Knowledge Management System Use as a Key Driver of Professional and Organizational Cognitive Engagement

This study aims to contribute to the literature on knowledge management systems (KMS) through investigating the role of engagement as an important intermediary in the relationship between KMS use and outcomes. Building on prior literature, we propose a theoretical model that conceptualizes KMS use as a valuable resource and distinguish between two types of cognitive engagement: professional cognitive engagement and organizational cognitive engagement. These, in turn, mediate the KMS use-job performance and KMS use-organizational commitment relationships. We tested the model on a sample of 3354 real estate agents using an extensive dataset comprised of primary and secondary data. The findings show that KMS use has an impact on individuals’ professional and organizational cognitive engagement, which then impacts their job performance and organizational commitment. However, our findings indicate that professional cognitive engagement only partially mediates the relationship between KMS use and job performance. We conclude the paper with a discussion of theoretical contributions and practical implications

The Role of Basic Human Values in Knowledge Sharing: How Values Shape the Postadoptive Use of Electronic Knowledge Repositories

A growing body of literature examines how to elicit knowledge contributions to electronic knowledge repositories (EKRs) with the goal of helping organizations increase implementation benefits. While this literature has explained in detail the initial EKR adoption by knowledge contributors, it has not yet examined the drivers of postadoptive EKR usage for contributing knowledge. Postadoptive EKR usage, such as innovative feature use, can potentially result in richer contributions to EKRs. To aid understanding of how to unlock the benefits of EKRs for organizations, this study examines the impact of basic human values on one type of postadoptive behavior that goes well beyond basic usage: trying to innovate with EKR features. We develop a research model that integrates human values and trying to innovate with EKRs, suggesting that human values indicate modes of independent thought and action and can lead to attempts to innovate in EKR use by increasing the frequency of EKR usage. Data collected from 233 knowledge workers support the model. Our findings shed light on how to encourage innovative EKR usage and underscore the importance of human values for the success of knowledge management initiatives

Advances in Technology Enhanced Learning

‘Advances in Technology Enhanced Learning’ presents a range of research projects which aim to explore how to make engagement in learning (and teaching) more passionate. This interactive and experimental resource discusses innovations which pave the way to open collaboration at scale. The book introduces methodological and technological breakthroughs via twelve chapters to learners, instructors, and decision-makers in schools, universities, and workplaces. The Open University's Knowledge Media Institute and the EU TELMap project have brought together the luminaries from the European research area to showcase their vision of the future of learning with technology via their recent research project work. The projects discussed range widely over the Technology Enhanced Learning area from: environments for responsive open learning, work-based reflection, work-based social creativity, serious games and many more

Organizational Strategies for Developing New STEM Talent

U.S. business leaders are experiencing a deficiency within STEM skill sets in newly hired employees, resulting in challenges to business sustainability. The purpose of this case study was to identify strategies used to develop new STEM employees for business sustainability. Participants included 5 IT business leaders who had experience developing new STEM employees in a technology organization in New York. The 3-part theory of knowledge management, knowledge creation, and knowledge transfer was the conceptual framework for this study. Data collection included face-to-face interviews and analyses of company training plans, videos, and internal websites. Methodological triangulation of the analysis technique included organizing, collecting, and comparing data. Data analysis included a generic coding process to identify 3 themes: (a) strategies for organizational effectiveness, (b) strategies for new IT employee enrichment, and (c) strategies for improving business productivity. The results of the study indicated strategies to deliver employee training and development systems leveraging internal knowledge management and transfer could provide business leaders with effective ways to increase productivity and maintain organizational effectiveness. The social implications of the study include the potential to improve the economic strength of the local community because new insights on the development of STEM employees may lead to increased hiring and business sustainability

NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse