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TACIT KNOWLEDGE FOR BUSINESS INTELLIGENCE FRAMEWORK USING COGNITIVE-BASED APPROACH
DOCTOR OF PHILOSOPHY UNIVERSITI UTARA MALAYSIA
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Pengetahuan tersirat menjadi isu utama dalam pendekatan kecerdasan perniagaan terhadap sistem pengetahuan. Bagi menangkap pengetahuan tersirat bukanlah tugas yang mudah. Ini kerana, melibatkan data di dalam bentuk yang tidak terstruktur dan juga pelbagai sumber maklumat yang tidak mudah untuk diakses melalui cara tradisional. Penyelidikan ini menerangkan pendekatan sistematik untuk menangkap pengetahuan tersirat dengan membuat Kerangka Kecerdasan Perniagaan yang menunjukkan pengetahuan tersirat dapat diakses melalui berbagai kaedah dan kriteria pemilihan. Pendekatan ini adalah berdasarkan teori Fungsi Linguistik Sistematik, yang dibangunkan menjadi protokol temu ramah untuk pemilik pengetahuan. Data diubah menjadi peta kognitif untuk dibekalkan kepada Gudang Data. Kerangka ini diuji oleh 23 orang pustakawan dari perpustakaan di universiti Jawa Barat dan Yogyakarta, Indonesia. Algoritma dimulakan dengan temu ramah yang dikendalikan bagi mengenal pasti senarai masalah yang dihadapi oleh para pustakawan. Masalah-masalah tersebut kemudian diubah menjadi soalan soal selidik untuk mengenal pasti kualiti masalah seperti kekerapan, mendesak, keparahan, dan kepentingan. Seterusnya, dari hasil soal selidik, pemilik pengetahuan tersirat yang terbaik dikenal pasti. Mereka merupakan responden yang dapat menyelesaikan masalah yang tidak dapat diselesaikan oleh majoriti responden. Pemilik pengetahuan yang terbaik menjalani temu ramah mengikut tatabahasa untuk mengumpulkan penyelesaian yang mereka menggunakan untuk masalah tersebut. Transkripsi hasil temu ramah diubah menjadi peta kognitif yang memvisualisasikan penyelesaiannya.
Peta kognitif ini disimpan di Gudang Data untuk siap dikumpulkan pada bila-bila masa untuk tujuan analisis. Kerangka ini disahkan melalui perisian Power BI dan diulas oleh tujuh pakar domain. Hasil pengesahan ini menunjukan Power BI boleh memaparkan kepentingan fenomena dan membuat hubungan dengan tahap penyelesaian yang disediakan oleh amalan pustakawan. Kebolehgunaan ke domain lain dibenarkan selagi domain tersebut mengalami masalah yang berkaitan dengan teknikal, pengurusan, dan empirik yang sering dihadapi oleh pekerja. Penyelidikan ini menyumbang kepada kaedah menangkap pengetahuan tersirat dengan menggunakan pendekatan berasaskan kognitif untuk memastikan kelangsungan perniagaan dalam pelbagai domain.
Kata kunci: Kecerdasan Perniagaan, Pengurusan Pengetahuan, Pendekatan Berasaskan Kognitif, Fungsi Linguistik Sistematik, Pengetahuan Tersirat.
Tacit knowledge becoming a key issue in business intelligence approach to knowledge systems. Capturing the tacit knowledge is not a straightforward task, since it consists of unstructured data and related to a variety of information that is not always accessible through traditional means. This work presents a systematic approach for capturing tacit knowledge to be used in a business intelligence framework. The approach is based on the theory of systematic functional linguistics, developed into interview protocols to be asked to tacit knowledge owners. The data transformed into cognitive maps to supply the data warehouse. The framework was tested on 23 librarians from several university libraries in West Java and Yogyakarta, Indonesia. The algorithm starts with a content targeted interview to identify the list of problems faced by librarians. The problems were then converted into a questionnaire to identify qualities of the problems such as frequency, urgency, severity, and importance. From the questionnaire results, the best tacit knowledge performers were identified. They are respondents who can solve the problems, while the majority of the respondents are unable to solve them. The best performers were then subjected to grammar targeted interview to collect the solutions they made to the problems. The transcription of the interview results is then converted into cognitive maps that visualize the solutions. These cognitive maps are then stored in a data warehouse and ready to collect anytime for analytics purposes. The framework is validated through Power BI and reviewed by seven experts. Its applicability to other domains is justified as long as the domain, e.g., manufacturing, have experienced problems related to technical, managerial, and empirical problems faced by employees at work. This research contributes to the methods of capturing tacit knowledge using a cognitive-based approach, which important to ensure the continuity of business in various domains.
Keywords: Business Intelligence, Knowledge Management, Cognitive-based Approach, Systemic Functional Linguistics, Tacit Knowledge.
Firstly, I would like to express my sincere gratitude to my supervisor, Prof. Madya Ts. Dr. Azman B Ta'a for the continuous support of my Ph.D. study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better supervisor and mentor for my Ph.D. study.
A special thanks to my wife and my son. Words cannot express how grateful I am to my family for all the sacrifices they’ve made on my behalf. Their prayer for me was what sustained me thus far. In the end, I would like to express appreciation to my beloved siblings, dad, mom, and my parents-in-law, who has supported me along the way.
My sincere thanks also go to my fellow doctoral students for their feedback, cooperation, and friendship. All my friends provided me an opportunity to share my research struggle and stimulate discussions. Without their precious support, it would not be possible to conduct this research.
TABLE OF CONTENTS
Permission to Use ... i
ABSTRAK ... ii
ABSTRACT ... iii
ACKNOWLEDGEMENT ... iv
TABLE OF CONTENTS ... v
List of Tables ... x
List of Figures ... xii
List of Appendices ... xvi
List of Abbreviations ... xvii
CHAPTER ONE INTRODUCTION ... 1
1.1 Background ... 1
1.2 The motivation of the Study ... 3
1.3 Problem Statement ... 6
1.4 Research Questions ... 10
1.5 Research Objectives ... 11
1.6 Research Gap ... 11
1.7 Research Strategy ... 13
1.7.1 Phase I – Defining the Capturing Issues in BI ... 13
1.7.2 Phase II – Develop Cognitive-Approach Method for Capturing Tacit Knowledge ... 15
1.7.3 Phase III – Evaluate the Cognitive Based Approach in BI ... 16
1.8 Contribution of the Study ... 16
1.8.1 Theoretical Contribution ... 17
1.8.2 Practical Contribution ... 18
1.9 Thesis Organization ... 19
CHAPTER TWO LITERATURE REVIEW ... 23
2.1 Introduction ... 23
2.2 Knowledge Management in Academic Libraries ... 25
2.3 Managing the Knowledge ... 28
2.4 Types of Knowledge ... 31
2.5 Capturing Tacit Knowledge ... 35
2.5.1 Capturing Tacit Knowledge Techniques ... 36
2.6 Intelligence in Information Technology... 44
2.6.1 Business Intelligence (BI) ... 45
2.6.2 Business Intelligence Framework ... 46
2.7 Data Sources... 51
2.8 Extraction, Transformation, Load (ETL) ... 52
2.9 Data Warehouse (DW) ... 54
2.10 Data Analysis in Business Intelligence ... 56
2.10.1 Type of Data Analysis ... 56
2.11 Tools Used in the Research ... 59
CHAPTER THREE COGNITIVE-BASED APPROACH FOR BUSINESS INTELLIGENCE FRAMEWORK ... 62
3.1 Introduction ... 62
3.2 Cognitive Approach ... 65
3.2.1 Cognitive Mapping ... 66
3.2.2 Cognitive Analytics ... 74
3.2.3 Cognitive Model ... 75
3.3 Theoretical Framework ... 76
3.3.1 Knowledge, Information, Data Model ... 79
3.4 Linguistic Source of Tacit ... 85
3.4.1 Phonological and Non-Verbal Source of Tacit ... 92
3.5 Contextual Resources ... 94
3.6 Systematic Literature Review ... 95
3.7 Conclusion ... 96
CHAPTER FOUR RESEARCH METHODOLOGY ... 98
4.1 Introduction ... 98
4.2 Research Methodology... 99
4.3 Research Methodology Summary ... 101
4.4 Design for Capturing Tacit Knowledge ... 104
4.5 Research Design ... 118
4.6 Conclusion ... 130
CHAPTER FIVE IMPLEMENTATION OF COGNITIVE-BASED APPROACH FOR BUSINESS INTELLIGENCE (COBASE-BI): CASE STUDY IN LIBRARY OF HIGHER EDUCATION ... 131
5.1 Introduction ... 131
5.2 Respondent Survey ... 133
5.3 Interview Result ... 134
5.4 Questionnaire Design ... 136
5.5 Questionnaire Data Acquisition ... 144
5.5.1 Problem Faced by Librarians (Q1 and Q2) ... 145
5.5.2 Inter librarian relationship (Q3 and Q4) ... 152
5.5.3 Librarian and Patrons Relationship (Q5 and Q6) ... 156
5.5.4 Emergency events (Q7 and Q8) ... 161
5.5.5 External Relations (Q9 and Q10) ... 167
5.5.6 Library service (Q11 and Q12) ... 170
5.6 Questionnaire Result ... 177
5.7 Second Interview ... 181
5.8 Second Interview Result ... 189
5.9 Cognitive-based Analysis... 190
5.9.1 Visitors do not know what they are searching for (PD6) ... 190
5.9.2 Fuss Among Visitors that not relevant with the library (PD8) ... 191
5.9.3 Less budget (PD9) ... 193
5.9.4 Limited collection for new books (PD10)... 194
5.9.5 Fewer Visitors (PD11) ... 196
5.9.6 Interruptions at work(PD16) ... 196
5.9.7 Social relations between staff are not good (PD20) ... 197
5.9.8 Staff also teaches (PD22) ... 198
5.9.9 Lazy Staff (PD24) ... 199
5.9.10 Providing information needed by visitors (SD1) ... 200
5.9.11 Literacy programs for students (SD2) ... 201
5.9.12 Computer courses for research (SD3) ... 202
5.9.13 Conservation of books (SD4) ... 203
5.9.14 Making information guide (SD8) ... 204
5.9.15 Development of collections (SD9) ... 205
5.9.16 Information Technology Course (SD14) ... 207
5.9.17 Creating Living and Crowd Library (SD15) ... 208
5.9.18 Data management (SD16) ... 209
5.9.19 Activities that create knowledge (SD17) ... 211
5.10 Lesson Learned ... 212
5.11 Cognitive-based Approach and Business Intelligence ... 213
5.12 Conclusion ... 224
CHAPTER SIX VALIDATION AND EVALUATION ... 226
6.1 Introduction ... 226
6.2 Data Collection for the Case Study ... 227
6.3 Data Modelling for Library Data Warehouse ... 229
6.4 Business Intelligence Descriptive Analysis ... 232
6.4.1 Problems Librarians face ... 232
6.4.2 Relationship between Librarians ... 236
6.4.3 Relationship between librarians and patrons ... 240
6.4.4 Emergency Situations in the library ... 242
6.4.5 External activities are undertaken by librarians ... 246
6.4.6 Providing services for librarians in the future ... 248
6.5 Cognitive Mapping for Business Intelligence ... 250
6.6 Cognitive Map ... 265
6.7 Business Intelligence and Predictive Analysis ... 269
6.8 Experts Review ... 275
6.8.1 The setting of The Questionnaires ... 275
6.8.2 Expert Review Results ... 277
6.8.3 Analysis of Comments ... 281
184.108.40.206 Compliments about the research... 282
220.127.116.11 Enhancement ... 284
6.9 Summary and General Findings ... 287
6.10 Conclusion ... 289
CHAPTER SEVEN CONCLUSION AND RECOMMENDATION ... 291
7.1 Research Objectives Examination... 291
7.2 Method to Capture Tacit Knowledge ... 292
7.3 Construction of a Capturing and Codifying Tacit Knowledge Framework for Managing Tacit Knowledge ... 293
7.4 Evaluation of the Capturing and Codifying Tacit Knowledge Framework ... 295
7.5 Research Contribution ... 295
7.5.1 Review of the Current Status of HEI Libraries in Indonesia ... 296
7.5.2 Design of a Framework that Capture Tacit Knowledge for BI Purposes 296 7.5.3 Application of Capturing Tacit Knowledge in BI Field ... 297
7.6 Research Limitations ... 299
7.6.1 The Use of Librarians as Research Sample ... 299
7.6.2 The Use of Interview as the Tool to Capture Tacit Knowledge ... 299
7.7 Further Research ... 300
7.7.1 Consolidation Method ... 301
7.7.2 Further Exploration of the Framework ... 301
7.7.3 The Impact of Tacit Knowledge for Enterprise and Decision Making Process ... 301
7.7.4 Real Data from Other Disciplines ... 302
REFERENCES ... 303
List of Tables
Table 2.1: Summary of techniques for capturing and converting tacit knowledge
(Pourzolfaghar, Ibrahim, Abdullah, & Adam, 2014) ... 38
Table 3.1: Summary of traditions in linguistics and its relations to capturing tacit knowledge ... 87
Table 4.1: Summary of Research Methodology ... 102
Table 4.2: Previous Research ... 107
Table 5.1: Research Sample ... 133
Table 5.2: First Stage Interview Results and Basic Materials for the Questionnaire ... 137
Table 5.3: Problems frequency that faced by librarians ... 145
Table 5.4: Difficulty level problems faced by librarians ... 147
Table 5.5: Matrix Combination of Frequency and Difficulty of Problems ... 148
Table 5.6: Urgency Value of each problem ... 149
Table 5.7: Detailed Respondents' Answers for Every Urgent Problem ... 151
Table 5.8: Frequency of Relations between Librarians ... 153
Table 5.9: Importance of Activities among Librarians ... 133
Table 5.10: Matrix of Combination of Frequency and Interest of Activities ... 155
Table 5.11: Urgency Value of each activity ... 156
Table 5.12: Frequency of Relationships between Librarians and Patrons ... 157
Table 5.13: Importance of Relationship between Librarians and Patrons ... 158
Table 5.14: Matrix of Combination of Frequency and Interests of Activities ... 159
Table 5.15: Urgency Value of each activity ... 160
Table 5.16: Frequency of Emergency Events in the Library ... 161
Table 5.17: Level of Readiness in Dealing with Emergency Situations ... 163
Table 5.18: Matrix of Combination of Frequency and Event Readiness ... 164
Table 5.19: Urgency Value of Each Problem ... 165
Table 5.20: Frequency of External Activities Undertaken by Librarians ... 167
Table 5.21: Importance of Librarian external activities ... 168
Table 5.22: Matrix of Combination of Frequency and Difficulty of Problems ... 169
Table 5.23: Value of the Urgency of Each Problem ... 170
Table 5.24: Degree of Interest Problems Faced by Librarians ... 170
Table 5.25: Difficulty Levels Problems Faced by Librarians ... 172
Table 5.26: Matrix of Combination of Interest and Problem Difficulty ... 173
Table 5.27: Urgency Value of each service ... 174
Table 5.28: Detailed Respondents' Answers for Each Urgent Problem ... 176
Table 5.29: Questionnaires Result ... 177
Table 5.30 Samples for Second Interview ... 182
Table 6.1: Problems faced by librarians ... 233
Table 6.2: Relationship between librarians ... 237
Table 6.3: Relationship between librarians and patrons ... 240
Table 6.4: Emergencies in the library ... 243
Table 6.5: External activities undertaken by librarians ... 246
Table 6.6: Provision of services for libraries in the future ... 249
Table 6.7: The number and percentage of experts and answers who supported (“Yes”), opposed (“No), and were “Neutral” to the questions on the capabilities of the methodology ... 277
Table 6.8: Themes and Frequencies of Qualitative Comments as Shared by Experts ... 284
List of Figures
Figure 1.1: Proposed Framework: Data Management Using Cognitive Approach ... 12
Figure 1.2: Research Strategy ... 14
Figure 2.1: Differentiation among Explicit, Implicit, and Tacit Knowledge (Nickols,2013) 31 Figure 2.2: Relationship among Data, Information, Knowledge, and Wisdom (Bellinger, 2004) ... 34
Figure 2.3: Inputs and Outputs of Business Intelligence (Nguyen, 2011) ... 45
Figure 2.4: Gartner BI Framework (February, 2019) ... 50
Figure 2.5: The Multi-Layer BI Framework (February, 2019) ... 49
Figure 2.6: Business Intelligence Architecture ... 61
Figure 2.7: ETL processes (Xu & Yu-Shi, 2016) ... 54
Figure 2.8: Magic Quadrant for Analytics and Business Intelligence Platforms (Gartner, 2020) ... 61
Figure 3.1: Systemic Functional Linguistics (SFL) System (Nam & Park, 2015)... 65
Figure 3.2: Managing the Knowledge Model (Egbu, Hari, & Renukappa, 2005; Hernandez- Matias, Vizan, Perez-Garcia, & Rios, 2008) ... 67
Figure 3.3: Semantic map for different types of transportation (Esfahanipour & Montazemi, 2015) ... 70
Figure 3.4: Example of a causal map (Pillutla & Giabbanelli, 2019) ... 70
Figure 3.5: Concept map for plants (Esfahanipour & Montazemi, 2015)... 71
Figure 3.6: A cognitive model of the memory system (Sato & Huang, 2015a)... 80
Figure 3.7: The proposed generic KID Model (Sato & Huang, 2015b) ... 81
Figure 3.8: KID Model (Sato & Huang, 2015b) ... 82
Figure 3.9: Research Data Collection Scheme ... 84
Figure 3.10: Flow of Capturing Tacit Knowledge for Business Intelligence ... 95
Figure 3.11: Cognitive-based Approach for Business Intelligence (Cobase-BI) framework 95 Figure 4.1: Design for Capturing Tacit Knowledge... 112
Figure 4.2 General flow of information to collect problem-focused tacit data ... 114
Figure 4.3: Comprehensive tacit data collection ... 116
Figure 4.4: Simple Diagram for Tacit Knowledge Capturing ... 118
Figure 4.5 Scope of tacit knowledge ... 119
Figure 4.6: Research Design ... 120
Figure 5.1: PD6 Cognitive Map based on two respondents’ Tacit Knowledge ... 190
Figure 5.2: PD8 Cognitive Map based on two respondents’ Tacit Knowledge ... 192
Figure 5.3: PD9 Cognitive Map based on one of respondent's Tacit Knowledge ... 194
Figure 5.4: PD10 Cognitive Map based on three respondents’ Tacit Knowledge ... 195
Figure 5.5: PD11 Cognitive Map based on three respondents’ Tacit Knowledge ... 196
Figure 5.6: PD16 Cognitive Map based on one respondent's Tacit Knowledge ... 197
Figure 5.7: PD20 Cognitive Map based on one of respondent's Tacit Knowledge ... 198
Figure 5.8 PD22: Cognitive Map based on one of respondent's Tacit Knowledge ... 199
Figure 5.9: PD24 Cognitive Map based on one of respondent's Tacit Knowledge ... 200
Figure 5.10: SD1 Cognitive Map based on one of respondent's Tacit Knowledge ... 200
Figure 5.11: SD2 Cognitive Map based on one of respondent's Tacit Knowledge ... 201
Figure 5.12: SD3 Cognitive Map based on one of respondent's Tacit Knowledge ... 202
Figure 5.13: SD4 Cognitive Map based on three respondents’ Tacit Knowledge ... 203
Figure 5.14: SD8 Cognitive Map based on four respondents’ Tacit Knowledge ... 205
Figure 5.15: SD9 Cognitive Map based on three respondents’ Tacit Knowledge ... 206
Figure 5.16: SD14 Cognitive Map based on one of respondent's Tacit Knowledge ... 207
Figure 5.17: SD15 Cognitive Map based on one of respondent's Tacit Knowledge ... 208
Figure 5.18: SD16 Cognitive Map based on two respondents’ Tacit Knowledge ... 209
Figure 5.19: Visual Management Example ... 210
Figure 5.20: SD17 Cognitive Map based on three respondents’ Tacit Knowledge ... 211
Figure 5.21: Enhanced BI Framework comprising of tacit knowledge as the final output the research ... 216
Figure 5.22: Steps in capturing tacit knowledge ... 227
Figure 5.23: Tools for capturing tacit knowledge ... 219
Figure 5.24: Cognitive Map algorithm flowchart ... 223
Figure 6.1: Institutions of the Librarians ... 227
Figure 6.2: Potential Number of Patrons of the Librarians ... 228
Figure 6.3: Problem Difficulties Data Warehouse Design... 231
Figure 6.4: Frequency of problems librarians face ... 234
Figure 6.5: Ease of resolving problems librarians face ... 235
Figure 6.6: Difficulty in resolving problems librarians face ... 236
Figure 6.7: Importance of the phenomenon that requires knowledge transfer among librarians ... 238
Figure 6.8: Difficulty of resolving phenomenon that requires knowledge transfer among librarians ... 239
Figure 6.9: Frequency of phenomenon that required knowledge communication between
librarians and readers ... 241
Figure 6.10: Importance of the phenomenon that required knowledge communication between librarians and readers ... 242
Figure 6.11: Frequency of events or phenomenon classified as an emergency in the library ... 244
Figure 6.12 level of preparation to manage emergency events or phenomenon in the library (A) ... 245
Figure 6.13: Level of preparation to manage emergency events or phenomenon in the library (B) ... 246
Figure 6.14: Frequency of the phenomenon required coordination between librarians and the surrounding environment, organizations, or other libraries ... 247
Figure 6.15: Importance of the phenomenon that required coordination between librarians and surrounding environment, organizations, or other libraries ... 248
Figure 6.16: Ease of providing new services for libraries in the future ... 249
Figure 6.17: Difficulty in providing new services for libraries in the future ... 250
Figure 6.18: Visitors did not know what they were searching for ... 253
Figure 6.19: Fuss among visitors that are not relevant to the library ... 254
Figure 6.20: Frequency of not good social relationship between staff and Less Budget ... 255
Figure 6.21: Difficulty of not good social relationship between staff and Less Budget ... 256
Figure 6.22: Frequency of interruption at work and limited collection for new books ... 257
Figure 6.23: Difficulty in resolving interruption at work and limited collection for new books ... 257
Figure 6.24: Frequency of staff also teaches, and social relations between staff were not good ... 258
Figure 6.25: Difficulty of resolving staff also teaches, and social relations between staff were not good ... 259
Figure 6.26: Frequency of lazy staff ... 260
Figure 6.27: Difficulty in resolving lazy staff ... 260
Figure 6.28: Frequency of providing the information needed by visitors, literacy programs for students, and computer courses for research ... 261
Figure 6.29: Difficulty in resolving to provide the information needed by visitors, literacy programs for students, and computer courses for research ... 262
Figure 6.30: Frequency of making information guide, development of collections, conservation of books ... 263
Figure 6.31: Difficulty of resolving making information guide, development of collections,
conservation of books ... 263
Figure 6.32: Frequency of occurrence of information technology course, data management and creating living and crowd library ... 264
Figure 6.33: Difficulty of resolving information technology course, data management, and creating living and crowd library ... 265
Figure 6.34: Cognitive mapping showing lazy staffs in library ... 266
Figure 6.35: Cognitive mapping showing percentage of librarians involved ... 267
Figure 6.36: Cognitive mapping showing patrons did not know what they were searching for ... 268
Figure 6.37: Cognitive mapping showing patrons did not know what they were searching for ... 269
Figure 6.38: Prediction for Fuss among Visitors that were not relevant to the library ... 271
Figure 6.39: Lazy Staff ... 272
Figure 6.40: Visitors did not know what they were searching for ... 273
Figure 6.41: Computer courses for research ... 273
Figure 6.42: Conservation of books ... 274
Figure 6.43: Providing information needed by visitors ... 275
Figure 6.44: Answers Based on KM Experience of the Experts ... 278
Figure 6.45: Answers Based on BI Experience of the Experts ... 279
Figure 6.46: The Most and Least Answered “Yes” Questions Answers ... 280
Figure 6.47: All Expert Reviews Feedbacks (N = 7) ... 281
List of Appendices
Appendix A.1 First Interview Guide ... 331
Appendix A.2 Consent Form ... 337
Appendix A.3 Questionnaire ... 346
Appendix A.4 Questionnaire Datasheet ... 360
Appendix B Data Warehouse Model Appendix B.1 Problem Difficulty & Frequency Data Warehouse Model ... 368
Appendix B.2 Peer Communication Importance& Frequency Data Warehouse Model…..369
Appendix B.3 Patron Communication Importance& Frequency Data Warehouse Model...370
Appendix B.4 Emergency Preparedness& Frequency Data Warehouse Model...371
Appendix B.5 Environment Relation Importance& Frequency Data Warehouse Model...372
Appendix B.6 Service ImportanceDifficulty & Frequency Data Warehouse Model...373
Appendix C Business Intelligence Descriptive Dashboards ... 374
Appendix D Business Intelligence Cognitive Mapping Dashboards ... 382
Appendix E Business Intelligence Service Difficulty Likelihood Analysis ... 401
Appendix F Business Intelligence Problem Difficulty Likelihood Analysis ... 410
Appendix G Questionnaire for Experts Review ... 418
List of Abbreviations
AI Artificial Intelligence AKPAR Akademi Pariwisata
AMIK Akademi Manajemen Informatika dan Komputer BI Business Intelligence
CCTV Closed-Circuit Television CM Cognitive Map
CTI Content Targeted Interview CTK Comprehensive Tacit Knowledge CTQ Content Targeted Questioning DBA Database Administrators DM Data Mining
DSS Decision Support System DW Data Warehouse
EF Emergency Frequency EP Emergency Preparedness
ERF Environmental Relations Frequency ERI Environmental Relations Importance ETL Extract, Transform, and Load
FDIII Fakultas Diploma III
FKIP FakutlasIlmu Pendidikan GTI Grammar Targeted Interview GTQ Grammar Targeted Questioning HEI Higher Education Institutions IS Information System
IT Information Technology
KB Knowledge Bank
KID Knowledge, Information, and Data
KM Knowledge Management
KMS Knowledge Management System LCM Linguistic Categorization Model MFA Most Frequent Answer
NCF Patron Communication Frequency NCI Patron Communication Importance OLAP Online Analytical Processing OLTP Online Transaction Processing PCI (a) Peer Cognitive Interviews
PCI (b) Peer Communication Importance PCF Peer Communication Frequency PD Problem Difficulty
PF Problem Frequency PO Participant Observation
RDBMS Repository Data Base Management Systems SD Service Difficulty
SECI Socialization, Externalization, Combination, and Internalization SFL Systemic Functional Linguistics
SI Service Importance
SLR Systematic Literature Review SOP Standard Operating Procedures STAI Sekolah Tinggi Agama Islam STH Sekolah Tinggi Hukum STIE Sekolah Tinggi IlmuEkonomi STIKES Sekolah Tinggi Ilmu Kesehatan
STKIP Sekolah Tinggi Keguruan dan Ilmu Pendidikan
STMIK Sekolah Tinggi ManajemenInformatika dan Komputer STQ Semantic Targeted Questioning
CHAPTER ONE INTRODUCTION
This chapter presents the background, problem statement, and motivation of this research. The chapter defines the research problems, the research gaps, and the research questions and objectives. Then, the research strategy is discussed in three phases, followed by the scope and the research contributions. This chapter ends with an overview of the thesis organization and a summary of the thesis.
Business intelligence (BI) is a crucial part of the knowledge system in business operations. BI becomes an essential aspect of an organization that produces knowledge for decision purposes. The knowledge production comes from various tasks of analysis that required well-defined structured data. However, there has been a problem in the integration of the new types of data with internal transactional data such as motion data and uncaptured data, which consist of various types of structured and unstructured data (Raghupathi & Raghupathi, 2014). The evolution in business is mandatory to match the pace of global requirements. However, using technology as a solution to enhance the business is a significant challenge considering that every organization has individual and organizational knowledge either in the form of raw data or in the form of information. However, BI has the most significant function of data analysis for transforming the raw data into knowledge. Dearth research had been conducted to explore the use of knowledge and manage it as a new raw data to manage within BI and its applicability within various application domains.
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