臺灣博碩士論文加值系統

Human resources has been evolving from not just an administrative discipline, but increasingly oriented around building relationships and employee management strategy. Human resources is beginning to flex new muscles backed with people analytics and organizations are further realizing they are as strong as the people within them. Leading global companies are starting to ask questions such as, “How can we make our workers improve performance and work better together?” and, “What can we do to make our employees feel happier to work here?” Google released their results from a two year project which discovered the eight criteria that determines what makes a good manager at Google (Garvin, Kind, & Wagonfeld, 2013). This research paper demonstrates a Fuzzy Set Theory Multi-criteria Decision Making (MCDM) tool to define the best managers according to that eight criteria. This MCDM tool is built to work with qualitative as well as quantitative information. The alternatives being analyzed give fuzzy values which are then converted back into crisp values via a Maximizing Minimizing Set method (Chen, 1985). This allows to easily discern a ranking of the best managers using the initial input of a set of decision makers.

Human resources has been evolving from not just an administrative discipline, but increasingly oriented around building relationships and employee management strategy. Human resources is beginning to flex new muscles backed with people analytics and organizations are further realizing they are as strong as the people within them. Leading global companies are starting to ask questions such as, “How can we make our workers improve performance and work better together?” and, “What can we do to make our employees feel happier to work here?” Google released their results from a two year project which discovered the eight criteria that determines what makes a good manager at Google (Garvin, Kind, & Wagonfeld, 2013). This research paper demonstrates a Fuzzy Set Theory Multi-criteria Decision Making (MCDM) tool to define the best managers according to that eight criteria. This MCDM tool is built to work with qualitative as well as quantitative information. The alternatives being analyzed give fuzzy values which are then converted back into crisp values via a Maximizing Minimizing Set method (Chen, 1985). This allows to easily discern a ranking of the best managers using the initial input of a set of decision makers.

TABLE OF CONTENTS
ABSTRACT II
ACKNOWLEDGEMENTS III
TABLE OF CONTENTS V
LIST OF TABLES VII
TABLE OF FIGURES VIII
CHAPTER One 1
1.1. Research Description 5
1.2. Research Objectives 6
1.3. Objectives 7
1.4. Research Layout 8
CHAPTER Two 9
2.1. Multiple Criteria Decision Making 9
2.2. Maximizing Minimizing Ranking Approach 14
2.3. Introducing Criteria in the Study 15
2.4. The Eight Qualitative Criteria 16
2.4.1. Coaching 16
2.4.2. Empowerment 19
2.4.3. Empathy 20
2.4.4. Results-Oriented 22
2.4.5. Communication 23
2.4.6. Support of Employee’s Career Development 24
2.4.7. Vision: Strategy and Innovation 26
2.4.8. Technical Skills 26
2.5. Quantitative Criteria 28
2.5.1. Years of Experience in Leadership Role 28
2.5.2. Number of Direct Reports/ Span of Control 29
2.5.3. Salary and Bonuses 31
CHAPTER Three 33
3.1 Definitions and Functions of Fuzzy Sets 33
3.2. Mathematical properties of Complementation, Union, and Intersection 36
3.3. Algebraic Methods of Fuzzy Numbers 36
3.4. Alpha Cuts 37
3.5. Linguistic Variables 37
CHAPTER Four 40
4.1. MCDM Matrix 40
4.2. Average of Importance Weights 41
4.3. Average Ratings of Alternatives amongst the Qualitative Criteria 42
4.4. Normalization of Quantitative Formula 42
4.5. Average Criteria Weights Multiplied by Alternative Rating Averages 43
4.6. Defuzzification 47
CHAPTER Five 51
5.1. Avoiding Bias 51
5.2. Qualitative Ratings from the Decision Makers 52
5.3. Criteria Weights 53
5.4. DMs Decide Managers on Each Qualitative Criterion 54
5.5. Discerning Quantitative Values 56
5.6. Criteria Weights Multiplied by Entire Criteria Averages 57
5.7. Solving for xR and xL 59
5.8. Solving for UR and UL 59
CHAPTER Six 61
6.1. Discussion 61
6.2. Research Limitations 61
6.3. Practical Uses of this Manager Fuzzy MCDM Tool 63
REFERENCES 65
Appendix A 81
Appendix B 82

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