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Massive Online Open Courses (MOOCs) platforms are workplace for many instructors. In the platform, underrepresented instructors face an environment where they have to reveal their identity online to attract students. However, limited efforts have been devoted to understanding the unwanted consequences of the disclosed identity, especially under contexts of course subscription. Using matched sample approaches, we find evidence of negative gender bias against female instructors in course subscriptions. Further, under the stereotype content model and role congruity theory, we find a moderating effect of the IT course type and warmth in gender bias. We found a larger bias for the female instructors who teach IT courses. Additionally, the instructors who display low warmth are subject to larger bias than those who display high warmth. Our results supplements online bias literature by adding empirical evidence regarding mechanisms of gender bias. Specifically, we highlight the importance of warmth. 

ABSTRACT   i
TABLE OF CONTENTS   ii
LIST OF TABLES   iii
LIST OF FIGURES   iv
CHAPTER 1. INTRODUCTION   1
CHAPTER 2. LITERATURE REVIEW   4
CHAPTER 3. CONCEPTUAL FRAMEWORK  AND HYPOTHESIS   7
CHAPTER 4. DATA AND VARIABLES   10
 4.1. Data   10
 4.2. Variables   11
CHAPTER 5. MAIN ANALYSIS   15
 5.1. Methodology   15 
 5.2. Result   18
CHAPTER 6. MODERATION ANALYSIS   20
 6.1. Methodology   20
 6.2. Result   22
CHAPTER 7. ROBUSTNESS CHECKS   25
 7.1. Alternative Matching Strategy   25
 7.2. Falsification Test   26
CHAPTER 8. CONCLUSION   28
REFERENCES   30