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.
Table of Contents
1 Introduction
1.1 Research Question
1.1.1 Research challenge
1.2 Structure of this paper
2 Artificial Intelligence
2.1 The beginning of design
2.1.1 Foundations
2.1.2 History
2.2 Definition
2.3 Intelligent agents
2.3.1 Task environments
2.3.2 Architecture
2.3.3 Learning agents
2.3.4 Conversational Agents - chatbots
2.3.5 “Can digital computers think?”
3 Knowledge Acquisition through intelligent agents
3.1 Exemplars of utilized intelligent agents
3.1.1 ELIZA
3.1.2 A.L.I.C.E.
3.1.3 Jabberwacky
3.1.4 Lingubot™
3.2 Knowledge Delivery and Management through a dialog system
3.2.1 High-level dialog systems
3.2.2 Mid-level dialog systems
3.2.3 Low-level dialog systems
3.3 Performance variables (based on the AZ-ALICE experiment)
3.4 Intelligent tutoring systems
3.4.1 Learner modeling
3.4.2 Open Learner Model
3.4.3 Natural language implementation
3.4.4 Requirements for implementation
3.4.5 The uncertainty factor
4 Knowledge Flows within MNCs
4.1 Absorptive capacity
4.2 The subsidiary’s role
4.3 Interaction between the MNC and local networks
4.3.1 The concept of localized learning
4.3.2 The concept of embeddedness
4.4 Barriers to learning
4.4.1 Cultural barriers to knowledge management
4.5 Enabling and development of subsidiary learning
5 Empirical Study
5.1 Defining tacit knowledge
5.1.1 Distinction of tacit knowledge
5.2 Approaches to measuring tacit knowledge
5.2.1 The Yale group
5.2.2 Team-level and proxy measures
5.3 Testing common sense
5.3.1 Procedure of scoring
5.3.2 Repertory Grid technique
5.4 Synopsis of the research strategy
5.4.1 Scenarios
5.4.2 Scaling methods and formats
5.4.3 Validation analysis and correlated group scoring
5.4.4 Knowledge modeling using Formal Concept Analysis
5.4.5 Dependence Techniques
5.4.6 Multinational development of scenarios and response items
5.4.7 Target group
5.4.8 Survey
5.4.9 Data
5.5 Experimental hypothesizes
5.5.1 Hypothesis H1 (automated agent implementation)
5.5.2 Hypothesis H2 (turning tacit to explicit knowledge)
5.6 Statistical evaluation
5.6.1 Demographic data
5.6.2 Results – H1
5.6.3 Results – H2
6 Testability of the experimental hypothesizes
6.1 Process method – hypothesis H1
6.1.1 Phi coefficient φ
6.1.2 Cronbach’s alpha coefficient α
6.1.3 Pearson chi-square χ2 test
6.1.4 Fisher's exact test
6.2 Results – hypothesis H1
6.2.1 Results H1
6.3 Process method – hypothesis H2
6.3.1 Kolmogorov-Smirnov Test
6.3.2 Linear regression analysis
6.3.3 Variable definition
6.3.4 ANOVA
6.3.5 Adjusted R Square (R2)
6.3.6 Cronbach’s alpha coefficient α
6.4 Results – hypothesis H2
6.4.1 Analysis H2M1
6.4.2 Results H2M1
6.4.3 Analysis H2M2
6.4.4 Results H2M2
6.4.5 Analysis H2M3
6.4.6 Results H2M3
6.4.7 Analysis H2M4
6.5 Results of testability of the experimental hypothesizes
7 Conclusion
7.1 Areas of discussion
8 List of abbreviations
9 References
10 Appendix
Objectives and Topics
The primary objective of this thesis is to research and evaluate the potential of using artificial intelligence, specifically in the form of automated intelligent agents, to support knowledge acquisition and management within multinational corporations (MNCs), with a focus on cross-cultural collaboration and the conversion of tacit knowledge into explicit knowledge.
- Role of artificial intelligence and intelligent agents in knowledge management.
- Knowledge flow dynamics within multinational corporations.
- Empirical analysis of tacit knowledge acquisition through automated systems.
- Evaluation of user acceptance and the impact of interactive dialog systems.
- Statistical investigation of tacit-to-explicit knowledge transfer probabilities.
Excerpt from the book
1 Introduction
“In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge” (Tuomi, 1999, p. 107).
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. Concomitantly, the exploitation and protection of intellectual property also described as assets, as well as how they are being managed, depends a firm’s economical future. Declining costs of information flow, due to implementation of technological innovations, raising and liberalization of markets and unification of financial streams amongst countries are eradicating away many conventional sources of competitive differentiation (Govindarajan and Gupta, 2000).
“Competitive advantage is not just a function of how well a company plays by the existing rules of the game. More important, it depends on the firm’s ability to radically change those rules.”(Govindarajan and Gupta, 2001b, p. 3)
Summary of Chapters
1 Introduction: This chapter establishes the importance of knowledge management in the modern global economy and defines the research questions regarding the support of knowledge acquisition through artificial intelligence.
2 Artificial Intelligence: Provides a foundational overview of AI, including its history, definitions, and the architecture and behavior of intelligent agents.
3 Knowledge Acquisition through intelligent agents: Details specific types of conversational agents and dialog systems, examining their application in knowledge delivery and management.
4 Knowledge Flows within MNCs: Explores the complexities of knowledge management within multinational companies, including barriers to learning and the role of subsidiaries.
5 Empirical Study: Outlines the methodological approach for evaluating tacit knowledge acquisition, including the study design, participant demographics, and experimental hypotheses.
6 Testability of the experimental hypothesizes: Presents the statistical evaluation and verification of the experimental hypotheses against the null hypothesis using various statistical methods.
7 Conclusion: Summarizes the study's findings and offers a critical reflection on the role of intelligent agents in future knowledge management strategies.
Keywords
Knowledge Management, Multinational Corporations, Artificial Intelligence, Intelligent Agents, Chatbots, Tacit Knowledge, Explicit Knowledge, Knowledge Flow, Absorptive Capacity, Cultural Barriers, Dialog Systems, Empirical Study, Formal Concept Analysis, Organizational Learning, Knowledge Acquisition
Frequently Asked Questions
What is the core focus of this research?
The research examines how artificial intelligence, particularly autonomous conversational agents, can facilitate knowledge acquisition and transfer in multinational corporations, focusing on managing complex intellectual capital.
What are the primary themes discussed?
The paper addresses artificial intelligence architecture, knowledge management strategies in cross-cultural settings, the measurement of tacit knowledge, and the efficacy of automated agents in organizational learning.
What is the main research question?
The study investigates whether cross-cultural knowledge collaboration can be supported by intelligent integrated agents and if these systems are more effective at turning tacit knowledge into explicit knowledge than human counterparts.
Which scientific methods were applied?
The research utilizes an empirical quantitative approach, employing situational judgment tests based on the Yale group’s methodology and Formal Concept Analysis to model and interpret data regarding articulate tacit knowledge.
What does the main body cover?
The main body explores the theoretical foundations of AI and agent design, delves into existing knowledge management models in MNCs, and presents a detailed empirical analysis of survey data collected from international professionals.
What are the key terms?
Key terms include "Tacit Knowledge," "Intelligent Agents," "Multinational Corporations," "Knowledge Transfer," and "Formal Concept Analysis."
How does the study evaluate the impact of 'small-talk' in agent interactions?
The data suggests that over 55% of respondents perceived small-talk as unnecessary in a professional context, indicating a preference for task-oriented and domain-specific interactions with the intelligent agent.
What conclusion does the author draw regarding human-agent collaboration?
The author concludes that while intelligent agents show potential as apparatuses for improvement and support, they are not a complete substitute for human interaction, but rather a tool to aid the effectiveness of knowledge transfer.
- Arbeit zitieren
- Alexandra Arbter (Autor:in), 2009, Knowledge Transfer through Artificial Intelligence (A.I.) in Multinational Companies, München, GRIN Verlag, https://www.grin.com/document/1299511
