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研究生：	高于勝 Kao, Yu-Sheng
論文名稱：	使用者技術接受影響因素預測及品牌策略訂定 User Technology Acceptance Influence Factors Prediction and Branding Strategies Formulation
指導教授：	黃啟祐
學位類別：	碩士 Master
系所名稱：	工業教育學系 Department of Industrial Education
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	224
中文關鍵詞：	平板手機、品牌策略、第二代整合科技使用模型(UTAUT2) 、決策實驗室分析法、決策研究室分析法之網路流程、偏最小平方法、粗糙集合法、模糊積分法、多準則決策分析、修正式德爾菲法
英文關鍵詞：	Phablet, Branding Strategies, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Decision Making Trial and Evaluation Laboratory (DEMATEL), Decision Making Trial and Evaluation Laboratory (DEMATEL) based on Network Process (DNP), Partial Least Squares (PLS), Rough Sets Theory (RST), Fuzzy Integral Technique (FIT), Multiple Criteria Decision Making (MCDM), Modified Delphi
論文種類：	學術論文
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隨著智慧型行動裝置使用數量增加，智慧型裝置已經變成人們日常生活中的一部份。許多電子廠商與企業嘗試著想要了解並且找出能夠影響消費者購買動機的潛在因素，此目的是為了要能夠吸引消費者的目光以及能夠改善他們的產品。平板手機，是一種整合型的智慧裝置，它結合了平板電腦與智慧型手機。平板手機目前也已經成為了智慧型手機使用中的未來趨勢。在此情形下，預測平板手機的採用因素與偏好即成為電子廠商不可或缺的工作。然而，預測平板手機的使用因素並非一件簡單的工作。為了能夠有效地了解這些因素，本研究將利用第二代整合科技使用模型 (UTAUT2) 作為研究架構來預測消費者的使用意圖與使用行為。除了預測消費者偏好以及產品改善之外，電子廠商也應該制定合適的品牌策略來提升良好的產品口碑以及刺激產品的銷量。因此，本研究將基於新型多準則決策架構來訂定平板手機的品牌策略。為了解決上述提及的議題，本研究首先將利用修正式德爾菲法來評估合適的準則與構面。其次，為了比較與建構因果模型，也將利用決策實驗室分析法 (DEMATEL) 以及偏最小平方法 (PLS)。第三，利用決策實驗室之網絡分析法 (DNP) 將獲得準則與構面的關聯權重，能夠找出其中最重要的準則與構面，以利進行評估與改善。第四，本研究將利用粗糙集合法來預測使用者採用行為的關聯規則，此目的是為了找出可能相關性最高的準則群。為了進一步制定產品行銷策略並且改善其策略以達到最理想水準，本研究最後會利用模糊積分法來推導出最佳的行銷策略並了解其他策略與理想策略存在的差距，以利進行策略改善。根據本研究的分析結果顯示，使用習慣構面對其他的構面皆有直接影響關係，使用意圖與績效期望屬於最容易被影響的構面。實務上而言，最容易影響其他因素的構面應該優先被改善。另外，關聯權重分析結果顯示使用意圖、享樂動機與績效期望構面為最重要構面。關聯規則的推導結果顯示出對平板手機擁有強烈偏好程度的覆蓋率為10.49%，中等偏好程度的覆蓋率為5.05%，低度偏好程度的覆蓋率為7.02%。最後，品牌策略的選擇以及排序之結果顯示出對於平板手機品牌策略擬定中，以顧客價值主張為基礎的穩定創新策略是最重要的品牌行銷手段。本研究未來的研究結果能夠作為平板手機相關產品的策略訂定以及產品改善之參考依據。而本研究所提出的方法論也能夠作為用來預測消費者科技採用行為偏好以及改善採用因素缺口的參考方法。

With the increase of smart mobile device in use as a regular part of people’s daily life, many electronic firms and businesses have attempted to understand and discover the potential factors affecting consumer’s purchase motivation to capture the users’ attention and to improve incrementally their product. Phablet, is an integrated smart device of combining the tablet PCs and smartphone, has gradually become a future trend of smartphone use. In this situation, predicting the phablet adopting factors and preferences will become an indispensable work for electronic firms. However, phablet use factors forecast is not a simple work. For the sake of understanding efficaciously the factors influencing the users’ technology adoption, this research will explore and forecast the intention to use and use behavior using the second generation of unified theory of acceptance and use of technology (UTAUT2) as a research model. In addition to customers’ preference prediction and the smart mobile device enhancement, electronics firms should also develop appropriate branding strategies to stimulate the product sales and increase their reputation. Therefore, this research will define the branding strategies for phablet based on MCDM framework. In order to solve above issues, this study first will employ Modified Delphi to evaluate the applicable dimensions/criteria. Second, to construct and compare the causal model, the DEMATEL and Partial Least Squares (PLS) will be introduced. Third, the influential weights versus each criterion being obtained by using DEMATEL based Network Process (DNP) method for understanding the importance among the dimensions/criteria. Fourth, this research will apply the Rough Sets Technique (RST) to forecast the association rules of users’ adopting behavior. To formulate the branding strategies and improve gaps among strategies for achieving aspiring level, the Fuzzy Integral Technique (FIT) finally will be leveraged. In light of the analytical findings, the causal relationships derived by DEMATEL show that the aspect of habit has direct influence on other aspects. The aspects of use intention and performance expectancy have least impact on other aspects. In practice, the least influential aspects should be prioritized in improvement than the rest of aspects. Also, the associated weights among the each criterion and construct reveal that the use intention, hedonic motivation, and performance expectancy are the most important aspects. The relation rules among the each preference level derived by RST method show that the coverage rates of reduction rules are 10.49% for strong preference in Phablet acceptance, 5.05% for moderate preference in Phablet acceptance, and 7.02% for low preference in Phablet acceptance. Finally, the empirical results of branding strategies being selected and ranked by Fuzzy Integral approach reveal that the strategy of creating a steady stream of innovations with strong values position is the most appropriate alternatives for Phablet branding strategies formulation. The research results can serve as a basis for related phablet devices’ branding strategy definition and product improvement. The proposed methodology can also be used for predicting users’ adopting behavioral preferences and be employed for improving the gaps among the phablet use factors.

Table of Contents
中文摘要    2
Abstract    4
Table of Contents    7
List of Figures    10
List of Tables    12
Chapter 1 Introduction    13
1.1    Research Background and Motivations    14
1.2    Research Objectives    17
1.3    Research Limitations and Future Research    20
1.4    Research Framework    21
1.5    Research Process    22
1.6    Overview of the Research    24
Chapter 2 Literature Review    24
2.1    Prediction of High-Technology Consumer Behaviors    25
2.2    The Lead User Method    29
2.3    Theory of Reasoned Action (TRA)    33
2.3. Theory of Planned Behavior (TPB)    35
2.3.    Technology Acceptance Model (TAM)    37
2.4.    The Extension of TAM Model    40
2.4.1    Technology Acceptance Model 2 (TAM2)    40
2.4.2    Unified Theory of Acceptance and Use of Technology (UTAUT)    43
2.4.3    Technology Acceptance Model 3 (TAM3)    45
2.4.4    Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)    48
2.5.    Branding Strategies of Phablets    50
2.5.1.    Branding In High-Technology Markets    51
2.5.2.    Developing the Branding Strategies    53
2.6.    Research Model and Hypothesis    58
2.6.1.    Performance Expectancy    58
2.6.2.    Effort Expectancy    60
2.6.3.    Social Influence    61
2.6.4.    Facilitating Conditions    62
2.6.5.    Hedonic Motivation    63
2.6.6.    Price Value    64
2.6.7.    Habit    66
2.6.8.    Behavioral Intention    67
2.6.9.    Use Behavior    69
Chapter 3 Research Methods    72
3.1    Data Collection    73
3.2    Measurements    74
3.3    Modified Delphi Technique    75
3.4    Partial Least Squares (PLS)    77
3.5    Decision Making Trial and Evaluation Laboratory (DEMATEL)    83
3.6    Analytic Network Process (ANP)    88
3.7    DEMATEL based Network Process (DNP) Technique    95
3.8    Rough Sets Theory (RST)    100
3.6.1.1    Information and Decision System    101
3.6.1.2    Indiscernibility Relation    101
3.6.1.3    Approximations of Sets    102
3.6.1.4    Attribute Reduction and core    103
3.6.1.5    Decision Rules    104
3.6.1.6    Flow Graphs    105
3.9    Fuzzy Integral Technique (FIT)    107
3.6.2    Fuzzy Measure    108
3.6.3    Fuzzy Integral    109
Chapter 4 Empirical Study    111
4.1    Background and Problem Description    112
4.2    Factors Determination for Phablet Acceptance and Branding Strategies Selection by Modified Delphi    113
4.3    Constructing the Causal Relationships between Dimensions and Criteria by DEMATEL and Deriving the Influential Weights by DNP method    118
4.4    The analytical results by PLS method    131
4.5    The Analytical Results by RST Method    137
4.5.1 Attributes Selection    138
4.5.2 The Associate Rules Derivations by RST Method    141
4.5.3 The Results of Flow Graph    143
4.6 The Results by Fuzzy Integral    149
Chapter 5 Discussion    154
5.1    Managerial Implication    154
5.2    Advances in Research Method    166
Chapter 6 Conclusion    167
Reference    168

List of Figures
Figure 1-1 Research Framework    22
Figure 1-2 Research Process    23
Figure 2-1 Characterizing the High Technology Market    29
Figure 2-2 Theory of Reasoned Action (TRA)    35
Figure 2-3 Theory of planned behavior (TPB)    37
Figure 2-4 Technology Acceptance Model (TAM)    40
Figure 2-5 Technology Acceptance Model 2 (TAM2)    43
Figure 2-6 UTAUT Model    45
Figure 2-7 Extended TAM Model    46
Figure 2-8 TAM3 Model    48
Figure 2-9 Research Model    70
Figure 3-1: Example of a PLS Path Model.    79
Figure 3-2 An example of the directed graph    85
Figure 3-3 The control hierarchy    90
Figure 3-4 Connections in a network    91
Figure 3-5 Concept of fuzzy integral    110
Figure 4-1 The Casual Relationship Network versus Each Dimension and Each Criterion    125
Figure 4-2 PLS Path Modeling Analytical Results for Structural Model    136
Figure 4-3 The Flow Graph based on Reduced Decision Rules of Low Preference in Phablet Acceptance    145
Figure 4-4 The Flow Graph based on Reduced Decision Rules of Moderate Preference in Phablet Acceptance    145
Figure 4-5 The Flow Graph based on Reduced Decision Rules of Strong Preference in Phablet Acceptance    146
Figure 4-6 Comparison between Different Branding Strategies based on Criteria by Non-Additive Fuzzy Integral Method    154
Figure 5-1 The original model    156
Figure 5-2 The Comparison of DEMATEL and PLS Methods based Lead Users’ and Mass Users’ Opinions    156
Figure 5-3 The Casual Relationship Network for Performance Expectancy    157
Figure 5-4 The Casual Relationship Network for Effort Expectancy    158
Figure 5-5 The Casual Relationship Network for Social Influence    159
Figure 5-6 The Casual Relationship Network for Facilitating Conditions    160
Figure 5-7 The Casual Relationship Network for Hedonic Motivation    160
Figure 5-8 The Casual Relationship Network for Price Value    161
Figure 5-9 The Casual Relationship Network for Habit    161
Figure 5-10 The Casual Relationship Network for Use Intention    162
Figure 5-11 The Casual Relationship Network for Use Behavior    162

 
List of Tables
Table 2-1The Number of Innovations Across a Various Industries    31
Table 2-2 Strategies for Branding in the High Technology Environment    53
Table 2-3 Dimensions and Criteria for Analyzing Users’ Phablet Acceptance    70
Table 4-1 The Evaluative Results of Dimensions based on Fifteen Experts by Modified Delphi    115
Table 4-2 The Evaluative Results of Criteria based on Fifteen Experts by Modified Delphi    116
Table 4-3 The Evaluative Results of Alternatives Strategies based on Fifteen Experts by Modified Delphi    117
Table 4-4 The Average Initial Direct Influence Matrix      122
Table 4-5 The Normalized Direct Influence Matrix      123
Table 4-6 The Total Influence Matrix      124
Table 4-7 The Total Influence Matrix      126
Table 4-8   and   Versus Each Dimension    126
Table 4-9   and   Versus Each Criterion    127
Table 4-10 The Unweighted Supermatrix      128
Table 4-11 The Weighted Supermatrix      129
Table 4-12 The Influential Weights versus Each Dimension and Criterion    130
Table 4-13 Descriptive statistics of mass users’ characteristics    133
Table 4-14 Factor loading and T-statistic for Model Measurement    134
Table 4-15 AVE, CR, and Cronbach’s   for Model Evaluation    135
Table 4-16 The Square Root of AVEs and Dimension Correlation Coefficients    136
Table 4-17 Direct, Indirect, and Total Effect of dimensions    137
Table 4-18 The Attributes Specification for Mass Users’ Characteristics    139
Table 4-19 Quality and Accuracy Measurement for Classification    143
Table 4-20 Decision Rules for Low Preference in Phablet Acceptance    147
Table 4-21 Decision Rules for Moderate Preference in Phablet Acceptance    147
Table 4-22 Decision Rules for Strong Preference in Phablet Acceptance    148
Table 4-23 Fuzzy Measure for criteria among each dimension    151
Table 4-24 Performance Scores Derivations by Fuzzy Integral    153
                                

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