative study of factors that influence contraceptive choice among adolescent school-based health center patients. Journal of pediatric and adolescent gynecology, 29(3), 259-264.

Jiang, Y., & Johnston, C. (2010). Parents' dilemmas in choosing empirically supported treatments for child ADHD. The ADHD Report, 18(4), 5–9.

Kalema, B. M., & Mokgadi, M. (2017). Developing countries organizations' readiness for big data analytics. Problems and Perspectives in Management, 15(1), 260–270.

Lengoatha, L., & Seymour, L. F. (2020). Determinant factors of intention to adopt blockchain technology across academic libraries. In Proceedings of the South African Institute of Computer Scientists and Information Technologists (SAICSIT) '20 (pp. 244–250). Association for Computing Machinery.

Lewis, M., Sycara, K., & Walker, P. (2018). The role of trust in human-robot interaction. In H. A. Abbass, J. Scholz, & D. J. Reid (Eds.), Foundations of trusted autonomy (pp. 135–159). Springer.

Lokuge, S., Sedera, D., Grover, V., & Xu, D. (2019). Organizational readiness for digital innovation: Development and empirical calibration of a construct. Information & Management, 56(3), 445–461.

Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26–29.

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.

Mann, C., & Stewart, F. (2000). Internet communication and qualitative research: A handbook for researching online. Sage.

Marshall, B., Cardon, P., Poddar, A., & Fontenot, R. (2013). Does sample size matter in qualitative research? A review of qualitative interviews in IS research. Journal of Computer Information Systems, 54(1), 11–22.

Matta, V., Koonce, D., & Jeyaraj, A. (2012). Initiation, experimentation, implementation of innovations: The case for Radio Frequency Identification systems. International Journal of Information Management, 32(2), 164–174.

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage.

Mogali, S. S. (2014, February). Artificial intelligence and its applications in libraries [Paper presentation]. Bilingual International Conference on Information Technology: Yesterday, Today and Tomorrow, Delhi, India.

Motau, M., & Kalema, B. M. (2016). Big data analytics readiness: A South African public sector perspective. In 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech) (pp. 265–271). IEEE.

Mustafa, A., & Noorhidawati, A. (2020). Adoption and implementation of evidence-based library acquisition of electronic resources. Malaysian Journal of Library & Information Science, 25(1), 1-29.

Okunlaya, R. O., Syed Abdullah, N., & Alias, R. A. (2022). Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education. Library Hi Tech, 40(6), 1869–1892.

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110–121.

Omame, I. M., & Alex-Nmecha, J. C. (2020). Artificial intelligence in libraries. In N. E. Osuigwe (Ed.), Managing and adapting library information services for future users (pp. 120–144). IGI Global.

Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.

Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74.

Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467–484.

Salman, M. S., Sani, M. K. J. A., & Sahid, N. Z. (2022). Assessing the big data analytics readiness based on technology-organization-environment (TOE) framework of Malaysian libraries: Descriptive analysis. International Journal of Academic Research in Progressive Education and Development, 11(2), 1465–1484.

Singeh, F. W., Abrizah, A., & Kiran, K. (2020). Bringing the digital library success factors into the realm of the technology-organization-environment framework. The Electronic Library, 38(3), 659–675.

Soares, A. L. V., Mendes-Filho, L., & Gretzel, U. (2021). Technology adoption in hotels: Applying institutional theory to tourism. Tourism Review, 76(3), 669–680.

Sox, C. B., Crews, T. B., & Kline, S. F. (2014). Virtual and hybrid meetings for Generation X: Using the Delphi method to determine best practices, opportunities, and barriers. Journal of Convention & Event Tourism, (15)2, 150–169.

Sözüer, A., & Pınar, İ. (2016). Teknoloji-organizasyon-çevre modeli perspektifinden kütüphane 2.0 [Library 2.0 through the perspective of technology-organization-environment framework]. Türk Kütüphaneciliği [Turkish Librarianship], 30(1), 20–32.

Tella, A. (in press). Adoption of a quadratic usage framework for predicting blockchain use intention from the perspective of librarians in Southwest Nigerian universities. Journal of Librarianship and Information Science.

Thornberg, R., & Charmaz, K. (2014). Grounded theory and theoretical coding. In U. Flick (Ed.), The SAGE handbook of qualitative data analysis (pp. 153–169). Sage.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and justifying sample size sufficiency in interview-based studies: Systematic analysis of qualitative health research over a 15-year period. BMC medical research methodology, 18, 1-18.

Vijayakumar, S., & Sheshadri, K. N. (2019). Applications of artificial intelligence in academic libraries. International Journal of Computer Sciences and Engineering, 7(16), 136–140.

Wang, Y., Meister, D., & Wang, Y. (2008). Relative advantage and perceived usefulness: The adoption of competing ICTs. In Diffusion Interest Group in Information Technology (DIGIT) 2008 proceedings.

Wang, Y.-S., Li, H.-T., Li, C.-R., & Zhang, D.-Z. (2016). Factors affecting hotels' adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management, 53, 163–172.

Wiese, M., & Humbani, M. (2020). Exploring technology readiness for mobile payment app users. The International Review of Retail, Distribution and Consumer Research, 30(2), 123–142.

Wynn, E., & Hult, H. V. (2019). Qualitative and critical research in information systems and human-computer interaction: Divergent and convergent paths. Now Publishers.

Yakubu, A. S., Kassim, A. M., & Husin, M. H. (2023). Conceptualizing hybrid model for influencing intention to adopt cloud computing in North-Eastern Nigerian academic libraries. The Journal of Academic Librarianship, 49(4), Article 102747.

Yoon, J., Andrews, J. E., & Ward, H. L. (2022). Perceptions on adopting artificial intelligence and related technologies in libraries: Public and academic librarians in North America. Library Hi Tech, 40(6), 1893–1915.

Zhao, Y., Deng, S., Gao, T., & Zhou, R. (2016). Research on user needs for mobile information services in Chinese university libraries: Comparison between existing user and potential users. The Electronic Library, 34(4), 617–635.




How to Cite

Jan, S. U., Khan, M. S. A., & Khan, A. S. (2024). Organizational Readiness to Adopt Artificial Intelligence in the Library and Information Sector of Pakistan. Evidence Based Library and Information Practice, 19(1), 58–76.



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