大規模開放在線課程(MOOC)近年來已逐漸成熟，人們開始關注這種新型態的學習方法是否是成功的。現有文獻使用了數據分析方法來定義MOOC的成功，例如:預測學生何時輟學，預測學生學習成績和預測用戶滿意程度。但是，對於預測誰將為MOOC付費的研究很少。我們的研究建立了預測模型的研究程序，以識別願意為不同的MOOC付費方式（即一次性付費，每月付費，完成證明付費，證書證明付費）付費的客戶。
此外，一般的純數據預測分析模型要求公司收集盡可能多的數據，以建立功能強大的預測模型，這非常耗時，而且缺乏理論依據。在沒有理論依據的情形下，這種預測模型有時很難清楚地證明結果的合理性。因此，我們提出了一種基於理論的預測方法，該方法基於理論來預測使用者是否會付費。在此篇研究中，我們使用TAM(技術接受模型)建立基於理論的預測模型。結果顯示在MOOC中，基於理論的模型可以在兩個付費方式(即每月付費和證書證明付費)中獲得與純數據預測分析相同的預測能力。我們的研究還討論並了解如何基於預測結果來擴展TAM理論，以及向MOOC的公司提出意見，即不同的MOOC平台應採用不同的支付策略以最大化收益。

MOOC, massive open online course, has gradually matured in recent years. Extant literature has spent efforts on predicting the success of MOOCs, such as dropout rate, academic performance, and satisfaction, using data mining methods. However, the attention on predicting who will pay for MOOCs is scarce. Our study demonstrates a research routine to build predictive models in order to identify customers who are willing to pay for different MOOCs package (i.e. adoption, monthly, achievement, certificate). In addition, pure data mining modelling requires company to collect as much data as possible in order to build a powerful prediction model, which is time-consuming. Without a theoretical guidance, this predictive modelling sometimes is very difficult to justify the results clearly. Therefore, we propose a theory-based prediction method that builds on theory to predict future observations. Our findings show that, in MOOC, theory-based models can gain the same predictive power as pure-data models can in two payments Monthly and Achievement. Our study also discusses how we can extend the TAM theory based on predictive results as well as signals to MOOCs practitioners that different MOOCs platforms should pursue different payment strategies to maximize the revenue.

Chapter 1 – Introduction 8
Chapter 2 – Literature Review 12
2.1 Explanatory modeling and Predictive modeling 12
2.1.1 Explanatory modeling 12
2.1.2 Predictive modeling 13
2.2 Theoretical foundation 15
2.2.1 Research Gap of Model 15
2.2.2 Technology acceptance model (TAM) 16
2.2.3 Model description 16
2.2.4. TAM in Explanatory studies 17
2.3 Prior Prediction model in MOOCs 18
2.3.1 Dropout 19
2.3.2 Satisfaction 20
2.3.3 Academic Performance 21
2.3.4 Willing to Buy 22
Chapter 3 – Methodology 23
3.1 Data 23
3.1.1 Data Collection 23
3.2 Research design 23
3.2.1 Independent variables (IV) 24
3.2.1.1 Personal information 24
3.2.1.2 Personal Motivation 24
3.2.1.3 MOOCs Platform 25
3.2.1.4 MOOCs Course 25
3.2.2 Dependent variables (DV) 30
3.2.2.1 Adoption payment 31
3.2.2.2 Monthly payment 31
3.2.2.3 Certificate of Complete 31
3.2.2.4 Certificate of Achievement 31
3.3 Comparison of data mining methods 31
Chapter 4 – Results 33
4.1 Predictive power comparison 33
4.1.1 Naïve Classifier 33
4.1.2 Accuracy 34
4.1.3 Sensitivity 35
4.1.4 Specificity 36
4.1.5 Summary 37
4.2 Predictive Power of Theory-based model 38
4.3 Extending TAM theory 39
4.3.1 Combined with Pure-data model 40
4.3.2 Combined with Self-Regulated Learning 43
4.4 Prediction in a context of a MOOC company 44
4.4.1 Udemy and Coursera characteristics 45
4.4.2 Predictor selection 45
4.4.3 Prediction results 46
4.4.3.1 Adoption 47
4.4.3.2 Monthly 48
4.4.3.3 Certificate 49
4.4.3.4 Achievement 50
Chapter 5 – Conclusion 51
5.1 Theoretical Implications 51
5.2 Managerial Implications 53
5.3 Limitation and future research 54
Reference 56
Appendix 62

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