Integration of Knowledge Management and Business Intelligence for Lean Organizational Learning by the Digital Worker

2019 ◽  
pp. 130-140 ◽  
Author(s):  
Selvi Kannan ◽  
Shah J. Miah
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Labor Market ◽  
Organizational Learning ◽  
Behavioral Science ◽  
Content Management ◽  
Decision Makers ◽  
Digital World ◽  
Global Labor Market ◽  
Work Knowledge

The polarization of global labor market, hunt for talent, need to adapt quickly to changing environment is pressuring businesses more than ever before on their performance. This is further snowballed with the development of digitalization, automation, robotization, and artificial intelligence that offer approaches for addressing enormous industry challenges. These challenges create a push for organizational decision makers to rethink on the management of work. Knowledge management (KM) is understood to encourage content management, collaboration with inclusion of organizational behavioral science, and of course technologies. Complementing BI with knowledge management (KM) system in an organization can account for lean and accelerated performance. In this chapter, the authors present their position and insights in the integration of KM and BI suited for the worker in the digital world which possibly encourages lifelong learning with the focus on adaptability.

Social Knowledge Workspace

2011 ◽  
pp. 193-206
Author(s):  
Jagdish K. Vasishtha
Keyword(s):  
Knowledge Management ◽  
Organizational Learning ◽  
Relevant Information ◽  
Content Management ◽  
Social Knowledge ◽  
Knowledge Capture ◽  
Team Collaboration ◽  
Technological Advances ◽  
Key Characteristics ◽  
Content Management Systems

Over the years, knowledge management in organizations has picked up steam with implementation of various solutions like Content Management Systems, Wiki, etc. However, the ability to find relevant information and capture organizational learning still looks like a distant dream. Also, organizations worldwide are transforming due to changes in worker demographics, globalization of business and technological advances. The knowledge workers of today need tools for effective knowledge capture and team collaboration. Some of the key concerns which will be analyzed in this chapter are; (a) Knowledge fragmentation due to technology, (b) Relevancy of information to a user and (c) Push vs. Pull approach of accessing information. The chapter will also explore how these challenges can be addressed by social knowledge workspaces and what should be some of the key characteristics of these technologies under development.

scholarly journals Impact of Industrial Revolution 4.0 on the Labor Market in Vietnam

2021 ◽  
Vol 12 (1) ◽  
pp. 94
Author(s):  
Nguyen Minh Tri ◽  
Doan Thi Nhe
Keyword(s):  
Artificial Intelligence ◽  
Labor Market ◽  
Market Structure ◽  
Industrial Revolution ◽  
National Development ◽  
Strong Impact ◽  
The World ◽  
Global Labor Market ◽  
Catch Up ◽  
Automation Technology

Industrial Revolution 4.0 is taking shape and has a strong impact on the global labor market. The strength of the system connecting everything and artificial intelligence as well as automation technology is changing the labor market structure of countries in the world in general and of Vietnam in particular. For the labor market, the Industrial Revolution 4.0 has created many opportunities and challenges that require managers to catch up in time to have appropriate directions and solutions to develop the labor market, and meet the requirements of the current national development career.

Knowledge Management Software

2009 ◽  
pp. 438-449
Author(s):  
Rodrigo Baroni de Carvalho ◽  
Marta Araújo Tavares Ferreira
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Content Management ◽  
Knowledge Map ◽  
Management Software ◽  
Software Products ◽  
Software Market ◽  
Knowledge Processes ◽  
Innovation Support ◽  
Content Management Systems

Due to the vagueness of the concept of knowledge, the software market for knowledge management (KM) seems to be quite confusing. Technology vendors are developing different implementations of the KM concepts in their software products. Because of the variety and quantity of KM tools available on the market, a typology may be a valuable aid to organizations that are searching and evaluating KM software suitable to their needs. The objective of this article is to present a typology that links software features to knowledge processes described in the SECI (socialization, externalization, combination, internalization) model developed by Nonaka and Takeuchi (1995). KM solutions such as intranet systems, content-management systems (CMSs), groupware, work flow, artificial intelligence- (AI) based systems, business intelligence (BI), knowledge-map systems, innovation support, competitive intelligence (CI) tools, and knowledge portals are discussed in terms of their potential contributions to the processes of socialization, externalization, internalization, and combination.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

scholarly journals Sustainable Innovation in Small Medium Enterprises: The Impact of Knowledge Management on Organizational Innovation through a Mediation Analysis by Using SEM Approach

2020 ◽  
Vol 12 (6) ◽  
pp. 2407 ◽  
Author(s):  
Jaffar Abbas ◽  
Qingyu Zhang ◽  
Iftikhar Hussain ◽  
Sabahat Akram ◽  
Aneeqa Afaq ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Organizational Learning ◽  
Structural Equation ◽  
Organizational Innovation ◽  
Garment Industry ◽  
Equation Modeling ◽  
Sustainable Innovation ◽  
Study Results ◽  
The Impact ◽  
The Relationship

This current study is among the very few investigations, which seeks the relationship between knowledge management and sustainable organizational innovation in garment business firms. This investigation focused on examining how organizational learning mediates the relationship between knowledge management and sustainable organizational innovation. This research establishes that knowledge management and organizational innovation procedures are integral parts of the progress and survival of the organizations. The received data of this population reports on the garment firms, operating their businesses in Lahore and Gujranwala. The study applied a stratified random sampling method for data collection and employed structural equation modeling (SEM) to examine the hypothesized relationships. The results specify that knowledge management shows a significant positive association with organizational learning, which in turn reveals a positive linkage to sustainable organizational innovation in SMEs of the garment industry. The study results also specify that organizational learning mediates the relationship between knowledge management and sustainable organizational innovation. This research survey identifies the significance of knowledge management and organizational learning in executing the process of organizational innovation, and it helps business managers to understand organizational learning as a mediator, which in turn indicates the benefits of knowledge management in achieving sustainable organizational innovation. This review provides an empirical indication of original data to investigate the linkage between knowledge management, sustainable innovation process, and organizational learning culture in the Pakistani garment sector. The generalizability of the study fallouts is restricted to the garment industry, and it offers valuable insights for imminent researchers.

Sign in / Sign up

Export Citation Format

Share Document