1. Introduction

Information retrieval is the process of accessing data resources. It generally means collection, storage and access of information. It assists the users in finding the information they require form large database collections. In the digital age, the efficiency of information retrieval has become a critical aspect of academic and professional success. The ability to effectively navigate databases and online resources can significantly influence the quality of research and learning outcomes. This study aims to explore the database search behaviors of individuals, with a particular focus on how these behaviors vary by gender and whether individuals plan their searches in advance. Previous research has shown that search behaviors can be influenced by a variety of factors, including educational background, internet proficiency, and access to digital resources. However, there is limited literature on how planning habits impact search efficiency and the preferences for different information retrieval methods. Additionally, understanding gender differences in these behaviors can help in designing more inclusive and effective digital literacy programs.

The present study analyzes survey data from 198 respondents, segmented into various categories such as discipline, schooling background, internet usage, and preferences for information retrieval. By examining these variables, the study aims to provide a comprehensive understanding of how planning habits and gender influence database search behaviors. This analysis not only sheds light on the current state of information retrieval practices but also offers practical insights for educational institutions and libraries to enhance their support services. The results reveal significant differences in how males and females approach information retrieval, with females generally exhibiting more structured and planned behaviors. These findings have important implications for the design of digital literacy programs and the development of user-centric information retrieval systems. The study concludes with recommendations for optimizing resources and services to better meet the needs of different user groups.

1.1. Background

In today’s information-rich society, the ability to efficiently retrieve relevant information from databases is paramount for academic and professional success. The digital revolution has transformed the landscape of information access, with online databases, digital libraries, and search engines becoming essential tools for research and learning. As the volume of accessible information grows, understanding the factors that influence effective information retrieval becomes increasingly important.

Previous research has highlighted several factors that affect information retrieval behaviors, including educational background, familiarity with technology, and the availability of digital resources. However, there is a gap in the literature regarding the specific impact of planning habits on search efficiency and how these behaviors vary across different demographic groups, particularly between genders. This study seeks to fill this gap by providing a comprehensive analysis of database search behaviors based on gender and planning habits.

Effective information retrieval is an essential skill in the digital age, influencing academic and professional success. The ability to efficiently navigate and utilize databases has been the focus of numerous studies, highlighting various factors that impact search behavior and outcomes.

Hargittai [1] emphasized the importance of digital literacy, noting significant disparities in online skills among different demographic groups. Van Deursen and Van Dijk [2] further explored these disparities, identifying a shift from mere access to technology to differences in usage and proficiency.

Gender differences in digital literacy and search behaviors have been widely studied. Jackson et al. [3] found that males and females approach online searches differently, with males often exhibiting higher confidence but not necessarily higher accuracy. Ayyanar [4] focuses on the majority of Mechanical Engineering (Mech) students depending on information literacy skills to get the desired and relevant information for their research.

The impact of planning on search efficiency has also been documented. Bates [5] introduced the concept of “search tactics”, highlighting the benefits of a structured approach to information retrieval. More recent studies, such as those by Bilal [6] and Fidel et al. [7], have reinforced the importance of planning, particularly in complex search tasks.

In the context of educational settings, Head et al. [8] identified that students’ information-seeking behaviors are influenced by their familiarity with digital resources and their ability to effectively plan and execute searches. Additionally, research by Leeder [9] and Tsai et al. [10] has shown that instructional interventions can significantly improve students’ search strategies and overall digital literacy.

1.2. Objectives

The primary objective of this study is to investigate the differences in database search behaviors between males and females and to determine how the habit of planning searches in advance influences these behaviors. Specifically, the study aims to:

1) Identify the percentage of respondents who plan their searches in advance and analyze this behavior by gender.

2) Examine the impact of planning habits on the preferred methods of information retrieval.

3) Explore the differences in search behaviors based on respondents’ educational disciplines, schooling backgrounds, and internet usage patterns.

4) Provide insights for educational institutions and libraries to enhance their digital literacy programs and support services based on the findings.

1.3. Literature Review

Effective information retrieval is a multifaceted process influenced by cognitive, behavioral, and technological factors. Previous studies have shown that individuals with higher levels of digital literacy tend to be more efficient in retrieving relevant information [1] [2]. Digital literacy encompasses not only the ability to use digital tools but also the skills to evaluate and integrate information from various sources [11]. Chu et al. [12] studied the development of information searching expertise by 12 postgraduate research students, they have shown that Findings reveal that, in the beginning, students performed more questionable subject searches and fewer keyword searches; later, as they understood more about subject searching and the power of keyword searches. In case of graduate students, Catalano [13] highlighted a study to draw out patterns of information seeking behavior of graduate students. This review revealed that graduate students begin their re-search on the internet much like any other information seeker, consult their faculty advisors before other people, and use libraries in diverse ways depending on the discipline studied. Motamedi F. et al. [14] studied the number of keywords used by the users in the database of SID and Magiran studied this effect on Information Retrieval. Su W. and Sun Y. [15] studied the Information Retrieval behaviour of the library users using the concept of their use of bibliometric contents. Warwick C., et al. [16] discuss numerous aspects of students’ information work, including information seeking, evaluation of information, and the use of a variety of materials, both digital and in print. It also was important that the study not only concern seeking but a broader range of information use so that we could determine how expertise changed in different aspects of information behavior. There are of significant value, as they show what a less expert searcher may do when overwhelmed by the complexity of a task or search or when under pressure of time. The students did not necessarily complete their information tasks but deployed considerable ingenuity in finding ways to avoid or limit complexity [17]. The study was conducted by means of an online multiple-choice survey, completed during the first few weeks of their course and both undergraduates and postgraduates have problems with basic information literacy skills, particularly those related to the use traditional library tools, such as library. The postgraduate students cannot be assumed to possess all the basic information literacy skills necessary to succeed in their studies. This may prompt librarians and academic teaching staff to devote time to identifying and addressing gaps in their postgraduate student’s knowledge. This study suggests that some older students may benefit from increased support from librarians and teaching staff [18]. It gives detailed understanding of the theory, implementation, and evaluation of information retrieval systems in biomedicine and health. It covers basic information retrieval, but with a distinct focus on the biomedical and health domain. Defines where current applications and research systems are heading in digital libraries, and text mining systems [19]. To clarify the conceptual issues of information behaviour research by examining how researchers have characterized the construct of interaction as a component of information seeking and Information Retrieval (IR) [20]. The present study shed new light on tactic transitions in the cross-app interactive environment to explore information search behaviour. The findings of this work provide targeted suggestions for optimizing APP query, browsing and monitoring systems [21]. The study found a satisfactory level of students searching skills. There was no significant difference in the skills based on various variables like gender, age, type of university and level of degree. However, short courses and training workshops had a positive impact on the level of skills. This study will also be helpful for Higher Education Commission (HEC), national digital library for selection of appropriate databases for business students. Hyman, H. et al., [22] deals with the IR problem of balancing recall with precision in electronic document extraction and examine the IR constructs of uncertainty, context and relevance, proposing a new process model for context learning, and introducing a new information technology (IT) artifact designed to support user driven learning by leveraging explicit knowledge to discover implicit knowledge within a corpus of documents. Yuan X. and Belkin, N. J., [23] suggested an evaluation model and methodology grounded in the nature of information seeking and centered on usefulness. They also believed; this model has broad applicability in current IR research [24]. IR is generally concerned with the searching and retrieving of know-ledge-based information from database. It will discuss about the various models and techniques and for IR. It also providing the overview of traditional IR models. Kolomiyets, O. and Moens, M.-F. [25] provides a comprehensive and comparative overview of question answering technology. It presents the question answering task from an IR perspective and emphasizes the importance of retrieval models, i.e., representations of queries and information documents, and retrieval functions which are used for estimating the relevance between a query and an answer candidate. The survey suggests a general question answering architecture that steadily increases the complexity of the representation level of questions and information objects Bouadjenek, M. R. et al. [26] reviews different efforts in various domains like IR and social networks build a clearer picture and synthesize the efforts in a structured and meaningful way. To help them structuring the domain, position themselves and, ultimately, help them to propose new contributions or improve existing ones [27]. In the IR system, how to satisfy an information requirement from a query to voluminous document sets. In this regard, the importance is on improving the relevance quality of the results (i.e. retrieved documents). The effectiveness of the IR system depends on the efficacy of the respective adopted IR model and strategy. Understanding of the IR models, strategies and their challenges are important in choosing an appropriate and viable strategy toward the development of effective IR systems for a specific/ predefined role. This paper surveys the basic IR models, challenges and adopted strategies to enhance the IR systems are also highlighted. Gao, J. et al., [28] focused on the tremendous improvements in conversational artificial intelligence (CAI), leading to a plethora of commercial conversational services that allow naturally spoken interactions, increasing the need for more human-centric interactions in IR. It witnessed a resurgent interest in developing modern conversational information retrieval (CIR) systems in research communities and industry. It focusing mainly on neural approaches and new applications developed in the past five years. To provide a thorough and in-depth overview of the general definition of CIR, the components of CIR systems, new applications raised for its conversational aspects, and the (neural) techniques recently developed for it. Liu J. et al. [29] present an empirical analysis of publication metadata obtained from 6 top-tier journals and 9 conferences for the first 16 years of the 21st Century, and evaluate the dynamic characteristics of Data Mining and Information Retrieval. Also, find a steady growth both in terms of productivity and impact, evidenced by the unabated number of publications/citations over the period of study. Ahmad, W. and Ali, R., [30] discuss different types of social networking services and user’s information shared on these services. We categorize the content-based information into two categories, namely, textual content-based information and visual content-based information. Then, we discuss the major efforts made for retrieval of these information from different social networks. We also outline a procedure for content-based information retrieval from multiple social networks. Sharma, M. and Morwal, S., [31] studied Cross Language Information Retrieval (CLIR), whose goal is to find relevant information written in a language different from the language of query. CLIR can be used to enhance the ability of users to search and retrieve documents in many languages. Different type of translation techniques can be used to achieve CLIR. This paper describes the work done in CLIR and translation techniques for CLIR [32]. Large collection of unstructured, structured and semi-structured data the diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. Information Retrieval can be classified into different classes such as monolingual information retrieval, cross language information retrieval and multilingual information retrieval (MLIR) etc. In the current scenario, to solve such barriers, CLIR system are nowadays in strong demand. CLIR refers to the information retrieval activities in which the query or documents may appear in different languages. This paper takes an overview of the new application areas of CLIR and reviews the approaches used in the process of CLIR research for query and document translation. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. Ghorab, M. R. et al. [33] reports a survey featuring a critical review of PIR systems, with a focus on personalised search. The survey provides an insight into the stages involved in building and evaluating PIR systems, namely: information gathering, information representation, personalisation execution, and system evaluation. The survey proposes a classification of PIR systems into three scopes: individualised systems, community-based systems, and aggregate-level systems. Paper was highlighting challenges and future research directions in the field of PIR. Sharma, A. [34] presents use of Intelligent Information Retrieval (IIR) systems to find out the more relevant information’s. In this paper, we present a brief survey of Intelligent Information Retrieval Systems and Intelligent agent models based on semantic web and ontology. The performance of such intelligent systems is calculated in terms of Quality of Search, Efficiency, Effectiveness, and satisfaction of users according to search result. Riloff, E. [35] Natural-language processing methods (such as information extraction), case-based reasoning techniques, and machine learning algorithms are all being applied to information retrieval tasks in the hopes of building more effective retrieval systems. Intelligent information retrieval is an exciting new direction for IR research. Tao, C. et al. [36] presents a comprehensive survey of recent advances in response selection for retrieval-based dialogues. In particular, we first formulate the problem of response selection and review state-of-the-art context-response matching models categorized by their architecture. Then we summarize some recent advances on the research of response selection, including incorporation with extra knowledge and exploration on more effective model learning. It highlights the challenges which are not yet well addressed in this task and present future research directions. Patel, V. et al. [37] discuss the importance of information on IR on the web. Also, discuss the importance of various traditional information retrieval models with their pros and cons to enhance the research area for future research work. This paper also briefs on information retrieval, types of data used in the IRS, IRS process, and preprocessing of IR. The article also discusses the literature survey of information retrieval based on the system, traditional models, recent trends, and applications of IR. Current IR models suffer from significant problems of not having accurate information retrieval. This paper focuses on the need for various machine learning techniques, including advanced ML (deep learning), to overcome the exact and actual retrieval of information from a vast heterogeneous collection of databases, including various web-based applications, by improving performance. Fan, Y. et al. [38] studies that pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Recently, a large number of works, which are dedicated to the application of PTMs in IR, have been introduced to promote the retrieval performance. Considering the rapid progress of this direction, this survey aims to provide a systematic review of pre-training methods in IR. To be specific, we present an overview of PTMs applied in different components of an IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leader boards. Moreover, we discuss some open challenges and highlight several promising directions, with the hope of inspiring and facilitating more works on these topics for future research.

Gender differences in digital literacy and information retrieval behaviors have been a topic of interest in several studies. For instance, studies have found that males and females may use different strategies for online searches, with males often being more confident in their search abilities but females showing higher accuracy in evaluating information sources [1] [2]. Additionally, females are more likely to engage in planned and structured search behaviors compared to males, who may adopt a more exploratory approach [39].

The habit of planning searches in advance is another critical factor that can influence search efficiency. Planned searches typically involve setting clear objectives, identifying key search terms, and selecting appropriate databases or search engines before initiating the search. This structured approach can lead to more efficient and effective retrieval of relevant information [5]. However, there is limited empirical evidence on how this habit varies between different demographic groups and its impact on search behaviors.