The subject of this paper is to research and evaluate, the results and the potential of knowledge acquisition through a framework by involving artificial intelligence (in the form of an automated intelligent agent) to acquire and manage intellectual capital for future organizational research and development, availability and competitiveness.
Focus of this study is multinational corporations, which are complex knowledge-driven industries, managing their intellectual capital over multiple cultures, locally and globally. Even though codified knowledge and its acquisition is widespread applied, tacit knowledge has showed intangible in its inclusion within the organization‘s knowledge base. The aim is to determine in which relation or what kind of potential there is to locate in an artificial intelligence approach, in order to obtain the most precious knowledge, which is tacit.
Market complexity as well as internal organizational trends and developments have changed the way firms organize their activities locally as well as globally, both in terms of tangible and intangible assets. The dynamics of multinational companies (MNCs) within the markets and their efforts to manage their intellectual capital has become a competitive factor in the present economic globalization. Their competitive advantage is dependent on their employee‘s expert knowledge, which is mostly tacit. Concomitantly, the exploitation and protection of intellectual property or more also described as assets, as well as how they are being managed, depends a firm‘s economical future. The science of artificial intelligence aims to attempt not just to comprehend how humans think, but also to construct and design intelligent entities.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Research Question
- Research challenge
- Structure of this paper
- Artificial Intelligence
- The beginning of design
- Foundations
- History
- Definition
- Intelligent agents
- Task environments
- Architecture
- Learning agents
- Conversational Agents - chatbots
- "Can digital computers think?"
- The beginning of design
- Knowledge Acquisition through intelligent agents
- Exemplars of utilized intelligent agents
- ELIZA
- A.L.I.C.E.
- Jabberwacky
- Lingubot™
- Knowledge Delivery and Management through a dialog system
- High-level dialog systems
- Mid-level dialog systems
- Low-level dialog systems
- Performance variables (based on the AZ-ALICE experiment)
- Intelligent tutoring systems
- Learner modeling
- Open Learner Model
- Natural language implementation
- Requirements for implementation
- The uncertainty factor
- Exemplars of utilized intelligent agents
- Knowledge Flows within MNCs
- Absorptive capacity
- The subsidiary's role
- Interaction between the MNC and local networks
- The concept of localized learning
- The concept of embeddedness
- Barriers to learning
- Cultural barriers to knowledge management
- Enabling and development of subsidiary learning
- Empirical Study
- Defining tacit knowledge
- Distinction of tacit knowledge
- Approaches to measuring tacit knowledge
- The Yale group
- Team-level and proxy measures
- Testing common sense
- Procedure of scoring
- Repertory Grid technique
- Synopsis of the research strategy
- Scenarios
- Scaling methods and formats
- Validation analysis and correlated group scoring
- Knowledge modeling using Formal Concept Analysis
- Dependence Techniques
- Multinational development of scenarios and response items
- Target group
- Survey
- Data
- Experimental hypothesizes
- Hypothesis Hi (automated agent implementation)
- Hypothesis H2 (turning tacit to explicit knowledge)
- Statistical evaluation
- Demographic data
- Results H1
- Results H2
- Defining tacit knowledge
- Testability of the experimental hypothesizes
- Process method - hypothesis H1
- Phi coefficient o
- Cronbach's alpha coefficient a
- Pearson chi-square ✗² test
- Fisher's exact test
- Results-hypothesis H1
- Process method – hypothesis H2
- Kolmogorov-Smirnov Test
- Linear regression analysis
- Variable definition
- ANOVA
- Adjusted R Square (R²)
- Results hypothesis H2
- Cronbach's alpha coefficient a
- Analysis H2M1
- Results H2Mi
- Analysis H2M2
- Results H2M2
- Analysis H2M3
- Results H2M3
- Analysis H2M4
- Results of testability of the experimental hypothesizes
- Process method - hypothesis H1
- Conclusion
- Areas of discussion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This paper explores the potential of artificial intelligence (AI) in knowledge acquisition and management within multinational corporations (MNCs). The study focuses on utilizing an automated intelligent agent to acquire and manage intellectual capital, particularly tacit knowledge, which is crucial for organizational competitiveness.
- The role of AI in knowledge transfer within MNCs
- The acquisition and management of tacit knowledge
- The challenges and opportunities of AI-driven knowledge management in a multinational context
- The impact of AI on organizational competitiveness and development
- The ethical considerations of AI-based knowledge management
Zusammenfassung der Kapitel (Chapter Summaries)
The paper begins with an introduction outlining the research question and the research challenge, followed by a structure of the paper.
Chapter 2 provides a comprehensive overview of artificial intelligence, exploring its origins, definitions, and key concepts. It delves into the history of AI research and discusses the development of intelligent agents and their various architectures and applications.
Chapter 3 examines knowledge acquisition through intelligent agents, with specific emphasis on conversational agents (chatbots). The chapter discusses various exemplars of these agents, including ELIZA, A.L.I.C.E., Jabberwacky, and Lingubot™. It further explores knowledge delivery and management through dialog systems, categorizing them into high-level, mid-level, and low-level systems.
Chapter 4 focuses on knowledge flows within MNCs, analyzing the concept of absorptive capacity, the role of subsidiaries, and the interaction between MNCs and local networks. The chapter delves into the concept of localized learning and embeddedness, as well as the barriers to knowledge management, particularly cultural barriers.
Chapter 5 presents an empirical study examining the potential of AI in acquiring tacit knowledge within MNCs. It defines tacit knowledge, outlines approaches to measuring it, and describes the research strategy used to test the hypotheses.
Chapter 6 examines the testability of the experimental hypotheses and presents the statistical evaluation of the results.
Schlüsselwörter (Keywords)
This paper focuses on the topics of knowledge transfer, artificial intelligence, multinational corporations, tacit knowledge, intellectual capital, knowledge management, intelligent agents, and organizational competitiveness.
- Quote paper
- Alexandra Arbter (Author), 2009, Knowledge Transfer through Artificial Intelligence (A.I.) in Multinational Companies, Munich, GRIN Verlag, https://www.grin.com/document/1299511
