Automated gathering of Web information: An in-depth examination of agents interacting with search engines

The Web has become a worldwide repository of information which individuals, companies, and organizations utilize to solve or address various information problems. Many of these Web users utilize automated agents to gather this information for them. Some assume that this approach represents a more sophisticated method of searching. However, there is little research investigating how Web agents search for online information. In this research, we first provide a classification for information agent using stages of information gathering, gathering approaches, and agent architecture. We then examine an implementation of one of the resulting classifications in detail, investigating how agents search for information on Web search engines, including the session, query, term, duration and frequency of interactions. For this temporal study, we analyzed three data sets of queries and page views from agents interacting with the Excite and AltaVista search engines from 1997 to 2002, examining approximately 900,000 queries submitted by over 3,000 agents. Findings include: (1) agent sessions are extremely interactive, with sometimes hundreds of interactions per second (2) agent queries are comparable to human searchers, with little use of query operators, (3) Web agents are searching for a relatively limited variety of information, wherein only 18% of the terms used are unique, and (4) the duration of agent-Web search engine interaction typically spans several hours. We discuss the implications for Web information agents and search engines.

Human information behavior: Integrating diverse approaches and information use

For millennia humans have sought, organized, and used information as they learned and evolved patterns of human information behaviors to resolve their human problems and survive. However, despite the current focus on living in an "information age," we have a limited evolutionary understanding of human information behavior. In this article the authors examine the current three interdisciplinary approaches to conceptualizing how humans have sought information including (a) the everyday life information seeking-sense-making approach, (b) the information foraging approach, and (c) the problem-solution perspective on information seeking approach. In addition, due to the lack of clarity regarding the role of information use in information behavior, a fourth information approach is provided based on a theory of information use. The use theory proposed starts from an evolutionary psychology notion that humans are able to adapt to their environment and survive because of our modular cognitive architecture. Finally, the authors begin the process of conceptualizing these diverse approaches, and the various aspects or elements of these approaches, within an integrated model with consideration of information use. An initial integrated model of these different approaches with information use is proposed.

Information and non-information multitasking interplay

Purpose - During multitasking, humans handle multiple tasks through task switching or engage in multitasking information behaviors. For example, a user switches between seeking new kitchen information and medical information. Recent studies provide insights these complex multitasking human information behaviors (HIB). However, limited studies have examined the interplay between information and non-information tasks. Design/methodology/approach - The goal of the paper was to examine the interplay of information and non-information task behaviors. Findings - This paper explores and speculates on a new direction in HIB research. The nature of HIB as a multitasking activity including the interplay of information and non-information behavior tasks, and the relation between multitasking information behavior to cognitive style and individual differences, is discussed. A model of multitasking between information and non-information behavior tasks is proposed. Practical implications/limitations - Multitasking information behavior models should include the interplay of information and non-information tasks, and individual differences and cognitive styles. Originality/value - The paper is the first information science theoretical examination of the interplay between information and non-information tasks. © Emerald Group Publishing Limited.

Towards an evolutionary perspective for human information behavior: An exploratory study

Purpose - Since the beginning of human existence, humankind has sought, organized and used information as it evolved patterns and practices of human information behaviors. However, the field of human information behavior (HIB) has not heretofore pursued an evolutionary understanding of information behavior. The goal of this exploratory study is to provide insight about the information behavior of various individuals from the past to begin the development of an evolutionary perspective for our understanding of HIB. Design/methodology/approach - This paper presents findings from a qualitative analysis of the autobiographies and personal writings of several historical figures, including Napoleon Bonaparte, Charles Darwin, Giacomo Casanova and others. Findings - Analysis of their writings shows that these persons of the past articulated aspects of their HIB's, including information seeking, information organization and information use, providing tangible insights into their information-related thoughts and actions. Practical implications - This paper has implications for expanding the nature of our evolutionary understanding of information behavior and provides a broader context for the HIB research field. Originality/value - This the first paper in the information science field of HIB to study the information behavior of historical figures and begin to develop an evolutionary framework for HIB research. © Emerald Group Publishing Limited.

Information Behavior: A Socio-Cognitive Ability

Abstract: How has human information behavior evolved? Our paper explores this question in the form of notions, models and theories about the relationship between information behavior and human evolution. Alexander’s Ecological Dominance and Social Competition/Cooperation (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality. His model provides a basis for explaining the evolution of human socio-cognitive abilities, including ecological dominance, and social competition/cooperation. Our paper examines the human trait of information behavior as a socio-cognitive ability related to ecological dominance, and social competition/cooperation. The paper first outlines what is meant by information behavior from various interdisciplinary perspectives. We propose that information behavior is a socio-cognitive ability that is related to and enables other sociocognitive abilities such as human ecological dominance, and social competition/cooperation. The paper reviews the current state of evolutionary approaches to information behavior and future directions for this research . Keywords: information behavior, socio-cognitive ability, ecological dominance, social competition, social cooperation.

Human Information behavior (introduction to special issue)

Information behavior studies are a growing body of research that highlights the importance of information for everyone in the information age. This e-book presents an international and diverse range of studies and insights into the current state of theories and models of information behavior. There is an emphasis on the socialpersonalhuman dimensions of information seeking using social science methods and theoretical frameworks. The studies particularly draw on the methods and theories of anthropology, sociology and psychology to produce interpretations of the way in which information is experienced in the lives of individuals working as critical care nurses in a medical environment, the information seeking behavior of the visually impaired, the social interactions within knitting circles in public libraries, and attempts to apply information behavior theory to the design of information solutions. Collectively the papers contribute more generally to our understanding of information behavior theory and models, including the medical and retrieval contexts.

Cognitive shifts related to interactive information retrieval

Purpose – Interactive information retrieval (IR) involves many human cognitive shifts at different information behaviour levels. Cognitive science defines a cognitive shift or shift in cognitive focus as triggered by the brain's response and change due to some external force. This paper aims to provide an explication of the concept of “cognitive shift” and then report results from a study replicating Spink's study of cognitive shifts during interactive IR. This work aims to generate promising insights into aspects of cognitive shifts during interactive IR and a new IR evaluation measure – information problem shift. Design/methodology/approach – The study participants (n=9) conducted an online search on an in-depth personal medical information problem. Data analysed included the pre- and post-search questionnaires completed by each study participant. Implications for web services and further research are discussed. Findings – Key findings replicated the results in Spink's study, including: all study participants reported some level of cognitive shift in their information problem, information seeking and personal knowledge due to their search interaction; and different study participants reported different levels of cognitive shift. Some study participants reported major cognitive shifts in various user-based variables such as information problem or information-seeking stage. Unlike Spink's study, no participant experienced a negative shift in their information problem stage or level of information problem understanding. Originality/value – This study builds on the previous study by Spink using a different dataset. The paper provides valuable insights for further research into cognitive shifts during interactive IR.

The resource boom's underbelly : criminological impacts of mining development

Australia is currently in the midst of a major resources boom. Resultant growing demands for labour in regional and remote areas have accelerated the recruitment of non resident workers, mostly contractors, who work extended block rosters of 12-hour shifts and are accommodated in work camps, often adjacent to established mining towns. Serious social impacts of these practices, including violence and crime, have generally escaped industry, government and academic scrutiny. This paper highlights some of these impacts on affected regional communities and workers and argues that post-industrial mining regimes serve to mask and privatize these harms and risks, shifting them on to workers, families and communities.

Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Using the work system method with freshman information systems students

Recent surveys of information technology management professionals show that understanding business domains in terms of business productivity and cost reduction potential, knowledge of different vertical industry segments and their information requirements, understanding of business processes and client-facing skills are more critical for Information Systems personnel than ever before. In an attempt to restrucuture the information systems curriculum accordingly, our view it that information systems students need to develop an appreciation for organizational work systems in order to understand the operation and significance of information systems within such work systems.

From bust to boom : towards a strategic cognition perspective on Australian mining firms’ adaption

While the role of executives’ cognition in organisations’ responses to change is a central topic in strategic cognition research, changes in firms’ environment are typically not measured directly but described either as an event (for example, new industry legislation) or represented by a time period (e.g. when a new technology impacted an industry). The Australian mining sector has witnessed a historically significant change in demand for its products and we begin by developing measures of changes in supply and demand for key commodities during the period 1992-2008. We identify sub-groups of firms based on their activities and commodity sector and examine the relation of these variables to executives’ cognition and to firms’ CapEx. We find industry, firm and cognitive variables are related to both strategic cognition and firms’ CapEx.

Safety enhancement of operator protection systems on self-propelled mining equipment

In the long term, with development of skill, knowledge, exposure and confidence within the engineering profession, rigorous analysis techniques have the potential to become a reliable and far more comprehensive method for design and verification of the structural adequacy of OPS, write Nimal J Perera, David P Thambiratnam and Brian Clark. This paper explores the potential to enhance operator safety of self-propelled mechanical plant subjected to roll over and impact of falling objects using the non-linear and dynamic response simulation capabilities of analytical processes to supplement quasi-static testing methods prescribed in International and Australian Codes of Practice for bolt on Operator Protection Systems (OPS) that are post fitted. The paper is based on research work carried out by the authors at the Queensland University of Technology (QUT) over a period of three years by instrumentation of prototype tests, scale model tests in the laboratory and rigorous analysis using validated Finite Element (FE) Models. The FE codes used were ABAQUS for implicit analysis and LSDYNA for explicit analysis. The rigorous analysis and dynamic simulation technique described in the paper can be used to investigate the structural response due to accident scenarios such as multiple roll over, impact of multiple objects and combinations of such events and thereby enhance the safety and performance of Roll Over and Falling Object Protection Systems (ROPS and FOPS). The analytical techniques are based on sound engineering principles and well established practice for investigation of dynamic impact on all self propelled vehicles. They are used for many other similar applications where experimental techniques are not feasible.

The Measurement of Information System Use: Preliminary Considerations

DeLone and McLean (1992, p. 16) argue that the concept of “system use” has suffered from a “too simplistic definition.” Despite decades of substantial research on system use, the concept is yet to receive strong theoretical scrutiny. Many measures of system use and the development of measures have been often idiosyncratic and lack credibility or comparability. This paper reviews various attempts at conceptualization and measurement of system use and then proposes a re-conceptualization of it as “the level of incorporation of an information system within a user’s processes.” The definition is supported with the theory of work systems, system, and Key-User-Group considerations. We then go on to develop the concept of a Functional- Interface-Point (FIP) and four dimensions of system usage: extent, the proportion of the FIPs used by the business process; frequency, the rate at which FIPs are used by the participants in the process; thoroughness, the level of use of information/functionality provided by the system at an FIP; and attitude towards use, a set of measures that assess the level of comfort, degree of respect and the challenges set forth by the system. The paper argues that the automation level, the proportion of the business process encoded by the information system has a mediating impact on system use. The article concludes with a discussion of some implications of this re-conceptualization and areas for follow on research.

United we stand, divided we fall: the failure of an accounting information system in a major radiology provider

This article explores the way in which a major Australian radiology organization implemented a complex accounting information system and how workers in the 72 radiology practises that had to use it resisted the change. The study reports on the issues that led to the circumvention of the system by individuals and, after only three years, complete withdrawal of the accounting information system by the parent organization. This article has implications for firms in the health care and other sectors considering implementing new accounting information systems. Organizations need to incorporate change management techniques and provide open communication to all stakeholders to minimize disruption and potential problems.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.