State of the art

2.2.1 Information-seeking behaviour models

Ellis’s model: Ellis’s model is based on the first investigation conducted by Ellis et al. on empirical studies covering a population of social scientists [Ellis 1989].

A second study was conducted in [Ellis et al.1993], in which the authors com-pared scientists in the physical sciences to those in the social sciences. In [Ellis & Haugan 1997], the authors compared engineers and research scientists in an oil company. The methodology used to interview the scientists was semi-directive interviews. In the first study [Ellis 1989], the authors modelled six features (start-ing, chain(start-ing, brows(start-ing, differentiat(start-ing, monitoring and extracting). In the 1993 study [Elliset al. 1993], the authors added two more features (verifying and end-ing). The extended (last) model (see figure 2.1) described eight features of the information-seeking patterns of scientists:

Figure 2.1: A stage process version of Ellis’s behavioural framework (1993) [Elliset al. 1993]

• Starting: Identifying references that could serve as a starting point of the research cycle; asking colleagues and consulting literature reviews, online cat-alogues, indexes and abstracts.

• Chaining: Following footnotes and citations in known material, the direction of which can be forward or backward.

• Browsing: Looking for information in areas of potential interest.

• Differentiating: Using known differences in information sources, such as au-thors’ names and journal hierarchies, to filter information.

• Monitoring: Keeping knowledge about users’ area of research up to date by regularly following particular sources.

• Extracting: Selecting and identifying relevant material.

• Verifying: Checking that the information is correct.

• Ending: Activities that complete the information-seeking process.

Ellis suggests that the interaction and the order of the eight features can vary.

This model can be useful in providing a set of categories that allows for the analysis of information seeking at the individual level. In [Ellis & Haugan 1997], the authors conclude that even if sources of information differ between different fields of research, the behavioural characteristics remain similar. The authors consider the behavioural model to be quite robust in relation to the information-seeking patterns of physical scientists, engineers and social scientists.

Kuhlthau model: Based on different longitudinal⁴ empirical studies, Kuhlthau [Kuhlthau 1991] proposed a model composed of six stages (see figure 2.2). The first study concerned high-school students who had to write an essay. The model developed by Kuhlthau suggests that people search and use information differently depending on the stages of the search process, which are defined as follows:

• Initiation: Users become aware of the need for information when they face a problem.

• Selection: Users identify and select the general topic for seeking information.

• Exploration: Users seek and investigate information on the general topic of interest.

• Formulation: Users fix and structure the problem to be solved.

• Collection: Users gather pertinent information for the topic of interest.

• Presentation: Users complete their search and implement the results of their search.

This model, in contrast to Ellis’s model, proposes features in an order that cannot be changed. This difference between the two models may derive from the differences in the populations studied and in the tasks performed to retrieve infor-mation. Kuhlthau’s model also acknowledges the subjective characteristics (feelings and thoughts) of the user at each step.

In his article, Wilson [Wilson 1999] compares Ellis’s model (see figure 2.1) and Kuhlthau’s model (see figure 2.2) to determine the similarities between them (see figure 2.3) and to merge the models, although the hypotheses underlying the two models are slightly different.

4The study was conducted over several years.

Figure 2.2: Information Search Process (ISP), Kuhlthau’s model, 1991 [Kuhlthau 1991, p.367]

Figure 2.3: A comparison of Ellis’s model and Kuhlthau’s stage process model in [Wilson 1999, p.256]

Wilson’s model Wilson began developing his model in 1981 (see figure 2.4) [Wilson 1981]. The aim of his model was not to describe information behaviour but to draw attention to the interrelationships among concepts used in the field. He suggests that information behaviour results from users’ recognition of needs. This behaviour can take several forms: 1) through a formal system, such as an informa-tion system or other informainforma-tion sources, or 2) through informainforma-tion exchange. In both cases, failure or success can be tested. When users are successful (i.e., when they believe that the information retrieved can fully or partially satisfy their needs), the search process can end. However, if the information does not satisfy the user’s needs, the user performs the search process again.

Figure 2.4: Wilson’s model of information behaviour (1981) [Wilson 1999, p.251]

In the same paper, Wilson presents a second (see figure 2.5) model for information-seeking behaviour that takes into account basic needs:

• Physiological states, such as the need for food, water, and shelter;

• Affective states (sometimes called psychological or emotional needs), such as the need for attainment or domination;

• Cognitive states, such as the need to plan or to learn a skill.

Wilson argues that these three types of needs are interrelated. When an in-dividual engages in information-seeking behaviour, these needs may be fulfilled or may remain unfulfilled. The other factors that may intervene are the role of the person and the environment within which the person is living (see Figure 2.5). This

Figure 2.5: Wilson’s model of information-seeking behaviour (1981) [Wilson 1999, p.252]

model, as described by Wilson, is a macro-model that describes information-seeking behaviour and suggests how information arises and what may prevent or aid the actual search for information.

Wilson’s 1996 model [Wilson 1999] (see figure 2.6) represents a major revision of Wilson’s second model of 1981 (see figure 2.5), although the basic framework persists. The barriers are represented as "intervening variables", which, in the previous model, could be preventive or supportive of information-seeking behaviour.

Wilson incorporates some concepts that activate information-seeking behaviour: 1) risk/reward theory, which may help to explain which sources of information may be used more often than others, and 2) social learning theory, which incorporates the concept of self-efficacy⁵

The incorporation of different variables that may or may not help the user to initiate searching behaviour is a good start to account for the barriers that can arise in users’ information-seeking process, which should be considered in the construction and evaluation of IRs. These "intervening variables" can have implications for the decisions involved in the construction of IRs and the methods and variables measured for evaluation.

5"Perceived self-efficacy is defined as people’s beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives."

[Bandura 1994, p.71].

Figure 2.6: Wilson’s model of information behaviour (1986) [Wilson 1999, p.257]

The Ellis model has been used in other studies, such as the study by [Meho & Tibbo 2003], in which the authors addressed the change introduced by the Web in information-seeking behaviour. The authors interviewed by email social scientists who were working in the same field but in different areas of the world.

The authors’ findings are based on the eight features of the Ellis model and two additional features they identified for different purposes:

• Starting: Scientists first search through their own personal collection. They also search online catalogues, indexes and abstracts. Scientists primarily use literature searches as a way to determine what has been published on a given research topic or to find background information on a certain topic.

• Chaining: Chaining is used to identify new sources of information or to sat-isfy a potential information needs. Chaining is performed by following the references obtained through reading and personal content (literature already known and owned by scientists in their own database and files). The resulting chains of citations are primarily based on topical relevance, the importance of the authors’ research, novelty, the publisher’s reputation, the time it takes to locate the information / material, or citation frequency). The authors show that the most important features for the chains of citations are topical rele-vance, authors’ reputation, and availability.

• Browsing: All of the interviewed scientists were engaged in this part of the process, and it was an important aspect for them. Authors divided this part of the process into two different processes: 1) browsing journals by searching through new issues of journals and the table of contents of relevant books and 2) browsing online catalogues, indexes and abstracts.

• Monitoring: In their results, the authors demonstrate the different ways sci-entists maintain awareness of research developments in their topic of interest.

One of the ways that scientists maintain this awareness is by consulting lists of distribution, journals (based on their own subscriptions), conferences and colleagues.

• Accessing: The authors show that accessing information can be an issue; de-pending on the source, access can be difficult to obtain.

• Differentiating: The scientists interviewed in the [Meho & Tibbo 2003] study evaluate and judge sources according to their nature, quality, relative impor-tance and usefulness. They also use different sources to overcome problems of bias.

• Extracting: Scientists identify and select relevant material from selected sources. The authors of the study analysed two types of extraction activ-ities: those applied to direct sources (e.g., books and journal articles) and those applied to indirect sources (e.g., bibliographies, indexes and abstracts, and online catalogues).

• Verifying: The scientists interviewed in this study were preoccupied with the reliability and accuracy of their sources because of the nature of their research.

In fact, the extent of this preoccupation can vary with the nature of the research topic and the nature of the relation that scientists may have with national and government organisations.

• Networking: Scientists communicate and maintain a close relationship with a broad range of people, such as friends, colleagues, and intellectuals working on similar topics, members of national organisations, government officials, and booksellers.

• Information managing: In the interviews, scientists expressed the need for systems that help them filter, archive and organise the information they have collected.

Meho and Tibbo find that the personal collection is the most important source of information for scholars. The authors also show that the activities in which scientists are involved are not necessarily or entirely sequential, as Ellis argued, but they can be generally divided into four categories: 1) searching, 2) accessing, 3) processing, 4) ending.

2.2.2 Scientific inquiry models, nature of the needs of information, new forms of behaviour

Scientists have created strategies to read documents rapidly. In [Tenopiret al. 2009], the authors analysed the reading patterns of science faculty members from 1977 through 2006 and found that the amount of reading increased, but the time spent reading an article diminished from 48 minutes in 1977 to 31 minutes in 2005. How-ever, because scientists read more articles, they spent 120 hours annually reading in 1977 versus 144 hours annually in 2005. The authors found that the amount of read-ing varied with the field of research; for example, medical faculty read 414 articles per year versus 331 read by faculty in the natural sciences and 233 read by faculty in the social sciences. In [Nicholaset al. 2007], the authors found that users who tended to visit a Web site just once tended to publish the fewest papers. The au-thors found that the average time spent online for each full text item was 20-30 sec, which suggests that participants were checking documents online but not reading them online. The authors also found that the longer an article was, the more likely participants were to read the abstract or the summary and the less likely they were to look at the full text. Participants also spent more time online (42 sec) reading short articles (4-10 pages) than reading large articles (32 sec). The authors showed that search behaviour can vary as a function of academic field and the amount of experience users have in their research field. The study shows that scientists have developed different strategies to read faster and to avoid unnecessary reading. It also appears that scientists tend to avoid reading long articles. By analysing these types of strategies, we can assume that the more targeted information is, the more useful it will be to scientists. Indeed, scientists need techniques that will help them directly target the information they are seeking. Consequently, IR designers must understand the nature of the information needed.

In the literature, we can find several studies that describe the nature of the information needs of scientists.

In [Garvey et al. 1974], the authors defined the nature of information needed by scientists as follows:

• To aid in the perception or definition of a problem

• To formulate a scientific or technical solution

• To place work in its proper context with similar work that has already been completed

• To relate work to ongoing work in an area

• To select a design/strategy for data collection

• To select a data-gathering technique

• To design equipment or apparatus

• To choose a data analysis technique

• To enable full interpretation of collected data

• To integrate findings into the current state of knowledge in an area and the stages of research in which scientists may be involved:

1. Preliminary planning (general)

2. Specific planning: Theoretical/conceptual 3. Preparation of written research proposal

4. Preliminary experimentation/field trials or mockups 5. Calibration, pretesting, etc.

6. Design and development of equipment/apparatus 7. Formulation of experimentation/study design 8. Collection of data

9. Analysis of data

10. Interpretation of results 11. Preparation for report of work

In [Garveyet al. 1974], the authors also discuss the interaction between the na-ture of the information needs of scientists and the stage of research scientists have reached. For example, we can observe (see figure 2.7) that in the preliminary plan-ning stage, the nature of the information needs of scientists involve the perception or definition of a problem, the formulation of a scientific or technical solution, the placement of their work in its proper context with similar work that has already been completed (this nature of information need is involved in almost all stages except for the design and development of the equipment or apparatus) and the relation of their work to ongoing work in an area.

Figure 2.7: Garvey’s table for the nature of information needed at each stage by the majority of scientists involved in that stage of scientific work ([Garvey et al.1974, p.120])

In a thorough and wide-ranging study on changes in scholars’ article-seeking and reading patterns, [Tenopiret al. 2009] showed that scientists in "hard" sciences in the US (n=880) read scientific documents for the following purposes: research (48.5%), teaching (22.5%), writing articles, proposals, or reports (10.8%), current awareness, keeping up to date (8.0%), continuing education (3.0%), advising oth-ers (2.9%), and other reasons (4.2%). The authors also asked scientists how the last article they had read had affected their ways of thinking: 54.5% were inspired with new ways of thinking/new ideas, 39.9% obtained improved results, 26.6% nar-rowed/broadened/changed their focus, 12.4% saved time or other resources, 11.6%

resolved technical problems, 7.4% were able to complete their work more quickly, 6.0% resulted in collaboration/joint research, and 5.6% provided other responses.

In [Palmeret al. 2007], the authors interviewed subjects and found that scien-tists review documents for different purposes: to assess their own work and the work of others, to solve short-term instrumental problems, to consult and talk shop and to form complex intellectual relationships among the vast body of neuroscience findings.

In contrast with Ellis’s results [Ellis & Haugan 1997], which show no differences between academic fields in information-seeking behaviour, other studies show that information-seeking behaviour can vary depending on the field of research or can be affected by the personality of the seeker. This difference may be due to the fact that the more recent studies focus on a different level of analysis from Ellis’s study.

Whereas the Ellis model analyses information-seeking behaviour at the meta-level, more recent studies analyse this behaviour at a lower meta-level, taking into account what type of information scientists seek and how they do so.

In [Garvey et al.1974], the authors found that depending on the field of research, scientists’ experience in research, and scientists’ experience in their exact field of research, scientists do not share the same information needs. In fact, these needs vary depending on the stage of research in which scientists are involved.

In [Bates 1996], the authors assume that the information-seeking process should be even more complex for interdisciplinary studies than for "classical academic disci-plines". In [Whitmire 2002], the authors study the difference in information-seeking behaviour between different academic disciplines. The authors classify academic fields of research into the three dimensions described by Biglan⁶. The authors sur-veyed a total of 5176 undergraduate students in four-year institutions and found that differences exist between the "hard" and "soft" disciplines, between life and non-life sciences, and between pure and applied science. Students in "soft" disci-plines and "life" and "pure" science used online catalogues to a greater extent, asked librarians for help, read reference sections more often, used indexes more often, de-veloped their bibliographies more extensively, checked for citations more often, and checked out books more often than students in the "hard", "applied" and "non-life"

sciences did.

6Biglan [Biglan 1973]: 1) discipline-level paradigm (hard science and soft science); 2) practical application of research (pure and applied science); and 3) degree of involvement with the living or organic object of study (life versus non-life)

In [Kembellec 2012], the authors also detected some differences between different fields of research (196 PhD students and researchers in the human sciences, social sciences and hard sciences were surveyed). The authors found that 89% of searches were performed using a commercial information retrieval system, with an overall preference for Google Scholar. For the hard sciences, the authors found no differences between PhD students in the beginning stages of their programmes, PhD students at the end of their programmes, and researchers. For the social sciences and human sciences, searches were also performed using other systems, such as OPAC. The authors found differences between PhD students who were beginning to write their theses and those in the final stages of writing their theses, with the number of PhD students using systems such as OPAC decreasing from (44%) to (33%). Among researchers, 44% used OPAC.

In [Nicholas et al. 2007], the authors address the bouncing⁷ behaviour of users of ScienceDirect. The authors analysed the logs of 800 senior academics in the life sciences, mathematics and medicine. [Nicholaset al.2007] found that bouncing behaviour varies different between fields of research. The authors also found that the number of times an item is viewed during a session changes as a function of the field of research. Medical scientists tend to show the strongest bouncing behaviour.

The authors also found that graduate students exhibit more significant bouncing behaviour than senior scientists do.

Heinström [Heinström 2005] defined three different types of patterns for surfing the Web. She analysed the relations between information-seeking behaviour and personality traits and found that psychological features had a stronger influence on student information-seeking behaviour than students’ academic background or their stage of thesis writing. The author categorised the participants into three groups:

• Fast-surfing students: Students who wanted information available quickly and easily with the least effort. Depth of information was not a concern. Students judged the relevance of the documents by their look, the type of document and whether documents were easily available.

• Broad-scanning student: Students with exhaustive and flexible information-seeking behaviour over a wide range of sources. These students developed their search gradually rather than planning it.

• Deep-diving student: These students went beneath the surface in seeking in-formation. Students of this type showed quality and conscientiousness in their searches and worked hard to obtain high-quality information. These students

• Deep-diving student: These students went beneath the surface in seeking in-formation. Students of this type showed quality and conscientiousness in their searches and worked hard to obtain high-quality information. These students