Submitted in partial fulfillment of the requirements for the degree of

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MULTITASKING, COGNITIVE COORDINATION AND COGNITIVE SHIFTS DURING WEB SEARCHING

By

Jia Tina Du

B.S., M.L.I.S

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Written under the direction of Professor Amanda Spink

and approved by

________________________

Faculty of Science and Technology Queensland University of Technology

Brisbane, Australia

2010

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2010

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SUPERVISORY PANEL

Principal Supervisor

Professor Amanda Spink

Research Capacity Building Professor of Information Science Vice-President - Queensland Academy of Arts & Sciences

Faculty of Science and Technology Queensland University of Technology

Associate Supervisor

Dr. Dian Tjondronegoro Faculty of Science and Technology Queensland University of Technology

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STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution.

To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

………..

Signature Jia Tina Du

………

Date

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ACKNOWLEDGEMENTS

My sincere appreciation is given most to my principal supervisor, Professor Amanda Spink. Thank you Amanda for your continued support, guidance and encouragement, both intellectually and socially. During the years of working with you, I was challenged, encouraged, tested, and most importantly helped. Your emphasis and attention to rigour have helped me grow towards being a capable researcher. Great appreciation is also given to my associate supervisor, Dr. Dian Tjondronegoro, for his efforts with my thesis, his confidence in me and his warm friendship. I am grateful for the teaching opportunities that he offered me. I would also like to thank my thesis final seminar panel members, Associate Professors Sylvia Edwards, Yuefeng Li and Shlomo Geva, and two anonymous external examiners, for their valuable contributions and advice.

Warm thanks are given to my fellow Ph.D. colleagues, Bhuva Narayan, Awadh Alharbi and Kinley Kinley, for their generous help no matter whether in my research or my life. Their company has made my Ph.D. journey a very pleasant and memorable one.

Thanks to Assistant Professor Jingfeng Xia, from Indiana University, and Professor Qinghua Zhu, from Nanjing University, for their insightful and challenging comments on my thesis at various workshops. I would further like to thank the group researchers, Associate Professor Yuefeng Li, Drs. Daniel Tao and Susan Zhou, and Lifeng Ai, for their support and help during the years that I have studied in the information science group.

Thanks to the School administrators Agatha Nucifora, Sara Thomas, Matt Williams, Ilana Bolingford, and Therese Currell, who have assisted me with many of the administrative aspects of this thesis and my Ph.D. study at QUT.

Appreciation is given to my husband Roger Yongjian Chen for accompanying me through periods, whether happy or tough. Your optimism and persistence have helped me to concentrate on completing this work. This is a cherished memory in our continuing life.

Finally, I want to thank QUT for the financial support provided for this research. My special thanks are due to the forty-two anonymous study participants for their time

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and efforts. Without their participation and cooperation, this project would have not been possible.

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DEDICATION

This dissertation is dedicated to my father, Weicheng Du, my mother, Huiling Chen, and my husband, Roger Yongjian Chen, for their love and support.

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ABSTRACT

Multitasking, Cognitive Coordination and Cognitive Shifts During Web Searching

by Jia Tina Du

PhD Thesis Supervisor:

Professor Amanda Spink

As Web searching becomes more prolific for information access worldwide, we need to better understand users’ Web searching behaviour and develop better models of their interaction with Web search systems. Web search modelling is a significant and important area of Web research. Searching on the Web is an integral element of information behaviour and human–computer interaction. Web searching includes multitasking processes, the allocation of cognitive resources among several tasks, and shifts in cognitive, problem and knowledge states. In addition to multitasking, cognitive coordination and cognitive shifts are also important, but are under-explored aspects of Web searching. During the Web searching process, beyond physical actions, users experience various cognitive activities. Interactive Web searching involves many users’ cognitive shifts at different information behaviour levels. Cognitive coordination allows users to trade off the dependences among multiple information tasks and the resources available.

Much research has been conducted into Web searching. However, few studies have modelled the nature of and relationship between multitasking, cognitive coordination and cognitive shifts in the Web search context. Modelling how Web users interact with Web search systems is vital for the development of more effective Web IR systems. This study aims to model the relationship between multitasking, cognitive coordination and cognitive shifts during Web searching. A preliminary theoretical model is presented based on previous studies.

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The research is designed to validate the preliminary model. Forty-two study participants were involved in the empirical study. A combination of data collection instruments, including pre- and post-questionnaires, think-aloud protocols, search logs, observations and interviews were employed to obtain users’ comprehensive data during Web search interactions. Based on the grounded theory approach, qualitative analysis methods including content analysis and verbal protocol analysis were used to analyse the data. The findings were inferred through an analysis of questionnaires, a transcription of think-aloud protocols, the Web search logs, and notes on observations and interviews.

Five key findings emerged.

(1) Multitasking during Web searching was demonstrated as a two-dimensional behaviour. The first dimension was represented as multiple information problems searching by task switching. Users’ Web searching behaviour was a process of multiple tasks switching, that is, from searching on one information problem to searching another. The second dimension of multitasking behaviour was represented as an information problem searching within multiple Web search sessions. Users usually conducted Web searching on a complex information problem by submitting multiple queries, using several Web search systems and opening multiple windows/tabs.

(2) Cognitive shifts were the brain’s internal response to external stimuli. Cognitive shifts were found as an essential element of searching interactions and users’ Web searching behaviour. The study revealed two kinds of cognitive shifts. The first kind, the holistic shift, included users’ perception on the information problem and overall information evaluation before and after Web searching. The second kind, the state shift, reflected users’ changes in focus between the different cognitive states during

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the course of Web searching. Cognitive states included users’ focus on the states of topic, strategy, evaluation, view and overview.

(3) Three levels of cognitive coordination behaviour were identified: the information task coordination level, the coordination mechanism level, and the strategy coordination level. The three levels of cognitive coordination behaviour interplayed to support multiple information tasks switching.

(4) An important relationship existed between multitasking, cognitive coordination and cognitive shifts during Web searching. Cognitive coordination as a management mechanism bound together other cognitive processes, including multitasking and cognitive shifts, in order to move through users’ Web searching process.

(5) Web search interaction was shown to be a multitasking process which included information problems ordering, task switching and task and mental coordinating;

also, at a deeper level, cognitive shifts took place. Cognitive coordination was the hinge behaviour linking multitasking and cognitive shifts. Without cognitive coordination, neither multitasking Web searching behaviour nor the complicated mental process of cognitive shifting could occur.

The preliminary model was revisited with these empirical findings. A revised theoretical model (MCC Model) was built to illustrate the relationship between multitasking, cognitive coordination and cognitive shifts during Web searching.

Implications and limitations of the study are also discussed, along with future research work.

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KEYWORDS

Interactive information retrieval (IR), Web search interaction, Web search behaviour, Multitasking, Cognitive shifts, Cognitive coordination, Cognition, Web search modelling, Human information behaviour, Human–computer interaction

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LIST OF TABLES

Table 2-1. Search terms and amount ... 46

Table 2-2. Statistics on dissertations and theses... 47

Table 2-3. The annual number of published dissertation and thesis related to this study ... 47

Table 3-1. Coding scheme... 79

Table 4-1. Study participant profiles... 89

Table 4-2. Number of study participants in each gender category... 90

Table 4-3. Number of study participants in each age category... 90

Table 4-4. Number of study participants in each academic status category... 90

Table 4-5. Number of study participants in each faculty category ... 91

Table 4-6. Years of Web use by study participants ... 92

Table 4-7. Number of study participants: frequently used Web browsers... 92

Table 4-8. Number of employed Web search systems ... 93

Table 4-9. The employed Web search systems during the Web searches... 94

Table 4-10. Study participants’ information problems ... 100

Table 4-11. Information problem topic area ... 108

Table 4-12. Status of original information problems prior to the searching... 109

Table 4-13. Number of study participants with related or unrelated information problems searching ...110

Table 4-14. Factors affecting information problem search ordering ...114

Table 4-15. Summary of the factors affecting multiple information problems search ordering ...117

Table 4-16. Evolving information problems generated per study participant ... 125

Table 4-17. Number of study participants with evolving or non-evolving information problems developed ... 128

Table 4-18. Information problem searching task switching pattern... 137

Table 4-19. The percentage of each information problem searching task pattern 140 Table 4-20. Reasons for information problem searching task switching... 141

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Table 4-21. Summary of reasons for searching tasks switching ... 143 Table 4-22. Number of search sessions conducted during the current Web

searching ... 149 Table 4-23. Mean queries, Web search systems, and windows/tabs for an

information problem searching per study participant ... 150 Table 4-24. Holistic cognitive shifts data ... 156 Table 4-25. Cognitive state data... 166 Table 4-26. Shifts between cognitive states ... 168 Table 4-27.Summary of cognitive state shifts occurrence... 169 Table 4-28. Overall results ... 173 Table 4-29. Time allocation between multiple information problems searching (Global Strategy) ... 179 Table 4-30. Cognitive coordination behaviour data ... 187 Table 4-31. Number and type of cognitive coordination occurrences per study participant ... 195 Table 4-32. Summary of cognitive coordination types and occurrences ... 197 Table 4-33. Summary of cognitive coordination transition steps per study participant

... 199 Table 4-34. Sequence on cognitive coordination levels per study participant (Study Participants 1 to 21) ... 201 Table 4-35. Sequence analysis on cognitive coordination levels per study

participant (Study Participants 22 to 42) ... 204 Table 4-36. Summary of each type of cognitive coordination level sequences... 208 Table 4-37. Summary of cognitive coordination transition on three levels ... 209 Table 5-1. Cognitive State Shift (this study) vs. Information Problem Shift (Robins, 2000)... 221 Table 5-2. Cognitive coordination mechanism vs. feedback mechanism... 225

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LIST OF FIGURES

Figure 2-1. Year-Number (from 1970 to 2008) ... 48 Figure 2-2. A conceptual model of multitasking, cognitive coordination and cognitive shifts during Web searching ... 52 Figure 2-3. Dynamic Web search interactions... 53 Figure 3-1. An example of an open coding outcome ... 81 Figure 4-1. Ordering of information problems...112 Figure 4-2. Forty-two study participants’ information problem searching task

switching... 132 Figure 5-1. Two-dimensional Multitasking Web search behaviour ... 214 Figure 5-2. Holistic cognitive shifts and cognitive state shifts ... 223 Figure 5-3. Flowchart example of interplay between the three coordination levels (Study Participant 36) ... 228 Figure 5-4. Shifts between the three coordination levels (Study Participant 36) .. 229 Figure 5-5. Interplay between three cognitive coordination levels... 230 Figure 5-6. Multitasking, Cognitive Coordination and Cognitive Shifts (MCC) Model ... 232

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Chapter 1 Introduction

This chapter provides an overview of the dissertation research, including the problem statement, the aims of research, the research questions, the significance of the study, and the contribution to research. Lastly, the structure of the document is outlined.

1.1 Problem Statement

Humans have been seeking, organising and using information as they learned and evolved their patterns of information behaviour while resolving problems for survival, work and everyday life (Case, 2002). Information behaviour studies have become a key research area within the field of information science. Researchers have sought to study the human behaviour related to seeking, searching, foraging, retrieving, organising and using information. Information seeking studies focus on purposive information behaviour, while information searching is a sub-set of information behaviour, and is associated with the human–system interaction process.

In recent years, there has been an explosive growth of information on the Web.

Web searching is a predominant tool and channel for people to acquire information.

Web searching has the characteristics of both information seeking and information searching. Web searching is represented as a series of actions between logging on and logging off a Web search system.

Research shows that users’ actions are engaged in multitasking information behaviour during Web searching episodes (Ozmutlu, Ozmutlu & Spink, 2003b;

Spink & Jansen, 2004; Spink, Ozmutlu & Ozmutlu, 2002; Spink, Park, Jansen &

Pedersen, 2006; Spink, Park & Koshman, 2006). Web searching behaviour is described as a multitasking process which includes searching for information

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related to one information task, and then switching to search for items on another information task (Spink, Cole & Waller, 2008). Multitasking is a prevalent phenomenon in the Web searching context. Multitasking has been recognised as important user behaviour during Web searching (Ozmutlu, Ozmutlu & Spink, 2003a, b; Spink, Bateman & Greisdorf, 1999; Spink, Park, Jansen & Pedersen, 2006;

Spink, Park & Koshman, 2006). Web users prefer to search for multiple information problems concurrently during single or multiple Web search sessions.

User studies can tell us a lot about the actual and typical characteristics of the Web searching process. Web search studies are concerned with how people search the Web, especially the cognitive processes involved in Web search activities. Recently, efforts have been made to build Web search models with an emphasis on illustrating the dynamic interaction between the information problem, the user, and the information environment, and on the iterative effect on user search strategies, processes and outcomes. Ford, Miller and Moss (2001, 2005) modelled how users’

individual differences have an effect on Web search performance. Originally, Spink and her colleagues developed a model of Web search as multitasking (Spink &

Park, 2005; Spink, Park & Koshman, 2006), showing that the user may pool together more than one related or unrelated topic when searching on the Web.

There is still a significant gap between the Web search model and the real users’

dynamic Web searching process. Web searching is proposed as an important element of interactive information retrieval which includes multitasking processes, and the allocation of cognitive resources among several tasks, and shifts in cognitive, problem and knowledge states (Du & Spink, 2009). Multitasking involves cognitive shifts in task focus. During the Web searching process, users experience various cognitive, emotional and physical reactions when they identify a gap in knowledge that needs to be filled with the information they are searching for (Spink

& Dee, 2007). Interactive Web searching involves many human shifts on cognition 2

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at different levels of information behaviour (Du & Spink, 2009; Robins, 2000; Spink, 2002; Spink & Dee, 2007). Cognitive shifts are an important but under-explored research area for understanding the cognitive processes associated with Web searching. The identification of types of cognitive shifts may be meaningful in understanding the outcomes of user–Web interaction.

Wickens and Gopher (1977) argued that one of the important insights into people’s ability to dual-task was that while there was some interference between the two tasks that were being performed people could consciously trade off performing one task for the other. The key point of such trading off stems from the person’s coordination capability. Cognitive coordination allows humans to manage dependences among information tasks and the resources available. A key issue for cognitive coordination research in the Web search context concerns those coordination mechanisms that move users through a multitasking Web search, while experiencing various shifts in cognition. An exploration of this issue may be significant in developing a comprehensive, deep understanding of Web searching behaviour. Spink and Du (2007) proposed that humans must cognitively coordinate a number of elements, both internal (cognitive) and external (environmental), into a coherent Web search process. However, it is not clear what kinds of elements are cognitively coordinated, or how these elements interplay in order to achieve a coherent Web search process.

As Web searching becomes the predominant form of information access worldwide, we need to develop better user interaction models of a Web search. Much research has been conducted into Web searching. However, few studies have modelled the relationship between multitasking, cognitive coordination and cognitive shifts in a Web search context. And this is something that needs to be explored. Cognitive coordination, in conjunction with multitasking and cognitive shifting, may form a

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theoretical framework for understanding how Web searching behaviour is constructed.

1.2 Aims of Research

Broadly, the objective of this research is to develop a framework for theory building and research work in Web searching involving multitasking, cognitive coordination and cognitive shifts. Specifically, this study aims to model the relationship between multitasking, cognitive coordination and cognitive shifts during a Web search. The model aims to depict how users conduct multitasking information behaviours and how they perform cognitive coordination between multiple information problems and the resources available. The study also investigates the various types of cognitive shifts and how these shifts occur during Web searching. Finally, the research is expected to discover how multitasking, cognitive coordination and cognitive shifts interplay to influence users’ behaviours during Web search interactions.

1.3 Research Questions

The major research problem underpinning this study is:

What is the relationship between multitasking, cognitive coordination, and cognitive shifts during Web searching?

Four minor research questions addressed in this study are:

(1) How do users conduct their Web searches on multiple information problems?

(2) What types of cognitive shifts occur during Web searching?

(3) What levels of cognitive coordination occur during Web searching?

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(4) How do multitasking, cognitive shifts and cognitive coordination interplay during Web searching?

1.4 Contributions and Significances

Saracevic, Kantor, Chamis and Trivison (1988) suggested that the key to the future of information systems and searching processes lay not in the increased sophistication of technology, but in the increased understanding of human involvement in relation to information. Users play a predominant role during Web searching interactions. These conclusions form the rationale of this study and indicate why this rationale is significant.

1) This dissertation contributes theoretically to interactive IR research. By providing a comprehensive picture of a Web searching interaction, the exploration of multitasking, cognitive coordination and cognitive shifting extends the user Web search model to include cognitive mechanisms within searching interactions.

2) This study contributes to the formal characterisation and better understanding of elements and processes involved in the Web searching process in relation to cognitive IR interactions. Such an understanding is fundamental to basic research directed toward theories, models, and practices on human information behaviour involving Web IR systems, and to applied development directed toward improvement of human–computer interaction involved in Web searching.

3) This research analyses the users’ Web searching as they attempt to solve their information problems, and looks for patterns involving multitasking, cognitive coordination and cognitive shifts. Comprehensive patterns lead to the development of a theoretical model depicting the inter-relationship between multitasking, cognitive coordination and cognitive shifts. This study theoretically underpins

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interactive Web searching studies, and it might also expand the theoretical basis of multitasking and coordination theory in cognitive science.

4) In practical terms, the results of the study will not only show how people search information which might help users understand their own Web searching, but will also provide insights into the design of Web technologies. Modelling how users interact with Web search engines from different cognitive perspectives is important in the development of more intelligent and effective Web based IR systems.

5) Users’ multitasking, coordination and management of different information search tasks is little understood or supported by current search technologies (Spink, Park & Koshman, 2006). Modelling users’ cognitive Web searching process that integrates multitasking, cognitive coordination and cognitive shifts could impact on the development of technologies that support coherent Web searching and that lead to improvements in the performance of search technology. Web search systems must evolve out of the users’ needs and the common characteristics of Web searching, rather than from expecting the user to adapt to their singularities.

6) The Web search patterns and models proposed in this study allow the possibility of predicting users’ Web searching. Web search engine companies could benefit from knowing how their products are used, which may help designers to redesign and reconstruct search engines in order to attract visitors to their sites. The efforts to model Web searching behaviour are important for the design of Web search systems and for information providers in general. If a system is familiar with users’

behavioural patterns, it may more easily adapt and personalise users’ interactive process.

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1.5 Structure of the Work

The thesis comprises six main chapters. Chapter 1 provides an introduction to the thesis research. Chapter 2 presents a review of the literature, with a focus on the behavioural characteristics of multitasking, cognitive coordination, and cognitive shifts research. In particular, the research is contextualised information retrieval.

Chapter 3 describes the research design, including data collection instruments, selection of study participants, data analysis techniques, and justification of the methodology. Results are reported in Chapter 4. Chapter 5 contains a discussion of the key findings. The terms of these findings are guided by the research questions.

Finally, in Chapter 6, a conclusion is made which outlines how this research improves our knowledge of Web search behaviour, the research’s limitations and some further research directions.

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Chapter 2 Literature Review

2.1 Introduction

This study is concerned with users’ actual information search behaviours and processes within the context of searching the Web and focuses on the underlying coordination mechanisms which may be involved in users’ Web search interactions.

This chapter contains a review of interactive information retrieval and Web search literature; a discussion of representative interactive IR models and recent Web search models; and research about multitasking, cognitive coordination and cognitive shifts in general, and in the context of interactive information retrieval in particular. Investigation into relevant state of the art dissertations and theses is then presented. This is followed by an outline of the research gap identified from the critical analysis of the previous studies, and an argument for the research proposed will be put forward. The last section will explore the theoretical model which has emerged: this is based on the analysis of the studies reviewed and forms the foundation for the theoretical framework in this study.

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2.2 Interactive Information Retrieval (IR) 2.2.1 Overview

Information retrieval (IR) is a fundamental component of information behaviour (Ruthven, 2006). Information retrieval is defined as the process involved in representation, storage, searching, finding, filtering and presentation of potential information perceived to be relevant to a requirement of information desired by a user in context (Ingwersen & Jarvelin, 2005). Interactive information retrieval is viewed as information acquisition via formal channels and in organised knowledge sources such as information systems like the Internet (Ingwersen & Jarvelin, 2005).

Research on interactive information retrieval originated in the early 1980s, when a theoretical direction was generated to address the dynamic of the searcher/user and system interaction. In the last two decades, research on interactive IR has mainly concentrated on understanding the ways and processes that searchers/users iteratively search IR systems, modelling user and search intermediary behaviours and using information behaviour models to design automated intermediary devices and IR systems which train and educate information professionals on information user literacy.

Searcher/user-oriented IR research has been aimed at understanding the searchers/users’ behaviour and the dynamic and evolving search characteristics (Bates, 1989; Fidel, 1985). Efforts have been made to recognise the complex dynamics and interactive processes involved in this communication and to look for a more accurate way of capturing the cognitive developments involved in the user/searcher’s information problems. Past attempts to build user and search intermediary interactive IR models were carried out in more natural situations instead of simulated laboratory settings (Belkin, 1984; Saracevic & Kantor, 1988).

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The recording of “talk aloud” discourse and interviews became principal tools of experimental methodology. The scope of the study participant group was extended to include users from all levels of society, going beyond the experienced researchers normally analysed by traditional IR studies.

Subsequently, a cognitive focus took place in the interactive IR research. Saracevic and Kantor (1988) correlated users’ and searchers’ cognitive styles to their search results. Ingwersen (1992) described the impact of cognitive science on IR research, analysing IR as a process involving cognitive states and complex interactions. In 1996, Ingwersen (1996) proposed a first formulation of a cognitive theory for information retrieval interaction. Later, these models led to a more sophisticated model of the cognitive communication system (Ingwersen, 2001). In 2005, Ingwersen and Jarvelin built an integrated cognitive information seeking and retrieval (IS&R) research framework involving the concepts of “cognitive actor” and

“context” (Ingwersen & Jarvelin, 2005, p. 19).

2.2.2 Interactive IR Model

This section presents a discussion about several representative interactive IR models. The interactive nature of the users’ information searching behaviour has become a primary focus of the interactive IR models that have been developed since late 1980s. The IR models have an emphasis on the dynamic interaction between the searcher/user, the information need, and the information environment.

2.2.2.1 Bates (1989) Berry-picking Model

Bates (1989) described the online search as an evolving berry-picking process in which the changes in the search strategy were due to the experience of a variety of sources and were the result of new information encountered which provided new ideas and directions to the original search query. Her model illustrates that the

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results of each search query would provoke cognitive thoughts in the user to make continual judgments regarding the relevance and interoperability as the information was sought and used.

Based on the basic implication that the information searcher is also the information user, the berry-picking model more closely discovers users’ actual information behaviour than previous traditional linear IR models, in that it considers the users’

dynamic and continuous cognitive responses during IR interaction and their effects on the follow-up search queries. Yet Bates’ ideas on the model have never been empirically validated. Thus, whether the users’ cognitive thought is the factor resulting in changes to the search query still remains a question. Additionally, the forms of the users’ cognitive response have not yet been investigated. A major aim of this dissertation is to investigate the interplay of multiple cognitive aspects on the users’ information search behaviour.

2.2.2.2 Ingwersen (1992, 1996) Cognitive IR interaction Model

As one of the earliest IR interaction models, Ingwersen’s (1992, 1996) model was the first to illustrate that a dynamic interaction process occurred at multiple levels within the “cognitive space” of the user and the “information space” of the IR system.

The multiple levels of interaction were said to occur not only between the user and the IR system, but also between the user and the information objects within the system. The model focused more on understanding the actual information system being used during the interactive cognitive processes. Ingwersen’s model provides a way to understand the process of how information is being retrieved and ultimately used.

Ingwersen (1996) claimed that a wide ranging influence of factors should be considered in IR research, such as social environment, IR system, information

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objects, search intermediary and user. He incorporated all these variables into the notion of polyrepresentation. His models have presented a reasonable synthesis of studies regarding IR interactions with empirical evidence. As Robins (2000) pointed out, however, the problem with Ingwersen’s model lies in determining the way to get input from the users’ cognitive space into the request model builder, since the differences among the four components of user cognitive space put forth by Ingwersen are subtle. The study presented in this dissertation attempts to better understand the components of the users’ cognitive space in Ingwersen’s model.

2.2.2.3 Saracevic (1996) Stratified Interactive IR Model

Conceptually borrowed from human–computer interaction, Saracevic’s (1996) theoretical model described the IR interaction between the user, the IR system and the information objects through the system. His model was also based on the assumption that users interacted with IR systems in order to use information. He originally emphasized that understanding the reason why a user sought out information was an important part of discerning the influencing factors during that interaction.

The stratified model involved three strata of IR interaction:

• a surface level—the interactions between the user and the interface of the IR system.

• a cognitive level—user-made judgments regarding the results given by the system. Both the users’ thinking and system’s information objects were identified as cognitive entities.

• a situational level—a context-driven interaction, influenced by the need for original information and how the user or system might categorise, or even iteratively change the need.

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In terms of a theoretical framework, it was a comprehensive model covering all of the three IR interaction levels. However, the details on what and how the changes occurred as a process of IR interaction were not fully established.

2.2.2.4 Belkin (1996) Episodic Model of IR Interaction

Belkin is another pioneer who advanced the interactive viewpoint in information retrieval. Belkin’s (1996) episodic model was based on his anomalous states of knowledge (ASK) hypothesis (Belkin, 1980), which modelled a user who turned to the information system with a high level of cognitive uncertainty. The users’ state of knowledge was anomalous. As such, he/she could not adequately present his/her information need to the information system. Belkin considered that the real problem in IR was how to represent the users’ anomalous state of knowledge.

Interaction with the information system led to the users’ altered state of knowledge, which enabled him/her to define, reformulate and re-focus the information need, and eventually to contribute to his/her underlying information problem solving.

Although the model provides a research framework for interactive IR, it lacks a treatment of the social/contextual facets of the user information problem and the corresponding effects on IR interactions.

2.2.2.5 Spink (1997) Interactive Feedback and Search Process Model

While focusing on understanding how the interactive process actually took place, Spink’s search process model (1997) was developed based on the empirical research. User judgments, search strategies and the interactive feedback loops within the search process were presented. The model reflected that a variety of feedback mechanisms were the major influencing factors in the interactive IR process.

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Importantly, the model demonstrated that a users’ interaction with the system could consist of multiple feedback transactions, leading to additional inputs or queries which could in turn result in different feedback and new inputs. The strength of Spink’s model is that it observed IR from an interactive point of view and, in particular, that it provided a complete investigation of the feedback mechanisms.

Yet the feedback loops were identified within the discussions between the user and the search intermediary. That is to say, in her model, a feedback loop was incurred when one of the participants gave feedback to the other, followed by a judgment or an action taken. The recognition of feedback was based on analysis of the discourses between the two participants. Apparently, it was not applicable under a Web searching context in which the searcher is the actual user. Another weakness of the model is that it lacks appropriate explanations about the underlying cognitive changes during the occurrence of feedback loops. The present study seeks to address both of these issues.

The above interactive IR models have laid the foundation for the development of later Web search models, which are discussed in the following section.

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2.3 Web Search

As special forms of IR systems, Web search engines are designed specifically for the hypermedia environment of the Web and are considered to be the major portals for users of the Web, with 71% of Web users accessing Web search engines to locate other Web sites in 2000 (Spink & Jansen, 2004). Thus it is critical to have an understanding of how people search information on Web search systems. Web searching is the context for the present research project. The study of Web searching focuses mainly on users’ information behaviour while interacting with Web search systems.

2.3.1 Overview

Web searching refers to users' actions during the logged-on to logged-off period on a Web information system (Spink, Ozmutlu & Ozmutlu, 2002). The current state of Web search studies can be summarised within the following categories: (1) Web search behaviour; (2) single Web site search studies; (3) information foraging studies; (4) children’s Web search behaviour; (5) Web search training and learning;

and (6) Web search evaluation (Spink & Jansen, 2004).

Amongst the above categories, Web search behaviour studies are becoming a flourishing research area which is concerned with why and how people search the Web and the process of their interaction with Web search tools. Researchers have argued that Web searching behaviour is separated into information searching, seeking and retrieving behaviour on the Web. A significant number of Web users begin their interactions with the Web with searching and retrieving type behaviours, such as a query formulation, and then shift to seeking type moves, such as

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scanning or browsing the returned results by the search engine (Choo, Detlor &

Turnbull, 2000b; Choo & Marton, 2003).

Research has been conducted in order to understand the elements and patterns of the users’ Web search. For instance, Catledge and Pitkow (1995), as well as Montgomery and Faloutsos (2001), investigated Web search browsing patterns of adult Web searchers and concluded that they relied mainly on hyperlink structures;

Tauscher and Greenberg (1997) revealed that people repeatedly returned to the same Web pages and conducted short searches; Hawk and Wang (1999) identified people’s Web searching patterns with ten strategies, including exploring, link following, back and forward movements, engine seeking and using, shortcut seeking and surveying or scanning Web pages; Rieh and Xie (2001) examined patterns and sequences of query reformulation during users’ interactions with the Excite Web search engine; White and Iivonen (2002) pointed out that users regard closed or predictable topics as easy to search and open or unpredictable topics as difficult to search.

The users’ tasks and goals are mentioned and investigated in the studies. Rose (2006) investigated many of the characteristics of users' Web search behaviour, including the variety of information seeking goals, the cultural and situational context of the search and the iterative nature of the search task. Kim (2008) found that, to experienced Web users, the effects of emotional control and search tasks were significant on Web search behaviour, but not on the search performance, and that the effects of users' emotional control on the search behaviour varied depending on search tasks.

Some of the individual differences which are viewed as important factors affecting the Web users’ searching are personality (Amichai-Hamburger, 2002), age and gender (Roy, Taylor & Chi, 2004; Tillotson, Cherry & Clinton, 1995), and level of

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experience (Hill & Hannafin, 1997; Hoelscher & Strube, 2000; Kellogg & Richards, 1995; Navarro-Prieto, Scaife & Rogers, 1999). Ford, Miller and Moss (2001, 2002, 2003 & 2005) explored the relationship between Web search strategies and human individual differences from multi-perspectives of cognitive and demographic factors, Internet attitudes and approaches to study. Morgan (2008) explored Web searching behaviour in correlation with individual differences within the context of health information searching. This evaluated the ways in which individual differences influence user Web search behaviour, determining that differences such as race, gender, age, socio economic class and geographic location all influence searching behaviour. However, it was found that those factors work together to influence behaviour, rather than independently.

In particular, research identifies a link between individual differences in cognitive styles and their Web search outcomes. Cognitive styles are characteristic ways in which different individuals engage in information processing and representation. As to the holist/analytic cognitive style, in relation to Web searching, Ford and Miller (1996) found relationships between a holist cognitive style and a preference for browsing over keyword searching, a broad-based approach to Internet exploration and lower levels of reported distraction by irrelevant material. Wang, Hawk and Tenopir (2000) linked the holist cognitive style to greater levels of difficulty and confusion experienced during Web searching. Palmquist and Kim (2000) studied the effects of cognitive style on search performance and found that less time was taken and fewer nodes were traversed in locating information by analytic novice searchers.

Kim and Allen (2002) found that the cognitive individual differences during Web searching consisted of holist/analytic and imager/verbaliser cognitive styles and cognitive complexity. In terms of a verbaliser/imager cognitive style, links were found between an imager cognitive style and low levels of reported disorientation,

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information overload, avoidance of unplanned Internet browsing and poor retrieval effectiveness in a study of Web searching that focused on retrieval performance rather than strategy and the use of the internet when required to rather than through intrinsic interest (Ford & Miller, 1996; Ford, Miller & Moss, 2001).

Modelling the mutual impact of users’ information behaviour and Web searching process is another important research topic that several studies have pursued.

Choo, Detlor and Turnbull (2000a) developed one of the first behavioural models of Web interaction which depicted how users translated their information needs into search strategies. Wang, Hawk and Tenopir (2000) designed a multi-dimensional model of user–Web interaction, consisting of users, the interface and the Web.

Hodkindon and Kiel (2003) modelled consumer Web search behaviour, including personal demographic, behavioural, use and experience variables. Lau and Coiera (2006) developed a Bayesian model for predicting the impact of Web searching on human decision making. Aula and Nordhausen (2006) chose to model Web search success with the concept of task completion speed (TCS) and the results showed that the variables related to Web experience had the expected effects on TCS.

2.3.2 Web Search Model

Web search models should be different from previous information seeking and retrieving models, which partly derive from the distinct users under the two contexts.

Before the advent of the Web, IR system searchers comprised information professionals, including intermediaries and educated professionals, who usually had formal training in developing appropriate search queries or retrieval strategies.

The enormous growth of the Web has since provided an environment for a whole new user group with a vast computational capacity to search for information instead of asking librarians for help related to how to refine a query or improve a search result. New dynamic variables of different users’ interactions have to be considered

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that involve the users’ cognitive ability, personality, information task, and search outcomes, all of which are key to developing sound Web searching models (Knight

& Spink, 2008).

This section provides a discussion of more recent Web search models. It seeks to trace their contribution to theoretical construction on information seeking, including Web searching behaviour research.

2.3.2.1 Choo et al. (2000b) Web Behavioural Model

As one of the earliest behavioural models of Web interaction, Choo, Detlor and Turnbull’s model (2000b) depicted how knowledge workers used the Web to seek information. The behavioural model of information seeking on the Web combined and extended Aguilar and Francis' (1967) modes of scanning and Ellis et al.'s seeking behaviours (Ellis, 1989; Ellis, Cox & Hall, 1993), and consisted of the four main modes of undirected viewing, conditioned viewing, informal search and formal search. For each mode, information seeking activities or moves occurred frequently and included starting, chaining, browsing, differentiating, monitoring and extracting.

The strength of this model is that it related motivations (the strategies and reasons for viewing and searching) and moves (the tactics used to find and use information) and this was helpful in analyzing Web-based information seeking.

2.3.2.2 Wang et al. (2000) Multidimensional User–Web Interaction Model Taking a holistic approach, Wang, Hawk and Tenopir (2000) proposed a multidimensional user–Web interaction model encompassing users, the interface and the Web. The user is the first and foremost element in this model; the Web space is what the user interacts with to obtain wanted information between the user and the Web and there is an interface which has been designed to mediate communication

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between the two. User–Web interaction is viewed as a communication process facilitated through an interface.

The model related users’ behaviour to deficiencies in the design of interfaces and the Web. It designated that Web browser designers needed to understand the users’ mental models. An effective interface must provide great affordance and facilitate correct mental model development by presenting appropriate messages/clues and providing context-sensitive help. Whilst pointing to the important role of the users’ mental model in the provision of information, Wang et al.

(2000) did not elaborate the users’ cognitive processes in detail.

2.3.2.3 Ford et al. (2001, 2005) Individual User Differences Web IR model Ford and his colleagues made continual efforts to study the impact of individual characteristics on Web search strategies and search performance (Ford, Miller &

Moss, 2001, 2005). Key characteristic differences were identified in the users’

cognitive styles, study approaches, prior experience, internet perceptions, gender and age which were linked to users’ internet-based information seeking.

What the model of Ford and his colleagues lacks, however, is an understanding of why users choose their search strategies in a given way and to what extent personal attributes affect the Web search process. Nevertheless, this research provides an approach for studies about individual differences. The technology acceptance model was later integrated into an interdisciplinary investigation of the impact of user perceptions of information quality on IR strategies.

2.3.2.4 Knight & Spink (2008) Web IR Model

Different to Saracevic’s model, which suggested the reason why a user sought out information was an important part of discerning the influencing factors during the

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interaction, Knight and Spink’s (2008) Web IR model contends that the users’ pre- existing cognitive style is seen as influencing the Web search strategies and is followed by two types of system interaction, browse-seek and search-seek.

Knight and Spink’s (2008) model hopefully provides a better understanding of the impact of the users’ pre-existing cognitive style on the users’ Web search strategies, such as the adoption of certain search engines and the perception of the value of the search engine’s results to their query. A weakness of the model is its lack of accounting for the influence of current knowledge and a dynamic cognitive state on the users’ Web searching performance. In addition, it stays only on a theoretical hypothesis level.

2.3.2.5 Park (2008) Prioritising and Coordinating Information Behaviour Model

Empirically validated, Park’s (2008) model describes the processes that individuals engage in to manage multiple information task Web searching under time pressure.

The inner level of the model indicates that self-regulating individuals engage in information task perceptions, emotional, mental and temporal reactions. The initial processes at the inner level then send out a signal to the outer level to prioritise and coordinate multiple information tasks.

Park’s (2008) model illustrated dynamic internal and external processes that users employ in order to efficiently and effectively deal with multiple information tasks while interacting with Web search systems. The strength of her model is that it examines Web searching behaviour from a multitasking point of view. Park acknowledged that individuals monitor and coordinate their internal (i.e., emotion, effort, and time) and external (i.e., task performance) activities through continuous self-feedback. The self-feedback mechanism, however, was not clearly examined or presented.

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The examples outlined above indicated that current Web search models concentrate more on the users’ cognitive efforts involved in a single topic Web search interaction. Few studies have examined how users’ cognitive efforts are made during a multiple topics’ Web searching process. Additionally, current Web technologies and interface design are generally based on the assumption that most users are engaging in single Web searches on a single topic. Yet an increasing number of recent studies show that most people engage in multiple search activities. Multitasking is observed as an important attribute of Web searching behaviour (Spink, Ozmutlu & Ozmutlu, 2002). A comprehensive multitasking Web search model may provide implications for the development of adaptive Web technologies.

Literature about multitasking, including up-to-date research about multitasking in a Web search context, is reviewed in the following section.

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2.4 Multitasking

2.4.1 Multitasking Research in Cognitive Science and Psychology

The research on multitasking has a decades-long history in the field of cognitive sciences and psychology. Cognitive psychologists have provided extensive research literature on multitasking, concurrent information processing, task switching (Burgess, 2000; Pashler, 2000) and sequential actions (Carlson & Sohn, 2000).

Multitasking is the ability of humans to handle the demands of multiple tasks concurrently through task switching or interleaving if necessary (Just, Carpenter, Keller, Emery, Zajac & Thulborn, 2001; Lee & Taatgen, 2002). Lee and Taatgen (2002) argued that multitasking behaviours are a product of skill acquisition.

Multiple task situations are faced frequently in daily life. For example, having snacks when watching a TV program, or using multiple information systems simultaneously, for example, checking emails while chatting with friends online.

Neuro-cognitive psychologists have investigated the human brain’s activation mechanism which is associated with multitasking behaviour. They found that when humans conduct multiple tasks at the same time, the activation volume in the cortical systems underlying the execution of tasks decreased compared to that in single task conditions. That is, the cognitive limitation of multiple task performance causes a brain activation decline. They concluded that the productivity level of multitasking performance is accordingly reduced (Just, Carpenter, Keller, Emery, Zajac & Thulborn, 2001). Engineering psychologists found, however, that people may adopt strategies of time sharing or time swapping to manage their multitasking situations effectively. Time sharing is for performing multiple tasks simultaneously, while time swapping is for performing multiple tasks sequentially (Wickens, 1989,

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1991). Hunt and Joslyn (2000) identified the characteristics of individuals who did well in multitasking and decision making situations under considerable time pressured conditions.

Task switching has been recognised as an important element of multitasking.

Monsell (2003) reviewed the notion of task switching in cognitive science research in which multitasking was considered as switching behaviour from one task to another in rapid succession. The costs to the individual of switching tasks compared to non-switch or task repetition trials are a focus of task switching research in cognitive science. To explain how such multiple tasks and task switching are performed, experimental psychologists have proposed that cognitive executive control systems govern processes including the selection, initiation, execution, and termination of each task (Rubinstein, Meyer, & Evans, 2001). The cognitive executive control system provides a supervisory function controlling other perceptual/motor and cognitive processes when switching from one task to another.

2.4.2 Multitasking Studies in Human Information Behaviour

Spink, Cole and Waller (2008) elucidated information behaviour as a multitasking process. Multitasking information behaviour is emerging as an important information behaviour research area.

In the realm of human information behaviour, the process of seeking information concurrently over time in relation to more than one, possibly evolving, set of information tasks, including shifts in beliefs, cognitive, affective and/or situational states, is called multitasking information behaviour (Spink, Ozmutlu & Ozmutlu, 2002). Multitasking information behaviour includes searching for information related to one information task and then switching to search for items on another information task (Spink, Cole & Waller, 2008).

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Information scientists have observed the phenomenon of multitasking information behaviour by employing different methodology in a number of different environments, such as library use, database searching and the work environment.

Spink (2004) reported results from a case study exploring the multitasking information behaviour by one information seeker in a public library. The results showed that people engaged in multitasking information behaviours consisting of electronic search, physical library search, serendipity browsing and information task switching in libraries as they seek and search for information on more than one information task. A process of seventeen information task switches over two library visits was identified. Spink, Alvarado-Albertorio, Narayanan, Brumfield and Park (2007) investigated the multitasking information behaviours of ninety-six public library users through diary questionnaires and found that 63.5% of the 96 library users sought information on multiple topics and engaged in multitasking behaviours.

Waller (1997) examined how air crew work groups managed multiple tasks under dynamic and deadline conditions through two field studies. Her model suggested that work groups engaged in information gathering, task prioritisation and resource allocation activities in order to perform multiple tasks. The task characteristics linked to multiple task switching were revealed: (1) the familiarity of the task and its relative difficulty; (2) the source of the task; (3) the task deadline; (4) the status of the task in terms of its potential completion; and (5) the sequence of the task in terms of any interdependence among the tasks being prioritised.

González and Mark (2004) presented results of fieldwork observation of information workers in three different roles: analysts, software developers and managers. They introduced the concept of working spheres to explain the inherent way in which individuals conceptualize and organise their basic units of work. People worked in an average of ten different working spheres and they spent about twelve minutes in

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a working sphere before they switched to another. Spink and Park (2005) conducted a study investigating business consultants’ multitasking information and non-information task switching and their interplay. They found that information seeking tasks occurred within multitasking and task switching sequences with non- information tasks, including computing and communication tasks. The execution of information seeking tasks often supported or responded to communication or computing tasks.

Spink and Cole (2005) proposed a model of multitasking and task switching information behaviour. They argued that information behaviour may involve a combination of cognitive and physical actions on multiple tasks concurrently or sequentially, including switching between different information tasks. Information seekers have to coordinate a number of factors, including their cognitive state, level of knowledge and understanding of their information problem, into coherent processes of human information seeking, searching, retrieving and usage behaviours. Spink, Park & Cole (2006) argued that multitasking is an essential element of the information behaviour process that must be closely examined, allowed for and facilitated in the design of IR systems.

2.4.3 Multitasking Studies in Web and Information Retrieval

This section investigates how multitasking research has been conducted in information retrieval and Web search studies.

Limited studies have shed light on multitasking information behaviour during Web search and IR sessions. Spink, Ozmutlu and Ozmutlu (2002) originally suggested that IR searches often include multiple topics during a single search session or multitasking search. They found that multitask searching is common human information retrieval behaviour as many IR system users conduct information

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searching on multiple related or unrelated topics and also switching among the topics. In addition, IR multitasking search sessions are longer than single topic sessions, with a mean of 2.1 topic changes per search session. Their study represents advances in providing a research framework for multitasking during Web and information retrieval.

Subsequently, multitask searching was examined on the Excite and AlltheWeb.com Web search engines. Ozmutlu, Ozmutlu and Spink (2003b) found that multitasking Web searches are noticeable user behaviours, as one tenth of Excite users and one third of AlltheWeb.com users conducted multitasking searches. Additionally, multitasking search sessions with a broad variety of search topics are longer than regular search sessions in terms of queries per session and duration. Koshman, Spink and Jansen (2006) also reported that 11.1% of search sessions over the Vivisimo search engine were multitask searches, including a broad variety of search topics in multitask search sessions.

In 2006, Spink, Park, Jansen and Pedersen (2006) conducted separate studies of two-query search sessions and three or more query search sessions on the AltaVista Web search engine. The degree of multitasking search and information task switching was examined, based on these two sets of search sessions.

Findings included (1) a high degree of multiple topics existed in both two-query sessions (81%) and three or more query sessions (91%); (2) three or more query sessions sometimes contained frequent topic changes; and (3) multitasking was found to be an un-ignored but growing element in Web searching.

Further, Spink, Park and Koshman (2006) investigated multiple information problems ordering which is engaged in Web search by observing forty study participants’ Web searching behaviour. They concluded that information task ordering was affected by the following factors: personal interest, problem

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knowledge, perceived level of information available on the Web, ease of finding information, level of importance and seeking information on information problems in order from general to specific. Personal interest and problem knowledge were the two major factors affecting multiple information problem prioritisations.

Park (2008) specifically studied human prioritising and coordinating information behaviour among multiple information tasks in a Web information seeking and retrieval context. She found that human prioritising behaviour was affected by multiple factors, such as task attributes, emotions and time. Users’ dynamic Web search interactions existed among the components of their prioritising and coordinating information behaviour. Multiple tasks prioritisation and coordination was operationalised as being composed of the level of each task, cognitive, affective, and temporal and behaviour dimension. The affective factor was considered as an important factor affecting task performance. She stated that how we manage our emotions ultimately yielded successful performance. Her discussion of coordination information behaviour was more on the task level, such as coordinating activities of task switching, and tabbed browsing.

2.4.4 Coordination Viewpoint in Multitasking Research

According to Wickens (1989), multitasking research includes both task characteristics and coordination processes. Multitasking information behaviour is conceptualized as a binding process that works with human coordination behaviours to construct an information behaviour process. It provides a framework for coordinating and integrating the different levels within information behaviour.

Information science researchers such as Spink, Park and Cole (2006) and Spink and Du (2007) incorporated the concept of coordination into multitasking information behaviour. They discussed the role of multitasking and coordination as conceptualizing and binding elements in the integrated information behaviour

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framework. People coordinate the translation of their information problem(s) by performing search term selection tasks, tactic and strategy tasks, search engine interaction tasks, and relevance judgments. Effective interactive IR must be a successful process of coordinating the switching between related or unrelated tasks.

Multitasking processes, in general, involve a person’s allocation of his/her own scarce cognitive resources among several tasks and the moderating impact of task elements, task processes, and task resources on multiple-task performance. The task coordination research concerns how people coordinate their activities to perform tasks, in particular, decision-making and problem-solving tasks (Waller, 1997). Iani and Wickens (2004) pointed out that the performance of multiple tasks was controlled by cognitive executive processes that enable humans to choose and prioritise tasks, and monitor, interrupt and adjust task performance. Hence, it is necessary to identify how such cognitive executive control processes establish priorities among multiple information tasks and allocate resources to them, thus allowing efficient multiple-task performance.

The following section discusses in detail the research on coordination and its theory, mechanism of coordination, especially the coordination in information retrieval, and ends up with recent studies on cognitive coordination within Web searching context.

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