Research Article
Organizational Readiness to Adopt
Artificial Intelligence in the Library and Information Sector of Pakistan
Saeed Ullah Jan
Assistant Professor
Department of Library and Information
Science
Khushal Khan Khattak
University Karak
Karak, Khyber Pakhtunkhwa,
Pakistan
Email: dr.saeedullah@kkkuk.edu.pk
Muhammad Sajjad Ali Khan
Assistant Professor
Department of Mathematics
Khushal Khan Khattak
University Karak
Karak, Khyber Pakhtunkhwa,
Pakistan
Email: sajjadalimath@yahoo.com
Ali Saeed Khan
Student of BS-Law
Law College
University of Peshawar
Peshawar, Khyber
Pakhtunkhwa, Pakistan
Email: alisaeedkhan2004@gmail.com
Received: 11 Aug. 2023 Accepted: 12 Jan. 2024
2024 Jan, Khan, and Khan. This is an Open
Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly attributed, not used for commercial
purposes, and, if transformed, the resulting work is redistributed under the
same or similar license to this one.
DOI: 10.18438/eblip30408
Abstract
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.
Introduction
Artificial
intelligence impacts a wide range of industries. Recent studies have explored
AI applications in such areas as information systems, marketing, and
exhibitions (Collins et al., 2021; Davenport et al., 2020; Hradecky et al.,
2022). The library sector is also embracing this trend, as it recognizes the
potential of AI in revolutionizing library services and management (Harisanty
et al., in press).
The goal of this
study is to determine whether organizations in Pakistan are ready to utilize AI
in the library sector. This study aims to understand the existing state of AI
adoption from the viewpoint of library managers and administrators and investigate
important elements on organizational readiness to use AI. Due to its
exploratory nature, this study utilized a qualitative research design. It
incorporated the technology-organization-environment (TOE) framework and the
Technology Readiness Index (TRI), both of which have been previously employed
in quantitative research concerning technology adoption readiness (Dewi et al.,
2018; Hradecky et al., 2022). This study represents a novel application of
these frameworks by employing qualitative methods and focusing specifically on
libraries.
This exploratory
research study aims to make three distinct contributions to the existing
literature. Firstly, it addresses the existing gap in sector-specific research
concerning the readiness of organizations in the library sector to adopt AI.
Remarkably, there has been a significant lack of attention given to
decision-makers' and managers' perspectives on AI adoption within the broader
library field in Pakistan. The appropriateness of employing an exploratory
approach in this study allows for theoretical exploration within an empirical
context (Elman et al., 2020). This is especially relevant given the early stage
of AI research in the social sciences overall, as well as in the specific
domains of business and library management research. Secondly, this study,
which focuses on organizational preparedness, intends to fill a gap in the
literature by examining decision-makers' assessments of AI and their desire to
adopt it. Finally, the study seeks to qualitatively investigate the synthesis
of the TOE and TRI frameworks. Through this empirical exploration, the study
aims to enhance our understanding of organizational readiness for adopting new
technologies by emphasizing the intricate and context-dependent nature of this
phenomenon. By focusing on the situated and contextual complexities, the study
aims to advance our knowledge in this area.
The projected
outcomes of this study are expected to contribute to the development of best
practices for effectively preparing librarians for adopting AI in library and information centres. This study seeks to elicit
the key elements that influence policy makers and librarians regarding the
adoption of AI technologies in libraries of Pakistan.
Literature Review
Artificial Intelligence in Librarianship
The term
"artificial intelligence" was introduced to replace the analogue
concept of "cybernetics." George Boole, Allen Newell, and Herbert
Simon were early innovators of AI as an experimental endeavor (Kumar, 2004, as
cited in Mogali, 2014). According to Asemi and Asemi (2018), the idea of
implementing AI systems in libraries originated in 1990; these intelligent
library systems provide knowledge-based services to both library staff and
patrons. The exploration of AI in libraries by library and information
scientists, as well as academics, has been a long-standing pursuit. The
American Library Association's Center for the Future of Libraries recognizes
the significance of emerging AI for library systems (Andrews et al., 2021).
Now, training programs designed to equip librarians with the skills necessary
for working with AI and related technologies are needed, coupled with a
thorough examination of the evolving role of librarians in this new era (Yoon
et al., 2022).
With the ability
to access services anytime, anywhere, libraries are transitioning their
services to AI. For libraries, AI opens up a wide
range of intriguing options that could lower technology costs and improve
performance and capacity for various types of automation activities (Tella, in
press). The implementation of AI in library systems encompasses a wide range of
areas, such as descriptive cataloging, subject indexing, reference services,
technical services, shelf reading, collection development, and information
retrieval systems, among others. These applications have advanced beyond
natural language processing (NLP) and knowledge-based services (Omame &
Alex-Nmecha, 2020). There is significant untapped potential for enhancing
existing information systems through the integration of AI technologies. Recent
research efforts have primarily concentrated on enhancing the technology
related to book gripping, precise localization, and enhancing human-robot
interaction in the context of librarian robots (Asemi et al., 2021).
There are many
new technical innovations in library settings that are still in the research
and development stages, but there are also cases where these developing
technologies have already been used and their effects seen. AI tools will
likely have a great impact on librarianship (Cox, 2023). Librarians must be
ready to design, implement, and assess improvements that expand services to
users as new technologies develop (Andrews et al., 2021). AI and other emerging
technologies have the potential to impact the accessibility of library
resources, enhance document delivery services, and improve user experiences
(Bolt, 2014; Collins et al., 2021). For example, the application of AI and big
data analytics enables efficient collection management and facilitates
data-driven marketing decisions (Crawford & Syme, 2018). ChatGPT, as an
AI-based tool, possesses significant potential to drive advancements in
academia and librarianship, ushering in both potentially challenging and
exhilarating new avenues (Lund & Wang, 2023). AI is also used in online
reference assistance (Vijayakumar & Sheshadri, 2019) and as an online
chatbot (Andrews et al., 2021). An AI-based smart and intelligent library
system is perceived to offer several benefits, including privacy protection,
knowledge creation and dissemination, inclusivity, the development of a
seamless interface between library users and AI-generated information,
promotion of social justice, and facilitation of machine-based scholarly
communications (Bourg, 2017). Mogali (2014) notes that the use of AI in
libraries is becoming more widespread and useful in areas including collection
development, automation, and technical document processing; it is anticipated
that effective AI-based library expert systems will considerably aid library
and information science (LIS) professionals in the near
future.
Considerable
focus has been placed on conceptualizing the notion of a smart library, leading
scientists and professionals to design systems that
can think and make decisions in place of human librarians (Cox et al., 2019).
Chatbots—referred to as digital assistants, virtual agents, or intelligent
agents—are software applications capable of mimicking intelligent dialogue.
They can engage in conversations using text, speech, or even embody a physical
representation (Asemi & Asemi, 2018). An endeavor is underway to create an
Innovative Conceptual Framework for Artificial Intelligence Library Services
(AI-LSICF), which involves integrating AI applications and functionalities into
the elements of the digital transformation framework (Okunlaya et al., 2022).
The practical uses of AI in different areas of libraries, including cataloguing
and classification of documents, collection development, documentation, and
automation of library material, continue to advance (Mogali, 2014). It is
anticipated that LIS professionals and administrators will consider the
adoption of AI in Pakistan and greatly benefit from the implementation of
efficient AI-based library systems in the near future.
Technology-Organization-Environment (TOE) Framework and the Technology Readiness Index (TRI)
To evaluate the
readiness of librarians and administrators to adopt AI, we developed a
theoretical framework that integrates the technology-organization-environment
(TOE) framework and the Technology Readiness Index (TRI). The TOE framework was
created to describe the elements that influence organizational decision-making
around the use of technology (Tornatzky & Fleischer, 1990). To examine
organizational adoption and implementation of technological innovations, the
framework combines technological, organizational, and environmental elements.
The TOE framework has undergone extensive theoretical and empirical
examination, making it widely utilized in various sectors including information
technology (IT), manufacturing, healthcare, hospitality, and financial services
(Aboelmaged, 2014; Wang et al., 2016).
The
technological dimension investigates the internal and external equipment,
processes, and practices within the organization. The technology
infrastructure, which significantly influences technology adoption by reducing
costs, comprises hardware, software, and networking technologies within the
existing environment (Bhattacherjee & Hikmet, 2008). The organizational
component focuses on the company's size, structure, communication, and
decision-making, along with its assets (Aboelmaged, 2014). Top management
support involves leaders who possess the authority to make critical decisions
and create a positive environment that fosters innovation (Chaubey & Sahoo,
2021). The environmental dimension relates to the external business
environment, encompassing factors such as competitors, suppliers, customers,
and regulatory subjects (Oliveira & Martins, 2011).
The rationale
behind using the TOE framework instead of established technology acceptance
theories like the Unified Theory of Acceptance and Use of Technology (UTAUT) or
the Technology Acceptance Model (TAM) is based on the findings of previous
studies (Hradecky et al., 2022). Gholami et al. (2018) found that the
integrated model of TAM and TOE is a promising approach for recognizing the key
factors that influence the adoption of mobile technology in a library setting.
Singeh et al. (2020) used the TOE model in assessing factors that contribute to
the successful implementation of digital libraries. The TOE model was also
employed to evaluate the readiness of Malaysian libraries for big data
analytics (Salman et al., 2022).
One notable
reason for selecting the TOE framework over other theories and models is its
specific emphasis on technological acceptance within the context of the
academic library in an organization, aligning well with the organizational
focus of this study. This sets it apart from alternative theories and models.
Second, in addition to considering technology adoption, the TOE framework also
incorporates the dimensions of organization and environment. These dimensions
encompass the organization's financial and physical resources, services, and
the greater academic environment. Since this study concentrates on assessing an
organization's readiness to adopt AI in libraries, examining the three
dimensions of TOE offers a comprehensive understanding of the various factors
influencing a library's inclination towards AI utilization.
Parasuraman
(2000) first introduced the Technology Readiness Index (TRI), which measures
people's propensity to embrace and use new technologies for accomplishing goals
in home, in life, and at work. The TRI has found application
in various industries, such as e-tailing, e-banking, and e-services (Wiese
& Humbani, 2020). The TRI measures decision-makers' conscious willingness
and mental state at the time they make decisions (Jiang & Johnston, 2010).
In contrast to other theories that primarily focus on general perceptions or
usage, our approach places specific emphasis on the four dimensions of the TRI:
optimism, innovativeness, discomfort, and insecurity. By utilizing these
dimensions, we establish a framework to analyze the drivers and barriers that
influence the mindset of decision-makers when it comes to adopting AI (Hradecky
et al., 2022). The combination of the TOE and TRI frameworks offers a robust
model to explore how the availability of AI artifacts, organizational
resources, and external factors collectively serve as influential elements that
impact and potentially constrain an organization's decision-making process in
relation to AI adoption in the academic libraries of Pakistan.
The TOE model
was selected for its strong track record of reliability and validity, which has
been documented in previous studies (Kalema & Mokgadi, 2017; Motau &
Kalema, 2016). Strengths of the TOE framework include
its comprehensive perspective, customization, and interdisciplinary approach;
its weaknesses are its complexity, limited focus on human factors, and static
nature. Positive aspects of the TRI are its global benchmarking and simplicity,
whereas simplistic metrics, limited scope, influence of data quality (i.e.,
poor data quality can lead to inaccurate conclusions and unreliable results),
and static measurement (i.e., the evaluation or measurement being performed on
the TOE is done at a fixed point in time, without considering dynamic changes
or variations over time) are its weaknesses. In the library context, Salman et
al. (2022) divided the TOE framework into four distinct domains: Library
Technology Capabilities, Library Characteristics, Library Environment
Characteristics, and Big Data Analytics Readiness. Within the Library
Technology Capabilities domain, subcategories are Information Communication
Technology (ICT) infrastructure, security, reliability, and data scalability.
The Library Characteristics domain comprises five variables, including
management support, magnitude, budgeting, strategies, and talent. Operational
acceptance and cultural variables serve as key metrics for assessing Library
Environment Characteristics. The significance of this framework can also be
understood by examining earlier research (e.g., Aboelmaged et al., in press;
Baharuddin et al., 2018; Chen et al., 2018; Gitonga et al., 2023; Lengoatha
& Seymour, 2020; Mustafa & Noorhidawati, 2020; Singeh et al., 2020;
Sözüer & Pınar, 2016; Yakubu et al., 2023).
Methods
This exploratory
study, which aimed to collect data from library managers and administrators
about the organizational readiness to adopt artificial intelligence in the
library and information sector of Pakistan, was suited for qualitative
research. Qualitative research in library systems holds significance as it
enables the capture of detailed insights into how things work at a specific
level of granularity. It allows researchers to represent the complexities of
real-world situations that cannot be easily reproduced, thereby reinforcing its
seminal position in the field (Wynn & Hult, 2019).
Due to the
widespread nature of the target population, all interviews in this study were
conducted online. Online interviews offer several advantages, particularly in
overcoming geographical distance, time constraints, and the associated costs of
traveling for in-person interviews (Mann & Stewart, 2000). Conducting
interviews online eliminates the need for physical presence, allowing for
greater flexibility and convenience for both the researcher and the
participants. It also facilitates the inclusion of participants from diverse
locations, making it easier to reach a broader and more representative sample.
In this study, a purposive sampling technique was employed. The participants
selected for the study held senior management positions in academic libraries
of Pakistan. Participants with seniority were selected to enhance the
trustworthiness of the findings. These participants were chosen based on their
substantial and comparable experience in library management, which ensured that
the insights gathered would be reliable and meaningful. By focusing on
participants with senior management positions, the study aimed to capture
valuable perspectives and insights from individuals who have a deep
understanding of library operations and can provide relevant and informed
insights into the research topic.
The size of an
organization plays a crucial role in technology adoption. Larger organizations
tend to have greater adoption potential, as they possess more resources,
skills, and experience to support the implementation and utilization of new
technologies. Their size often provides them with a competitive advantage in
terms of financial capabilities, infrastructure, and human resources, which can
facilitate successful technology adoption. Moreover, larger organizations are
generally more resilient and better equipped to handle potential challenges or
failures that may arise during the adoption process (Matta et al., 2012).
Consequently,
all the participants selected from library and
information centres in this study represent organizations that have a history
of more than 25 years and possess large libraries. Marshall et al. (2013)
recommends that qualitative case studies have sample sizes ranging from 15–30
participants, and contemporary qualitative research (Hoopes et al., 2016;
Vasileiou et al., 2018) on the adoption of technology in contexts related to
the service sector confirm this figure. Some studies have used much smaller
sample sizes, but few fall outside this range (Eze et al., 2019; Soares et al.,
2021; Sox et al., 2014). The sample for this study consisted of 27 interviews
including 17 senior library managers from academic libraries and 10
administrators at universities across the country (Table 1).
Table 1
Details about
Participants
|
Participant |
Organization
Type |
Role |
Geographic
Zone |
|
(P1) |
Public
Sector University |
Chief
Librarian |
Islamabad |
|
(P2) |
Private
Sector University |
Library
Director |
Islamabad |
|
(P3) |
Public
Sector University |
Chief
Librarian |
Punjab |
|
(P4) |
Public
Sector University |
Librarian |
Punjab |
|
(P5) |
Public
Sector University |
Library
Manager |
Punjab |
|
(P6) |
Private
Sector University |
Information
Executive |
Punjab |
|
(P7) |
Medical
College |
Senior
Librarian |
Sindh |
|
(P8) |
Public
Sector University |
Chief
Librarian |
Sindh |
|
(P9) |
Engineering
University |
Librarian |
Sindh |
|
(P10) |
Private
University |
Director
of Information |
Sindh |
|
(P11) |
Agricultural
University |
Librarian |
Sindh |
|
(P12) |
Agricultural
University |
Chief
Librarian |
Khyber
Pakhtunkhwa |
|
(P13) |
Public
Sector University |
Librarian |
Khyber
Pakhtunkhwa |
|
(P14) |
Engineering
University |
Librarian |
Khyber
Pakhtunkhwa |
|
(P15) |
Private
University |
Library
Manager |
Punjab |
|
(P16) |
Public
Sector University |
Chief
Librarian |
Balochistan |
|
(P17) |
Public
Sector University |
Library
Manager |
Azad
& Jammu Kashmir |
|
(P18) |
Public
Sector University |
Registrar |
Islamabad |
|
(P19) |
Public
Sector University |
Registrar |
Punjab |
|
(P20) |
Private
Sector University |
Registrar |
Punjab |
|
(P21) |
Engineering
University |
Registrar |
Sindh |
|
(P22) |
Medical
University |
Registrar |
Sindh |
|
(P23) |
Institute
of Management Sciences |
Registrar |
Khyber
Pakhtunkhwa |
|
(P24) |
Public
Sector University |
Registrar |
Khyber
Pakhtunkhwa |
|
(P25) |
Public
Sector University |
Registrar |
Balochistan |
|
(P26) |
Public
Sector University |
Registrar |
Azad
& Jammu Kashmir |
|
(P27) |
Private
Sector University |
Registrar |
Islamabad |
After
transcribing the interviews, we conducted a reflexive thematic analysis as
outlined in Braun and Clarke (2021). During the familiarization stage, the
transcripts were scrutinized with the research objectives in mind, aiming to
pinpoint any data gaps or limitations while also identifying emerging patterns
within the data (Lewis et al., 2018). We implemented two rounds of coding. In
the initial round, a blend of provisional and open coding techniques was
utilized. The provisional coding process was guided by the theoretical
framework, utilizing essential terms and concepts as a foundational reference
point (Miles & Huberman, 1994). Subsequently, a phase of manual open coding
was undertaken to enable the emergence of additional codes through an inductive
process. In the second round of coding, we employed axial coding following the
strategic data assembly approach, as outlined by Thornberg and Charmaz (2014).
Our aim was to uncover connections between the codes to create thematic
elements for analysis. During this coding phase, our primary focus was on
synthesizing the relationship between the TRI and TOE frameworks, with a
specific emphasis on investigating the four readiness elements of the TRI
across the three dimensions of the TOE framework. Following two rounds of
coding, the themes were systematically arranged within the three dimensions of
the TOE model. These organized themes serve as the structural foundation of our
findings.
Results
In response to
the query "What do you think of when I mention artificial intelligence in
the library context?" the respondents were of the view that the
development of computer systems or computers that think, behave, and actually challenge human intelligence is the ultimate
promise of AI in libraries, and this clearly has significant consequences for
librarianship.
Technological Parameters
Connectivity
Internet
connection speed is the basic tool required for AI adoption and implementation
(Brown, 2021; Collins et al., 2021). The availability of 5G internet connection
provides an opportunity for AI adoption in libraries. Participants (P6) from
Punjab, (P18) from Islamabad, and (P23) from Khyber Pakhtunkhwa are of the view
that, "Our libraries will implement 5G internet connections as and when
Government launch it." A majority of the
respondents have claimed 4G connections are sufficient for the smooth
implementation of modern technologies in library and
information centres. They are of the view, "We are now one step closer to
implementing AI and the Internet of Things and other innovative technologies.
Thus, having a solid IT infrastructure is crucial." The adoption of AI is
closely coupled to other components of the Internet of Things (IoT) ecosystem
and is highly dependent on the technological capabilities that support
autonomous networks (Bello & Zeadally, 2019).
Respondents were
well aware of the importance of technical support by
the organization for adoption of AI in libraries (Hradecky et al., 2022). Seven
organizations were using and creating networking systems at the same time.
Participant (P27) was making investments in their system, while Participant
(P18)'s organization opted to use a contractor company for the same purposes.
Participant (P12)'s university initiated a significant project five years ago
to revamp their facility management. Additionally, Participant (P22) reported
having a high-speed internet connection four years ago. The respondents from
these various libraries are highly enthusiastic about the implementation of AI
in their respective institutions.
Lack of Awareness About AI and Discomforts
Organizational adoption of AI is also impacted by
technological practices and a lack of knowledge. Many participants felt
overawed by the prospect of adopting AI and lacked clarity regarding how it
functions and how, specifically, it would benefit their library systems. For
instance, Participant (P15) made the following observation:
“As a manager,
you are unsure about just where to apply AI technology. You obviously
understand how the library system works. It is challenging to match the AI
technology on your "unique" library object, though. I don't fully
understand all the things that technology can accomplish for me as a library
manager. Library managers are open to AI, but they are not yet able to fully
use its potential. Additionally, it causes other and financial insecurities.”
The discomfort is caused by a lack of confidence in
the deployment of AI and numerous mistakes in AI pilot tests (Parasuraman,
2000). Participants agreed that there is a need for training programs to
improve employees' ability to use AI efficiently in order to
deal with the discomfort of AI adoption.
Organizational Dimensions
Perceptions about AI
This study
demonstrates that library professionals are generally optimistic and curious
about the adoption and use of AI. The majority of our
participants have an intention of using AI in their libraries. Most
participants concurred that AI will be gradually adopted in the library and is
expected to be useful. According to Participant (P16), a chief librarian, AI
can have a significant impact on how libraries and library managers are
perceived: "AI not only surpasses its designated scope by performing all intended
functions, but also has a positive external impact on promoting the library or
library managers." Respondents also expressed agreement that AI technology
will enhance visitor experiences, expedite decision-making processes, reduce
costs, and replace time-consuming tasks (Davenport et al., 2020; Makridakis,
2017).
Organizational Size and Financial Resources
Participants
stated that their readiness for adopting AI is significantly influenced by
organizational size and financial resources. The majority of
participants believed that larger universities would adopt new technologies,
including AI, sooner and with greater financial resources. Participants from
larger and established universities intend to adopt and create their own AI
platforms instead of utilizing contractors. On the other hand, individuals from
smaller and more newly established universities also show a strong desire to
incorporate modern technology, particularly AI, if required by human resources
and as funding permits. In terms of adopting AI, Participant (P13), a librarian
from a public sector university in Khyber Pakhtunkhwa, assessed the advantages
and disadvantages of small and large university libraries:
“Smaller institutions have an edge in agility, faster responsiveness,
and more risk taking; on the other side, they lack cash. Larger players have
resources in both money and people. Not usually, but at least in terms of
numbers, very slow decision-making. As a result, both sides have advantages and
drawbacks. However, I do believe there is a chance for new players to obtain a
competitive edge.”
Strategic Plan of the Organization
One of the main
barriers to the adoption of AI in university libraries, according to our
participants, is the lack of vision, planning, and proactivity of the
university and library administrators. According to many participants in our
interviews, the library and information sector as a whole is
not very progressive in embracing new technologies. Participant (P5) stated:
“Established IT departments would be beneficial to organizations.
According to my observations, this is not a typical practice. IT departments
frequently lack the time and resources necessary for the development and
deployment of new technologies due to their overwhelming digital commitments.”
Senior
management has the authority to decide whether to adopt new technology and
foster an environment that is conducive to innovation (Premkumar & Roberts,
1999). Most top management may experience insecurity towards the adoption of AI
due to the possible harm and disruption that AI could cause to current
organizational structures and activities (Parasuraman & Colby, 2015).
Although their library has a three-year business plan, Participant (P9)
revealed that AI is not included in this strategy. The existing organizational
culture "is not ready to drive digital business models," according to
Participant (P18), a registrar.
According to our
investigation, some universities are planning to create adoption strategies
after seeing the potential of AI. As part of a five-year AI strategy,
Participant (P8), a chief librarian, is focusing on creating an AI-powered
marketplace and migrating their library catalogue to directories; in 2022, a
sizeable amount of funds was proposed for this initiative. Participant (P21), a
registrar, plans to recruit a futurist to help them create their strategic plan
for 2025, and they identified AI as one of the industry's disruptors and
game-changers. Participant (P20), a registrar from a public sector university
in Punjab, was of the view that they have been preparing themselves for the
adoption since 2016:
“We started with cleansing our vast amount of data. Our company bought a
data management system, which we then connected with our current management
system. As a result, we can help recommend services that our library customers
need from us more accurately.”
From a financial
standpoint, AI is primarily viewed as a tool for cost reduction (Davenport et
al., 2020). Participant (P25), a registrar from a university in Balochistan,
lamented that, "The top management lacks the know-how to effectively
implement AI in practical situations," but he still was hopeful that the
IT department could cooperate with and persuade the management board.
Participant (P26), a registrar, was unable to envisage AI as a tool for
cost-cutting in the next few years.
Environmental Dimensions
Data Management and Privacy
University libraries are interconnected with
university management and policymakers. The significant data demands of AI
systems make it imperative for lawmakers to be involved, prioritizing the
protection of their constituents. Moreover, the university's administrators
possess the authority to endorse and facilitate the development of innovative
and emerging technologies (Hradecky et al., 2022). Participant (P21) responded
that, "Examining the matter from a different perspective, university
authorities pursue their distinct agendas. Consequently, if the adoption of new
technology aligns with their objectives, they are more inclined to allocate
funding and support the proposal's implementation." The challenges of data
management and privacy concerns in the context of library management and
operations were known to our participants. When considering privacy concerns
from the viewpoint of library users, Participant (P17), a library manager,
expressed unease about the current level of AI readiness. He emphasized that
library patrons might be hesitant to embrace facial recognition technology.
Moreover, the organization would need to employ a specialist to ensure secure
data management after its use and transactions. Participant (P14) positively
narrated his point of view:
“We concentrate
on data collection as it is what we need to do. Our library maintains a
database of its users, and based on their actions, we can suggest networking
opportunities to them, for example via push notifications. AI algorithms
possess remarkable capabilities with such data. They can rapidly analyze,
evaluate, and predict users' desires, needs, and future actions.”
COVID-19 as a Catalyst for Profound Transformation
The library
sector has been heavily impacted by COVID-19 as an environmental concern
(Hradecky et al., 2022). Participant (P19) is of the view that participants saw
the epidemic as a force of transformation to adopt AI despite its destructive
effects: "The pandemic's effects have demonstrated the necessity of going
more digital and developing fresh strategies for making library contents
accessible to users." To enhance socially remote working practices,
participants have recognized the pressing need for digitization and the
upskilling of employees in digital competencies. As mentioned by Participant
(P14), their organization recently embraced new technology to align with the
changes in the library landscape brought about by COVID-19. Significant
investments were made in virtual meetings, virtual reality, and augmented
reality.
Discussion
Theoretical Implications
The theoretical synthesis (Figure 1) of the
technology-organization-environment (TOE) framework and the Technology
Readiness Index (TRI) used in this research has shed light on the context for
AI adoption in the library sector. This theoretical lens offers a novel
perspective for understanding the application of AI in this domain. While the
TOE framework has been widely used (Aboelmaged, 2014; Hradecky et al., 2022;
Oliveira & Martins, 2011; Y. Wang et al., 2008; Y.-S. Wang et al., 2016;
Zhao et al., 2016), this study is an addition to the existing theory in the
context of Pakistani libraries. The model used in the current study offers a
framework applicable to diverse service industries, where interactions between
internal and external TOE components are similar. It is crucial for influential
decision-makers to consider the implementation of AI in customer-facing
businesses. The model efficiently demonstrates how TOE dimensions affect TRI
factors, which have been thoroughly investigated by numerous research projects
(Aboelmaged, M. G., 2014; Ahmad, H. et al., 2020).
The three TOE elements determine the readiness for AI
adoptions as well as the decision-making process for adopting AI in libraries
of Pakistan. On one hand, certain factors present
specific challenges that result in discomfort (e.g., lack of confidence in new
technology) and insecurity (e.g., data management concerns). On the other hand,
there are factors that foster innovation and optimism (e.g., facilities with
improved connectivity and perceived AI benefits). Conversely, some factors act
as double-edged swords (e.g., library size and COVID-19 impacts),
simultaneously deterring and encouraging AI adoption in libraries.
We suggest three potential directions for future
research, accompanied by corresponding propositions that can guide these
investigations. The first avenue involves recognizing the significant influence
of the IoT ecosystem's affordances on the effectiveness of AI functionality.
The potential for incorporating AI into routine jobs
has considerably increased due to advancements in high-speed internet
infrastructure, complex network systems, and emerging technologies. It is
crucial to look into how these workflow integrations
might function as drivers of AI adoption. Since AI implementation is relatively
new in the library sector, the level of confidence and familiarity with
emerging technological practices has a significant impact on librarians'
feelings of readiness for adoption. Our research indicates that most adoptions
are still in their initial 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.
In future research, it would be beneficial to conduct
a comparative study between the positive perceptions of AI and the actual
experiences of AI implementations. Special attention should be given to areas
such as AI competence and the existing infrastructure. Such an investigation
would provide valuable insights into the factors that contribute to favorable
outcomes of AI adoption readiness.
Practical Implications
For library
managers and administrators in Pakistani universities, as well as those working
in large library systems, the findings from this study are quite relevant.
Universities can use the method employed in this study to evaluate and
strengthen their adoption of AI. The TRI elements in the model represent the
psychological factors influencing personnel, while each TOE element creates
concrete linkages in the operation of the library. Additionally, the model may
be used holistically by library professionals to assess whether their
facilities are prepared to adopt AI-powered technologies into their services.
The lack of a
concrete intention to adopt AI in Pakistani libraries and information centres
is a significant practical implication of this study. Libraries in the
universities of Pakistan should either create specific AI adoption strategies
or include these factors in their overall strategic planning for two reasons.
Firstly, when compared to other easy-to-deploy technologies, AI requires
significantly more attention and resources due to its higher level of
complexity (Lokuge et al., 2019). Despite library and information centres'
experience in incorporating previous technological advancements into their
systems and practices, this study highlights the complex interaction of
technological and non-technological elements. Consequently, it underscores the
necessity of employing a strategic approach to effectively tackle these
challenges when implementing AI. Secondly, AI technology operates within a
complex regulatory space, involving multiple stakeholders with interests in its
regulation and application. To navigate this space effectively, university
library systems must adopt a strategic approach that takes
into account the perspectives of these stakeholders. Effectively
handling intricate stakeholder relationships can prove challenging,
particularly due to the demands of AI's nature and regulations, which
necessitate establishing connections between library professionals and
administrators. To manage the networks of internal and external stakeholders
essential for a successful AI adoption, organizations should consider
appointing individuals with a specialized knowledge of AI within the context of
libraries, especially if they possess sufficient resources.
Limitations and Future Research Directions
Certain limitations have been found during
the course of this study. First, conducting virtual interviews can
present challenges in capturing nuanced data from posture, body language,
gestures, facial expressions, and voice tone. Second, due to the relatively
recent adoption of AI in libraries, participants' responses tended to be
abstract and lacked concrete evidence. Additional research should be conducted
on the readiness of other organizations for the adoption of AI in their
respective libraries. More settings can benefit from the applications of the
TOE (technology-organization-environment) framework and the TRI (Technology
Readiness Index) scale. Research should examine the decision-making process for
AI adoption within various organizations. Further research on the readiness of
libraries concerning AI adoption should be conducted in different geographical
contexts or within smaller universities of Pakistan.
Conclusion
The primary
objective of this study was to assess the organizational preparedness for AI
adoption in university libraries across Pakistan. The findings indicate that
while most participants have not encountered AI in their institutions, their
comprehension of its application remains limited and cautious. These results
align with previous studies, suggesting that technology adoption, especially in
the context of AI, is relatively recent and progressing slowly in this area.
The progress of digitalization in university libraries is significantly
delayed, and their technology infrastructure is not well suited for adoption of
AI in libraries. Although AI has the potential to increase productivity, cut
costs, and improve user experiences, most organizations don't have a clear plan
on how to put it into practice. This is surprising, considering the recent
surge in technology adoption within the libraries, largely driven by the
COVID-19 pandemic. Additionally, we found that the degree to which technical
practices are trusted, available funding, university size, and data management
and security concerns might either encourage or hinder the preparedness of AI
adoption in university libraries.
This study has
made significant contributions to the existing literature by addressing the
lack of library-related research on AI adoption preparedness in libraries,
particularly in the context of university libraries. By bridging this gap in
the existing knowledge, this research has also generated valuable insights
applicable to other parts of the library sector with similar topography.
Additionally, this model can be used to assess the organization preparedness
for AI adoption in different library settings. This research adds to the
limited body of knowledge on library-level AI adoption by exploring how
decision-makers assess various factors and their willingness to adopt AI.
Lastly, through an empirical investigation of the integration of the TOE framework
and the TRI scale, this study highlights the intricate and context-dependent
nature of understanding organizational readiness for adopting new technologies.
Authors Contributions
Saeed
Ullah Jan: Conceptualization, Methodology, Writing – original
draft, Formal analysis Muhammad Sajjad Ali Khan: Visualization,
Interview-Investigation, Formal analysis Ali Saeed Khan: Validation,
Literature search, Writing – review & editing
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Appendix
Interview Questions
Section 1
Introduction to
the research study; confirmation of participant information and consent.
General
familiarization questions.
Section 2
1.
What do you think of when I mention
artificial intelligence in the library context?
2.
How do you perceive artificial
intelligence in general?
3.
Thinking about adopting artificial
intelligence: What are the benefits that you or your organization/university
expect to gain by adopting AI?
4.
What other technological innovations,
excluding AI, has your library adopted in recent years? (Hardware, software,
Wi-Fi connection, or similar.)
5.
Do you consider your organization
sufficiently technologically developed for adopting AI?
6.
Where do you see the advantage or the
disadvantage of your university's size for adopting artificial intelligence?
Are you too small to adopt AI? Or would the AI
adoption be easier if you were a larger entity?
7.
What kind of influence does senior
management in your university have over artificial intelligence adoption?
8.
How do government policy and regulation
influence your university's decision-making in adopting artificial
intelligence?
9.
What other external influences are there
on adopting AI in the library of your university?
10.
How could AI support employees and
increase their productivity within the organization you represent?
11.
Have you established any training
program for your employees to improve their skills before adopting AI? If not,
are you planning to do so?
12.
Where do you see challenges in adopting
AI for your employees?
13.
What are the negative impacts that artificial
intelligence could cause in your library?
14.
Do you perceive any risks to your
organization if your organization adopts AI?
15.
Does your university have a strategy, or
strategic perspective, for adopting artificial intelligence in the upcoming
five years?
16.
How, in your opinion, could AI impact
your library services in the upcoming five years?
17.
How does your library possess financial,
human, and technological resources for adoption of AI?