2.1. Information Behavior Model

The IB model is one of the indispensable components for study on the implementation of IB since the model profoundly reflects a core principle, logic behind functionalities, work processes, and workflow. It also explains the relationship with users for each step clearly. Due to the constant change of both user behavior and digital context, the IB model inevitably needs to evolve accordingly in order to effectively sustain its compatibility with user behavior in the digital environment. A summary of five well-known IB models under the digital context is described in Table 1. All of these five models required certain changes and modifications to comply with not only the present user behavior but also the digital environment.

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Table 1.

Summary of information behavior (IB) models in the digital context

The evolution of digital technologies determines the growth in transparency of the IB of a person as a subject of the digital environment and as a user of electronic resources. The digital development of society has required the technological development of the monitoring of the IB of a person in various spheres of activity. Astakhova (2018) proposed that the cognitive determinants of the IB of subjects of different activities in the digital environment are as follows: (1) the epistemological status of information; (2) the cognitive transformation of social and information needs, interests, and motives of subjects; (3) the cognitive managerial paradigm of the development of modern society; and (4) the imperative of the development of an individual’s cybersecurity culture.

2.2. Information Behavior Models and Information Retrieval Systems in the Digital Environment

Developing an IR system to be able to support, store, track, and analyze user IB in the context of the digital environment by applying an IB model as a blueprint still needs to be investigated. Further research into approaches for the IR system design revealed a number of new approaches proposed by previous researchers under the goal of covering as much of user IB as possible, whether it is a contextual design involving the system or information seeking context (Spink & Wilson, 1999), or a participatory design allowing those involved to take part in designing, etc. The reason behind this necessity of new approach designs is tied to the ever-changing nature of IB and the notion that an effective IB model’s framework must, by all means, demonstrate the work processes right from the beginning of information needs to information usage. If it can only respond to certain aspects of human IB, much more effort and time on the part of humans will be demanded for information seeking, which may result in failure in accessing the information needed (Spink & Cole, 2001). Dresang (2005) studied the research about youth information-seeking behavior with the Radical Change digital age. The use of computer technology provides greater connectivity in a social environment. Specific factors, for example, gender and collaborative behavior, may alter the interpretation and application of earlier research and may bring new and perhaps more positive perspectives to researchers. Yuan and Belkin (2014) applied an information-seeking dialogue model that integrated the system and user to evaluate the IR system. Results demonstrated that the dialogue structures indeed supported effective human IB patterns in a variety of ways. It is important to design an interactive IR system that can provide different types of support in a manner that searchers can easily understand, navigate, and use as they change information seeking strategy.

What has been discussed above testifies to how significant an IB model is and how it can dictate the developed IR system’s capacity and success. As a result, it is highly crucial that the design and development guidelines for the IB model and the IR system be in absolute concordance with each other. Nevertheless, as a result of changes in the digital environment generating changes to user IB, IR systems which provide users with services that are adapted to user behavior must change as well. This is consistent with the concept put forward by Saracevic (1996), where he proposed a solution to seek information with the consideration of the electronic environment.

Although studies and research on IB models have been around for a long time, there are still many more behavioral models that are presented. However, by changing the context and human behavior, research on human IB is still a challenging task and needs to be constantly developed. It is preferable to integrate new technology into a behavior model’s work processes (e.g., data mining and log file) so that the IR system developed under the model’s framework can comprehend and serve users to its fullest potential.

3.1. Document Analysis

In order to study the characteristics and capacities of current IR systems, the study was divided into two major parts, i.e. study of a current IB model in the digital environment, and study of the characteristics and capacities of IR systems.

3.2. The Delphi Technique

The Delphi technique is a forecasting process framework based on the feedback of multiple rounds of questionnaires and in-depth interviews with experts. This research used the Delphi technique in order to evaluate and improve the proposed framework for an IB model in the digital environment. This emphasized a cutting-edge design for an IR system, encapsulating absolute intelligence that can search for patterns and understand personalized IB.

The research population and samples in this study were comprised of experts and instructors equipped with years of extensive experience in IR, IB, and data mining. Purposive sampling was used to recruit the study samples theoretically described as “information-rich cases.” In other words, with their expertise and insight, exceptionally in-depth and the most illustrative data were contributed. There were six instructors specializing in IB, library and information science, data mining, and computer science.

The research instrument used in this study was questionnaires based on the results from Section 3.1.3. The questionnaires were divided into three parts, namely (1) an overall feature of the conceptual framework for an IB model supporting and covering user IB in an IR system, (2) work stage and workflow evaluation based on the conceptual framework for the IB model, and (3) log file storage and the concept of applying data mining methods for the conceptual framework for the IB model.

Data collection in the Delphi process was conducted in three rounds by using the questionnaires, in which the responses were analyzed and then used to construct the next round of questionnaire. For the first round, the experts provided a list of as many suggestions as they could for each one of the open-ended questions addressing human IB in order to identify a diversity of ideas, characteristics, and other issues about the proposed conceptual framework. The researcher adjusted the proposed conceptual framework to align with the list of crucial suggestions provided. For the second round, the experts were asked to rate and rank the importance of each one of the issues relating to the various items of the proposed conceptual framework, such as consolidated lists of work stage, alternative workflows, log file data which need to be stored, and the intelligent techniques which are important in each stage of the design of sophisticated IR systems in order to learn, recognize, search for patterns, and understand each personalized IB. The researcher analyzed the responses from the experts and adjusted the proposed conceptual framework accordingly. For the third round, final questions along with in-depth interviews were designed for the experts to confirm the accuracy of the proposed conceptual framework and to recommend more details about the components of the framework in all items from the previous round.

3.3. Focus Group with Experts

A focus group with the experts was organized to conduct a qualitative study that was based on the analysis of the results from the Delphi technique in Section 3.2 in order to confirm the proposed conceptual framework for the IB model based on the collaboration between the user and system for IR.

This study was conducted with a team of ten experts with practical experience in an IR system, which included system analysts, programmers, database designers, and also stakeholders who are involved with system usability (e.g., project managers, librarians, lecturers, and instructors). Before the study was conducted, all participants in the focus group were provided with a debriefing sheet, which reminded them of the purpose of the study and provided them with the research results from the Delphi technique.

During the processes of the focus group, the debriefing sheet was disseminated to all participants. The items consisted of (1) an overview of the proposed conceptual framework for the IB model, principles, work stages and procedures, and workflow; (2) data mining and intelligent techniques that might be used in each step; and (3) log file and data storage under the conceptual model of the collaboration between the users and the system. Participants were also asked questions pertaining to their experiences to evaluate the proposed conceptual framework for coping with techniques, scope of data set, database design, system testing, and also their expectation about the user-friendly characteristics of the IR system.

Based on the research results from Section 3.1 and 3.2, the data analysis was summarized, and it focused on improving the model and transforming the suggestions and opinions of the experts concerning human IB and the IB model together with the suggested log file, into an IB model based on the collaboration perspective between the user and system for an IR system which is capable of learning, recognizing, searching for patterns, and understanding personalized IB. Data mining methods could be applied to promote system performance for predicting and generating the results that met user needs. Results drawn from this study are intended to be used as fundamental data upon which other IB models can be developed and presented based on the collaboration perspective between the user and system.

4.1. Analysis of the Current Information Behavior Models in the Digital Environment

The results of the analysis and synthesis of the current IB model in the digital environment showed that it can categorize the characteristics of IB according to similar and different behaviors. This made it possible to determine the stages of the process of the IB model that could cover IB in the digital environment (Yangyuen et al., 2016) as displayed in Table 2. The eight stages are composed of: (1) Starting & Understanding, which is the starting process where users need to understand their problems and determine relevant information such as choosing and entering interesting websites; (2) Searching & Chaining, which consists of searching for the topic or other interesting data and chaining the information to sources related to that information; (3) Browsing & Scanning, which consists of browsing through data or exploring what users are searching for in order to locate featured contents; (4) Differentiating & Choosing, which consists of differentiating the gathered information and choosing what is considered useful; (5) Monitoring, which consists of monitoring new and updated information associated with the usage information, and staying attuned to the information’s instant changes by means of pushing from the system or entering websites marked as favorites; (6) Extracting, which consists of extracting useful information or information related to user needs; (7) Verifying & Evaluating, which consists of checking and evaluating the accuracy of the information received and the information sources; and (8) Ending & Using, which consists of ending the search process and using the retrieved information.

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Table 2.

Comparative analysis of the information behavior model’s operating stages

S stands for Stage (Example: S.1=Stage 1).

All of these eight stages were deemed to cover the entire IB model’s work processes, complying with human IB and effectively serving user IB in the digital environment, under the condition that certain sequences of the work processes of the model were adjusted. This is because today’s human IB positions itself under the streaming digital environment, which is defined by constant growth in terms of culture, technology, and information and communication systems. Furthermore, when there is integration between an IB model’s work stage and an IR system, it must be conceived with a bigger scale of flexibility, and internal work stages must be interleaved.

4.2. Analysis of the Current Information Retrieval System

The researcher used the system trail to investigate the characteristics of online databases. Two studied perspectives included: (1) characteristics and functionalities providing for information search and IR, displaying, and interacting between the user and system; and (2) log file storage and use. Data analysis was conducted to compare the systems’ similar and different characteristics as determined by the important stages of the work processes for an IB model covering IB in the digital environment. The outcomes yielded from the study are described as follows.

4.2.1. Characteristics and Functionalities of Online Databases

To analyze these aspects, the researcher utilized an Input-Process-Output: IPO concept. The subject matters used in the analysis included login, search and methods of search, displaying, sorting, scanning, differentiating, extracting, verifying, evaluating, using, and other related stages. Rigorous comparison with the IB model’s eight work processes was carried out (Yangyuen et al., 2016). The characteristics and functionalities of the online databases are shown in Table 3. The abbreviations of the online databases are as follows. ABI: ABI/INFORM Complete; ACM: ACM Digital Library; EDS: EBSCO host; EM: Emerald Management (EM92); IEEE: IEEE/IEE Electronic Library (IEL); PQ: ProQuest; SD: ScienceDirect; SL: SpringerLink; and WoS: Web of Science.

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Table 3.

Characteristics and functionalities of online databases

ABI, ABI/INFORM Complete; ACM, ACM Digital Library; EDS, EBSCO host; EM, Emerald Management (EM92); IEEE, IEEE/IEE Electronic Library (IEL); PQ, ProQuest; SD, cienceDirect; SL, SpringerLink; WoS, Web of Science.

The following findings encapsulated all featured characteristics of the present IR system.

• (1) Starting and Understanding: The researcher focused on design and system services regarding member subscription, login, and user account setup. Findings showed that every online database was equipped with member subscription and account creating functions. In some databases, the detail saving process simultaneously progressed with the search process, such as keeping records of an output display, which was processed for future relevant notifications when the user logged back into the system. Such a process was in congruence with the making of a user model for recommendation systems, with emphasis on a personal profile’s database in order to analyze and predict the system’s user behavior, which was also proposed by Knijnenburg, Willemsen, Gantner, Soncu, and Newell (2012).

• (2) Searching and Chaining: The researcher investigated function designs and searching and chaining techniques or methods. It was found that every online database encompassed identical search patterns and functions. Both basic search and advanced search functions were available. Apart from this, a search history saving service in several databases was also available. There was search query saving consistent with the notions proposed by Sugiyama, Hatano, and Yoshikawa (2004) and Speretta and Gauch (2005), which had taken search history into account for future relevant searches. Of all the available functions, it was evident that the IR system needed to have saved search history and the chaining of user data to get it oriented toward the users’ behavior effectively.

• (3) Browsing and Scanning: The researcher studied the use of conditions in browsing and search result scanning. Every online database was found to have the same functionalities. Browsing under determined conditions or filters could be done, such as for titles, authors, A-Z alphabetical sorting, or fields of study. Users could access the targeted data in a timely manner.

• (4) Differentiating and Choosing: An investigation in this aspect was dedicated to the system’s tools defined to differentiate data to properly choose genuinely useful and relevant information. The results of the analysis affirmed similar functionalities for all the online databases. Users were entitled to specify search filters and sorting or displaying through a result refining, sorting, and showing function. In addition, there was also a recommendation function that was designed to assist users in choosing information so that related information was selected. The function would constantly analyze other users’ search results and propose related data. This function required less effort on the user’s part and allowed for quicker searches. Therefore, the information proposing function was deemed to be indispensable in today’s IR so that the system could properly match dynamic user IB.

• (5) Monitoring: Information tracking was studied with methods for tracking new information that met the needs or interests of users. Most databases were designed to have alerts in various formats that tracked information and alerted users through various channels, especially via e-mail. Monitoring the systems’ newly added information was by all means of absolute importance for present IR systems.

• (6) Extracting: The study focused on function designs, techniques, or methods for extracting relevant useful information. It was found that at this stage, users needed to rely on themselves and no online database has been designed to extract information yet.

• (7) Verifying and Evaluating: The study found that the information system’s work processes could assist the users through its various functions, such as highlighting, biometrics, usage, or citations, to help users identify and select the relevant information. These data assisted learners in evaluating and choosing relevant information.

• (8) Ending and Using: This aspect was concerned with functions for data saving, using, sharing, ending, and logging out. It was found that every online database adopted the same fundamental functions, such as downloading, saving, exporting, printing, logging out, and sharing on social media.

The analysis suggested that the characteristics and functions of online databases comprehending user IB were similar, which was in accordance with the principles of the process of the IR system. Further analysis demonstrated that each database’s unique function stemmed from different IB for that particular database. Examples included the display function, which showed relevant data to promptly facilitate the inexperienced user’s seeking of information. Sharing data on social media is a vital function for the digital era’s users, who are generally oriented to data sharing. It was evident that several online databases endeavored to design a system’s work processes in parallel with user IB for the best search experience to be achieved.

It is utterly significant that the user IB analysis concept and IB model be employed as the foundation for the IR system’s design and development. Such practices establish congruence between the system and user IB, especially those engulfed in the digital environment. Nevertheless, analysis and synthesis of all the eight stages of an IB model and IR systems strongly indicated the necessity for modification of the models’ work processes, including modifications in process switching, flowing, and classifying of the models’ work processes. Principal reasons to affirm the modifications included:

• (1) User IB does not naturally occur in a fixed sequence from Stage 1 (Starting & Understanding) to Stage 8 (Ending & Using) in a linear fashion. It, in fact, functions in a loop manner. For example, users will be compelled to navigate back to the search and chaining stages after having acknowledged data irrelevance from their first search attempts. Therefore, the models’ process flow should develop flexibility and non-sequential work processes.

• (2) Human IB inherently evolves in close relation to the digital environment contexts. Information, for instance, is being increasingly shared on social media to other interested users.

• (3) The currently available IB model’s capacity has been intensively governed by human abilities. This fact implies both benefits and drawbacks. Inexperienced users were likely to face certain difficulties in their search information attempts, and so practical behavior model design and analysis carried out under collaboration perspectives were deemed suitable for user behavior.

• (4) In Stage 1 (Starting & Understanding), the users were required to actively acknowledge their problems and be able to identify the information they needed. However, the IR systems should allow easier and more convenient access to source data for searches.

• (5) In Stage 2 (Searching & Chaining), it was found that the chaining process could be conducted under multiple processes within the behavior models. For example, after having differentiated the data, the users proceeded to chain them to choose information. Furthermore, the chaining could also be carried out in the search stages to choose information from search results. Consequently, a number of sub-processes could be embedded within more than one core process.

• (6) In Stage 3 (Browsing & Scanning) and Stage 4 (Differentiating & Choosing), the holistic foundation of the user work behavior was not covered. Quite a few subprocesses existed within each of the major processes, such as displaying and sorting.

• (7) In Stage 5 (Monitoring), keeping track of newly updated information and making suggestions to users were an exceptional part of the work process. Its work sequence must, however, be reordered. That is because potential information monitoring should function 24/7 even when users are not active.

• (8) In Stage 6 (Extracting), the users’ ability was intensively involved in order to extract relevant information. Nowadays, it has become a habit for users to export all the data before logging out. That process occurs in the Ending & Using stage, reflecting the obvious change in user behavior.

From what has been discussed above, the IR system must accommodate the aforementioned user’s IB in the digital environment. It has been proven that the gap between the evolving IR system and today’s IB model is still huge. Such a gap urgently requires analysis and synthesis in order to develop new IB models that are commensurate with the digital environment’s contexts. That includes a redesign for more practical work processes and workflows within the behavior model.

4.2.2. Log File Storage of Online Databases

Because of the limitation of revealing log file data from the online databases, the researcher used the system trail to observe the response behavior of the system instead. The researcher analyzed log file data storage by means of studying the systems’ functions, allowing users to save data such as member subscription, search history, subscribing for interesting information, and also observing the interaction between the proposed information from the systems and users. The analysis revealed findings which are detailed in Table 4.

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Table 4.

Analysis of log files by observing the interaction between the information retrieval system and the user of online databases

IP, internet protocol.

The following findings revealed observable stored data of the online databases.

• (1) Starting and Understanding: Regarding signing up, the researcher studied the data storage of member signup, login, user account setup, and data saving. All databases were equipped with sign-in and personal setting functions, which basically consisted of the name and e-mail address of users. Some databases stored additional personal data (e.g., job title, subject areas of interest).

• (2) Searching and Chaining: During the use of the systems, the users entered keywords and operators which are referred to as a query, and then the systems displayed search results. There are several sub functions; for example, in some databases, search results featured vividly highlighted search terms, or the features of display and query saving were adopted. Systems also had a search saved function for storing queries and search results, and a search history function was available to save keywords, search options, and results. The systems could notify and also remember the search terms entered for each search. Such capacity allowed an easier and faster process in case users needed to use certain outputs again. In the searching and chaining process, with a data storing system designed to holistically save the user’s log file, the systems’ capacity will be elevated to achieve better orientation toward user behavior or interest and analyze and predict behavior patterns. Hence, IR systems in the present digital environment must be designed to effectively save users’ searching and chaining log file data (e.g., keywords, related keywords, search terms, search operators, queries, search histories, and URLs). This needs to be carried out in order for the systems to assist users in achieving better and faster work and to perform analysis based on the stored data to propose search terms relevant to the user’s interest.

• (3) Browsing and Scanning: An array of browsing functions was available, but the log of data for browsing was not included. If the system stored the log of data for browsing in that regard (e.g., topics, subjects, journals, and books), then a user behavior analysis could be performed to determine the user’s marked data browsing characteristics. An IR system with such features possessed the capacity to automatically propose user matching data browsing patterns. This was consistent with the notions put forward by Jiang, Pei, and Li (2013), which involved analyzing data for browsing and estimating behavior patterns based on it.

• (4) Differentiating and Choosing: The databases provided a function for choosing, filtering, sorting, and displaying data. The system trial revealed a lack of data storage. If an IR system possessed the ability to save data on filtering, sorting, and displaying, it would help users save time. This was consistent with Jiang et al. (2013), who stipulated about extracting data from the users’ system and recording search history to analyze user behavior. Moreover, when users chose information from the databases equipped with the recommendation function, data playing a large role (e.g., titles, keywords, download histories, related keywords, etc.) were presented. This was to facilitate the system to analyze and search relevant information more effectively.

• (5) Monitoring: The online databases stored data (e.g., alert settings – journal alert, book alert, citation alert, search alert, and subject alert; my subscribed; and my favorite) and used the monitoring function to track new information and push information that matched the interests of users via e-mail.

• (6) Extracting: The researcher studied data storage in terms of the users’ useful data for extracting. The online databases were not equipped with this capacity.

• (7) Verifying and Evaluating: The result was clear that the users’ ability played a role in this stage. The systems could only propose data concerning usage history (e.g., metrics or bibliometrics, citation count, and cited by). This was to demonstrate the estimated level of the information’s reliability as well as its relevance.

• (8) Ending and Using: The data stored by the systems were usage statistics, including the statistic of download, save, print, and export results.

It was concluded that all online databases were equipped with user accounts and personal profiles, where data regarding the users’ interests or favorites were stored. However, each online database was characterized by its uniqueness and different purposes for information usage. For example, ScienceDirect and Emerald Management provided “recommended articles” and “your favorites” functions, where both functions recorded data regarding the users’ favorite information, indicating each user’s interest and associating them with IB. This demonstrated that all online databases were launched with the design, data storage, and analysis of the users’ system usage in order to facilitate the users’ work.

Therefore, the design and development of IR systems required proper log file data storage analysis and design. The stored data must accommodate system user IB analysis and allow data mining as well as other applicable approaches to be applied in order to accommodate each personalized IB. Without the storage of data useful for data analysis, an IR system will not be able to accommodate each personalized IB.

4.3. Study Results on the Information Behavior Model’s Framework Development

The researcher assembled and took all the results into consideration on the basis of the system approach’s participatory principle, which was utilized in an information system’s abilities in enabling an IR system to learn, search for patterns, recognize, and understand an individual’s human IB in terms of the behavior and interaction between the users and systems. This is to empower the users’ ability and cognitive process to be considered as a principle guideline for IB model development. The created behavior model must appease and support all aspects of human IB in the digital environment, which is highly beneficial for further development of IR systems, such as online databases and library automation systems, as illustrated in Fig. 1.

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Fig. 1.

Conceptual framework for an information behavior model based on the collaboration perspective between user and system for information retrieval.

Fig. 1 illustrates the IB model’s internal functionalities right from the start until the end. Within the process, the model can work in a loop fashion. The IB model was comprised of seven stages which included: (1) Starting and Suggesting, (2) Filtering, (3) Differentiating, (4) Selecting and Proposing, (5) Verifying and Evaluating, (6) Using and Ending, and (7) Monitoring. The goal was to align the functionalities with the IR system’s user’s IB under the framework of the IB model’s functionalities. Symbols that were used included: S, symbolizing actions carried out by the system; U, symbolizing those taken by the users; and a disk representing the saving of the users’ log files for later user behavior analysis. Each stage can apply data mining techniques to support the work processes to be more efficient. The collaboration between the system and user in the IB model, based on the collaboration perspective of the user and system for IR and the related log data recommendation, is shown in Table 5. According to the conceptual framework for the behavioral model, the details for the seven steps are as follows:

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Table 5.

Collaboration between the system and user and the related log data recommendation according to the proposed conceptual framework

All of these seven stages of the IB model’s conceptual framework were unique and consistent with the digital environment in order for the IR system to be able to learn, search, recognize, and understand an individual’s human IB. The model’s featured characteristics were as follows: