This presentation was presented PhD Open Defense presentation at The Islamia University Bahawalpur on 31 July 2023. The title PhD study was "Service innovation and performance-based evaluation of university libraries in the age of Artificial Intelligence". The PhD scholar successfully defended his dissertation.

1. Service innovation and performance-based evaluation of university libraries in the age of Artificial Intelligence by Muhammad Yousuf Ali PhD Scholar DLIS, IUB 31 July 2023
PhD Research Open Defence
Service innovation and performance-based evaluation of university libraries in the
age of Artificial Intelligence
Presented to
Department of Library and Information Science,
The Islamia University Bahawalpur, Punjab,
Pakistan
Presented by
Research Scholar
Muhammad Yousuf Ali
PhD Scholar (Library & Information Science)
Department of Library & Information Science
The Islamia University, Bahawalpur Punjab,
Pakistan
Prof Rubina Bhatti
Supervisor
Dean, Faculty of Social Sciences
Professor & Chairperson,
Department of Library & Information Science
The Islamia University, Bahawalpur Punjab,
Pakistan
Prof Salman Bin Naeem
Co-Supervisor
Department of Library & Information Science
The Islamia University, Bahawalpur Punjab,
Pakistan

2. CONTENT
1. INTRODUCTION
2. LITERATURE REVIEW
3. RESEARCH QUESTIONS
4. RESEARCH HYPOTHESES
5. RESEARCH METHODOLOGY & DESIGN
6. RESULTS/ FINDING
7. RECOMMENDATIONS
8. LIMITATIONS AND DELIMITATIONS OF THE STUDY
9. IMPLICATIONS OF THE STUDY
10. FURTHER STUDY

3. My PhD Journey 01 Oct 2018 to 31 July 2023

4. Dissertation Publications Details
• 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. (cited 35 times)
• Ali, M. Y., Naeem, S. B., & Bhatti, R. (2021). Artificial Intelligence (AI) in Pakistani
university library services. Library Hi Tech News, 38(8), 12-15. (cited 4 times)
• Ali, M. Y., Naeem, S. B., Bhatti, R., & Richardson, J. (2022). Artificial intelligence
application in university libraries of Pakistan: SWOT analysis and implications.
Global Knowledge, Memory and Communication, Advance online. (Cited 3 times)

5. Dissertation Publication under review

6. IFLA Paper Presentation
• Ali, M. Y., Bhatti, R., & Richardson, J. (2019). New Avenue for
Reference and Information Services When Most Content is Open
Access. 85th IFLA WLIC Athens Greece
IFLA Paper Presentation
• Ali, M. Y., & Bhatti, R. (2017). HEC Digital Library, Pakistan: An
Integrated Information Source for University Students in Pakistan.
85th IFLA WLIC Athens Greece
ASIS&T SIG AI Symposium
• Ali, M. Y., Naeem, S.B., Bhatti, R., & Richardson, J. (2022). Artificial
Intelligence Adoption Factor in the University Libraries of Pakistan:
UTAUT Framework. ASIS&T SIG AI Symposium

7. 1. Introduction to Artificial Intelligence(AI)
The term AI was first used in 1950 by John McCarthy, when preparing a
research proposal for the (US) Dartmouth Summer Research Conference.
• According to Hilker (1986, p.15), “Artificial intelligence is a branch of
computer science that concerns the ability of computers to perform
intelligent tasks, such as those requiring recognition, reasoning, and
learning.”

8. 1. Introduction to Artificial Intelligence(AI)
United Nations’ Information Economy Report (UNCTAD 2021, p. 17) suggests:
“AI is defined as the ability of machines and systems to acquire and apply
knowledge, and to carry out intelligent behaviour. 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.”

9. 1. Artificial Intelligence(AI) in Libraries
Emerging trends in library and information services are demonstrating an
increased reliance on machines. For example,
• computers
• laptops
• tablets
• cellphones
• robotics
and other devices are replacing human beings, not only in libraries but also
in all other walks of life.

10. 1. Artificial Intelligence(AI) in Libraries
Early Phase Middle Phase Modern Phase Advance Phase

11. Artificial Intelligence (AI) in Libraries
Natural Language
Processing
Pattern
Recognition
Text Data
Mining
Image
Processing
Big Data
Analytics
Chatbot
Robotics
Libraries

12. Artificial Intelligence(AI) in Libraries
Artificial Intelligence
Tool
Technical Services User Services
Chatbot Acquisition Descriptive
Cataloguing -
Query Services Library Instructions Information Retrieval
Robotics Library Stocktaking 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/Warehouse
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 for 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
-
Source : 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.

13. 2. Background of the study
Beyond the Digitalization and Automation
The successful transformation of academic libraries around the world to
computerization, automation, and digitalization for more than three decades.
New Information Ecosystem
Given this changing Information environment and a new information
ecosystem of libraries, librarians can potentially manage their libraries through
artificial intelligence (Wood & Evans, 2018; Ratledge, 2017).
Strong information and communication technology (ICT)
The strong information and communication structure like 5G, Cloud
computing, IoT and data science.

14. 3. Statement of the Problem
In Pakistan, university libraries are starting to introduce AI tools into their services. As a new
innovative technology, there is a strong need to know about the adoption of AI tools and
technologies, and especially the perspective on their usage in university libraries. Therefore,
this research has explored the potential usage and application of AI tools, as well as overall
issues in adopting new technologies, in university libraries in Pakistan.
The universal model of the Unified Theory of Acceptance of Technology (UTAUT) framework
has been used as a good fit in examining the adoption of various technologies. In the current
research, the researcher has used this model to explore AI adoption by Pakistani LIS
professionals.
This empirical study has been designed to determine the adoption of AI, barrier, awareness,
attitude, intention to use, and behaviour to use toward AI tools by LIS Professional under the
framework of the (UTAUT) model (Venkatesh et al., 2003).

15. 4. Significance of the Study
This Study is significant for LIS professionals to introducing AI-based innovative
library services in their respective libraries.
This Study is equally important to LIS leaders, policy makers, university
administration, and HEC regarding which types of tools are used in the library
and what factors affect the adoption of AI technologies.
It also helps to set the direction for Pakistani university libraries in their
adoption of AI-based technologies.

16. 5. Objectives of the Study
1. To identify the relationship between Librarian behaviour and
intention to use and adopt AI tools for library services in
Pakistan.
2. To present a model for Pakistani university libraries for the
adoption of AI technology under the UTAUT framework.
3. To determine the relationship of endogenous variables (anxiety,
trust, and perceived risk) with the mediating variable of
attitude.
4. To measure the most influential factors for the adoption of
technology.

17. 6. Theoretical Framework
Unified Theory of Acceptance of Technology (UTAUT) model (Venkatesh et al., 2003)
is considered one of the strong models for the adoption of technologies. This model
is a combination of eight models:
i. The Theory of Reasoned Action (TRA) (Fishbein, 1975)
ii. The Theory of Planned Behaviour (TPB) (Ajzen, 1991)
iii. The Technology Acceptance Model (TAM) (Davis, 1989)
iv. The Model of PC Utilization (MPCU) (Thompson et al., 1991)
v. The Diffusion of Innovation Theory (DOI) (Rogers, 1995)
vi. The Motivation Model (Vallerand, 1997)
vii. The Social Cognitive Theory (SCT) (Bandura, 1986)
viii. The Combined TAM-TPB (Taylor & Todd, 1995)

18. Independent Variable
Effort
Expectancy
Social
Influence
Performance
Expectancy
Intention to
Use
Behavior to
Use
Facilitating
Condition
Gender Age Experience Voluntariness of use
Dependent Variable
6. Theoretical Framework
Independent variables
Performance Expectancy
Effort Expectancy
Social Influence
Facilitating Condition
Moderating variables
Age
Gender
Experience
Voluntariness of use

19. 6. Theoretical Framework
Propose Theoretical Frame work
Independent variables
Performance Expectancy
Effort Expectancy
Social Influence
Facilitating Condition
Mediating variables
Attitude
Perceived Risk
Anxiety
Trust
Dependent variables
Behaviour Intention
Behaviour to use
Outcome variables
Service Innovation
Service Quality
Library Performance

20. Construct Code Definition Source
Performance Expectancy PE Performance expectancy is
defined as the degree to which
an individual believes that
using the Technology will help
him or her to attain gains in job
performance
Venkatesh et al. (2003);
Sohn & Kwon (2020)
Effort Expectancy EE Effort expectancy is defined as
the degree of ease associated
with the use of the System.
Venkatesh et al. (2003);
Gursoy et al. (2019).
Social Influence SI Social influence is defined as
the degree to which an
individual perceives that it is
important others believe he or
Venkatesh et al. (2003); Lu
et al. (2013); Gursoy et al.
(2019)
Constructs of Variables

21. Constructs of Variables
Perceived Risk PR Perceived risk (PR) is
commonly thought of as felt
uncertainty regarding possible
negative consequences of
using a product or service.
Featherman & Pavlou
(2003); Martins & et al.
(2014)
Trust TR Trust is a defining feature of
most economic and social
interactions in which
uncertainty is present.
Gefen (2004); Pavlou
(2003)
Anxiety AX It is somewhat intimidating to
me
Venkatesh et al. (2003);
Kohnke et al. (2014); Yoo &
Huang (2011).
Attitude AT Attitude is ‘the perceived
degree of positive and negative
feelings about the target
behavior’
Ajzen (1991); Yoo & Huang
(2011); Rahman et al.
(2017)

22. Constructs of Variables Facilitating Condition FC Facilitating condition indicates
the degree to which a person
believes that there are few if
any barriers to using the new
technology – personally,
socially, organisationally, or
technologically
Kohnke et al. (2014); Sohn
& Kwon (2020)
Behavioural Intention BI Individual difference is
regarded as a dominant factor
in the adoption behaviour of
Technology.
Venkatesh et al. (2003); Lin
et al. (2013); Sohn & Kwon
(2020)
Behavioural Use BU Individual Usage behaviour to
adopt the Technology
Awwad & Al-Majali (2015)
Service Innovation SN New or significantly improved
service concepts and offerings
as such, irrespective of
whether they are introduced
by service companies or
Chen & Shen (2019)

23. Constructs of Variables
Service Quality SQ “Quality” in
a service organization is a
measure of the extent to
which the service delivered
meets the customer's
expectations.
Jamaludin & Mahmud
(2011); Chen & Shen
(2019)
Library Performance LP A measurement of library user
satisfaction and library services
by the organization
Self-Developed

24. 6. Theoretical Framework
Independent variables
Performance Expectancy
Effort Expectancy
Social Influence
Facilitating Condition
Dependent variables
Behaviour Intention
Behaviour to use
Mediating variables
Attitude
Perceived Risk
Anxiety
Trust
Outcome variables
Service Innovation
Service Quality
Library Performance

25. 7. Literature Review
In searching of relevant literature in Google Scholar, author used following nested search
strategy
“UTAUT” AND “Libraries” = 3870
“UTAUT” AND “Librarian” = 1010
“UTAUT” AND “University Libraries” = 596
“UTAUT” AND “University Libraries” AND "Artificial Intelligence" = 60
"UTAUT" AND "Library" AND "artificial intelligence" = 1250 search results
“UTAUT” AND “Libraries” AND “Artificial intelligence = 481 search results
"UTAUT" AND ("Librarian*” OR “LIS Professional*”) AND “artificial intelligence”= 118 search results
In addition to journal articles, book chapters & books, grey literature was helpful for covering some of the
advanced research topics. This included blogs, websites, and dissertations.

26. 8. Research Questions
RQ1: What is the level of the existing AI tools usage and applications
in university library services in Pakistan?
RQ2: What comparisons can be made in Pakistan between private and
public sector university usage and applications of AI tools?
RQ3: What are the existing facilities available to support adoption of
AI tools and applications in Pakistani university libraries?
RQ 4: What are the most influential factors for the acceptance of the
adoption and use of AI by LIS Professional in Pakistan?

27. 8. Research Questions (cont’d)
RQ5: To what extent are the UTAUT model’s original relationship (performance
expectancy, effort expectancy, social influence, and facilitating condition)
associated with the behavioural intention and intention to use of LIS professional
in Pakistan to use AI tools in libraries?
RQ6: What is the relationship of behaviour to use of AI tools by LIS professional in
Pakistan with the outcome variables (service quality, service innovation, and
library performance)?
RQ7: To what extent are the endogenous variables (perceived risk, anxiety, and
trust) associated with the behavioural intention and intention of LIS professional
in Pakistan to use AI tools in libraries?

28. 9. Research Hypotheses
Hypotheses Statements
H1 Performance expectancy (PE) has significant positive relationship with
attitude (AT) in use of AI
H2
Perceived risk (PR) has significant positive relationship with attitude
(AT) in use of AI
H3 Anxiety (AN) has significant positive relationship with attitude (AT) in
use of AI
H4 Trust (TR) has significant positive relationship with attitude (AT) in use
of AI
H5 Effort expectancy (EE) has significant positive relation with attitude
(AT) in use of AI

29. Hypotheses
Statements
H6 Social influence (SI) has significant positive relation with behaviour
intention (BI) in use of AI
H7 Facilitating condition (FC) has significant positive relation with
behaviour intention (BI) in use of AI
H8 Attitude (AT) has significant positive relation with behaviour
intention (BI) in use of AI
H9 Performance expectancy (PE) has significant positive relation with
behaviour intention (BI) in use of AI
H10 Effort expectancy (EE) has significant positive relation with
9. Research Hypotheses (cont’d)

30. 9. Research Hypotheses (cont’d)
Hypotheses Statements
H11 Behaviour intention (BI) has significant positive relation with behaviour to
use (BU) of AI
H12 Service innovation (SN) has significant positive relation with behaviour to
use (BU) of AI
H13 Service quality (SQ) has significant positive relation with behaviour to use
(BU) in use of AI
H14 Service quality (SQ) has significant positive relation with service innovation
(SN) in use of AI
H15 Behaviour to use (BU) has significant positive relation with library
performance (LP) in use of AI
H16 Library performance (LP) has significant positive relation with service quality

31. 10. Research Methodology & Design
Research Methodology
Survey Research Methods has been selected as it is an easy way to collect the data.
Population of the Study
LIS professionals in Higher Education Commission (HEC) Recognized Public and Private
Sector Universities and Degree Awarding Institutions (DAI) are the population of the
study. 212 HEC institutions are the research population, with 245 responses received
from 175, and a response rate of 82.54 %.
Data Collection Instrument
Questionnaire was used as data collection instrument. Questionnaire is designed
under the construct and hypotheses designed under the UTAUT framework (Venkatesh
et al., 2003). Some instrument tools have been adopted from additional sources (Sohn
& Kwon, 2020; Ritter, 2019) and etc.

32. Data Collection Process
The data collection questionnaire form was send through email, social media librarian
groups, and personal visits to the respective university libraries and with their staff.
Data Analysis Procedure
Collected data was analysed using the Statistical Package for Social Sciences (SPSS) 26.
AMOS version 26 also used for Structure Equation Modelling (SEM) to check the
fitness for purpose of the proposed model (Schermelleh-Engel, Moosbrugger, &
Müller, 2003).
Data Analysis
Descriptive statistics, e.g., Mean and Standard Deviation, have been applied.
Inferential statistics t test has also used to find the mean difference between public
and private sector universities .

33. 10. FINDINGS /
RESULTS
Demographical
Demographic Frequency Percent
Gender
Male 169 69
Female 76 31
Designation
Assistant Librarian 63 25.7
Deputy Librarian 21 8.6
Associate Librarian 4 1.6
Librarian 79 32.2
Chief Librarian 32 13.1
Library In-charge 18 7.3
Senior Librarian 15 6.1
Other 13 5.3

34. Age Category Frequency Percent
< 30 Years 33 13.5
31 to 40 Years 134 54.7
41 to 50 Years 62 25.3
> 50 Years 16 6.5
N 245 100
University Type
Public 104 42.5
Private 141 57.5
10. FINDINGS/RESULTS
Demographical

35. 10. FINDINGS/RESULTS
AI Tools usage
What is the level of existing AI Tools usage and applications in
university library services in Pakistan?
• Natural language processing, voice searching, and chatbot are the most
familiar and popular tools among those currently being used, with mean value
of (4.02).
• Robotics technology is currently rarely used with mean value (1.62) because
of the financial investment and high level of IT skills required.

36. 10. FINDINGS/RESULTS
t Test Results
RQ2: What comparisons can be made in Pakistan between
private and public sector university usage and applications of AI
tools?
The mean difference between public and private university LIS
Professional groups using AI-based technology.
Results disclosed there is no significant difference between them;
however, private universities have a slight edge when comparing
the mean values in the t-tests.

37. 10. FINDINGS/RESULTS
RQ3: What are the existing facilities available to support
adoption of AI tools and applications in Pakistani university
libraries?
There are seven AI tools are use in the university libraries of
Pakistan.
LIS professional has positive behaviour to adopt AI based
technology.
Infrastructure, skills, training, funding and collaboration with IT
team is required effective application of AI.

38. 10. FINDINGS/RESULTS
Hypotheses
Hypotheses Factor Factor Estimate S.E. C.R. p Label
H1 Attitude <--- Performance Expectancy .306 .069 4.401 *** Supported
H2 Attitude <--- Perceived Risk .114 .052 2.195 .028 Supported
H3 Attitude <--- Anxiety -.113 .047 -2.418 .016 Supported
H4 Attitude <--- Trust .776 .093 8.337 *** Supported
H5 Attitude <--- Effort Expectancy -.104 .058 -1.799 .072 Not supported
H6 Behaviour Intention <--- Social Influence .276 .095 2.891 .004 Supported
H7 Behaviour Intention <--- Facilitating Condition .013 .041 .321 .748 Not supported
H8 Behaviour Intention <--- Attitude .752 .090 8.356 *** Supported
RQ 4: What are the most influential factors for the acceptance of the adoption
and use of AI by LIS professional in Pakistan?
Attitude is the most Influential factor for the adoption of AI Technology
among the LIS professionals

39. 10. FINDINGS/RESULTS
Hypotheses
Hypotheses Factor Factor Estimate S.E. C.R. p Label
H1 Attitude <--- Performance Expectancy .306 .069 4.401 *** Supported
H2 Attitude <--- Perceived Risk .114 .052 2.195 .028 Supported
H3 Attitude <--- Anxiety -.113 .047 -2.418 .016 Supported
H4 Attitude <--- Trust .776 .093 8.337 *** Supported
H5 Attitude <--- Effort Expectancy -.104 .058 -1.799 .072 Not supported
H6 Behaviour Intention <--- Social Influence .276 .095 2.891 .004 Supported
H7 Behaviour Intention <--- Facilitating Condition .013 .041 .321 .748 Not supported
H8 Behaviour Intention <--- Attitude .752 .090 8.356 *** Supported
RQ5: To what extent are the UTAUT model’s original relationship (performance expectancy,
effort expectancy, social influence, and facilitating condition) associated with the
behavioural intention and intention to use of University Librarians and library staff in
Pakistan to use AI tools in libraries?
Behaviour intention and SI , Behaviour Intention and Attitude has significant relationship
with use of AI

40. 10. FINDINGS/RESULTS
Hypotheses (cont’d)
Hypotheses Factor Factor Estimate S.E. C.R. p Label
H9 Behaviour Intention <--- Performance Expectancy -.107 .063 -1.714 .086 Not supported
H10 Behaviour Intention <--- Efforts Expectancy .024 .114 .208 .835 Not supported
H11 Behaviour to Use <--- Behaviour Intention .969 .122 7.920 *** Supported
H12 Service Innovation <--- Behaviour to Use 1.033 .114 9.051 *** Supported
H13 Service Quality <--- Behaviour to Use .219 .188 1.168 .243 Not supported
H14 Service Quality <--- Service Innovation .709 .171 4.137 *** Supported
H15 Library Performance <--- Behaviour to use .467 .097 4.819 *** Supported
H16 Library Performance <--- Service quality .423 .080 5.300 *** Supported
RQ6: What is the relationship of behaviour to use of AI tools by LIS
professional Pakistan with the outcome variables (service quality, service
innovation, and library performance)?
All the out come variables have signification relationship.

41. 10. FINDINGS/RESULTS
Hypotheses
Hypotheses Factor Factor Estimate S.E. C.R. p Label
H1 Attitude <--- Performance Expectancy .306 .069 4.401 *** Supported
H2 Attitude <--- Perceived Risk .114 .052 2.195 .028 Supported
H3 Attitude <--- Anxiety -.113 .047 -2.418 .016 Supported
H4 Attitude <--- Trust .776 .093 8.337 *** Supported
H5 Attitude <--- Effort Expectancy -.104 .058 -1.799 .072 Not supported
H6 Behaviour Intention <--- Social Influence .276 .095 2.891 .004 Supported
H7 Behaviour Intention <--- Facilitating Condition .013 .041 .321 .748 Not supported
H8 Behaviour Intention <--- Attitude .752 .090 8.356 *** Supported
RQ7: To what extent are the endogenous variables (perceived risk, anxiety, and trust) associated with
the behavioural intention and intention of LIS Professional in Pakistan to use AI tools in libraries?
All these variables are closely associated with Attitude having correlated with each other

42. SEM Analysis

43. Model fit Indices
Values
Ideal Value
(Alrawashdeh et al., 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 (X2/df <
1.714)Degree of
freedom 1570
Comparative fit index (CFI > 0.90) Comparative fit index
(CFI <0.70)
Comparative fit index
(CFI = 0.886)
Goodness of Fit Index (GFI > 0.90) Goodness of Fit Index
(GFI <0.70)
Goodness of Fit Index
(GFI =0.740)
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 = 0.054)

44. 11. Key Findings
1. NLP-based AI tools are most frequently used in the surveyed libraries.
2. Robotic technology is rarely used in these libraries because of two important
reasons: (a) robotics is based on advanced level of technology and therefore
librarians need more advanced IT skills and (b) robotic technologies are
expensive for countries like Pakistan.
3. LIS professionals in Pakistani university libraries are mostly familiar with AI
tools and their application. Such tools include voice assistant, chatbot, facial
recognition, and ChatGPT.
4. Mean comparing t test results indicated that slightly more private sector than
public sector universities were represented in the survey, and that the former
were in a slightly better position in terms of technology adoption.

45. 11. Key Findings
5. In this study, 76 (31%) of respondents were female; this reflects that
LIS female professionals are also well aware about the technology.
6. Attitude as a mediating variable is one of the most influential factors
in adoption of AI technology in Pakistani university libraries.
7. Perceived risk has a significantly negative relationship with attitude.
8. Anxiety is considered a major barrier to technology adoption; it is
closely associated with attitude and trust.

46. 12. Recommendations
1. In LIS Schools, educators should plan to integrate AI-based technologies in their
respective school’s curriculum at various academic levels.
2. A plan should be developed for training, including workshops, to implement AI
technologies in university libraries.
3. University Librarians should develop strategic approaches for addressing the fear and
anxiety felt by librarians toward AI.
4. Most of the surveyed libraries are in planning phase to implement AI tools in their
respective libraries, so this study may be both supportive and instructive for the
libraries and librarians regarding the adoption of AI technology.

47. 12. Recommendations
5. Librarians have good attitude towards AI technology adoption, so libraries
need proper funding and ICT infrastructure to establish AI technology-based
services.
6. The negative impact of perceived risk on attitude (H2) suggests that the
relevant authorities should investigate methods to promote privacy and
security measures to overcome any issues relating to perceived risk.
7. Library leaders and policy makers should address the negative
consequences of anxiety, as in H3, on the adoption of AI-based tools and
technology.
8. University Librarians and library staff should consider joining Special
Interest Groups (SIG) within various international organisations.

48. 13. Limitation of the Study
The study is limited to the University Librarians and library staff working in HEC-
recognised universities in Pakistan having library-based knowledge and academic
qualifications. This is a cross-sectional survey because of time constraints. A
longitudinal study could be carried out with the same population in 5 years to
determine the adoption level of AI.
Delimitation of the Study
Library schools and their faculty members are not part of this study. Library users
are also not part of this study. AI adoption other types of libraries, i.e., special,
media, college, public and school, are not part of this study.

49. 14. Implications of the Study
This research has implications in three main dimensions:
a) Business Model
b) Academic Model
c) User Model

50. 15. Further Studies
1. Further study could be conducted based on library users’
perspective about acceptance or adoption of AI technology.
2. This study only covered university libraries; further study could
be conducted with special and public libraries regarding the
adoption of AI technologies.
3. The proposed model could also be applied to other latest
technologies, such as blockchain and cloud computing.
4. A separate study is also suggested for each of the other AI tools,
e.g., Robotics, NLP, and chatbot.

51. 16. References
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(Vol. 4, pp. 1-23). JAI Press.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and
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Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders’ views on
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435. https://doi.org/10.1108/LHT-08-2018-0105
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
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54. Thank You