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中小企業中雲端商業智能採用對業績優化的多準則決策分析 —以巴布亞紐幾內亞為例 摘要

研究目的
本研究旨在基於技術、組織和環境（TOE）因素框架，以多準則決策分析（MCDA）方法，評估巴布亞紐幾內亞（PNG）中小企業（SME）業主和管理階層採用雲端商業智能（Cloud BI）的影響因素，以優化其業績功能。

研究方法
本研究使用在巴布亞紐幾內亞四個地區的50家SME終端用戶和服務提供商之間進行的在線兩兩比較訪談和模糊層級分析研究問卷評估。本研究採用戰略性的多準則決策分析（MCDA）方法來評估和確定採用雲端商業智能（Cloud BI）在SME中的影響決策領域和因素權重。首先，進行了系統性文獻回顧（SLR），以確定基於TOE框架的決策準則，據此確定了3個決策領域和9個決策因素，並將其應用於評估Cloud BI的採用。其次，結合模糊層級分析網絡過程（Fuzzy DEMATEL-based ANP）數據分析技術，用於示範影響網絡關係圖（INRM），並指定決策領域和因素之間最準確的影響權重。最後，將模糊修正的VIKOR（多標準優勢規劃）方法與影響權重相結合，評估決策領域和因素在SME業績功能方面的差異性，並確定需要改善的關鍵領域和因素，以實現SME順利過渡和採用Cloud BI以優化其業績。

研究成果
研究結果說明在採用Cloud BI方面，「組織」是最重要的決策領域，而「環境」則是最不重要但是主要的因果影響因素。在評估巴布亞紐幾內亞採用雲端商業智能工具的9個決策因素中，有4個因素被評估為在決策上具有重要程度，包括技術利益、技術可信度、高層管理支持和組織準備度。此外，我們的戰略方法顯示，政府支持、競爭壓力和商業位置是最不重要的因素，但對其他決策因素產生正向的因果影響程度，反映出巴布亞紐幾內亞SME對經濟狀態不佳、法規限制和基礎設施不足等問題的關注，這增加了採用Cloud BI過程的複雜性。本研究結果亦證明對於SME的業務增長和財務業績，雲端商業智能將非常適合。

研究限制
首先，本研究的範圍僅限於巴布亞紐幾內亞的中小企業部門。因此決策準則和策略解決方案可能不適用於其他國家的中小企業。此外，TOE評估標準是從先前的文獻中得出的，可能排除了對Cloud BI評估過程的幾個可能影響因素。因此需要進一步研究以充分了解巴布亞紐幾內亞中小企業部門，不斷變化的經濟環境中可能存在的因素的程度。

實際影響
本研究提供了一種 MCDA 方法，巴布亞紐幾內亞的中小企業可以利用該來找出最佳解決方案，以滿足其業績需求，並根據其各自的要求和環境分析決策準則中應首先改善的差距，以精確評估雲端商業智能的利用。

原創性和價值
本研究從管理的角度提供了有關巴布亞紐幾內亞雲端商業智能採用的原創觀點。

關鍵字：中小型企業（SMEs）；雲端商業智能（Cloud BI）；技術-組織-環境（TOE）；模糊多準則決策分析（基於模糊的MCDA）

A Multi-criteria Decision Analysis of Cloud Business Intelligence Adoption for SME Performance Optimization in Papua New Guinea
ABSTRACT
Purpose
The purpose of this research study focuses purely on evaluating the influential factors amongst Small to medium enterprises (SMEs) owners’ and managers’ decision to adopt Cloud Business Intelligence (Cloud BI) in Papua New Guinea (PNG) based on Technology, Organization, and Environment (TOE) factorial framework with a Multi-Criteria Decision Analysis (MCDA) approach to optimize their performance functionality.

Design methodology and approach
This study used an online pair-wise comparison interviews and a Fuzzy DEMATEL research questionnaire assessment accomplished by distinct senior professional decision makers across an array of 50 SME end-user and service providers in the four regions of Papua New Guinea. This study utilized a strategic Multiple-Criteria Decision Analysis (MCDA) approach to evaluate and determine the influential decision areas and factors weights, upon adopting Cloud Business Intelligence (Cloud BI) in SMEs. Primarily a systematic literature review (SLR) was conducted to determine the decision criterion based on the TOE framework upon which (3) decision areas and (9) decision factors were identified and implemented to assess the adoption of Cloud BI. Secondly, a combination of (Fuzzy DEMATEL-based ANP) data analysis techniques was employed to exemplify an influence network relations map (INRM) and designate the most precise influenced weights connecting the decision area and factors. Lastly, a Fuzzy modified VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method was integrated with the influential weights to evaluate the decision areas and factors disparities with respective to SME performance functions and identify the critical areas and factors that needs to be enhance for the smooth transition and adoption of Cloud Business Intelligence by SMEs to optimize their performance.

Research Findings
The study results signify that “Organization” is the most important decision area when it comes to adopting Cloud BI, whilst “Environment” is the least important but the main causal influencer. Whilst, (4) of the (9) decision factors evaluated posits a significant degree of importance on the decision to adopt cloud business intelligence tools in PNG. These vital factors entail technology benefits, technology credibility, top management support, and organization readiness. Moreover, our strategic approach indicated that government support, competitive pressure, and Business location, are the least important factors but the positive degree of causal influence factors on other decision factors for the adoption of Cloud BI reflecting PNG SMEs’ concerns about poor economic state, legal regulations, and inadequate infrastructure with high undue pressure goes to show the poor state of the business location and service linkage which adds complexity to the process of adopting Cloud BI. Our research findings also revealed that Cloud BI will be much suited for SMEs’ Business growth and financial performance.

Research Limitations
Primarily, this study’s scope is limited to the SME sector in PNG. As a result, the determined decision criterion and the strategic solution may less be beneficial to SMEs in other nations. Also, a TOE assessment criterion was derived from prior literature, which might have excluded several possible influences on the Cloud BI evaluation process. Thus, further research is required to fully comprehend the extent of probable factors involved especially with the ever-changing economic environment of PNGs SME sector.



Practical implications
This study provides a MCDA approach that SMEs in PNG can utilize to their advantage to determine the best solution to serve their performance needs according to their respective requirement and setting. A strategic approach that SMEs in PNG can use to ascertain the disparities in their key performance function to identify the best cost-effective alternatives and also analyze which gaps in the decision criterion should be improved first to precisely asses the utilization of cloud business intelligence.

Originality
This study offers novel managerial insights concerning the application of cloud business intelligence in PNG.

Keywords
Small and Medium Sized Enterprises (SMEs); Cloud Business Intelligence (Cloud BI); Technology-Organization-Environment (TOE); Fuzzy Multi-Criteria Decision Analysis (Fuzzy based MCDA)

TABLE OF CONTENTS

MASTERS THESIS VERIFICATION (i)
ACKNOWLEDGEMENTS (ii)
ABSTRACT (iii)
摘要 (vi)
TABLE OF CONTENTS (viii)
LIST OF FIGURES (x)
LIST OF TABLES (xi)
LIST OF ACRONYMS (xii)

CHAPTER 1. INTRODUCTION 1
1.1 Research Background 2
1.2 Research Problem 6
1.3 Research Purpose, Aim and Objectives 8
1.4 Proposed MCDA Model 9
1.5 Research Implication 10
1.6 Chapter Alignment 10

CHAPTER 2. LITERATURE REVIEW 11
2.1 Small and Medium-sized Enterprises (SME) 11
2.2 Cloud Business Intelligence 17
2.3 Technology - Organization - Environment (TOE) Framework 22
2.4 Fuzzy Multiple-Criteria Decision-Analysis (MCDA) 22
2.5 Systematic Literature Review (SLR) 24
2.6 Criteria for Study Selection 26
2.7 Data Extraction of Cloud BI Adoption and Influence Factors in SMEs 27
2.8 SLR Findings of Cloud BI Adoption Factors 29
2.9 Factors Extraction of Cloud BI Adoption and Influence Factors in SMEs 33

CHAPTER 3. RESEARCH FRAMEWORK 35
3.1 Research Framework and Variables for Assessing Cloud BI Adoption 35
3.2 Technological Factors 37
3.3 Organizational Factors 38
3.4 Environmental Factors 40
3.5 SME Performance Functions 42
3.6 Summary of Research Questions 43

CHAPTER 4. RESEARCH METHODOLOGY 45
4.1 The Fuzzy Multi-Criteria Decision Analysis 45
4.2 Fuzzy Set Theory 47
4.3 Fuzzy DEMATEL - based ANP Method 50
4.4 Fuzzy Modified VIKOR Method 58

CHAPTER 5. EMPIRICAL CASE STUDY ANALYSIS 64
5.1 Data Collection Analysis 64
5.2 Results Analysis 67
5.3 Comparison Analysis of Decision Areas 69
5.4 Comparison Analysis of Decision Factors 70
5.5 Influential Network Relationship Map (INRM 71
5.6 Decision Area Inter-Relationship Analysis 74
5.7 Decision Factors Inter-Relationship Analysis 76
5.8 Influential weights of Decision Criteria’s 79
5.9 SMEs Performance Function Analysis 81
5.10 Decision-Criteria Optimization and Compromise Analysis 83
5.11 SME Key Performance Function Gap Evaluation of Cloud BI 88

CHAPTER 6. CONCLUSION 92
6.1 Research Contribution 93
6.2 Managerial Implication 93
6.3 Research Limitation and Future Work 94

REFFERENCE 95
APPENDIX A: FUZZY AVERAGE RELATION MATRIX 105
APPENDIX B: FUZZY AVERAGE DECISION MATRIX 106
APPENDIX C: TOTAL DECISION MATRIX 106

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