Perceptions of University Digital Libraries as information source by international postgraduate student

Keywords: University Digital Libraries UDL, Google Scholar, individual differences, system features, technology adoption, technology acceptance, UTAUT, Wilson’s model, information seeki

Research Background

Introduction

Over the past decades, the evolution of information and communication technologies (ICT) has made ICT an integral part of both traditional and distance education (Hrtoňová, Kohout, Rohlíková, & Zounek, 2015; Nirban & Chasul, 2014) This development has been accompanied by a rise in the use of digital media in education, prompting educational institutions to prioritize the delivery of effective Web-based services that meet learners’ knowledge and education needs (Arif, Ameen, & Rafiq, 2018).

In other words, the notion of the electronic (e-library) or digital library which is a library that is

Digital learning is ubiquitous and accessible anytime and anywhere, allowing users to access it over the internet via their personal computers, mobile computers, and mobile devices This broad, device-inclusive access has made online learning an integral part of the educational context (Hwee & Yew, 2018, p.75).

E-library systems have become popular and offer convenient access to scholastic and research resources during the academic existence of an individual as student (Hwee & Yew, 2018) An e- library has also been described as an accumulation of information and services that facilitate the management of information objects which can be directly or indirectly accessed by end users through electronic or digital devices (Che Rusuli, Tasmin, Takala, & Norazlin, 2013; Miller & Khera, 2010; Ramayah, 2006) Similar to its traditional counterpart, the functions of an e-library encompass searching, locating and copying, requisitioning and obtaining in the context of e-books and e-journals (Park, Roman, Lee, & Chung, 2009; Sheeja, 2010) Significant advantages over conventional libraries include the ease with which digital resources can be monitored, the speed and unbiased access to library collections, and the provision for users to utilise search engines to locate required resources (Hwee & Yew, 2018; Thong, Hong, & Tam, 2002)

Alongside the development of university e-library services, the academic search engine Google Scholar (GS) emerged in 2004 and has since expanded rapidly in both size and popularity According to Cothran (2011), the use of federated and Google search tools by higher education students like GS is a prevailing topic in the academic library literature GS is widely used by academics (Ollé & Borrego, 2010) and by students (Cothran, 2011) alike By 2014, the GS database contained approximately 160 million documents.

Google Scholar has grown substantially, reaching almost 390 million records by 2019 (Gusenbauer, 2019; Martín-Martín & López-Cózar, 2015) Compared with the Web of Science Core Collection (WoSCC) and Scopus, Google Scholar is recognized for its wider coverage, inclusion of a broader range of languages and publication types, faster growth, and the retrieval of more citations (de Winter, Zadpoor & Dodou, 2014; Harzing, 2013; Meho & Yang, 2007; Orduña-Malea & López-Cózar, 2014).

Information behavior comprises the activities people undertake to identify their information needs, search for information, and use or transfer that information The information need is an anomalous state of knowledge, signaling that a person’s current understanding is insufficient to achieve a goal This need arises from an acknowledged variance in the user’s knowledge about a topic or situation, and, in general, the user cannot precisely specify what is required to resolve that gap.

While 1982 (p.62) hints at one view, Dervin (1983) reframes an information need as a course of sense-making that shapes a person’s personal perspective, and Wilson (1997) emphasizes that this need is a private, experiential phenomenon not evident to observers, arising only in the thinking of the individual in need.

Three kinds of motives can drive information needs: physiological (such as thirst and hunger), unlearned (such as sensory stimulation and curiosity), and social (such as the longing for affiliation, support or status, or hostility), as Morgan and King (1971) argued; these motives correspond to Wilson’s (1981) framing of needs as cognitive, affective, or physiological.

1997, p.553) Consequently, it would appear that the behaviour undertaken to satisfy the need for information is associated with an underlying motive Another perspective is provided by Case

(2012), who highlights that information need is an acknowledgment of the inadequacy of a person’s existing knowledge to fulfil that person’s goal Weijts, Widdershoven, Kok, and Tomlow

(1993) submit that the notion of information need could be broken down into three categories:

“requests for new information, requests for elucidation, and requests for confirmation” (p.403)

That is, information need encompasses a requirement for fresh information, and the necessity to interpret and verify existing information

A natural consequence of the information need is behaviour related to seeking information which, according to Wilson (2000), is:

Information seeking is purposive, driven by a need to satisfy a specific goal In pursuing information, individuals may interact with traditional, manual information systems such as newspapers or libraries, or with computer-based systems like the World Wide Web.

Case (2012) defines information seeking as a deliberate effort to obtain information that satisfies a need or bridges a knowledge gap Extending this view, Ingwersen and Jørvelin (2005) highlight the role of information sources and information retrieval systems in shaping human information behavior, describing it as the process of searching or seeking information through information sources and (interactive) information retrieval systems (p 21).

Some scholars, such as Sadeh (2010) and Wilson (2000), differentiate between information-seeking and information-searching behaviours, with Wilson defining information-searching behavior as the micro-level activity of a searcher interacting with information systems of all kinds: “information searching behaviour is the ‘micro-level’ of behaviour employed by the searcher in interacting with information systems of all kinds” (p 49) In this interpretation, information-searching behaviour encompasses all dealings with the information system, regardless of the level of interaction—whether it involves direct human–computer engagement or purely intellectual processing—and includes cognitive actions such as assessing the significance of retrieved information or data This distinction highlights that information searching goes beyond navigation to include evaluation and interpretation of results within the broader information-seeking process.

2010 highlighted that information searching behaviour is a facet of information seeking behaviour that specifically addresses active, directed searching in information systems for data that can be specified to some degree (p 20) In this study, we adopt the proposition of Ingwersen and Jørvelin (2005) that searching and seeking are synonymous, using these terms interchangeably to frame our analysis of information retrieval within information systems.

Adoption is the decision by an organization or individual to commit to and implement a new practice or technology Diffusion is the process by which that new practice or technology spreads over time and across people or settings within the organization or among individuals In essence, adoption represents the initial uptake, while diffusion describes the gradual, widespread spread that follows, shaping how quickly and where the innovation takes root.

A distinction has been made between the terms of adoption and diffusion by Kripanont (2007,

Rogers (1983) defines diffusion as the cumulative adoption of a technology through communication among members of a social system over time, transmitted via specific channels, while adoption refers to an individual’s use of a new technology within a defined period The decision to adopt an innovation is an internal cognitive process that begins when an individual becomes aware of the technology and ends with a choice to reject or adopt it The diffusion process, by contrast, unfolds across the components of a social community or nation as information and influence flow among people Kurtenbach and Thompson offer further elaboration on these concepts, clarifying how diffusion and adoption relate within the broader social context.

Technology adoption, as defined in 1999, is the stage where an individual, group, institution, or organization selects a technology for use, recognizes its usefulness for their work, and then integrates it into daily practice This perspective highlights the sequence of deliberate choice, perceived benefits, and active utilization, underscoring that impact comes from adoption as a committed, real-world application rather than mere awareness.

According to Swanson (1994), the adoption of information systems innovation by individuals or organisations can be classified into three main types:

• Innovations that occur within the information systems function (Type I);

• Innovations that occur at the individual user or work group level (Type II); and

• Innovations that occur at the organisational or institutional level (Type III)

Problem Statement

Over recent years, a behavioural shift in students' information needs has emerged, with electronic information increasingly preferred to traditional print formats As a result, university libraries must adapt by delivering services that meet these changing attitudes and information-seeking requirements Advances in information and communication technology (ICT) and easier access to online information repositories are attracting students and altering both the extent and nature of their use of library resources Google Scholar, in particular, has become extremely popular among university students, and multiple studies identify it as the most widely used and user-friendly information-seeking platform (Beckmann & von Wehrden, 2012; de Winter et al., 2014; Mayr & Walter, 2007; Mikki, 2009) Some research also suggests that Google Scholar's popularity among students is drawing them away from their institutional libraries (Dewan, 2012).

Google Scholar's online availability, ease of use, and ability to provide quick access to vast amounts of information are making it increasingly popular among students (Georgas, 2014; Cothran, 2011; van Aalst, 2010) At the same time, academic libraries in the United Kingdom, the United States, and the European Union face budget constraints driven by reduced funding and the rising cost of new technology (Jubb, 2010) Substantial investments are needed to reshape traditional libraries into digital libraries, including digitizing materials, training staff to manage digital resources, and maintaining online services (bandwidth, servers, and related infrastructure) Moreover, ongoing costs for hardware and skilled personnel are incurred as information must be migrated periodically to the most current digital media (Sun & Yuan, 2012).

Despite growing research, many questions remain about whether students—especially postgraduate students—use the University Digital Library (UDL) or online search engines like Google Scholar, how they use these resources, and which factors facilitate their adoption and effective use.

Rationale for the Study

Digital libraries have become the go-to solution for the library needs of students and individuals today, but they face competition from Google Scholar However, little is known about the factors that hinder students from using university digital libraries (UDLs) or why they prefer Google Scholar Rising costs of academic journal subscriptions limit the growth of electronic databases, since a large share of library budgets is devoted to these subscriptions In this context, libraries aim to ensure that these electronic resources are used effectively by students A library’s success depends on meeting user requirements, reflected in improved services and broader access to extensive resources.

Catalano (2013) shows that the information needs of postgraduate and undergraduate students differ considerably, with postgraduate learners requiring more advanced and intricate information Understanding their information-seeking behaviour helps stakeholders—such as librarians, educators, and university administrators—design targeted information services and resources that better support each group’s learning and research goals in higher education.

21 librarians, teaching staff, and supervisors, among others, to influence these behaviours by providing suitable and essential coaching, resources, and facilities

Across several countries, international students comprise a substantial portion of the higher education population, with most pursuing studies outside their home countries (OECD, 2018a) The UK remains the leading European destination for international students and the most popular choice globally after the US (Marginson, 2018) Data from HESA (2019) show that about 24% of UK higher education students in 2016/17 and 2017/18 were enrolled in postgraduate programmes, with international students accounting for roughly 19% of postgraduates (UKCISA, 2019).

This study investigates the factors that facilitate or hinder the use of university e-resources, focusing on how postgraduate international students in the UK use these resources compared with Google Scholar The findings reveal how students perceive the use of their university digital libraries (UDLs) and Google Scholar, providing actionable insights for decision makers responsible for the provision and management of the university e-library The research also informs a central library management challenge: whether to retain or cancel current e-resource subscriptions.

Across information system research, innovations such as digital and electronic library services must first be accepted by end users before they can be utilized When an organization introduces new technology, user acceptance is a prerequisite for actual use (Min & Qu, 2008; Tibenderana & Ogao, 2008b; Zhou, 2008) In addition to understanding how adoption decisions are made, it is crucial to examine how usage and values evolve post-adoption (Zhu et al., 2006) Accordingly, this study empirically investigates and validates the determinants of electronic library service acceptance and use, specifically focusing on international postgraduate students in the context of UDLs in universities in Manchester, and explicitly addressing Type II – individual adoption of technology, since the core dynamics of adoption operate at the individual level.

22 determinant of actual new technology usage is user acceptance of a new technology (Min & Qu,

International students are defined as individuals who either received their prior education in a country other than their current place of study and are not residents of the host country, or who have left their country of origin to relocate to another country specifically for the purpose of study (OECD, 2018a, 2018b).

Research Aim and Objectives

The aim of this study is largely twofold:

(i) To identify factors that affect international postgraduate students’ choice to use Google

Scholar over their University Digital Libraries (UDLs);

(ii) To develop an information driven framework to determine an information search strategy responsive to dynamic end-user (student) preferences in the library

Based on the aims, the following objectives have been drawn:

(i) To examine students’ online search behaviour, with specific reference to their use of Google Scholar and university digital libraries

(ii) To examine international students’ perspectives on the factors that affect their use of Google Scholar

(iii) To examine international students’ perspectives on the factors that affects their use of University Digital Libraries (UDLs)

(iv) To propose and test a conceptual model of the factors that affect international students’ use of Google Scholar as opposed to the University digital library, and vice-versa;

(v) To compare the factors that influence the use of Google Scholar and those that affect the use of University Digital Libraries (UDL)

(vi) To develop an information driven framework that can be used by libraries to determine an information search strategy responsive to dynamic end-user (student) preferences

Research Questions

Keeping in view the aim and objectives of the research, the following research questions have been developed:

This study examines the factors that affect the acceptance and use of University Digital Libraries (UDL) and Google Scholar among international postgraduate students at Manchester universities, and it investigates how effectively a modified UTAUT model evaluates their use of these information resources Specifically, RQ1 asks which factors influence the acceptance and use of UDLs and Google Scholar in Manchester’s universities Sub-question (a) analyzes how effectively the modified UTAUT model explains international postgraduate students’ use of UDLs in Manchester, and sub-question (b) analyzes how effectively the same model explains their use of Google Scholar in Manchester The goal is to understand the explanatory power of the modified UTAUT framework in this context and to inform strategies for improving access to digital libraries and scholarly search tools for international postgraduate students.

RQ2: What are the international postgraduate students’ perceptions of and attitudes towards the

University Digital Libraries (UDLs) and Google Scholar?

RQ3 examines the key factors that influence international postgraduate students’ acceptance and usage of University Digital Libraries (UDLs) and Google Scholar in Manchester’s universities, focusing on how individual differences (such as digital literacy, language proficiency, and motivation) and system features (including usability, interface design, search capabilities, and accessibility) affect engagement with these scholarly resources; specifically, it asks: a) to what extent can individual differences and system features increase the use of UDLs, and b) to what extent can these factors increase the use of Google Scholar?

RQ4: What is the current state of knowledge on student online search behaviour, with specific reference to their use of Google Scholar and university libraries?

The study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) as its primary theoretical framework after evaluating various theories and models related to information seeking and technology adoption to address the research questions To deepen the theoretical lens, Wilson’s (1999) information-seeking behavior model for international postgraduate students is employed as a complementary perspective, developed from a critical review of existing models Further details on the model selection and its grounding can be found in Chapter 2.

Significance of the Study

In the present day, there is a great influx of international students into UK universities in pursuit of higher education However, their previous experiences with information sources in their native

Across 24 countries, individuals’ information-searching behavior could influence their use and potential preference for university digital libraries (UDLs) and Google Scholar in their chosen universities While there are indications that Google Scholar is widely accepted as useful for meeting students’ research requirements within the scholarly community, to the researcher’s knowledge there is limited literature comparing the perceived usefulness of Google Scholar and UDLs (for example, Asher, Duke, & Wilson, 2013; Brophy & Bawden, 2005; Georgas, 2013, 2014, 2015; Wu & Chen).

These findings can illuminate postgraduate students’ perceptions of the relative usefulness of universal design for learning (UDLs) and Google Scholar, enabling institutions to better understand how learners actually use these tools The study will raise awareness about effective use patterns of UDLs and Google Scholar among postgraduate researchers, informing targeted support and training By analyzing the results, the research could offer insights that guide the design of UDLs to promote greater adoption and more efficient information searching, potentially increasing UDL usage over Google Scholar Additionally, it will shed light on the information-seeking behaviour of international students, informing user-centered UDL design that accommodates their specific needs Finally, the findings will help identify the benefits and reasons behind students’ preferences for Google Scholar or a UDL, supporting evidence-based decisions for libraries and institutions.

This study extends an extended UTAUT model to determine and explain the factors that influence the adoption of UDLs and Google Scholar by international postgraduate students It is believed that an empirical study focusing on students’ perspectives regarding UDL adoption will help university decision-makers understand the determinants of student uptake, thereby enhancing the usability and usefulness of UDLs The research principally scrutinizes students’ behavioural intention to use their UDL or Google Scholar Consequently, the UTAUT model will be used for development and assessment only and will not be refined during the course of this study.

Contributions of the Research

This study is expected to contribute to both knowledge and practice by identifying the factors that influence international students' use of information sources and by informing the design of an effective Universal Design for Learning (UDL) framework The anticipated findings aim to illuminate how students discover, evaluate, and engage with information resources and to translate those insights into actionable recommendations for educators and institutions seeking inclusive, accessible learning environments through UDL.

(i) Identify delay in the evolution of tools and techniques for capturing dynamic information needs of the library end-user;

(ii) Identify the ease of use platform for accessing information with limited restrictions;

(iii) Propose a simpler platform that recognises Domain Knowledge, Computer Efficacy and

(iv) Lack of awareness of the powerful search mechanisms available at UDL leading to a parallel typically use with

(v) Use multiple regression analysis (MRA) and the Structured Equation Modelling (SEM) to map the relationships between factors influencing information seekers.

Structure of the Thesis

The thesis is structured as follows:

Chapter 1 (Introduction) provides the context for the research, outlines the central problem and rationale, identifies aims, and articulates the research questions and objectives It introduces the approach used to address the research questions Finally, the intended contribution of this research to the body of knowledge and theory is outlined

Chapter 2 (Literature Review) offers a review of extant literature related to digital libraries and

This chapter centers on Google Scholar and its role in student information seeking behaviour, while offering a discussion of the theoretical underpinnings related to information seeking behaviour and the technology acceptance and adoption framework It also reviews existing literature on students’ usage of digital resources, information seeking behaviour, and technology adoption, tying these strands together to illuminate how students locate, assess, and adopt digital information tools in academic contexts.

Chapter 3 (Research Methodology): This chapter describes the methodology adopted for the investigation of comparison of postgraduate international students’ perceived use of Google Scholar and of their University Digital Libraries (UDL) Accordingly, the research design,

26 instruments and procedure adopted for this research, data collection, sampling techniques used for data collection, and the method used for data analysis, are each described in this chapter

Chapter 4 (Research Findings): This chapter presents the findings of the research from the data obtained using the questionnaires designed for the study

Chapter 5 (Discussion): This chapter discusses the findings with regard to existing literature to interpret the results

Chapter 6 (Conclusion): This final chapter summarises the study and details the conclusions derived from the findings Recommendations are also made in the light of the findings Suggestions for future research are provided

Libraries and the Technology for Information Searching Services

Introduction

Information seeking is a natural activity that students are expected to undertake in order for them to complete their studies However, we rarely stop to reflect on the key drivers for seeking information as well as the how such drivers combine with the technology of the day in order to create a workable platform for searching information Even though learning institutions provide platforms for accessing information through their libraries, students opt to search for it using other sources Currently, learners have a myriad of options, which could be used to successfully find the information they need Accordingly, the theoretical basis upon which information seeking and behaviour of those seeking it has been under review for decades For instance, models such as those developed by Wilson(1999), Kuhlthau (1991) , Ellis (1989) and Marchionini (1995) have been critical in explaining the rationale behind information seekers’ behaviour With the changing library platforms, it was critical that this chapter examines literature related to digital libraries and university libraries, external platforms such as Google Scholar, and the like This chapter, therefore, reviews information seeking patterns and behaviour that influence students’ usage of digital resources and technology adoption This chapter strives to examine literature that could be critical in the identification of factors ‘that affect international postgraduate students’ choice of using Google Scholar over their University Digital Libraries (UDL)’, as stated in the main aim (section 1.4) The chapter addresses objectives (i) and (ii) that state that the research would

“examine student online search behaviour, with specific reference to their use of Google Scholar and university libraries” and “examine international students’ perspectives on the factors that affect their use of Google Scholar” (section 1.4)

Using information-seeking behavior models, this chapter shows that students typically begin their information search with e-libraries and web search engines rather than with the digital library itself It also notes that the digital library is often not the first choice, with many students preferring internet search engines Among international postgraduate students, environmental, linguistic-cultural, and affective dimensions shape how they use the university e-library, including occasional unawareness of the library and its processes and technologies Finally, several studies use or extend Wilson’s information-seeking model to explain these usage patterns.

28 model of information seeking behaviour and it could be seen that the information-seeking context influenced the information seeking behaviour of individuals.

Basic Concepts and Definitions of Digital Libraries

Traditionally, libraries have been centers for storing, distributing, and sharing knowledge, preserving culture, facilitating information retrieval, supporting learning, and enabling social engagement (Neal, 1997) In the digital era, libraries perform these same functions, but through electronic means that reach users online A digital library is defined as a collection where some or all holdings exist in electronic form, with library services delivered electronically—often over the Internet—to provide remote access for users (Rosenberg).

Originating from a 1988 CNRI report by Kahn and Cerf, the term “digital library” gained traction as the NSF/DARPA/NASA Research in Digital Libraries Initiative (Griffin, 1998) advanced the field; however, the phrase has been used to describe a variety of entities and concepts For example, Lynch and Garcia-Molina (1996) define digital libraries as systems that provide “a community of users with coherent access to a large, organized repository of information and knowledge” (p.4).

Digital libraries are defined as content collected and organized on behalf of user communities, with librarians highlighting the institutional role of digital libraries as services Borgman (1999) emphasizes that digital libraries function as both curated content and supported services for users Building on this, Michael Lesk (1997) describes the digital library as a collection of information that is both digitized and organized, capturing the essential combination of digitization and organization that characterizes digital libraries.

Researchers contend that digital libraries improve access to print content by converting it into digital formats, enabling easier retrieval and use (Yeates, 2002) They also function as systems for displaying collections that can be archived across different kinds of media, broadening preservation options and audience reach (Passos, Carolino, & Ribeiro, 2008) Yao and Zhao contribute to this discourse by exploring how these libraries can integrate metadata and interoperable architectures to support diverse user needs.

Digital libraries are specialized organizations that harness modern information, computer, and network technologies to digitize, collect, sort, and preserve informational resources They provide access to a wide range of external information sources and build a comprehensive collection of digitized materials that are readily accessible to users of all kinds, with professional staff serving as stewards who ensure reliable, organized access and long-term stewardship of the collection.

29 of the intellectual and cultural heritage of the world” (Marcum, 2003, p.279) They are also said to behave as “cognitive tools, component repositories, and knowledge networks (Sumner & Marlino, 2004)

A digital library was defined by the DELOS Digital Library Reference Model as:

An organization, potentially virtual, systematically collects, manages, and preserves rich digital content for the long term, and provides its user communities with specialized tools and services built around that content, delivering measurable quality and governed by codified policies.

The Digital Library Federation defines it as:

Organizations that steward digital collections provide the resources and specialized staff needed to curate, structure, and preserve digital works, ensuring meaningful access to defined user communities They offer intellectual access, interpretive context, and responsible distribution while maintaining the materials' integrity and ensuring long-term persistence Through robust digital preservation, metadata-driven discovery, and governance, these institutions enable reliable reuse and discovery of digital collections over time by researchers, educators, and other stakeholders.

The Berkeley Digital Library Project, University of California, describes a digital library as a collection of information sources that are distributed (Trivedi, 2010)

These definitions reveal that a digital library is a networked system rather than a standalone unit, built on technology that links resources across multiple databases The connections between the digital library and its resources are seamless from the user’s perspective, ensuring transparent access and discovery Moreover, the digital library’s repository extends beyond bibliographic records to include actual digital objects such as texts, images, and other media.

Scholars (e.g Trivedi, 2010; Uzuegbu & McAlbert, 2012) suggest that digital libraries have various significant purposes, such as:

• Ensuring effective and economical delivery of information to users

• Supporting networking and communication between educational organisations

• Accelerating the systematic growth of techniques for collecting, storing and organising data digitally

• Promoting supportive efforts in computing, communication networks, and research resources

• Encouraging institutional networking and exchange programmes

• Acquiring the role of leadership in generation and distribution of information

According to Trivedi (2010), digital libraries serve multiple functions, including aiding students in information search and retrieval through a user-friendly interface They enable access to sources across both the internet and intranet, granting visibility to prominent information sources and allowing users to obtain large amounts of information whenever and wherever needed In addition, digital libraries support interoperability with other libraries and accommodate diverse content such as multimedia and text They typically operate on a client–server architecture and use hypertext links to provide intuitive navigation.

Digital libraries are perceived differently by different groups, producing distinct implications For students, they resemble a pool of databases, learning materials, digital documents, and video games accessible via computers For space scientists, they represent a collection of satellite images, video galleries, CAD and GIS data available online For business professionals, they may include stock and shares information, business deals, reports, and budget data on the Internet In short, a digital library is a curated collection of digital data organized for a community or group Across contexts, various terms describe digital libraries, such as data mining systems, data warehouses, digital archives, publisher records, eBooks, online data sources, multimedia records, electronic libraries, image applications, digital protection, e-Journals, and virtual libraries.

The World Wide Web is often described as a digital library—a vast collection of thousands of documents that people can search and access (Cleveland, 1998) Yet Clifford Lynch, a leading scholar in digital library research, disputed this label, arguing that the Internet “is not a digital library” because it was not designed to support the organized publication and retrieval of information, as libraries are (Lynch, 1997, p 72).

2.2.1 Digital Libraries in the University Context

Al-Qallaf and Ridha (2018) contend that libraries in colleges and universities must harness converging technologies to strengthen instructional, learning, and research environments in higher education Consequently, the academic library website becomes the central hub for resource discovery, access to services, scholarly communication, and collaborative learning, integrating digital tools to support students, faculty, and researchers.

Academic library websites serve as the dissemination hub for digital information, a portal to a multitude of e-resources and e-services, and the main gateway for virtual users, while also functioning as a marketing tool that projects the library’s image to the world They represent libraries’ virtual presentation, offering access to online catalogs, electronic databases, subject resources, library instruction and tutorials, and digital collections Together, these sites have the potential to form a centralized information ecosystem that minimizes users’ search effort and nurtures the development and sharing of learning, concepts, and experiences, while continuously supporting evolving user needs and creating opportunities for communication, instruction, and learning.

Academic libraries face challenges from the growing availability of information on the internet, which introduces a wide array of sources beyond traditional collections This change means that academics and postgraduate students increasingly supplement library resources with external online materials, using library websites alongside other information sources to support their research As Bates notes, institutions must adapt by improving discovery tools, evaluating information quality, and strengthening digital literacy so users can effectively navigate, compare, and integrate diverse resources.

A 2007 study by the European Library Automation Group (ELAG) highlighted that libraries face multiple challenges The internet not only gives users direct access to information but also enables them to obtain physical items through online services, reducing the need to visit libraries or search there Moreover, search engines offer a simpler, more intuitive search process, which can erode users' proficiency with traditional library search skills Online searching also alters communication, as users increasingly rely on citation metrics to gauge an article's usefulness rather than consulting an impartial reference librarian.

Google Scholar

Google Scholar, launched by Google in 2004, queries a web-based database of academic documents using a version of the Google search engine The databases may include journal articles, conference papers, book chapters, and theses, and the results provide links to full-text versions, citation totals, and lists of citing documents often sorted by relevance Google Scholar has attracted substantial research attention as a tool for surveying the status of research in specific areas and for identifying significant publications Its advantages include free access on the Web and broad coverage of scholarly resources, a significance reflected in publishers like Sage providing guidance to writers on leveraging Scholar search results.

Google and Google Scholar are the primary ways researchers find articles online today, and together they drive about 60% of referral traffic to SAGE Journals Online; since search engines are often the first place researchers look, making your article easy to locate in search results can significantly boost its visibility.

A study by Al-Moumen et al (2012) examined users' information needs and their information-seeking behavior, revealing that students find it difficult to locate information on library websites because of incomprehensible terminology As a result, they increasingly rely on Google and Google Scholar to discover relevant information, a shift also observed by Sadeh (2008).

The popularity of Google Scholar (GS) has been examined by main scholars since its debut Since

Google Scholar (GS) is a commercial tool whose coverage and ranking algorithms are proprietary and therefore inaccessible to researchers (Wenzler, 2008) Its strengths and weaknesses have been evaluated through comparisons with library subscription databases, alternative search engines, and library federated search tools Given that this study focuses on libraries, the analysis is restricted to examining GS within the library context.

Early studies by Mullen and Hartman (2006) and Neuhaus, Neuhaus, and Asher (2008) examined the acceptance of GS in academic institutions and found that only a small number of institutions offered direct access to GS on their homepages; instead, most placed GS on library websites Neuhaus and colleagues (2008) further noted that the placement of GS information tended to be centralized within library portals rather than on main institutional pages.

Google Scholar (GS) on library websites signified that these institutions regarded GS as a worthwhile resource for academic research A follow-up study by Hartman and Mullen (2008) of the same institutions studied earlier by Mullen and Hartman (2006) reported that GS penetration had increased over the two-year period.

Several studies have contrasted Google Scholar's retrieval and accuracy with those of subscription databases, indicating that Google Scholar has improved over time (Chen, 2010; Neuhaus, Neuhause, Asher, & Wrede, 2006) For example, Walters (2009) compared Google Scholar against a range of subscription databases and found that Google Scholar's performance exceeded many of these databases.

Research suggests that library users favor the simplicity of Google Scholar’s search interface and may prefer GS over more complex tools even when those tools offer greater usefulness (King, 2008) In a comparative study, Cooke and Donlan (2008) evaluated Google Scholar against Serial Solutions Central Search and Windows Live Search Academic and found that straightforward, efficient interfaces can be as useful as more complex ones, though the latter may yield more relevant results; ultimately, usefulness depends on users’ preferences and information needs Earlier work comparing GS with subscription-based federated search engines such as MetaLib and WebFeat highlighted GS’s ease of use, speed, and usefulness (Chen, 2006) Importantly, Giglierano (2008) demonstrated that the culture of a library can influence how GS is used.

Wang and Howard (2012) analyzed Google Scholar usage data from 2006 at San Francisco State University, focusing on three library tools—the SFX link resolver, the Web Access Management proxy server, and the ILLiad interlibrary loan server They found that Google Scholar’s usefulness as a research resource had grown, making it a significant addition to the library’s collection of research databases.

Several studies have juxtaposed Google Scholar (GS) with established bibliographic databases such as ISI Web of Science and Scopus Adriaanse and Rensleigh (2013) found that GS did not match the performance of the other two databases in terms of citation results, retrieval of unique items, or consistency and quality verification Martín-Martín, Orduna-Malea, Thelwall, & López-Cózar (2018) similarly reported that GS's unique citations tend to have substantially lower scientific impact on average than the citations indexed by Web of Science and Scopus, and that roughly half of GS's unique citations originate from non-journal sources, with many not in English Harzing (2013) showed that GS coverage has been steadily increasing and that it provides substantial disciplinary coverage, enhancing its suitability for both research evaluation and bibliometric studies Together, these findings reveal conflicting opinions about GS's usefulness as a research resource compared with traditional library databases.

Among university students, Google Scholar (GS) use and acceptance have been quantitatively examined Cothran (2011) reported that respondents perceived GS as easy to access and to use Shen (2012) explored the frequency of GS usage and the factors driving intention to use, identifying apparent ease of use, loyalty, and perceived advantages as key influences Tella, Oyewole, and Tella (2017) surveyed postgraduate students at the University of Ilorin in Nigeria and found that while most were aware of GS and used it, they were not satisfied with its performance because it did not simplify or accelerate their research; nonetheless, GS was viewed as useful for covering broad topics and providing relevant articles Ankrah and Atuase (2018) examined factors affecting awareness of electronic resources among postgraduate students and found that these students are more comfortable accessing information from Google.

Google Scholar (GS) often emerges as a preferred starting point for researchers, offering an alternative to traditional library databases Across studies, opinions about GS vary, but the evidence generally supports its ease of use and usefulness as a quick search tool for scholarly literature While debates continue about coverage and rigor, GS’s user-friendly interface and efficient access to sources make it a valuable asset for initial literature exploration and broad scholarly discovery.

Google Scholar (GS) has attracted criticism regarding its suitability as a sole tool for searches related to systematic reviews Giustini and Boulos (2013) argued that GS has not evolved sufficiently to be used exclusively for systematic-review searches, noting its continually changing content, shifting database structure, and evolving algorithm as factors that make GS a poor choice for this purpose In addition, Halevi, Moed, and Bar-Ilan (2017) highlighted GS’s limitations in advanced searching, its lack of support for data downloads, the absence of quality control, and unclear indexing guidelines, all of which limit its usefulness as a standalone bibliometric source.

Student Information Seeking/Searching Behaviour

Information seeking is inseparable from the context in which it occurs (Johnson, Case, Andrews, Allard, & Johnson, 2006) Yet, in most cases, people tend to turn to the internet as their primary information source, rather than to other individuals or libraries (Johnson et al., 2006).

Research on information seeking in academic contexts shows that information tasks are explorative, undefined, complex, rational, flexible, and continuous It also reveals that users’ searching behavior involves using multiple search systems, constructing varied search queries, employing basic search functions, and reformulating queries to refine results (Du & Evans, 2011).

Collaborative information seeking behavior typically unfolds at the initial stage, when an information need is identified, and again when that information is utilized in final reporting According to Saleh and Large (2011), the quality of search outcomes improves when searchers are effective and efficient, yielding higher-quality sources Leeder and Shah (2016) add that an individual’s attitude and experience also shape search quality Although the current study does not focus on collaborative information seeking, it underscores that the individual still plays a substantial role in search effectiveness even within a team setting.

Research has increasingly focused on information-seeking behavior across students' disciplines Majyambere and Hoskins (2015) examined the information-searching patterns of international postgraduate students in humanities and arts, finding both active and passive information-seeking behaviors Their analysis shows that academic information needs drive students to consult lecturers, supervisors, and subject librarians, while personal information needs are met through interactions with colleagues and the use of internet resources In line with these findings, Sahu and Nath Singh also report similar patterns of information seeking among students.

A 2013 study examined information-seeking behavior among academics in astronomy and astrophysics and found that the ways people search for information—and their needs—vary by discipline Researchers reported that the primary aim of information seeking is to support research and teaching, with web pages and other online resources identified as the most commonly used information sources The results imply that students’ information needs are shaped by their disciplinary focus, which in turn drives their information-seeking practices.

Consistent with the present study, Lacović (2014) found that academic libraries and the internet significantly shape university students' information behavior Catalano (2013) noted that graduate students typically start their research online, then consult faculty advisors and use library resources, with search behavior varying by discipline, student origin (international versus domestic), and degree level (master’s versus doctoral) Liao, Finn, and Lu (2007) identified internet searches and library electronic resources as the two primary entry points into the information-seeking process, with the internet leading; their work also showed that international students engage library services more frequently than national students, yet may require higher-level information literacy instruction—precisely defining research problems, developing effective search strategies, and evaluating and sorting academic sources Consequently, international students are more likely to need assistance to use library services effectively The following sections introduce theories of information seeking and technology adoption to connect these core concepts to the research that follows and to lay the theoretical groundwork for this study.

To address the study’s objectives and research questions, a thorough examination of information-seeking and information-searching models, alongside theories of technology acceptance and adoption, was conducted This scrutiny shapes the analysis and sets up the next section, which scrutinises the theories and models of information seeking.

Theories Related to Information Seeking/Searching Behaviour

Information seeking behavior is shaped by context and accompanying sub-contexts, a view supported by Abbas (2018) Over time, researchers have proposed multiple models to explain the information seeking and searching process The evolution of these models traces a broader shift in information behavior from system-centric approaches to user-centric ones, underscoring the central role of the user in information discovery (Abbas, 2018).

To ground this study, we examine a targeted set of salient information-seeking models, moving from general concepts to more specific insights These models cover core aspects of information searching, including preliminary actions, driving factors, obstacles, and the various phases of the information-seeking process Additionally, we review models that summarize the interaction between individuals and information systems and the stages involved in this facet of information seeking.

Generic models of information seeking behaviour are described first

2.5.1 Wilson’s Model of Information Seeking Behaviour

Wilson (1999) proposed several models of information-seeking behaviour An early model, proposed in 1981, stated that information-seeking behaviour arises from the information user’s perceived need To fulfil that need, the user searches within authorised or unauthorised information resources or facilities; this inquiry may or may not succeed in locating appropriate information If appropriate information is found, the user uses it and the perceived need is wholly or partly fulfilled If the information found does not fulfil the need, the search may be repeated This early model also suggested that other people may participate in information-seeking behaviour through information exchange and transfer.

40 included three entities identified by Wilson, namely the user of information, the user’s need for information, and the environment in which this information is sought (Figure 2.1)

Figure 2.1Wilson’s 1981 model of information behaviour

Wilson's model centers on information need, defined by the user's environment, role, and requirements across physiological, affective, and cognitive dimensions (Figure 2.2) This information need then drives information-seeking behavior, though the process is tempered by potential barriers—personal, interpersonal, and environmental—that can constrain or shape how users search for information.

Figure 2.2 Wilson’s 1981 model of information seeking behaviour

A 1996 extension of the core model (see Figure 2.3) adds intervening variables that can either support or hinder information use, broadening information-seeking behavior beyond active search to include additional forms of information activity and positioning the processing and use of information as a central element of the feedback loop that fulfills information needs The model also introduces three theoretical notions to explain why some requirements do not trigger information-seeking (stress and coping theory), which information sources may be preferred by a specific individual (risk/reward theory), and the belief that a person can effectively execute the necessary behavior to achieve anticipated outcomes (self-efficacy theory) Wilson (1999) evaluated these models using insights from subsequent research.

42 different fields, such as innovation, psychology, decision-making, consumer research, and health communication (Wilson, 1999; 2005)

Figure 2.3 Wilson’s 1996 model of information behaviour

Wilson’s model is complex, drawing on theories from psychology and consumer research to explain information-seeking behavior It uses stress and coping theory and social learning theory to show why information needs prompt seeking and why some individuals are better at tracking a goal based on their perceived self-efficacy (Case, 2012) It also leverages the risk and reward framework from consumer research to account for why people prefer certain information sources over others (Bloch et al., 1986).

2.5.2 Kuhlthau’s Information-Search Process (ISP)

Kuhlthau's Information Search Process (ISP) emphasizes how users cognitively engage with information and concepts while seeking meaning, describing information seeking as a construction that integrates affective, cognitive, and physical dimensions of the search experience According to Kuhlthau (2005), the ISP comprises six phases—initiation, selection, exploration, formulation, collection, and presentation—that span the full spectrum of feelings, thoughts, and actions users experience as they pursue information For example, starting a search may involve uncertainty and unclear thoughts, which can shift to optimism as search tasks are chosen, and later to confusion and frustration as exploration uncovers inconsistent information The actions in the model range from exploring information to documenting it This framework has been evaluated through mixed-method studies involving university, college, and secondary school students, as well as public library users.

Table 2.1 Kuhlthau’s Information-Search Process (adapted from Kuhlthau, 1991, pp.367, 369)

ISP Stage Feelings Thoughts Actions Task

Selection Confidence Isolate broad theme Exploration

Scrutinise information on broad theme

Formulation Clearness Focused/ Sharper Articulate emphasis

Looking for Appropriate or Concentrated Information

Collect information relating to area of emphasis

ISP Stage Feelings Thoughts Actions Task

2.5.3 Ellis’s Model of Information-Seeking Behaviour

The original purpose of this model was to examine retrieval of information from the perspective of social science Thus, its principal objective was to propose a behavioural method of information retrieval as opposed to a cognitive approach The design of the model was informed by semi- structured interviews with researcher groups from different academic and industrial disciplines (Ellis, 1989; Ellis, Cox, & Hall, 1993; Ellis & Haugan, 1997)

Ellis’ model (Figure 2.4) acknowledges the existence of eight kinds of activities related to information seeking: starting/surveying; chaining; monitoring; browsing; differentiating/distinguishing; filtering; extracting; and ending Starting/surveying pertains to the activities associated with the initial search for information, while chaining (which may be backward or forward) refers to using a preliminary resource as a point of reference to perform follow-up searches The next step, browsing, is a type of searching that is semi-directed; that is, the search is narrowed by this time through the use of contents, title lists, subject captions, and summaries On the other hand, filtering pertains to using certain methods or conditions to ensure the relevance and exactness of the information Relatedly, differentiating indicates sifting through the information on the basis of the features of the scrutinised material Monitoring encompasses tracking sources to remain aware of developments in the area, and extracting consists of methodically reviewing resources to select items of relevance Finally, verifying and ending pertain respectively to ascertaining the correctness of the information, and stopping the process at the end of a task It must be noted that the model “does not attempt to specify either the exact interrelationships of the activities or the order in which they are undertaken, because this might vary from project to project and to some extent depends on the phase and stage of the project” (Ellis & Haugan, 1997, p.388)

Figure 2.4 Ellis’ model for Information System Design

2.5.4 Belkin et al.’s Information-Seeking Strategies (ISS)

Belkin, Marchetti, and Cool (1993) propose that information-seeking strategies can be viewed as exchanges between a user and different facets of an information retrieval (IR) system They identify four facets of strategies for locating information: scanning–searching, learning–selecting, recognition–specification, and information items–meta-information In this view, every user interaction pairs an approach with a goal, a retrieval method, and a consideration of available resources The authors also suggest that distinct search behaviors emerge during information searching—ranging from seeking known items, to finding items similar to a known example, to pursuing items on identified topics, to browsing for interesting possibilities, and to inspecting contents or descriptors to identify useful material This conceptual model (Figure 2.5) was evaluated through observations and findings from additional experimental studies.

Figure 2.5 Information-Seeking Strategies (Adapted from Belkin et al., 1993)

The following subsections outline interactive models of information-seeking behaviour, describing how individuals interact with information resources or systems to locate and obtain the information they require.

Bates (1989) introduced the Berry-picking Model, an interactive information-seeking behavior framework that treats searching as an evolving process rather than a single-step task The model outlines a dynamic sequence of inquiry, reflection, assessment, and persistence that unfolds across diverse information sources Starting from an initial idea, the search can broaden or pivot as new information generates additional questions and insights, leading the original inquiry into unexpected directions (Knight & Spink, 2008).

Figure 2.6 Illustration of a classic model of information retrieval

Contrasting the linear model, Bates (1989) argued that information seeking is an evolving activity in which the outcome of each query triggers an intellectual reaction from the searcher That reaction can strengthen the current search, prompt its extension or modification, drive a complete overhaul of the inquiry, or even lead to abandonment (Knight & Spink, 2008) This view emphasizes the iterative, dynamic nature of information seeking, with search strategies adapting in response to each result.

Figure 2.7 Illustration of a berry-picking search

Marchionini (1995) categorizes information-seeking strategies into two main types: analytical strategies, which are methodical and deliberate, and browsing strategies, which tend to be less structured and exploratory Despite this dichotomy, the model presents information seeking as largely a linear process, with a flexible “reflect, iterate, stop” phase that allows users to reassess their information needs The search typically begins with identifying and defining the problem and continues until the need is resolved or discarded The model also accounts for a range of individual and environmental factors that shape how information is sought This framework is useful for illustrating how an end-user interacts with an electronic resource, as noted by Abbas (2018).

Figure 2.8 Information-Seeking Model (Source: Marchionini, 1995)

Beyond the models discussed earlier, researchers have proposed additional models that illuminate different aspects of information search behavior For example, the model proposed by Spink (1997) analyzes the strategic actions used during interactive information seeking (see Figure 2.9) Spink’s framework centers on user reasoning, search strategies, and feedback loops that connect the information retrieval process directly to overall information-seeking behavior.

Figure 2.9 Interactive Search Process – Elements

Theories of Technology Acceptance and Adoption

Earlier analysis shows that models of information-searching behavior are relevant to this study, since contemporary information seeking almost always involves technological systems As a natural progression, the researcher’s focus shifts to the factors that influence a user’s acceptance and adoption of these information tools.

53 of technologies – in this case, the UDL and Google Scholar Accordingly, this section discusses models and underlying theories of technology acceptance and adoption

Technology acceptance research spans multiple disciplines, with numerous models and theories designed to explain, predict, and understand how individuals adopt new IT products and services These frameworks have evolved through ongoing validation and refinement across information systems, sociology, and psychology In psychology, Ajzen and Fishbein’s Theory of Reasoned Action (1980) was extended into the Theory of Planned Behavior (1985) and later into Taylor and Todd’s Decomposed Theory of Planned Behavior (1995) In information systems, Davis’s Technology Acceptance Model (TAM) introduced core constructs such as perceived usefulness and perceived ease of use, shaping subsequent work on user adoption across technologies.

1986), which builds on the TRA, and this has been extended further both in the TAM2 (Venkatesh

Two foundational models—Davis's Technology Acceptance Model (TAM, Davis, 1989; revised 2000) and the Unified Theory of Acceptance and Use of Technology (UTAUT, Venkatesh et al., 2003)—offer integrated explanations of technology adoption by combining core determinants found across earlier theories These models synthesize insights from the Model of PC Utilisation (Triadis, 1979), Rogers's Diffusion of Innovations (DOI) (Rogers, 1983), and the Motivational Model developed by Deci and Ryan, among others By linking perceived usefulness, perceived ease of use, social influence, facilitating conditions, and intrinsic motivation, TAM and UTAUT provide a parsimonious yet powerful framework for predicting user acceptance and guiding design and implementation strategies The resulting integration helps researchers and practitioners anticipate uptake, tailor interventions to specific contexts, and support sustained technology use.

1985), and Social Cognitive Theory (Bandura, 1989)

Models and theories are built on distinct constructs and underlying philosophical assumptions, and these differ across frameworks because each is tailored to its respective discipline, as discussed in the following sections They have also faced criticism for potentially restricting explanations, predictions, and understanding of technology adoption at the individual level, which leads researchers to select the model or theory that most closely matches their specific study context.

2.6.1 Theory of Reasoned Action (TRA)

The Theory of Reasoned Action (TRA), introduced by Fishbein and Ajzen in 1975, arose from their dissatisfaction with existing research on behavior and attitude It identifies three core components—attitude toward the behavior, subjective norms, and behavioral intention—and explains how these elements collectively predict individuals' actions By linking beliefs, attitudes, and perceived social expectations, TRA provides a concise framework for understanding why people decide to perform or refrain from a given behavior.

TRA posits that an individual's behavioural intention is determined by their attitude toward the behaviour and the subjective norms surrounding it In equation form, BI = A + SN, indicating that a person’s likelihood of engaging in a behaviour depends on their evaluation of the behaviour and the perceived social pressure or expectations from others.

Miller (2005) analyzed the model by outlining three core components: attitude, the individual’s beliefs about a given behavior that are weighed by evaluations of those beliefs; subjective norms, the perceived social pressures from others shaping the intention to perform the behavior; and behavioral intention, the product of attitude and subjective norms toward the behavior, which in turn predicts actual behavior.

Many researchers have proven that the theory is effective in predicting human behaviour (Lin,

Although the Theory of Reasoned Action (TRA) offers demonstrable benefits in predicting deliberate behavior, it has notable limitations It applies mainly to behaviors that are consciously thought out before they occur and does not account for irrational actions, habitual behavior, or any behavior not consciously considered A further limitation is the 'problem of correspondence': for TRA to predict a specific behavior, intention and attitude must align on target, action, time frame, context, and level of specificity The theory also relies on self-report measures when analyzing participant attitudes, which can introduce bias (Abdulhafez & Gururajan, 2008).

The Technology Acceptance Model (TAM) has been developed to predict and explain behaviours specifically related to technologies (Davis, 1989) This theory came from Fishbein and Ajzen’s

Since its introduction in 1975, the Theory of Reasoned Action (TRA) has undergone revisions and extensions, including UTAUT, TAM2, and TAM3 The framework posits that an individual’s attitude toward a behaviour and their subjective norms influence their behavioural intention, a relationship originally proposed by Ajzen and Fishbein.

Ajzen and Fishbein (1980, p 6) define attitude toward a behavior as the individual's positive or negative evaluation of performing that behavior, while subjective norms reflect the person’s perceived social pressures to engage in or refrain from the behavior Both constructs arise from underlying beliefs, with attitude being attitudinal and subjective norms normative in nature They are not completely independent: a person may experience conflict or alignment between the perceived social pressure and their own attitudes when deciding whether to act.

Technology Acceptance Model (TAM) posits that actual technology use follows from a user’s intention to adopt it, with usage shaped by their attitudes toward the technology It explains how external factors alter internal beliefs, intentions, and attitudes, helping researchers and practitioners identify why a technology may be unsuitable for a given context and how to address it TAM centers on two key constructs: perceived ease of use, the degree to which using the system is expected to require little effort, and perceived usefulness, the extent to which the system is believed to enhance job performance (Davis, 1989) The principal hypothesis is that both attitudes and perceived usefulness drive behavioral intention to use the technology, with perceived ease of use influencing attitudes and also exerting a direct effect on perceived usefulness (as depicted in typical TAM models such as Figure 2.11).

The technology acceptance model is the most commonly used and widely accepted model for use in the field of technology adoption and acceptance (Conklin, 2006; Hong et al., 2002; Lin, 2005)

Sandberg and Wahlberg (2006) contend that the Technology Acceptance Model (TAM) is particularly valuable for IT acceptance research because it can be applied across a wide range of contexts and settings These contexts include internet usage behaviors, internet banking, online shopping, gaming, online learning, and digital libraries.

Thong et al (2002) demonstrated that perceived ease of use and perceived usefulness are both determinants of students’ acceptance of digital technologies In the domain of digital library technology, perceived ease of use is influenced by interface characteristics and individual differences, while both perceived ease of use and perceived usefulness are shaped by the organizational context.

A study by Hong, Thong, Wong and Tam (2002) applied the Technology Acceptance Model (TAM) to identify the determinants of user acceptance of digital libraries, focusing on two external variables—system characteristics and individual differences The results showed that these external variables significantly influence users’ perceived ease of use of the digital library technology.

Perceived ease of use was a significant antecedent of users’ intention to adopt digital technology, and content-based system characteristics had a greater impact on perceived usefulness than interface-based features, underscoring the importance of substantive design for user value The study by Hong et al (2002) adopted a user-centered approach rather than focusing primarily on the technology, suggesting that user acceptance is a key determinant of actual technology usage.

Previous Research on Students’ Usage of Digital Knowledge Resources

Information-seeking behavior has attracted considerable attention from researchers, yet studies differ in focus For instance, Sheeja (2010) examined the information-seeking behavior of research scholars from scientific and social science fields, considering service effectiveness, levels of satisfaction with various resources, and the approaches used to stay current in their research The study found that while there are similarities in information-seeking behavior across disciplines, there are significant differences in scholars’ perceptions regarding the adequacy of the available resources.

Analyzing 70 library databases and print journals, the study highlights the inadequacy of university libraries in helping research scholars stay current with the most recent developments in their fields In this context, the findings illustrate a clear link between successful information seeking and researchers’ perceptions of a library’s effectiveness.

Jamali and Asadi (2010) found that academics—including students, faculty members, and research staff—prefer using search engines like Google and web searching as their primary information-seeking tools A Tehran University study by Khosrowjerdi and Iranshahi (2011) examined graduate students’ information-seeking behavior alongside their prior knowledge and revealed positive, robust relationships between these variables Additionally, the researchers reported significant associations between certain aspects of information-seeking behavior and components of prior knowledge, such as familiarity, proficiency, and prior experience.

Orlu (2016) used a descriptor-explanatory design to explore the emotions driving information seeking, finding that postgraduate students’ information-seeking behavior is largely systematic but also exhibits random elements, especially in the planning phase The findings align with Kuhlthau’s (1991) model, indicating that searches at the planning stage often lack a well-defined focus and that emotional responses can provoke nervousness, anxiety, and bewilderment This perspective was extended by Orlu, Mafo, and Tochukwu (2017) in a follow-up study at Manchester Metropolitan University (MMU), which used a similar descriptor-explanatory design and reaffirmed prior observations about emotional reactions during the early stages of the search These initial phases are complex due to doubts about the topic and the ambiguity of ideas, during which students validate their information needs by identifying gaps in research and seeking contextual information, with anxiety arising from the non-specific nature of the information search.

Sabbar and Xie (2016) investigated the role of language in information-seeking strategies and found that language significantly shapes how users, especially those relying on non-native sources, search for information across multiple non-English disciplines They identified four formal system strategies, seven informal resource strategies, four interactive human strategies, and a hybrid strategy, with formal strategies tied to bibliographic devices and information-retrieval tactics across various sources, informal resource strategies encompassing print-oriented practices such as citation tracing, browsing, using bibliographies, indexes, and search aids, interactive human strategies involving direct consultation or intermediary help, and the hybrid strategy referring to inter-library loan processes that may require forms, emails, or conversations with library staff The study shows informal resource, formal system, and interactive human strategies are frequently used as initial information-seeking approaches, with informal strategies often serving as the final step, and that subjects shift between strategies under scheduled, disturbing, and challenging circumstances Notably, the most common formal system strategy was using a search engine to search the Web, which aligns with the present context and suggests international postgraduate students prefer GS to search for information.

Research on information-seeking behavior among students shows that discipline, the seeker’s prior knowledge, the emotions involved in searching, and language background all shape how information is sought In the present study, these factors map onto international postgraduate students who differ in their study fields, have varying levels of prior knowledge, may experience emotions during searches, and come from diverse native-language backgrounds These findings highlight how disciplinary context, prior knowledge, affective factors, and language diversity influence academic information seeking, underscoring the need for information literacy approaches tailored to international postgraduate learners.

72 libraries to be an inadequate resource for information seeking (Sheeja, 2010) and often rely on search engines and web searches for information (Jamali & Asadi, 2010)

2.7.1.1 Student Use of E-Libraries and Web Search Engines

Research on e-libraries and e-resources indicates that the e-library functions as a provider of information services and materials that bolster students' research objectives and learning needs while supporting teaching by staff In Bangladesh, Islam and Habiba (2015) found that students and faculty were generally satisfied with the current level of e-resources at a private university, yet constraints such as an insufficient number of titles, trouble locating information, limited computer access, and slow download speeds persisted Shuling (2007) showed that at Shaanxi University of Science and Technology, about 80% of students had limited knowledge of electronic resources, and roughly half used both print and electronic sources Mostafa (2013) observed widespread use of e-resources in a private Bangladeshi university, with many students relying on them for essential information, but the library's infrastructure was inadequate to optimize e-resource usage.

Ankara University researchers Turan and Bayram (2013) conducted a study of 280 students from the Letters, Pharmacy, and Veterinary Medicine faculties to identify the purpose of digital library usage, frequency of use, and the tools employed The results indicate that students primarily use internet resources for their assignments, while the digital library is not their first choice Key reasons include a lack of awareness about the digital library and the perception that their own resources suffice for their learning and research needs.

With regard to e-resources, a study of 182 students from Jimma University, Ethiopia, by Natarajan

(2017) showed that the use of e-journals had increased due to the students’ awareness of e- resources and services, but that this was accompanied by a decrease in visits to the library

Students need instruction on diverse search strategies, and slow downloads can hinder e-journal usage, highlighting the need for more computer systems and faster internet speeds In a 2017 study, Sohail and Ahmad compared e-resources and services used by Fiji National University students and faculty, finding that most participants were aware of advances in electronic resources and their proper use in academia and research, yet users faced problems such as insufficient IT infrastructure and occasional website blockages A 2019 study by Sohail, Maksood, and Salauddin comparing electronic journals used by postgraduate students and research scholars from Delhi University and Jamia Millia Islamia revealed higher satisfaction with e-journals and e-databases among Delhi students, while also identifying infrastructure and download-speed issues Together, these findings indicate that infrastructure, particularly internet speed, is a significant factor in the usage of e-resources.

A study by Kwadzo (2015) at the University of Ghana examined the usage and awareness of electronic databases among graduate students, revealing that awareness of university databases was high and that lecturers were the primary source guiding students to these resources However, students tended to focus on a limited number of databases The study recommends that librarians, particularly subject librarians, increase publicity of the databases to raise familiarity among both faculty and students and, as a result, boost their usage.

Perrusso (2016) tracked changes in reported research behavior over time to explore whether students' source selections were driven by librarians' instructions or by instructors' source requirements In a longitudinal study at a large public university, a cohort of 2008 freshmen was followed for four years to examine their use of websites and library resources, including journal articles and books, for research papers The findings showed that students' use of library resources increased as they matured, a maturation effect linked to diligence, motivation, and intellectual development The study also found that both faculty source requirements and librarian instructions shaped students' source choices and overall research behavior.

A score of 74 is related to the enhanced usage of library resources The data show that students are more likely to use library resources when they receive instruction from the librarian or when their courses require it This suggests that librarian-guided information literacy training and course-mandated resource use effectively boost engagement with academic libraries and support research outcomes.

Aba, Beetseh, Ogban, and Umogbai (2015) investigated how postgraduate students at the Francis Idachaba Library, University of Agriculture, Makurdi, use internet services for research, finding that only 22% used the internet daily but 87.41% reported that digital libraries markedly enhanced their academic performance More than half (51.11%) cited external internet facilities as primarily supporting educational and research activities, while users指出 common challenges such as long page load/download times and an insufficient number of computers The study also noted that internet usage contributed to a reduced reliance on conventional library facilities, yet 94% of students were fully satisfied with the internet facilities, even as 92.96% indicated a need for suitable guidance in utilizing e-resources.

A 2018 study by Ozonuwe, Nwaogu, Ifijeh, and Fagbohun examined the use of internet search engines among staff and students at a Nigerian university, finding widespread awareness of search engines and online resources while also highlighting barriers such as inadequate search skills, limited internet bandwidth, and information overload The authors argue that librarians and libraries should view search engines as complementary tools rather than threats to their roles and should provide comprehensive search-skills training for users to improve information retrieval.

Salehi, Du, and Ashman (2018) explored how university students use web search engines and personalized results to locate information for educational objectives, identifying an increasing reliance on online search as a learning tool The researchers argue this shift poses concerns for educators, since the drawbacks of web search and personalization are not fully offset by their benefits In a survey of 120 university students, the study examined students’ information-seeking behavior for educational objectives and found Google to be the primary information-seeking tool among participants.

Chapter Summary and Research Gap

This chapter defines digital libraries and explores their role within the university, including factors that influence students’ decisions to use digital libraries It also assesses Google Scholar and its popularity, and introduces student information seeking and search behaviour To build the study’s conceptual framework (see Chapter 4), it surveys theories of information seeking/searching and technology acceptance and adoption in detail, and then reviews prior research on students’ use of digital knowledge resources, information seeking behaviour, and technology adoption The information-seeking literature covers student use of e-libraries and web search engines, as well as international postgraduate students’ use of the university e-library, while technology adoption literature addresses the adoption and use of electronic library resources, the UTAUT/UTAUT2 models, and studies examining Google Scholar as a technology for adoption.

Research on student use of e-libraries and web search engines indicates that digital libraries are often not the first resource students turn to; instead, they rely on internet search engines for their information needs This pattern is supported by multiple studies (Aba et al., 2015; Hirsh, 2014; Islam & Habiba, 2015; Kumah, 2015; Kwadzo, 2015; Mostafa, 2013; Natarajan, 2017; Ozonuwe et al., 2018; Perrusso, 2016; Salehi et al., 2018; Shuling, 2007; Sohail & Ahmad, 2017; Sohail et al., 2019; Turan & Bayram, 2013), illustrating that the digital library is not the preferred option for many students.

International postgraduate students' use of the university e-library is shaped by three interrelated dimensions: environmental, linguistic-cultural, and affective They may be unaware of the library, its procedures, and its technologies, and may have limited familiarity with online resources, often relying on basic or non-critical search approaches From a linguistic-cultural perspective, divergent language practices, varying communication styles, nonverbal norms, and different learning approaches can constrain engagement with library resources (Lange et al., 2015; Michalak & Rysavy, 2018) The affective dimension encompasses emotional barriers that may arise from difficulties accessing library services, which can hinder usage (Noori et al., 2017).

Building on Wilson’s information-seeking behaviour model, numerous studies indicate that the information-seeking context influences how individuals pursue information From a technology-adoption perspective, various technology-acceptance models—TAM, UTAUT, and UTAUT2—have been used to explore the factors driving the use of electronic library resources and other technology systems However, except in a few cases, there are limited comparisons of perceptions across user groups (e.g., Al-Qeisi, 2009; Liao et al., 2007; Orji et al., 2010) and across different technology systems (Yan et al.).

By 2013, there had been few investigations, and studies examining Google Scholar as a technology system that could be adopted and used remained rare (e.g., Cothran, 2011; Wu & Chen, 2014), with the Technology Acceptance Model (TAM) appearing to be the model of choice when such inquiries were conducted (Cothran, 2011).

Extensive literature reviews present factual data on the use of digital libraries and their users, highlighting the factors that influence students’ decisions to engage with library services Although many studies apply theoretical frameworks to explain these factors or their interrelationships, few compare the intention to use a digital library with other information technologies such as Google Scholar A substantial number of studies have investigated Google Scholar and acknowledge that it often serves as students’ first recourse for information, yet there is limited scrutiny of the precise factors that drive its popularity and usage While there is considerable theoretical (e.g., Bates, 1989; Belkin et al., 1993; Ellis, 1989; Kuhlthau, 1991; Wilson, 1981; Marchionini, 1995) and empirical attention to information-seeking behavior in general and among students, research linking such behavior to information-providing technologies like UDLs and Google Scholar within the context of the present study remains scarce.

A review of the literature reveals a paucity of scrutiny into the use of University Digital Libraries (UDLs) and Google Scholar as technology systems Moreover, a direct comparison of the factors driving the usage of these two technologies has not been identified, even though earlier studies have compared the perceptions of two user groups concerning the same technology system Consequently, this study intends to provide insight into the key factors that influence international postgraduate students’ acceptance and usage of University Digital Libraries (UDLs) and Google Scholar by developing a conceptual framework that integrates technology acceptance theories with library usability and accessibility considerations.

This study evaluates a conceptual model based on the UTAUT framework and extends Wilson’s Information Seeking Behaviour model to investigate how the information-seeking practices of international postgraduate students may influence their inclination to adopt one technology system over another.

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