In this study, university marketing in recent years has undergone a major transformation. The reason is the introduction of digital marketing and intensified competition between universities. Inter-university competition can be attributed not only to a declining birthrate, but also to an increase in the influence of external institutions such as private school grants and accredited evaluation institutions, and the use of university rankings. The first half is summarized about the marketing of university-specific brand equity. In the second half, analysis methods used in recent data-driven marketing will be explained together with the analysis purpose. It will be explained about analysis techniques such as covariance structure analysis, text mining, multivariate analysis, factor analysis, and conjoint analysis are applied in actual business situations using diagrams and examples.

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本研究は、大学およびMBAのブランディング及びマーケティングに関する先行研究を概観する。具体的には、雇用主や検討者視点から見た大学の選好基準や大学プログラムベースのブランディング戦略、オンラインプログラム、ブランドパーソナリティやブランドのポジショニングについて、大学とMBAについてマーケティングの観点から幅広く先行研究を概観する。

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This study summarizes previous studies in two main areas where higher education institutions are devoting much resources. The first is what methods are effective in improving student performance. Second, we summarize previous studies on dropout prediction and suppression.

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This research is characterized in that it seeks closeness between the fields of machine learning, deep learning and reinforcement learning and the field of econometrics. If you are studying time series analysis or panel analysis in econometrics, machine learning has much in common with econometrics as well as analytical methods, so it is important to consider relationships more systematically in the future. I think it will be. In econometrics, machine learning is used in the field of causal inference, and the relationship is explained. In order to give an overview of recent advances in research on machine learning and econometrics, we have applied machine learning to business in image recognition and speech recognition, CATE and LASSO, ELM as a policy distribution problem, and CMA (causal inference in epidemiology). Mediation analysis), Causal Forest, SDD, structure estimation, etc.

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The purpose of this research is to organize collective intelligence from two viewpoints; Collective Intelligence and Wisdom of Crowds. Collective intelligence has two ideas with different characteristics; the Wisdom of Crowds by Surowiecki and Collective Intelligence by Pierre Levy. We summarize previous research on the requirements for making collective intelligence works.

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This paper provides learning effects on online case study for an MBA. When teacher guide to emphasize logical thinking as a learning outcome for a case study, it confirms to make logical thinking through discussion to emphasize the management perspective in the first year. In the second year, they had provided analyzed information for the market and the firm and had changed how to proceed with the group discussion. When the theme or industry for the case relates to their job experience, they provide specific information and the discussion qualification for group discussion improve. It confirms to discuss more overseas markets and competitors based on 3C analysis (Company, Customer, Competitory).

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This study clarifies the learning effects of online discussions in MBA, especially the learning effects of collective intelligence. For that purpose, we considered from two viewpoints. First, we proposed a speech criterion for collective intelligence and a speech criterion for individual knowledge. Secondly, we examined whether the differences in group members affected collective intelligence. Third, the factors that affect the breadth of the topics covered in group discussions were examined. As a result of classification according to the utterance standard for collective intelligence, it was shown that many students provided horizontal development and provided new viewpoints. In addition, as a result of examining each class year and position, there was a large difference in the number of remarks and the theme of remarks in all categories, and it was highly likely that collective intelligence that differs depending on the classmate's year and position was high. confirmed. It was also confirmed that the factors affecting the breadth of the theme were the number of utterances in the group and the average number of utterances in the thread.

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This paper confirmed about student motivations in Business School. It analysed about whether student job contents and positions relate the final scores and number of discussion, the degree of satisfaction for specific subject. It confirmed student subject motivation relates student job contents and position in Business School.

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