Ubiquitous search for smart workspaces

One key goal of personal information management is to let users search all of their information assets easily. As knowledge workspaces evolve, a need to provide ubiquitous search services that allow the user to search anything, from anywhere, at any time becomes increasingly important. A key feature of the smart workspace is the ability to support this access to personal content. Here, several current systems that provide some of these capabilities are examined, and implications for future information access systems are derived.     

The effects of positive affect and personal information search on outcomes in call centers: An empirical study

Despite the increasing availability of computer-based information sources within organizations, employees continue to rely on other people as important sources of information. However, relatively little is known about the relations among positive affect, personal information search, and related outcomes — especially in organizations. Using data from 276 service representatives in one organization we find that positive affect is positively related to the likelihood of searching for information through personal sources. Moreover, we find that the relations between the likelihood of searching for information via personal sources and work outcomes, such as productivity and job satisfaction, are moderated by positive affect. Specifically, we find that positive affect negatively moderates the relations between personal information search and productivity, but positively moderates the relations between personal information search and job satisfaction. As we will discuss, these findings not only contribute to the IT and organizational behavior literatures but also have significant implications for practitioners.     

Estimating incomplete information in group decision making: A framework of granular computing

A general assumption in group decision making scenarios is that of all individuals possess accurate knowledge of the entire problem under study, including the abilities to make a distinction of the degree up to which an alternative is better than other one. However, in many real world scenarios, this may be unrealistic, particularly those involving numerous individuals and options to choose from conflicting and dynamics information sources. To manage such a situation, estimation methods of incomplete information, which use own assessments provided by the individuals and consistency criteria to avoid discrepancy, have been widely employed under fuzzy preference relations. In this study, we introduce the information granularity concept to estimate missing values supporting the objective of obtaining complete fuzzy preference relations with higher consistency levels. We use the concept of granular preference relations to form each missing value as a granule of information in place of a crisp number. This offers the flexibility that is required to estimate the missing information so that the consistency levels related to the complete fuzzy preference relations are as higher as possible.     

Intelligent control of external software systems

This paper focuses on the relatively unexplored set of issues that arises when an intelligent agent attempts to use external software systems (EESs). The issues are illustrated initially in the context of the complex agent-ESS interactions in an engineering design example. Approaching the area from the perspective of artificial intelligence (AI) research, we find that in general, agent-ESS interactions vary widely. We characterize the possible variations in terms of performance capabilities required, skill levels at which performance is exhibited, and knowledge sources from which capabilities can be acquired. We are exploring these variations using Soar as our candidate AI agent; the document briefly describes seven Soar-based projects in early stages of development, in which agent-ESS issues are addressed. We conclude by placing agent-ESS research in the context of other work on software technology, and discuss the research agenda we have set for ourselves in this area.     

Design and implementation of a smart card based healthcare information system

Smart cards are used in information technologies as portable integrated devices with data storage and data processing capabilities. As in other fields, smart card use in health systems became popular due to their increased capacity and performance. Their efficient use with easy and fast data access facilities leads to implementation particularly widespread in security systems. In this paper, a smart card based healthcare information system is developed. The system uses smart card for personal identification and transfer of health data and provides data communication via a distributed protocol which is particularly developed for this study. Two smart card software modules are implemented that run on patient and healthcare professional smart cards, respectively. In addition to personal information, general health information about the patient is also loaded to patient smart card. Health care providers use their own smart cards to be authenticated on the system and to access data on patient cards. Encryption keys and digital signature keys stored on smart cards of the system are used for secure and authenticated data communication between clients and database servers over distributed object protocol. System is developed on Java platform by using object oriented architecture and design patterns.     

Deriving managerial benefit from knowledge search: A paradigm shift?

A conceptual model of knowledge/information (K/I) search and transfer was developed; it was based on three concepts: managers’ preferences for internal versus external sources, the importance of relationships between sources and recipients, and the derived managerial benefit. The results of an empirical study demonstrated a high degree of explained variance in managerial benefit and suggested managers’ preferences for external sources over internal ones. They also supported the notion that managers use internal, known knowledge search to enhance their reputation and status. These results suggested a shift away from the conventional wisdom of internal preference that is reflected in theories of in-group favoritism and out-group derogation.     

Personal information management effectiveness of knowledge workers: conceptual development and empirical validation

What critical factors contribute to knowledge workers’ effective information management and consequent job performance? This paper begins to address this important question by developing a conceptual definition of a new construct called personal information management effectiveness (PIME) and its constituent dimensions. Specifically, we theorize that PIME consists of two underlying dimensions: personal information management motivation (PIMM) and personal information management capability (PIMC). Synthesizing the extant literature on information management and information orientation, we further conceptualize PIMM as having four sub-dimensions of proactiveness, sharing, transparency, and formality, and PIMC as possessing five sub-dimensions of sensing, collecting, organizing, processing, and maintaining. Moreover, we develop a theoretical model that positions PIME as a mediator between two selected individual characteristics (IT self-efficacy and need-for-cognition) and job performance. New measures for PIME dimensions were developed and shown to have strong psychometric properties. The proposed model was empirically tested using data collected from 352 knowledge workers. As theorized, PIME was found to have significant effects on job performance (41%) and fully mediate the effects that the selected individual characteristics have on job performance. Responding to recent calls for advanced research on personal information management, the measures of PIMM and PIMC developed in this study have practical value as research and diagnostic tools and the findings provide useful insights to help organizations improve knowledge workers’ information management practices.     

Knowledge transfer in virtual settings: the role of individual virtual competency

Economic forces, competitive pressures and technological advances have created an environment within which firms have developed new ways of organizing (e.g. virtual work settings) and managing their resources (e.g. knowledge management) in order to maintain and improve firm performance. Extant research has highlighted the challenges associated with managing knowledge in virtual settings. However, researchers are still struggling to provide effective guidance to practitioners in this field. We believe that a better understanding of individual virtual competency is a potential avenue for managing the complexity of knowledge transfer in virtual settings. In particular, we suggest that optimal knowledge transfers can be achieved by individuals armed with the right personal capabilities and skills for virtual work, particularly when those knowledge transfers are emergent, bottom-up and cannot be specified a priori. The virtual competency exhibited by individuals can be the key to overcoming the constraints of knowledge transfers with such characteristics because underlying competency can facilitate effective action in unfamiliar and novel situations. In this conceptual research, we develop a theoretical model of individual virtual competence and describe its role in the communication process, which underpins effective knowledge transfer in virtual settings. Additionally, we consider the antecedent role that prior experience in virtual activity plays in aiding workers to develop virtual competence, which in turn engenders effective knowledge transfer. We conclude with implications for future research and for practicing managers.     

The search for knowledge,contexts, and Case-Based Reasoning

A major goal of this paper is to compare Case-Based Reasoning with other methods searching for knowledge. We consider knowledge as a resource that can be traded. It has no value in itself; the value is measured by the usefulness of applying it in some process. Such a process has info-needs that have to be satisfied. The concept to measure this is the economical term utility. In general, utility depends on the user and its context, i.e., it is subjective. Here, we introduce levels of contexts from general to individual. We illustrate that Case-Based Reasoning on the lower, i.e., more personal levels CBR is quite useful, in particular in comparison with traditional informational retrieval methods.     

Improving mobile search using content enrichment

Providing an effective mobile search service is a difficult task given the unique characteristics of the mobile space. Small-screen devices with limited input and interaction capabilities do not make ideal search devices. In addition, mobile content, by its concise nature, offers limited indexing opportunities, which makes it difficult to build high-quality mobile search engines and indexes. In this paper we consider the issue of limited page content by evaluating a heuristic content enrichment framework that uses standard Web resources as a source of additional indexing knowledge. We present an evaluation using a mobile news service that demonstrates significant improvements in search performance compared to a benchmark mobile search engine.     

Domain-specific entity extraction from noisy,unstructured data using ontology-guided search

Domain-specific knowledge is often recorded by experts in the form of unstructured text. For example, in the medical domain, clinical notes from electronic health records contain a wealth of information. Similar practices are found in other domains. The challenge we discuss in this paper is how to identify and extract part names from technicians repair notes, a noisy unstructured text data source from General Motors’ archives of solved vehicle repair problems, with the goal to develop a robust and dynamic reasoning system to be used as a repair adviser by service technicians. In the present work, we discuss two approaches to this problem. We present an algorithm for ontology-guided entity disambiguation that uses existing knowledge sources, such as domain-specific taxonomies and other structured data. We illustrate its use in the automotive domain, using GM parts ontology and the unit structure of repair manuals text to build context models, which are then used to disambiguate mentions of part-related entities in the text. We also describe extraction of part names with a small amount of annotated data using hidden Markov models (HMM) with shrinkage, achieving an f-score of approximately 80%. Next, we used linear-chain conditional random fields (CRF) in order to model observation dependencies present in the repair notes. Using CRF did not lead to improved performance, but a slight improvement over the HMM results was obtained by using a weighted combination of the HMM and CRF models.     

Unleashing the Power of mCRM: Investigating Antecedents of Mobile CRM Values from Managers’ Viewpoint

Mobile technology together with Internet-based electronic commerce has transformed the way businesses operate. Mobile Customer Relationship Management (mCRM) combines mobility and CRM (customer relationship management) to enable organizations to increase their business performance by delivering better products/services to their customers. Incorporating location-based information into CRM systems has created new values and business opportunities. Using location information, for example, businesses can offer more personalized and real-time location-based services (LBS) to their customers. In this study, we investigate how the IT infrastructure and LBS support of mCRM, as technical resource capabilities, and mCRM knowledge and education, as human resource capabilities, can enhance business performance of the organization by improving mCRM’s quality dimensions. We propose a research model that integrates the resource-based view (RBV) of a firm and DeLone and McLean IS success model to investigate the effects of mCRM quality dimensions on business performance. To validate the proposed research model, we collected a set of empirical data from managers in a wide range of organizations that use mCRM in South Korea. The results indicate that the IT infrastructure of an organization as a technical resource, along with mCRM users’ knowledge, as a human resource capability can significantly influence managers’ perceptions of quality along various dimensions of mCRM: customer data quality, system quality, and service quality. Consequently, improving these quality dimensions will significantly influence the performance of an organization in terms of financial performance, productivity, and customer satisfaction. The present study would help both academic and professionals to understand and improve business performance by employing the appropriate resources that can harness the full potential of mCRM.     

Making knowledge machine-processable: some implications of general semantic search

The central argument of this paper is that the design, implementation and use of technologies that underpin general semantic search have implications for what we know and the way in which knowledge is understood. Semantic search is an assemblage of technologies that most Internet users would use regularly without necessarily realising. Users of search engines implementing semantic search can obtain answers to questions rather than just retrieve pages that include their search query. This paper critically examines the design of the Semantic Web, upon which semantic search is based. It demonstrates that implicit in the design of the Semantic Web are particular assumptions about the nature of classification and the nature of knowledge. The Semantic Web was intended for interoperability within specific domains. It is here argued that the extension to general semantic search, for use by the general public, has implications for what type of knowledge is visible and what counts as legitimate knowledge. The provision of a definitive answer to a query, via the reduction of discursive knowledge into machine-processable data, provides the illusion of objectivity and authority in a way that is increasingly impenetrable to critical scrutiny.     

Adversarial-knowledge dimensions in data privacy

Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call adversarial knowledge. A robust privacy framework should allow publishing organizations to analyze data privacy by means of not only data dimensions (data that a publishing organization has), but also adversarial-knowledge dimensions (information not in the data). In this paper, we first describe a general framework for reasoning about privacy in the presence of adversarial knowledge. Within this framework, we propose a novel multidimensional approach to quantifying adversarial knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of adversarial knowledge. Our main technical contributions include a multidimensional privacy criterion that is more intuitive and flexible than previous approaches to modeling background knowledge. In addition, we identify an important congregation property of the adversarial-knowledge dimensions. Based on this property, we provide algorithms for measuring disclosure and sanitizing data that improve computational efficiency several orders of magnitude over the best known techniques.     

A dynamic capabilities perspective of IS project portfolio management

Organizations use information systems project portfolio management (IS PMM) to reconfigure their IS resources and capabilities to match changing market and economic conditions. IS PPM can therefore be characterised as a dynamic capability. We investigate how firms developed and adapted IS PPM to match the turbulent recessionary conditions witnessed after 2008–2009. This study contributes to an understanding of IS PPM by identifying the constituent dynamic capabilities and providing empirical examples of adaptation. To our knowledge, the study is the first to apply the notion of second order dynamic capabilities to the IS domain and also makes an important contribution to the more general concept of dynamic capabilities by providing empirical evidence and theoretical justification of the increased detailed, centrally controlled and analytical nature of IS PPM dynamic capabilities in recessionary conditions.     

Knowledge Domain Spanners in Ideation

Ideation is increasingly receiving attention as a management issue, and we can at present witness the emergence and diffusion of a range of different proactive approaches towards ideation. This development is hardly surprising in the light of the changed nature of innovation activities, including a higher reliance also on external sources for innovation and more focus on non‐technological types of innovation, such as business model innovations. Firms need to handle both a larger number of sources for innovation and more different types of innovations. This article investigates how spanning different knowledge domains influences individuals' ideation performance. A study has been performed using data on all ideas created within an organization during three years. From this data, two broad set of knowledge domains are identified and the influence on ideation of the individuals spanning these domains – the knowledge domain spanners in ideation – are investigated. The empirical results show that knowledge domain spanners in ideation have higher ideation performance than individuals engaged in only one knowledge domain.     

Experience effects on the accuracy of self-assessed user competence

Self-managed learning is the normal way that users learn to work with software within organizations. To be effective, self-managed, learning requires individuals to self-assess their IT knowledge; accurate self-assessment helps them optimize the capabilities they possess and be aware of those they do not. This study demonstrated that, in general, individuals did not accurately self-assess their knowledge of the software they used. However, we also found that the accuracy of self-assessment increased with greater experience in, and better understanding of, IT domains.Organizations need to recognize the self-assessment problem to facilitate effective software learning and to gain the most from their software investments.     

Towards Intelligent Semantic Caching for Web Sources

An intelligent semantic caching scheme suitable for web sources is presented. Since web sources typically have weaker querying capabilities than conventional databases, existing semantic caching schemes cannot be directly applied. Our proposal takes care of the difference between the query capabilities of an end user system and web sources. In addition, an analysis on the match types between a user's input query and cached queries is presented. Based on this analysis, we present an algorithm that finds the best matched query under different circumstances. Furthermore, a method to use semantic knowledge, acquired from the data, to avoid unnecessary access to web sources by transforming the cache miss to the cache hit is presented. To verify the effectiveness of the proposed semantic caching scheme, we first show how to generate synthetic queries exhibiting different levels of semantic localities. Then, using the test sets, we show that the proposed query matching technique is an efficient and effective way for semantic caching in web databases.