Ongoing Experts Strategies for Exploratory Information Seeking on the Web in the Field of Scientific Research / submitted by Julia Winter

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JOHANNES KEPLER UNIVERSITY LINZ Submitted by

Julia Winter

Submitted at

Institute for Innovation Management

Supervisor

Univ.-Prof. Dr. Matthias Fink

Co-Supervisor Dr. Michael Gusenbauer April 2018

Ongoing Expert’s

Strategies for

Exploratory

Information Seeking on

the Web in the Field of

Scientific Research

Master Thesis

to obtain the academic degree of

Master of Science

in the Master’s Program

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STATUTORY DECLARATION

I hereby declare that the thesis submitted is my own unaided work, that I have not used other than the sources indicated, and that all direct and indirect sources are acknowledged as references. This printed thesis is identical with the electronic version submitted.

Place, Date

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Table of Figures

Figure 1: Ellis's Behavioural Model of Information Seeking ... 4

Figure 2: 6 Stages of ISP Model, Overview of Behaviour per Stage (Kuhlthau, 1991)... 5

Figure 3: Marchionini's Process Model of Information Seeking, (Marchionini, 1995, p. 50) ... 6

Figure 4: Information Seeking Behaviours and Web Moves (Choo et al., 2000, p. 5) ... 6

Figure 5: Research on Expertise and Web Search ...11

Figure 6: Comparative Studies on Web Search Behaviour ...12

Figure 7: Sample: Covered Fields of Study ...14

Figure 8: Devices Used for the Purpose of Exploratory Online Information Search ...15

Figure 9: Devices Used for the Purpose of Exploratory Online Information Search_Primary and Secondary Choices ...16

Figure 10: Devices Used per Participant ...16

Figure 11: Underlying Reasons for the Choice of Secondary Device ...17

Figure 12: Browsers Mentioned ...17

Figure 13: Browser Choice per Participant ...18

Figure 14: Primarily Used Browser ...18

Figure 15: Pre-Settings on Browser/Device ...19

Figure 16: Usage of Multiple Windows / Tabs ...20

Figure 17: Purpose for Opening a New Tab (given in no. of participants mentioning the respective purpose) ...20

Figure 18: Purposes for Opening a New Window (given in no. of participants mentioning the respective purpose) ...21

Figure 19: Types of Information Searched For ...21

Figure 20: Website Types Used ...22

Figure 21: Primary Websites Used ...23

Figure 22: Underlying Purposes for Search Engine / Database Choice ...24

Figure 23: The Search for Scientific Articles by Means of Search Term Variation ...25

Figure 24: The Search for Books ...26

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Figure 26: The Search for Particular Scientific Articles ...27

Figure 27: Usage of Functions Provided by Search Engines/Databases ...28

Figure 28: Perceived Information Overload ...29

Figure 29: When Does Perceived Information Overload Occur ...29

Figure 30: Finding the Right/Relevant Search Terms ...30

Figure 31: Familiarizing with a Relatively New Topic...31

Figure 32: Usage of Boolean Operators / Other Command-Symbols (in percentage of all 13 participants using Boolean operators or command-symbols) ...32

Figure 33: Evaluating Quality / Relevance ...33

Figure 34: Evaluation of Quality / Relevance per Participant ...33

Figure 35: First Screening Criteria ...35

Figure 36: First Screening Criteria per Participant ...36

Figure 37: Second Screening Criteria (given in % of those engaging in a second screening regularly or exceptionally) ...36

Figure 38: Second Screening of the Material ...37

Figure 39: Finding Information Back: Breakup ...38

Figure 40: Finding Information Back (in no. of participants mentioning each action) ...38

Figure 41: No Access Right: How to Get the Material Anyway ...39

Figure 42: Basic Levels of Planning ...41

Figure 43: Degree of Planning: Three Planning Levels ...43

Figure 44: Degree of Planning: Distribution of Participants ...44

Figure 45: Keeping the Overview ...44

Figure 46: Types of Documentation in Percentage of Total Sample ...45

Figure 47: Documentation per Participant ...46

Figure 48: Classification based on two Dimensions: Planning & Documentation ...47

Figure 49: Classification Based on two Dimensions: Planning & Documentation Considering Fields of Study ...48

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List of Abbreviations

e.g. (for example) i.e. (id est)

JKU (Johannes Kepler University) L.o.A. (Level of Abstraction) no. (number)

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1. Introduction

The rapid progress of information and communication technology during the last decades brought along pervasive digitization and an ubiquitous use of the world wide web. A development which enables 24/7 availability, generation and exchange of information and hence entails exponentially growing amounts of data (Chen, Mao, & Liu, 2014). An IBM study found that 90% of the world data in 2013 had been created just in the two years preceding (Jacobson, 2013). Moreover, in 2014 the worldwide amount of data was forecasted to more than double every two years (EMC Digital Universe with Research & Analysis by IDC, 2014). This trend significantly effects business (e.g. Benselin & Ragsdell, 2016; Feldman & Sherman, 2001; Mayer-Schönberger & Cukier, 2013), overall society and not least science (Mayer-Schönberger & Cukier, 2013).

The days when scientists were dependent on brick-and-mortar libraries are long past. Instead, the internet permits an access to research and corresponding information from all over the world independent of time and place (Willinsky, 2006). The continuously rising amount of scientific information available certainly offers a lot of opportunities, but brings along the cost of messiness and increases the risk of information overload (Alhabashneh, Iqbal, Doctor, & James, 2017, p. 18; Mayer-Schönberger & Cukier, 2013). Hence, the ability of knowledge workers to effectively and efficiently search for information on the web increasingly gains importance, not least because unsuccessful or inefficient search can lead to high cost, e.g. of time loss and diminished result quality (Feldman & Sherman, 2001).

Research in the field of information behaviour has shown a positive relation between expertise and search success (e.g. Aula & Nordhausen, 2006; Hölscher & Strube, 2000). What has not been investigated so far is the online information seeking behaviour of experts in the field of scientific research itself. Thus, best practices or strategic guidelines on scientific in-depth web research have not been developed so far. Instead, Bates (2010, p. 2387) states, that “as a rule, people – even including PhD scholars – develop what search skills they have incidentally to their primary efforts at research or problem-solving” and that “in sum, people often vastly under-utilize available resources and are often quite inefficient in finding what they do find.” The purpose of the study at hand is to fill this gap at least partly by identifying strategies of ongoing experts in the field of scientific research for exploratory online information seeking. It is conducted in the form of semi-structured interviews with a sample of 25 PhD students from various fields of study. The study focuses on exploratory search, which is characterized by a high degree of complexity and versatility.

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To identify strategies for exploratory online information seeking, three research questions will be set up and answered:

RQ 1: Which technological means do ongoing experts make use of for the purpose of exploratory online information seeking?

RQ 2: Which common action patterns can be identified for the purpose of exploratory online information seeking by ongoing experts?

RQ 3: In how far do ongoing experts organize their exploratory online information seeking process?

Overall the thesis is structured as follows: Chapter two describes its integration into the scientific field of information seeking behaviour by providing an overview about basic and related research. Chapter three provides more detailed information on the methods applied for the study itself and the subsequent analysis. Chapter four offers a comprehensive presentation of the results, followed by a discussion of the findings in chapter five. The thesis will end up with the final conclusion.

2. Integration into the Scientific Context

Chapter two aims at the integration of the study at hand into the wider scientific context and correspondingly includes an overview of both, the underlying basic research on Information Seeking (chapter 2.1.) and related research to the study conducted (chapter 2.2.). Finally, in chapter 2.3., a definition of the term exploratory search as applied in this thesis will be given.

2.1. On Information Seeking

Research on information seeking behaviour is to be integrated into the overall field of Human Information Behaviour. While human information behaviour entails “the totality of human behaviour in relation to sources and channels of information, including both, active and passive” information absorption (Wilson, 2000, p. 49), information seeking as a subset of the prior comprises active, purposeful and directed actions of information gathering, consciously engaged in for fulfilling an underlying information need (Spink & Cole, 2006, p. 25; Wilson, 2000, p. 49).

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Topically, Chapter 2.1. will be divided into (1) research on factors/components of information seeking, (2) behavioural and process models of information seeking and (3) research on information seeking in electronical environments.

2.1.1. Factors/Components of Information Seeking

Marchionini (1995) suggests a comprehensive model of seven factors interacting in information seeking: (1) An information seeker, (2) an underlying problem or need, (3) a search task, (4) one or several search systems, (5) one or several domains, (6) an outcome and finally (7) the overall setting information seeking takes place in. These factors are interrelated; thus, their interactions constitute the overall information seeking activity, just broadly explained in the following:

The information seeker as individual represents the centre of information seeking. He/she disposes of an individual information infrastructure, consisting of more or less comprehensive experiences in and information on various domains (fields of knowledge). In the respective setting the information seeker finds his/herself in, he/she becomes aware of an information problem or need which requires to be solved/satisfied. It is worth mentioning that the setting does not only affect the evolving of an information need or problem. It represents the situational and physical context which limits the information seeking process itself and thus also affects the other factors of information seeking notably. The information problem/need will be translated into a more or less precise search task, a “verbal statement of the problem or set of purposeful actions related to solving it” (Marchionini, 1995, p. 32). For solving the search task, the information seeker interacts with one or several information systems which contain information on one or several domains. The information extracted from a search system finally interacts with the existing individual information infrastructure of the seeker. The seeker’s prior information infrastructure (knowledge of search systems and knowledge of domains) initially limits the process, but the process itself then enlarges the individual information infrastructure (it will be accrued, tuned or restructured; see also Zhang & Soergel, 2014). This interaction hence leads to an outcome. The outcome of an interaction with an information system might finally either be the solution to/satisfaction of the seeker’s problem/need or the extraction of information serving the overall search process., e.g. the awareness of other useful search systems or an enlargement or specification of the search task. (Marchionini, 1995, pp. 32–49) As it is the stimulus evoking information seeking behaviour, the information need has been of huge interest in research. Two basic approaches on conceptualizing this stimulus can be identified: (1) The Information seeking as sense-making approach (Cheuk & Dervin, 1999; Dervin, 1983) and (2) information seeking as problem solving approach (e.g. Belkin, 1980,

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Ingwersen, 1986, Wilson, 1999, Taylor, 1968). In this context, an overall question of interest is, if the information need itself changes during information seeking or if various levels of need are revealed. (Cole, 1998, 2011)

2.1.2. Behavioural and Process Models of Information Seeking

Widely recognized research on information seeking as an activity is found in Ellis’ Behavioural Model of Information Seeking. Ellis’ Behavioural Model of Information Seeking focuses on the differing, general kinds of behaviour information seekers engage in. The corresponding research conducted focused on repeatedly appearing action patterns in course of information seeking of various groups of researchers (engineers, research scientists, research physicists and research chemists). All in all, the model consists of eight behavioural categories: Starting, Chaining, Browsing, Differentiating, Monitoring, Extracting, Verifying and Ending. (ELLIS, 1989; ELLIS, COX, & HALL, 1993) Definitions as given by Ellis et al. (1993) are to be found in the table below:

Figure 1: Ellis's Behavioural Model of Information Seeking

In 2003, Meho and Tibbo added the following three behaviour categories to the ones mentioned above: accessing (getting access to the information sources or material identified to be useful), networking (the usage of the own network to support information seeking) and information managing (the organization/sorting/storage of information found). Ellis’ Behavioural Model does not imply any conclusion on a sequencing or a possible interrelation of those behavioural categories. Thus, this behavioural model is not process-, but solely activity focused.

Widely recognized research on Information Seeking Behaviour as a process is conducted by Kuhlthau (1991), who suggests a process model of information seeking which comprises six

Starting Chaining Browsing Differenti-ating

Monito-ring Extracting Verifying Ending

“activities characteris tic of the initial search for infor-mation” “following chains of citations or other forms of referential connection between material” “semi-directed searching in an area of potential interest” “using differences between sources as filters on the nature and quality of the material” “maintainin g awareness of developme nts in a field through the monitoring of particular sources” “systematic ally working through a particular source to locate material of interest” “activities associa-ted with checking the accuracy of infor-mation” “activities characteristi c of information seeking at the end of a topic”

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stages: initiation, selection, exploration, formulation, collection and presentation. Kuhlthau additionally links actions performed in those stages to feelings and thoughts of the information seeking individual. The behaviour among the six stages can be described as a funnel-shaped engagement in information seeking with a stepwise increase of focus: From Seeking Background Information over Seeking Relevant Information to finally seeking focused information.

Figure 2: 6 Stages of ISP Model, Overview of Behaviour per Stage (Kuhlthau, 1991)

2.1.3. Information Seeking in Electronical Environments

Marchionini (1995) suggests a process model of information seeking directed towards electronic environments, including eight steps: (1) Recognition and acceptance of the problem/need, (2) definition of the problem, (3) selection of a source, (4) formulation of a query, (5) execution of this query, (6) examination of the results, (7) extraction of information and (8) reflection of the information and stop. This model kind of represents a hybrid-style between the previous two: Ellis’ Behavioural Model solely targets actions taken within information seeking, without any considering of their sequencing. Kuhlthau’s ISP model targets information seeking as a process but stays on a quite broad phase level. Marchionini’s model finally takes a process view as Kuhlthau does, but on a much more action focused level. Furthermore, what characterized Marchionini’s process model is its flexible character, as it does not describe one strict sequence of actions but integrates backward-loops of repetition, thus allowing for systematics and opportunism at the same time. Thus, Marchionini’s Model might imply behaviours suggested by Ellis on one hand, and on the other hand the action process suggested by Marchionini can take place in Kuhlthau’s ISP Process repeatedly.

Initiation Selection Exploration Formulation Collection Presentation

The Recognition of an information need Seeking Background Information The identification of the general topic to focus on The investigation of information on the general topic which was identified in the previous stage Seeking Relevant Information The formulation of a search focus based on the previous exploration of the topic The gathering of information within the framework of the previously set focus Seeking Relevant / Focused Information A presentation of the complete information search findings.

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Figure 3: Marchionini's Process Model of Information Seeking, (Marchionini, 1995, p. 50)

In 2000, Choo et al. took Ellis’s Behavioural model to an online level by suggesting translations of six of Ellis’s initial behaviour categories to web moves, thus by extending the behavioural categories of Ellis to a web environment. An overview of these web moves is provided in the table below.

Figure 4: Information Seeking Behaviours and Web Moves (Choo et al., 2000, p. 5)

Starting Chaining Browsing Differentiating Monitoring Extracting Behavioural Categories Ellis Behavioural Model of Information Seeking Ellis et al (1989, 1993, 1997) Identifying sources of interest Following up references found in given material Scanning tables of contents or headings Assessing or restricting information according to their usefulness Receiving regular reports or summaries from selected sources Systematically working a source to identify material of interest Suggested Web Moves by Choo et al (2000) Identifying Web sites/pages containing or pointing to information of interest Following links on starting pages to other content-related sites Scanning top-level pages: lists, headings, site maps Selecting useful pages and sites by bookmarking, printing, copying and pasting, etc.; Choosing differentiated, pre-selected site Receiving Site Updates using e.g. push, agents, or profiles; Revisiting ‘favourite’ sites Systematically searches a local site to extract information of interest at that site

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A widely recognized process-model of web seeking is the Berrypicking Model of Bates (1989). The berrypicking model describes a very flexible, so-called “evolving search” or “bit-at-a-time retrieval” (Bates, 1989, p. 409). Here, the information found on the way determines the further direction of the search process itself. Just as a person picking berries in the forest, the information seeker is described to move through the information space and pick up information from a variety of sources. Each new information picked-up on the way is reflected on and contributes to the evolving of new ideas and directions to follow, e.g. new references to be used or new queries to be entered. In the course of Berrypicking, not only search terms might change on a regular basis for the aim of query filtering, but the query itself and with it even the information need might change. When considering berrypicking in relation to the process model by Marchionini (see above), one could say the process is characterized by many backward loops to query formulation, selection of source or even problem definition.

2.1.4. A Characterization of Exploratory Information Search

Exploratory information seeking is basically characterized by its high degree of process versatility and complexity, which will be illustrated in the following:

White and Roth (2009, p. 22) describe the underlying information need of an exploratory search as being “open-ended, persistent and multi-faceted”. An information need of these characteristics implicates a complex search task evocating uncertainty on the part of the information seeker. (Hendahewa & Shah, 2017, p. 905) Namely, the information seeking individual usually does not know specifically what he/she is searching for initially, the search goal is abstract. This point of departure evocates a relatively high degree of affective and cognitive uncertainty during the search process itself (White and Roth (2009, p. 10). The next action is not always projectable (Aula & Russell, 2008, June, p. 1), one or multiple tentative queries might contribute to a further understanding of the search task itself which might then determine the next search action (Hendahewa & Shah, 2017, p. 907). Hence an exploratory search requires learning and understanding, investigating and maybe even creativity which clearly differentiates this search type of simple search tasks comprising solely the location of information (White & Roth, 2009, p. 22; Hendahewa & Shah, 2017, p. 907). Due to the stepwise learning effects, action-wise White and Roth describe the exploratory search as a combination of browsing (for a reduction of uncertainty and confusion) and focused search. The overall process is characterized as “opportunistic, iterative and multitactical”, which leads to versatility and thus complexity (White & Roth, 2009, 6 + 22). Due to its unpredictability, an exploratory search usually covers multiple queries and/or search sessions over a relatively long period of time (from days to months). (White & Roth, 2009, pp. 21–22) Furthermore, the

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exploratory character might require the seeker to make use of external memory aids, e.g. notetaking (Aula & Russell, 2008, June, p. 1).

Aula and Russell (2008, June) suggest a differentiation between exploratory and complex search. In the context of this thesis however, the term “exploratory search” will imply both, explorativeness and complexity. This finally leads to the following listing of characteristics of exploratory search:

• Versatility & Complexity • Abstract search goal • Unpredictability • Persistency

• Possible requirement of external memory aids

2.2. Related Work

This chapter will provide an overview of selected already existing, related studies to the thesis at hand. The thesis at hand shall expand research in the field of web search behaviour and concretely aims at identifying strategies of PhD students, regarded as ongoing experts, for exploratory online information seeking. While the research presented in chapter 2.1. took a global perspective, examining how information seeking behaviour can be modelled, the study at hand takes a more focused view: not examining what is done, but how it is done. This chapter on related work shall thus include those studies which took a similar focus, which examined how online information retrieval is carried out. Related work will be categorized as follows: Chapter 2.2.1. presents studies which already identified expert’s strategies for online information seeking. The findings of the studies presented in this section shall be extended with the findings of the study at hand. In addition, chapter 2.2.2. identifies comparative studies which do not focus on experts and are thus less similar than those presented in chapter 2.2.1., but they do deliver findings on how online information retrieval is carried out by various focus groups and are thus worth mentioning.

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2.2.1. Research on Expertise and Online Information Seeking

A positive relation between expertise and web search success has been found by Aula and Nordhausen (2006) and Hölscher and Strube (2000). Expertise, in those cases, was measured in experience in the usage of the world wide web as an information source as well as regularity of usage. To this purpose, Nordhausen (2006) chose students and researchers as a study sample. Hölscher and Strube (2000) chose various kinds of internet professionals, namely information brokers, web masters, internet consultants, web content designers, librarians and authors of books about online searching.

Aula and Nordhausen (2006) conducted a log- and observational study to investigate the effect of selected variables related to web experience on the speed of task completion and found, that increasing years of web experience had a positive effect on the speed of task completion. Additional positively related variables were the speed of composing queries, the average number of query terms per query, the proportion of precise queries and the participants’ own evaluation of their search skills.

Hölscher and Strube (2000) investigated which abilities/factors affect search success with the help of an expert study in form of interviews and a subsequent log study comparing expert and novice web search behaviour. They found that both kinds of expertise, web experience and domain knowledge have positive effects on overall search success, in an independent and combined manner. Those participants disposing of both kinds of expertise conducted the most successful searches. If one kind of expertise was missing, participants were found to engage in compensatory behaviour.

In an observational study which was followed by interviews, Aula and Käki (2003, November) investigated expert search strategies of computer scientists. Strategies identified were “the usage of multiple search terms and operators, frequent query editing, using multiple windows, versatile result saving, and using the “Find” functionality”” (Aula & Käki, 2003, November, p. 759). The most frequently used browser was found to be the Internet Explorer. Although the experts participating in this study used operators regularly, Aula and Käki found that misconceptions about the usage of these operators and the subsequent ordering of the results were frequent. (Aula & Käki, 2003, November)

In 2005, Aula et al. applied a questionnaire with open-ended and closed questions to find out more about information search and re-access strategies of experienced web users. Key search strategies identified were the usage of multiple windows or tabs and the categorization of information. Key strategies for re-accessing information were the usage of search engines for this purpose (but with problems) and the usage of bookmarks (2-3 folders) despite associated

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burdens of organization and maintenance. Rarely used strategies found in this study are the usage of history tools and sending e-Mails to the own address.

Thatcher (2008) conducted a comparative study on web search strategy differences between participants with lower and higher levels of web experience. The methodology used were structured interviews followed by an observational- and log study. Strategies found to be used by experienced web users are:

• Parallel Player Strategies

The usage of multiple browser windows, used to conduct different searches parallelly, usually implying the application of different search approaches (e.g. specific vs. general term usage, usage of different search engines, general search term vs. known address) • Parallel hub-and-spoke Strategies

Opening links in a new browser window an leaving the original page opened • Known Address Search Domain Strategies

Directly entering of a known website (not a search engine) and the usage of this website as a portal for the respective search task

• Known Address Strategies

Directly enter a known site where the required information is expected to be located The table below provides an overview of the studies previously mentioned including information on the method and the kind of experts chosen as sample.

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Figure 5: Research on Expertise and Web Search

Author(s) Year Experts Method Findings

Aula and Nordhausen 2006 Students and Researchers

Log- and Observational Study

Investigation of effects of variables related to w eb experience on the speed of task completion. Findings: Increasing years of w eb experience had positive effect on speed of task completion. Other positively related variables: - speed of composing queries

- averge number of query terms per query - proportion of precise queries

- participants' ow n evaluation of their search skills Hölscher and Strube 2000 Information Brokers, Web

Masters, Internet Consultants, Web Content Designers, librarians and authors of books about online searching

all self-trained experts

Thinking-Aloud / Teaching-Aloud Scenario Experiments

Investigation of abilities/factors affecting search success. Findings: - Web experience and domain know ledge both have positive effects on overall search success. In an independent and combined manner - participants disposing of both kinds of expertise conducted the most successful searches

- if one kind of expertise w as missing, participants engaged in compensatory behaviour

Aula and Käki 2003 Computer Scientists Observational study follow ed by interview s

Identification of expert search strategies: - the usage of multiple search terms and operators - frequent query editing

- the usage of multiple w indow s - versatile result saving - usage of the "find" functionality

Additional finding: frequent misconceptions about the usage of operators and the subsequent ordering of the results

Aula et al. 2005 Experienced Computer- and Web Users SIGCHI-Finland Mailing list & other IT Specialists

Questionnaire Identification of information search and re-access strategies of experienced w eb users

Search Strategies: usage of multiple w indow s or tabs and categorization of information

Re-Access strategies: usage of search engines (but w ith problems) and usage of bookmarks (2-3) folders despite associated burdens of organization and maintenance

Thatcher 2008 Comparison of participants from various educational and occupational backgrounds w ith various levels of w eb experience Structured Interview , Observational- and Log Study

Compared to participants w ith low er levels of w eb experience, Participants w ith higher levels of w eb experience w ere more likely to use

(1) "Parallel player" strategies (2) "Parallel hub-and-spoke" strategies (3) "Know n address search domain" strategies (4) "Know n address" strategies

Participants w ith low er levels of w eb experience w ere more likely to use (1) "virtual tourist" strategies

(2) "link-dependent" strategies (3) "to-the-point" strategies (4) Sequential Player" strategies (5) "Search engine narrow ing" strategies (6) "Broad first" strategies

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2.2.2. Comparative Studies on Online Information Seeking Behaviour The table below provides an overview of comparative studies on web search behaviour in general, thus not related to expert search. The suggested studies compare web search behaviour with regards to differing dummy variables. In the study of Aula, Khan, and Guan (2010, April) the dummy variable (search difficulty) is related to search task characteristics. In all other studies, the dummy variable describes characteristics of the seeking person, namely age (Chevalier, Dommes, & Marquié, 2015), thinking style (Kao, Lei, & Sun, 2008) and gender (Zhou, 2014).

Figure 6: Comparative Studies on Web Search Behaviour

Author(s) Year Variable Method Findings

Aula et al 2010 Search Difficulty Log- and Observational Study

Effect of Rising Search Difficulty: (1) Formulation of more diverse queries (2) More usage of advanced operators (3) Spending longer time on the search result page Chevalier et al 2017 Age Observational

Study

Compared to younger individuals, older individuals (1) are less accurate

(2) used few er efficient strategies

(3) focused more on the result evaluations of Google

(4) used same strategies regardless the complexity of search question, thus controlled their ow n activities less often in order to improve the ow n strategies and obtain higher performances

Kao et al 2007 Thinking Style (Global vs. Local)

Questionnaires and Log Study

Compared to high local style users, high global style users (1) tend to disperse their targets to comprehend the search task (2) skim more

(3) require less explicit answ ers

(4) are less likely to explore an issue in depth

High Local Style users tend to elaborate on a few specific topics Zhou 2014 Gender Questionnaire +

Log Study

Investigation of Chinese students' gender differences in their actual use of the w eb for information seeking. Findings:

- w eb search efficacy varied by gender but not by performance levels. Significant gender differences only found in medium-performing students searches. Here, males engaged in more search activities than females. In high and low performance levels no significant difference w as found. Males in this performing group held a stronger and more positive belief about their search ability than females

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3. Method

3.1. Qualitative, Semi-Structured Interviews

The thesis at hand aims at the identification of ongoing expert’s strategies for exploratory online information seeking in the field of scientific research. For this purpose, 25 qualitative semi-structured interviews were conducted. The interview guideline (appendix 1) comprises 45 suggested questions plus 13 questions on personal data. An overview of the personal data according to the 13 final questions is illustrated in a table which can be found in appendix 2. On average, the interviews took about one hour.

The method was chosen because it allows for a flexible conversation and thus for an identification of the interviewee’s subjective evaluation on what is important with regards to the issue at hand and what is not (Bryman & Bell, 2007, p. 475; Flick, Kardorff, & Steinke, 2010, p. 180).

The questionnaire mostly consists of open questions which allow for a natural flow of the conversation. Especially questions on the course of purposeful actions taken during exploratory information search started on a very broad level, in order to not influence the interviewees in their evaluation of which points of their research are the most significant ones to talk about. Nevertheless, some closed-ended question options were added for matter of punctual comparison (Bryman & Bell, 2007, p. 258 - 262). Scaled questions can be mainly found in the additional questionnaire on personal data. The response scales found here were used as proposed by Rohrmann (1978), Dreyfus and Dreyfus (1980) and Prüfer, Vazansky, and Wystup (2003).

Each interview started with introductory information on the definition of exploratory information search for clarification of the interview topic in line with the characterization given in chapter 2.1.4..

Before starting the survey, the interview guideline was pre-tested with 8 students of 5 different fields of study and adjusted accordingly.

3.2. Sampling

The sample exclusively consists of PhD scholars, 24 of the Johannes Kepler University (JKU) in Linz (Austria) and 1 of the Technical University of Munich (Germany). Those PhD scholars are regarded as ongoing expert in the field of scientific online research, as they are assumed to dispose of long lasting experience in research through their preceding basic studies and as research is one of the core activities PhD students should perform during their doctorate. The

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sample includes 16 female participants and 9 male participants. The participants were aged between 24 and 58. The median is at the age of 27, the average age of the participants was 30 years. On average, the participants were in their 4.36th_{semester of PhD scholarship. At the}

time of the survey, 21/25 participants linked their PhD studies to an employment as assistant lecturer at their university. Three participants were not additionally linked to their university with an employment contract. Overall, the sample covers 5 different fields of study with a total of 11 fields of specialization:

Figure 7: Sample: Covered Fields of Study

The sample arose by non-probability snowball sampling, thus the researcher started with people from the own network which then recommended other participants from their network (Devlin, 2006). Those participants were then invited per e-mail to voluntarily participate in the study. The interview guideline was not provided beforehand.

3.3. Analysis

As proposed by Mayring (2002), in a preparative act for the analysis each interview was transcribed word by word (see Appendix 6). The analysis was subsequently conducted following Mayring’s mixed methods approach of inductive category formation process, which allows for a “true description without bias owing to the preconceptions of the researcher, an understanding of the material in terms of the material” (Mayring, 2014, p. 79). Thus, based on the interview transcription, categories were directly assigned from the material at hand. Instead of regarding the whole material for analysis, only those parts relevant for answering the research questions were considered. Coding unit were all semantic elements in the text, context unit was each transcribed interview and recording unit were all 25 interviews.

All coding-categories and their defined level of abstraction (L.o.A.) can be found in appendix three.

Business Engineering Natural Sciences Education Law

Social Sciences (1) Business Administration (6) Economics (1) Mechatronics (4) Computer Sciences (2) Polymer Engineering (2) Bioinformatics (1) Molecular Biology (1) Mathematics (2)

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A first revision of the categories was carried out after ten interviews. After some adjustments, the final working through the material took place, followed by a last category adjustment and grouping. Before starting the analysis, a final review of all codings in the 25 interviews was conducted, in conformance with the final categories and the defined L.o.A.. The following chapter will comprehensively present the analysis results.

4. Results

4.1. Technological Means

The first section of analysis concentrates on Research Question 1, thus the choice of technological means for exploratory online information seeking. As can be seen in appendix 3 this section is divided into three coding categories: (1) Hardware and Supporting Software, (2) Types of Information Searched for and (3) Websites, Search Engines and Databases.

4.1.1. Hardware and Supporting Software

HARDWARE

Overall, the usage of four different devices is mentioned for the purpose of exploratory online information seeking: (1) the workplace computer/laptop, (2) the private laptop, (3) the smartphone and (4) the tablet.

Figure 8: Devices Used for the Purpose of Exploratory Online Information Search

All participants use a computer or laptop as main device for the purpose of exploratory online information seeking. Only those participants not employed at their university (4/25 participants) use their private computer/laptop as main device. Thus, all participants who combine their PhD with an employment as research assistant at their University (21/25 participants) primarily use

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their working computer/laptop. The Smartphone (6/25 participants) and the tablet (2/25 participants) are only mentioned for additional and thus secondary usage.

Overall, 16/25 participants use at least one device in addition to their mainly used device on a secondary basis. Among the 21 university-employed PhD students participating, 14 additionally use their private computer/laptop on a secondary basis for the purpose of exploratory online information seeking.

Figure 9: Devices Used for the Purpose of Exploratory Online Information Search_Primary and Secondary Choices

The figure below gives an overview about the devices used per participant. If the “X” is bold and has a light grey background, this device represents the mainly used device of the respective participant. Only one participant uses a combination of all four device options. A combination of workplace pc/laptop, private laptop and smartphone is used by four participants. Among those participants not being employed at their university, three solely use their private laptop without any additional device for secondary use.

Figure 10: Devices Used per Participant

The underlying reason for the usage of an additional device is tied to the location the respective person finds his/herself in 14/16 cases. Stated differently, those 14 participants only deviate from their primary device if it is not available. Two participants use the private laptop now and then in course of their exploratory online search although their working computer/laptop is

Device Used / Participant 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Sum

Personal Computer / Laptop Workplace X X X X X X X X X X X X X X X X X X X X X 21

Smartphone X X X X X X 6

Tablet X X 2

Private Laptop X X X X X X X X X X X X X X X X X X 18

Sum 2 1 2 2 2 1 3 4 1 1 2 3 1 3 1 2 1 2 1 2 2 2 3 1 2

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available for reasons tied to the purpose of action (1x for using exclusive access right / 1x for bypassing search engine’s adaptation to past queries).

Figure 11: Underlying Reasons for the Choice of Secondary Device

SUPPORTING SOFTWARE

Browser Choice

Overall, four different browsers are mentioned to be used for the purpose of exploratory online information seeking. As illustrated in the figure below, Firefox is by far the mostly mentioned browser in the given sample (20/25 participants). About one quarter of the participants mentioned a usage of Chrome, Safari is used by 20% of the participants. The Internet Explorer, which is solely mentioned for usage by one participant, noticeably falls behind.

Figure 12: Browsers Mentioned

Overall seven participants mentioned the usage of more than one browser for the given purpose of exploratory information seeking. One of those uses Firefox and Chrome equally (random choice). All other participants using more than one browser change the browser when

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changing the device. This is the case for four users who use an apple device as secondary device and for one participant who uses Firefox at the workplace and Chrome as standard browser on the private laptop. One participant uses Windows Explorer as primary and Firefox as secondary choice on the same device in case if the Windows Explorer is not able to load contents. In the given Figure, a bold “X” with a light grey background always represents the primary browser choice.

Figure 13: Browser Choice per Participant

Finally, when only considering primarily used browsers, Firefox still is mostly mentioned and thus by far the mostly used primary browser for exploratory online information seeking in the given sample (19/25 participants). Google Chrome only looses four percentage points (equals one participant) and is hence primarily used by 20% of the participants, Windows Explorer and Safari are solely used as primary browser by 1 person respectively, thus by 4% of the overall sample.

Figure 14: Primarily Used Browser

Browser Choice / Participant 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Sum

Firefox X X X X X X X X X X X X X X X X X X X X 20

Chrome X X X X X X 6

Safari X X X X X 5

Internet Explorer X 2

Sum 1 1 3 2 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 2 1 1 2

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Pre-Settings used on Browser/Device

14/25, thus more than half of the participants, consciously support their exploratory online information seeking by using pre-setting options in their Browser or on their device. Among those, the most frequently mentioned pre-setting is the installation of quick-links to the most important search engines/databases, enabling a quick access (by 9/14 participants). 8 participants placed those quick-links on their bookmark bar, one participant placed them directly on the desktop. Other pre-settings mentioned by more than 1 person are the setting of Google as standard search bar, the installation of a citation assistant in the browser and the installation of Citavi Picker. The table below shows an overview of all pre-settings mentioned.

Figure 15: Pre-Settings on Browser/Device

Usage of the Possibility to use Multiple Tabs/Windows

24/25 participants (96%) consciously make use of the option to opened multiple tabs or windows parallelly during exploratory online information seeking. 13/25 participants (56%) use multiple tabs, 1/25 participants (4%) uses multiple windows, and 10/25 participants (36%) make use of both, multiple tabs and windows. Only one participant mentions not to consciously use multiple tabs or windows parallelly.

Type of Pre-Setting Frequency Percentage Percentage (valid)

Quick-Links to Search Engines/Databases 9 36,00 64,29

Citation Assistant 2 8,00 14,29

Citavi Picker 2 8,00 14,29

Google as Standard Search Bar 2 8,00 14,29

Pocket Application for Bookmarks 1 4,00 7,14

Firefox on Task Bar 1 4,00 7,14

Fixation of Tabs 1 4,00 7,14

Synchronisation of bookmarks with Google-Account 1 4,00 7,14

Frequent deletion of cookies 1 4,00 7,14

Feedly Staying Up to Date as Welcome-Page 1 4,00 7,14

Total (Valid) 14 56,00 100,00

Missing 11 44,00

-Total 25 100,00

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Figure 16: Usage of Multiple Windows / Tabs

Overall, 92% of the participants (23/25) consciously open additional tabs during their exploratory online information search. Four incentives were mentioned for this: to open the respective content (e.g. a scientific Paper) while leaving the result page open (by 21/23 participants), to open an additional search engine/database (by 13/23 participants), to compare contents (by 3/23 participants) or to leave tabs open for a later reading or for a re-access if not being sure about their respective relevance, yet (by 2/23 participants).

Figure 17: Purpose for Opening a New Tab (given in no. of participants mentioning the respective purpose)

In total, 40% of the participants (10/25) make use of multiple window opening. All in all, for this action six different motivations were mentioned: Most common is the conscious separation, either of topic areas within one search or of the search activity itself from other online activities (mentioned by 3/15 participants respectively). Additional motivations for the usage of multiple windows mentioned are the opening or comparison of contents, the opening of a new search engine/database or separating a page which shall not be closed from other tabs.

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Figure 18: Purposes for Opening a New Window (given in no. of participants mentioning the respective purpose)

2/9 participants who use multiple windows and tabs for their exploratory research do this in an uncoordinated manner, thus the choice of tab or window happens accidentally.

4.1.2. Types of Information Searched for

Overall, 15 types of information were mentioned to be searched for. Most frequently searched for are scientific articles, by all participants, and books, by 76% of the participants. Wikipedia entries, conference papers, expert opinions and videos are searched for by about 30 percent of the participants. An overview of all information types searched for and the proportion of participants mentioning them can be found in the table below.

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4.1.3. Websites, Search Engines, Databases

Overall, 63 websites were mentioned to be used on a regular basis for the purpose of exploratory online information seeking. A table listing all those websites can be found in the appendix 4. In this table, the websites are classified into seven categories:

Website Category No. of Websites Mentioned

Search Engines 6 Databases 18 Platforms/Forums 10 Journal Homepages 12 Publisher Homepages 5 Online Courses 4 Others 8 Total 63

Figure 20: Website Types Used

By far most often used are Google (by 80% of the participants), Google Scholar (by 60% of the participants) and the search engine provided by the university itself, e.g. JKU LISSS (by 80% of the participants). Taking Google and Google Scholar together as “Services Provided by Google”, 22/25 participants (88%) mentioned these to be a central part of their exploratory online search activities.

PRIMARY WEBSITES

Overall, 17 of the 63 websites mentioned in total are used as primary website for exploratory online information seeking. Primary websites in this thesis are those websites mainly used for the core online search activity of exploratory information seeking. (The usage of one or several primary websites might follow a previous basic study, which means a previous familiarization with the topic on other websites, might be supported by additional information from other websites and/or might be followed by a complementary search on other websites for matter of completeness). The table below gives an overview of all primary websites mentioned by the participants and the respective frequency (no. of participants mentioning the respective website in the given sense). The websites most commonly used for this purpose are Google, Google Scholar and Ebsco, mentioned by 6/25 participants respectively. Hence, 48% of the

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participants use a service provided by Google as primary access point for exploratory Online Information Seeking. An overview of the primary websites mentioned per participant is to be found in the appendix 5. 16/25 participants, thus 64%, mentioned to solely have one primary website, three participants mentioned two primary websites, two participants mentioned three primary websites and four participants mentioned to use four websites on a primary basis. Thus, on average each participant used 1.76 websites on a primary basis, but the median is clearly at the value of one website. All participants using four websites on a primary website are law students.

Primary Websites

Website Frequency Percentage Percentage (valid)

Standard Google 6 24,00 24,00 Google Scholar 6 24,00 24,00 Ebsco 6 24,00 24,00 RIS 4 16,00 16,00 RDB 4 16,00 16,00 LexisNexis 4 16,00 16,00 Web of Science 3 12,00 12,00 Science Direct 2 8,00 8,00

Journal of Economic Perspectives 1 4,00 4,00

Journal of Economic Literature 1 4,00 4,00

Scopus 1 4,00 4,00 Ecolex 1 4,00 4,00 MathSciNet 1 4,00 4,00 University Databases 1 4,00 4,00 BeckOnline 1 4,00 4,00 Linde Online 1 4,00 4,00 Hudoc 1 4,00 4,00

All Databases accessable through University Account 1 4,00 4,00

Total (Valid) 25 100,00 100,00

Missing 0 0,00 -

Total 25 100,00 -

Figure 21: Primary Websites Used

UNDERLYING PURPOSES FOR SEARCH ENGINE / DATABASE CHOICE

The search engine/database choice was found to vary by purpose of the respective search action. Overall, mainly four different purposes were identified: (1) The Search for Scientific Articles by means of search term variation, mentioned by all the participants, (2) the search for books, mentioned by 21/25 participants, (3) the topic search as a first contact with a topic for getting an overview, mentioned by 17/25 participants and (4) the search for particular scientific

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articles, mentioned by 12/25 participants. In the following, each of those purposes and the respective Website choices will be targeted in more detail.

Figure 22: Underlying Purposes for Search Engine / Database Choice

(1) The Search for Scientific Articles by means of search term variation

All participants mentioned the search for scientific articles to be one/the central part of their exploratory online information seeking. For this purpose, overall 20 different search engines/databases were mentioned. Most frequently mentioned were Google Scholar (32%), Google (28%) and Ebsco (24%). When counting together the participants using the services provided by Google in this context, 12/25 participants (48%) use the services provided by Google as means to search for scientific articles. Three participants use both, Google and Google Scholar.

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The Search for Scientific Articles by Means of Search Term Variation

Website Frequency Percentage Percentage (valid)

Google Scholar 8 32,00 32,00 Standard Google 7 28,00 28,00 Ebsco 6 24,00 24,00 RDB 4 16,00 16,00 LexisNexis 4 16,00 16,00 Science Direct 4 16,00 16,00 IEEE 4 16,00 16,00 Web of Science 4 16,00 16,00 Pubmed _NCBI 4 16,00 16,00

University-Provided Search Engines 3 12,00 12,00

Beck Online 2 8,00 8,00 Scopus 1 4,00 4,00 Anwaltblatt 1 4,00 4,00 Ecolex 1 4,00 4,00 ACM 1 4,00 4,00 IDEAS/RePEc 1 4,00 4,00 SSNR 1 4,00 4,00 MathScinet 1 4,00 4,00 arXiv 1 4,00 4,00 EconLit 1 4,00 4,00 Total (Valid) 25 100,00 100,00 Missing 0 0,00 - Total 25 100,00 -

Figure 23: The Search for Scientific Articles by Means of Search Term Variation

(2) The Search for Books

21/25 participants mentioned the usage of specific websites for the purpose of searching for books. 19/25 participants, thus 76% of all participants use the university-provided search engine for this purpose. Others mentioned are Google, Google Books, Publisher databases themselves, the Austrian National Library, Amazon, the Austrian Joint Library System, Google Scholar and Wikipedia.

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The Search for Books

Website Frequency Percentage Percentage (valid)

University-Provided Search Engines 19 76,00 90,48

Standard Google 3 12,00 14,29

Google Books 2 8,00 9,52

Publisher Databases 2 8,00 9,52

Wikipedia 1 4,00 4,76

Amazon 1 4,00 4,76

Austrian National Library 1 4,00 4,76

Google Scholar 1 4,00 4,76

Austrian Joint Library System 1 4,00 4,76

Total (Valid) 21 84,00 100,00

Missing 4 16,00 -

Total 25 100,00 -

Figure 24: The Search for Books

(3) The Topic Search as First Contact with the Topic for Getting an Overview

Overall, 17/25 participants mentioned the usage of specific websites for getting a first overview about a topic. In total, eight search engines/databases are suggested: Google, Wikipedia, Youtube, Google Scholar, Ebsco, the University-Provided Search Engine, EDX and Coursera. 14/25 participants, thus 56% use Google for a first contact with a topic. Among those, eight participants referred to the conscious usage of Wikipedia in this context. Youtube and Google Scholar were mentioned by 3/25 participants respectively.

The Topic Search as First Contact with the Topic for Getting an Overview Website Frequency Percentage Percentage (valid)

Google 14 56,00 82,35 Wikipedia 8 32,00 47,06 Youtube 3 12,00 17,65 Google Scholar 3 12,00 17,65 ebsco 1 4,00 5,88 Coursera 1 4,00 5,88 edx 1 4,00 5,88

University-Provided Search Engine 1 4,00 5,88

Total (Valid) 17 68,00 100,00

Missing 8 32,00 -

Total 25 100,00 -

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(4) The Search for Particular Scientific Articles

12/25 participants mentioned the usage of specific websites for the purpose of searching for particular scientific articles. Overall, five websites or website types are used in this context, most frequent is the usage of pirate sites (8/25) and Google (6/25). 3/25 participants mentioned to access the respective journal or publisher page directly, 3/25 participants mentioned to use Google Scholar and 1/25 mentioned the usage of PubMed for this specific purpose.

The Search for Particular Scientific Articles

Website (Type) Frequency Percentage Percentage (valid)

Pirate Sites 8 32,00 66,67

Google 6 24,00 50,00

Google Scholar 3 12,00 25,00

Journal/Publisher Page Directly 3 12,00 25,00

Pubmed 1 4,00 8,33

Total (Valid) 12 48,00 100,00

Missing 13 52,00 -

Total 25 100,00 -

Figure 26: The Search for Particular Scientific Articles

Furthermore, Google Scholar and ResearchGate were found to be used for three additional purposes:

• For Forward- and Backward Integration

ResearchGate mentioned by two participants, Google Scholar by three • For an author-related search

By one participant respectively

Additionally mentioned: IDEAS/RePec and MathScinet (1x respectively) • For the purpose of staying up to date

ResearchGate mentioned by three participants, Google Scholar by one

USAGE OF ADDITIONAL FUNCTIONS PROVIDED BY SEARCH ENGINES/DATABASES Among the additional functions provided by search engines/databases, the mostly used within the given sample is the filtering function (11/25 participants). The ordering function was used by 4/25 participants, generally with regards to the date of publication. Other functions used are create/save alert functions, the pre-setting of the search engine/database language, the “download all” function and a pre-setting of the citation style indicated. 44% of the participants did not mention any conscious usage of functions provided by search engines/databases.

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Figure 27: Usage of Functions Provided by Search Engines/Databases

4.2. Action Patterns for Efficient and Effective Exploratory Information

Seeking

The second section of analysis concentrates on Research Question 2, thus the detection of action patterns for efficient and effective exploratory information seeking online. As can be seen in appendix 3, this section is divided into three coding categories: (1) Fighting the masses of information, (2) Finding Information back and (3) No access right, how to get the material anyway.

4.2.1. Fighting the Masses of Information

More than 50 percent of the participants mentioned to feel information overloaded at least occasionally during exploratory online information seeking. 20% of the participants feel overloaded always or often. This first coding category of action patterns for efficient and effective exploratory information seeking focuses on actions taken by the sample of ongoing experts to fight the masses of information online and thus to reduce (the frequency of) perceived information overload.

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Figure 28: Perceived Information Overload

When asking for situations in which the participants felt information overloaded during exploratory online information seeking in the past, the most frequently mentioned ones were the availability of too many results after typing in a search term (mentioned by 11/25 participants) and an insufficient overview of the respective topic, thus a missing knowledge of relevant sub-topics and key words for pre-filtering (mentioned by 6/25 participants).

Figure 29: When Does Perceived Information Overload Occur

Further, when asked how to proceed for finding the right/relevant search terms, next to an intuitive trial and error search the mostly used strategy (by 56% of the participants) was to consciously familiarize with the respective topic beforehand.

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Figure 30: Finding the Right/Relevant Search Terms

Hence in the following, strategies identified for three actions will be focused on: (1) For familiarizing with a relatively new topic, (2) for pre-filtering the mass of potential results and (3) for filtering the mass of results when given, thus for evaluating quality and relevance of suggested content in course of the search.

(1) Familiarizing with a relatively new topic

The table below shows all approaches suggested for a conscious familiarization with a relatively unknown topic. More than half of the interviewees search for and read books for this purpose. 30% of the participants make use of the expertise of colleagues or their supervisor in this context, they thus use their network for a first orientation. Watching introductory videos for the purpose of familiarizing with a relatively new topic was mentioned by 30% of the participants (7/25). Other actions mentioned are reading through comments (PhD student law), reading literature reviews, reading blog-/ forum entries, reading through journals which target a relatively broad public, searching for lecture notes, watching online courses, delegating the familiarizing task to another person and reading through diverse material randomly found. 1

1_{Note: The actions of familiarization suggested do not necessarily happen online (e.g. reading books or use} network). Nevertheless, as they are taken as a preparative steps for online information seeking they are anyway considered.

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Figure 31: Familiarizing with a Relatively New Topic

(2) Pre-Filtering the Mass of Results

As illustrated in Figure 27, 44% of the participants consciously use filtering functions provided by search engines/databases for a pre-filtering of potential results. Further, about half of the participants (13/25) narrow their search and thus pre-filter potential results with a conscious integration of Boolean Operators into the search warrant and/or he usage of other command-symbols allowing for this purpose. By those 13 participants, overall the usage of six operators and symbols is mentioned: The Boolean Operators AND, OR and NOT, the usage of quotation marks for getting results with exactly the combination of words typed in, the operator “filetype:pdf” for only getting documents of the format PDF, and setting the star sign (*) as a place holder for missing/unfinished words. Mostly mentioned was the Boolean Operator AND, by about 77% (10/13) of the participants who mentioned to consciously use operators or other command-symbols. Overall, the usage of Boolean Operators was mentioned more often than the usage of the other command-symbols. Each Boolean Operator was at least mentioned by one third of the 12 participants consciously using operators or other command-symbols.

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Figure 32: Usage of Boolean Operators / Other Command-Symbols (in percentage of all 13 participants using Boolean operators or command-symbols)

(3) Evaluating quality and relevance of found material in course of the search for filtering the results

Overall, 9 different approaches for an evaluation of quality and relevance of results in course of the search itself are suggested. More than 60% of the participants lean on their scientific experience for an evaluation of quality and relevance by deciding subjectively when reading or skimming through the material (15/25 participants) and/or by deciding on the basis of meta-information on the material (16/25 participants), e.g. author, year of publication or publisher. 44 percent of the participants (11/25) consider Journal Rankings (VHS, EBS, Scimago) for evaluating the quality or relevance of given results. 28% (7/25 participants) consider by whom or how often the material is cited. Other criteria mentioned for quality/relevance evaluation are if the material is peer reviewed or not, the evaluation on basis of self-defined individual screening criteria for the respective search task and considering the scientific fundament, which means to verify the references of the respective material. 12% (3/25 participants) stated to generally trust in every published material to be of sufficient quality.

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Figure 33: Evaluating Quality / Relevance

72% of the participants do not base their evaluation on only one of those evaluation approaches but combine them instead. On average, the participants combine 2-3 evaluation approaches, the maximum is at five (by one participant). The table below provides an overview about the mix of evaluation approaches per participant.

Figure 34: Evaluation of Quality / Relevance per Participant

Five participants criticized the usage of Rankings for an evaluation of quality or scientific relevance. Two of those use Rankings themselves but carefully. The excerpts below serve as examples for the mostly mentioned criticisms: That the rankings of scientific journals were perceived more as a commercial than a scientific phenomenon, that even the highly-ranked journals were not always objective in their choice of scientific articles to publish because they did have an interest in publishing articles of well-known scientists for image reasons, that the competition among scientific journals was quite high and thus the quality difference not forcefully huge and that also second-rank journals could and did publish good articles.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Scientific Fundament X X

Self-Defined Screening Criteria X X

Trust Database X X X

Peer-Reviewed X X X

how often /from whom cited X X X X X X X

Rankings X X X X X X X X X X X

Subjective Impression X X X X X X X X X X X X X X X

Meta-Infos X X X X X X X X X X X X X X X X

Sum of Criteria Considered 1 4 3 5 2 3 1 4 3 1 2 2 2 2 1 1 1 5 1 2 2 3 2 3 3

Ongoing Experts Strategies for Exploratory Information Seeking on the Web in the Field of Scientific Research / submitted by Julia Winter

Ongoing Expert’s

Strategies for

Exploratory

Information Seeking on

the Web in the Field of

Scientific Research

Master Thesis

Master of Science

STATUTORY DECLARATION

Table of Contents

Table of Figures

List of Abbreviations

1. Introduction

2. Integration into the Scientific Context

2.1. On Information Seeking

2.2. Related Work

3. Method

3.1. Qualitative, Semi-Structured Interviews

3.2. Sampling

3.3. Analysis

4. Results

4.1. Technological Means

4.2. Action Patterns for Efficient and Effective Exploratory Information

Seeking