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The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

This study aims to investigate a startup accelerator’s decisions toward exerting effort in an information acquisition process and selecting an information disclosure strategy. In particular, the authors are interested in examining which factors may cause the accelerator to report more or less accurate information, which will subsequently affect the investment decision and the outcome of the ventures. This study examines the impact of the equity share taken by the accelerator on the effort level being exerted in the information acquisition process, as well as the accelerator’s decision on the information disclosure regime.

The authors use mathematical models built upon well-established theoretical and practical concepts to analyze the research problems and derive the findings.

The authors show that when the accelerator takes a sufficiently large equity share from the entrepreneur in exchange for admitting the entrepreneur’s venture into the acceleration program, the accelerator is motivated to exert a significant level of effort to observe an accurate signal for the quality of the venture, and then disclose the information about the venture’s quality consistently with the observed signal (informative disclosure regime). On the other hand, if the accelerator takes a small equity share, it is optimal for her to exert no effort in the information acquisition process and simply adopt the basic disclosure regime, where the accelerator reports the quality of the venture based solely on the ex ante expected payoff of the venture, regardless of the observed signal.

The results indicate that an equity sharing scheme, which awards a sufficient amount of equity to the accelerator, can be an effective tool to help obtain accurate information about the quality of a startup venture and make a well-informed investment decision.

This research illustrates that the ownership stake of the accelerator can potentially indicate the accuracy of the information about the venture provided by the accelerator to outside investors. That is, when the stake held by the accelerator is large, the investors can conjecture that the information about the venture reported by the accelerator may be highly accurate and reliable. In contrast, if the accelerator holds a small stake, then it is likely that the information provided by the accelerator may not add any value to the publicly available information. These insights can guide investors (e.g. angle investors, venture capitalists, etc.) in making well-informed startup investment decisions.

This study investigates the relationship between feelings of loneliness in the workplace, life satisfaction, affect, hope and expressivity among hotel employees.

The research model was tested via structural equation modeling based on the empirical data collected from hotel employees in Antalya, Turkey.

The research findings suggest that emotional deprivation and social companionship have a significant impact on life satisfaction, that life satisfaction has a significant impact on positive and negative emotions, and that positive and negative emotions have the same impact on pathways and agencies.

The research findings should assist researchers and practitioners to understand the behaviors of hotel employees in continuous interaction and relationship with individuals to motivate them while providing more effective services.