This study examines factors influencing the adoption of artificial intelligence (AI) technologies in university libraries in Pakistan using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The study collected survey responses from 187 university librarians and analyzed the data using structural equation modeling to test the UTAUT model. The results found attitude to be a major factor influencing behavioral intention to adopt AI, with performance expectancy and effort expectancy significantly impacting attitude. Social influence was also found to significantly relate to behavioral intention, while facilitating conditions did not relate to behavioral intention. The study concludes attitude is an important variable for AI adoption in Pakistani university libraries based on the UTAUT model.

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Mr. Muhammad Yousuf Ali
Associate Librarian
The Aga Khan University, Karachi, Pakistan
PhD Scholar, The Islamia University Bahawalpur
Artificial Intelligence adoption factor in the University libraries of
Pakistan: UTAUT framework
Dr. Salman Bin Naeem
Associate Professor
The Islamia University Bahawalpur
Prof. Dr. Rubina Bhatti
Chairperson & DEAN Social Sciences,
The Islamia University Bahawalpur
Dr. Joanna Richardson
Scholarly Communication Consultant
Brisbane | QLD | Australia

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Introduction
Artificial Intelligence (AI) is one of the most advance
technology impact in the all walk of life. AI is also impact the
libraries and librarian life. This study describe about the
artificial intelligence adoption factor in the university
libraries. The Unified Theory of Acceptance and Use of
Technology (UTAUT) model is used to the adoption of
technology which most suitable model for the adoption of
technology.
1st SIGAI Student Research Symposium 24 June 2020 SIG AI, ASIS&T

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United Nations’ Information Economy Report
(UNCTAD 2017, 5)
“AI is defined as the ability of machines and systems to acquire and
apply knowledge, and to carry out intelligent behavior. This may
involve performing various cognitive tasks, such as sensing, processing
oral language, reasoning, and learning, making decisions, and
demonstrating an ability to manipulate objects accordingly.”
(Ali, 2021)

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The Core study related to AI and Libraries
There different AI based study conducted in different
context of AI.
Since Last one decade AI debated become very hot issue in
the libraries.
Important study (Mogali, 2014; Johnson 2018; Asemi, &
Asemi, 2018; Cox et al. 2018) discussed the AI application in
libraries.

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UTAUT and Library Technology
UTAUT Model presented in Vanketesh et al. 2003 this model
is test by Zaniab et al. 2018 RFID adoption in libraries.
Andrews et al.(2021) determine AI and other related
technology in the libraries under the frame wrok of UTAUT.
Khan et al. (2017) digital reference service adoption
determined the university libraries of Pakistan
And many other study like e-book adoption, digital library
and etc.

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Artificial
Intelligence Tool
Technical Services User Services
Chatbot Acquisition Descriptive
Cataloguing -
Query Services Library
Instructions
Information
Retrieval
Robotics Library Stock
Taking
Shelving
-
Searching Library
Material
Check In/Check
Out -
Natural Language
Processing (NLP)
Knowledge
Management
Information/Book
Processing
Classification of
Books
Translation of Text
from Native
Language
Reading of
Material
Information
Retrieval
Big Data Library Resource
Usage
Managing
Repository
Library Data
Storage/Warehou
se
Managing
Repository
Library Usage
Report -
Text Data Mining
(TDM)
Altmetric,
Citations Support
& Analysis
OPAC Searching Metadata Reference
Services
#Library Trends Social media
Appearance
Pattern
Recognition
Library Security
Material
QR Code to the
Material
Indexing and
Abstracting of
Image
-
Security
Password/RFID
User
identifications
Image Processing Preservation and
Archival
Managing Image
and video library
database
Medical
Images/Scans
Records
Library User Facial
Recognition
3D-Printings
-
Usage of AI in Library Technical and User services

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UTAUT and Library Technology
Independent Variable
Effort
Expectancy
Social
Influence
Performance
Expectancy
Intention to
Use
Behavior to
Use
Facilitating
Condition
Gender Age Experience Voluntariness of use
Dependent Variable
Vankatesh et al. 2003 UTAUT Model

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Objective of the Study
Main objective of this study is to determine the Attitude of
the librarian to adoption of the libraries.
Attitude is additional construct use in this model.
To carry out this study hypotheses developed.

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UTAUT and Library Technology
Independent Variable
Effort
Expectancy
Social
Influence
Performance
Expectancy
Intention to
Use
Facilitating
Condition
Attitude
H1 Performance Expectancy (PE) is significant Impact on Attitude to use of AI
H2 Effort Expectancy (PE) is significant Impact on Attitude to use of AI
H3 Social Influence (SI) is significant relationship with Behavior Intention (BI)
H4 Facilitating Condition is significant relationship with Behavior intention
H5 Attitude (AT) is significant impact on Behavior Intention (BI)
H1
H2
H3
H4
H5

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Methodology
This is a quanative study and survey methodlogy is used.
Questionnaire is the data collection tool.
University librarian working in Public and private sector
university are the respondent.
Total 187 response received from the out of 245.
Data analysis in SPSS AMOS 23.00 and structure equation
modeling is used to test the fit model.

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Values Ideal Value
(Alrawashadeh,
2012)
Acceptable value
(Cao & Niu, 2019)
Model Value
Chi square t degree
of freedom (X2/df <
2.00)
Chi square t degree of
freedom (X2/df < 3.00)
Chi square t degree
of freedom (X2/df <
3.122)/346
Comparative fit index
(CFI > 0.90)
Comparative fit index
(CFI <0.70)
Comparative fit index
(CFI = 0.823)
Goodness of Fit Index
(GFI > 0.90)
Goodness of Fit Index
(GFI <0.70)
Goodness of Fit Index
(GFI =0.839)
Root mean Square of
Error Approximation
(RMSEA < 0.05)
Root mean Square of
Error Approximation
(RMSEA < 0.08)
Root mean Square of
Error Approximation
(RMSEA = 0.96)

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Values Ideal Value
(Alrawashadeh,
2012)
Acceptable value
(Cao & Niu, 2019)
Model Value
Chi square t degree
of freedom (X2/df <
2.00)
Chi square t degree of
freedom (X2/df < 3.00)
Chi square t degree
of freedom (X2/df <
2.061)/337
Comparative fit index
(CFI > 0.90)
Comparative fit index
(CFI <0.70)
Comparative fit index
(CFI = 0.914)
Goodness of Fit Index
(GFI > 0.90)
Goodness of Fit Index
(GFI <0.70)
Goodness of Fit Index
(GFI =0.839)
Root mean Square of
Error Approximation
(RMSEA < 0.05)
Root mean Square of
Error Approximation
(RMSEA < 0.08)
Root mean Square of
Error Approximation
(RMSEA = 0.066)

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Independent Variable
Effort
Expectancy
Social
Influence
Performance
Expectancy
Intention to
Use
Facilitating
Condition
Attitude
H1
H2
H3
H4
H5
Hypothe
ses
Supported p (Value) Supported /Not
H1 Performance Expectancy (PE)
is significant Impact on
Attitude to use of AI
*** Supported
H2 Effort Expectancy (PE) is
significant Impact on Attitude
to use of AI
*** Supported
H3 Social Influence (SI) is
significant relationship with
Behavior Intention (BI)
*** Supported
H4 Facilitating Condition is
significant relationship with
Behavior intention(BI)
0.496 Not supported
H5 Attitude (AT) is significant
impact on Behavior Intention
(BI)
*** Supported

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Finding
Finding of this study is that Attitude is one the major
Exogenous variable of important variable to adoption of AI
Technology adoption in the university libraries of Pakistan.

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Conclusion
AI is one of the emerging technology in the all walk of life
and libraries are also adopting this technology.
In Pakistani Libraries AI Technology attitude is one of the
major factor in the university libraries of Pakistan.
However facilitating condition and behavior intention does
not related each other.

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Recommendation
AI Technology under the UTAUT Frame can be tested in
Public and special libraries with additional variable of
Attitude.

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Reference
Ali, M. Y., Naeem, S. B., & Bhatti, R. (2021). Artificial intelligence (AI) Services in Pakistani
University Libraries. Library Hi Tech News, https://doi.org/10.1108/LHTN-10-2021-0065
Ali, M. Y., Naeem, S. B., & Bhatti, R. (2020). Artificial intelligence tools and perspectives of
university librarians: An overview. Business Information Review, 37(3), 116-124.
Asemi, A., & Asemi, A. (2018). Artificial Intelligence (AI) application in Library Systems in
Iran: A taxonomy study. Library Philosophy and Practice, Vol 2018, no. 1840, pp. 1-11.
Cox, A. M., Pinfield, S., & Rutter, S. (2018). The intelligent library: Thought leaders’ views
on the likely impact of artificial intelligence on academic libraries. Library Hi Tech (ahead
of print, pp 1-18). DOI 10.1108/LHT-08-2018-0105.
Johnson, B. (2018). Libraries in the age of artificial intelligence. Computers in
Libraries, 38(1), 14-16.

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Reference
Khan, A., & Qutab, S. (2016). Understanding research students’ behavioural
intention in the adoption of digital libraries. Library Review,65(4/5), 295-319.
Mogali, S. (February 2014). Artificial Intelligence and its applications in Libraries.
Bilingual International Conference on Information Technology: Yesterday, Today
and Tomorrow At: Defence Scientific Information and Documentation Centre,
Ministry of Defence, Delhi
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), 102437.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance
of information technology: Toward a unified view. MIS quarterly, 425-478.
Zainab, A. M., Kiran, K., Karim, N. H. A., & Sukmawati, M. (2018). UTAUT’S
performance consistency: Empirical evidence from a library management
system. Malaysian Journal of Library & Information Science, 23(1), 17-32.

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