School of Management and Technology
Organisational unit: Research School
- Institute for Auditing & Tax
- Institute for production technology and systems
- Institute of Experimental Industrial Psychology
- Institute of Information Systems
- Institute of Knowledge and Information Management
- Institute of Management, Accounting & Finance
- Institute of Management and Organization
- Institute of Marketing
- Institute of New Venture Management
- Institute of Performance Management
Organisation profile
EMPOWERING MINDS. INSPIRING INNOVATIONS. SHAPING TRANSFORMATIONS.
As part of Leuphana University, the School of Management and Technology is a dynamic and innovative community of students and faculty with high-level expertise in the fields of organization studies, responsible management, entrepreneurship, product development process, digital transformation and data science, and psychology and societal transformation. Our core aspiration is driving innovation in management and technology to shape responsible and sustainable transformations. In our research, we pioneer understandings of and solutions to the core challenges of our time, such as digitalization and sustainable production. In our teaching, we challenge conventional wisdom and inspire entrepreneurial thinking and responsible action. In business and society, we team up with local and international partners to contribute to the regional development of northern Germany. We value the interrelationships between disciplines, which is reflected in our interdisciplinary degree programs and collaboration in research.
The School of Management and Technology is home to the disciplines of Accounting and Finance, Business Psychology, Business Information Systems, Engineering, Management and Marketing. We support the respective identities and profile development of the disciplines, while also promoting interdisciplinary research and teaching in the shape of programs of study and research centers. This interdisciplinary approach is characterized by a commitment to responsibility and helping meet societal challenges.
Main research areas
The School of Management and Technology is the academic and professional home to 1,500 bachelor’s, master’s and doctoral students, more than 50 professors, more than 70 research associates and research assistants, 36 professional staff members.
The main themes of the school are reflected in its study programs: The 3 major and 7 minor programs at the College, 5 master's programs and 4 doctoral programs at the Graduate School provide academic training. The doctoral programs focus on (1.) Entrepreneurship, Management and Innovation (EMI), (2.) Information Systems and Data Science, (3.) Engineering and (4.) Management, Finance and Accounting.
In total, we offer 16 programs of study in the disciplines of Business Administration (in particular Accounting and Finance), Business Information Systems, Business Psychology, Engineering and Management.
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Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing
Hornstein, S., Scharfenberger, J., Lueken, U., Wundrack, R. & Hilbert, K., 12.2024, In: npj Digital Medicine. 7, 1, 9 p., 132.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring
Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R. D. & Brefeld, U., 15.08.2019, In: International Journal of Artificial Intelligence in Education. 29, 3, p. 342-367 26 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Predicting the future performance of soccer players
Arndt, C. & Brefeld, U., 01.10.2016, In: Statistical Analysis and Data Mining. 9, 5, p. 373-382 10 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Predicting the Individual Mood Level based on Diary Data
Bremer, V., Becker, D., Funk, B. & Lehr, D., 06.2017, Proceedings of the 25th European Conference on Information Systems, ECIS 2017. AIS eLibrary, p. 1161-1177 17 p. (Proceedings of the 25th European Conference on Information Systems, ECIS 2017).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis
Bremer, V., Becker, D., Kolovos, S., Funk, B., van Breda, W., Hoogendoorn, M. & Riper, H., 21.08.2018, In: Journal of Medical Internet Research. 20, 8, 11 p., e10275.Research output: Journal contributions › Journal articles › Research › peer-review
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Predicting Therapy Success For Treatment as Usual and Blended Treatment in the Domain of Depression
van Breda, W. R. J., Bremer, V., Becker, D., Funk, B., Ruwaard, J. & Riper, H., 01.06.2018, In: Internet Interventions. 12, p. 100-104 5 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Prediction of the tool change point in a polishing process using a modular software framework
Meier, N., Papadoudis, J. & Georgiadis, A., 2020, In: Procedia CIRP. 88, p. 341-345 5 p.Research output: Journal contributions › Conference article in journal › Research › peer-review
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Predictive Maintenance of Bearings Through IoT and Cloud-Based Systems
Meier, N., Georgiadis, A. & Sanchez, A., 03.2019, Proceedings of the 2019 International Conference on Industrial Engineering: A Regional Conference of Society for Industrial and Systems Engineering. Cordova, X., Garcia, G., Camacho, C., Vernaza, E., Fernandez, J. & Subramanian, A. (eds.). Quito, Ecuador: Society for Industrial and Systems Engineering, p. 23 1 p.Research output: Contributions to collected editions/works › Published abstract in conference proceedings › Research › peer-review
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Predictive modeling in e-mental health: A common language framework
Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J. & Riper, H., 01.06.2018, In: Internet Interventions. 12, Juni 2018, p. 57-67 11 p.Research output: Journal contributions › Scientific review articles › Research
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Predictors of adherence to public health behaviors for fighting COVID-19 derived from longitudinal data
Schumpe, B. M., Van Lissa, C. J., Bélanger, J. J., Ruggeri, K., Mierau, J., Nisa, C. F., Molinario, E., Gelfand, M. J., Stroebe, W., Agostini, M., Gützkow, B., Jeronimus, B. F., Kreienkamp, J., Kutlaca, M., Lemay, E. P., Reitsema, A. M., vanDellen, M. R., Abakoumkin, G., Abdul Khaiyom, J. H., Ahmedi, V., Akkas, H., Almenara, C. A., Atta, M., Bagci, S. C., Basel, S., Berisha Kida, E., Bernardo, A. B. I., Buttrick, N. R., Chobthamkit, P., Choi, H. S., Cristea, M., Csaba, S., Damnjanović, K., Danyliuk, I., Dash, A., Di Santo, D., Douglas, K. M., Enea, V., Faller, D., Fitzsimons, G. J., Gheorghiu, A., Gómez, Á., Hamaidia, A., Han, Q., Helmy, M., Hudiyana, J., Jiang, D. Y., Jovanović, V., Kamenov, Z., Kende, A., Keng, S. L., Kieu, T. T. T., Koc, Y., Kovyazina, K., Kozytska, I., Krause, J., Kruglanski, A. W., Kurapov, A., Lantos, N. A., Lesmana, C. B. J., Louis, W. R., Lueders, A., Malik, N. I., Martinez, A. P., McCabe, K. O., Mehulić, J., Milla, M. N., Mohammed, I., Moyano, M., Muhammad, H., Mula, S., Muluk, H., Myroniuk, S., Najafi, R., Nyúl, B., O'Keefe, P. A., Olivas Osuna, J. J., Osin, E. N., Park, J., Pica, G., Pierro, A., Rees, J. H., Resta, E., Rullo, M., Ryan, M. K., Samekin, A., Santtila, P., Sasin, E., Selim, H. A., Stanton, M. V., Sultana, S., Sutton, R. M., Tseliou, E., Utsugi, A., van Breen, J. A., Van Veen, K., Vázquez, A., Wollast, R., Yeung, V. W. L., Zand, S., Žeželj, I. L., Zheng, B., Zick, A., Zúñiga, C. & Leander, N. P., 01.12.2022, In: Scientific Reports. 12, 1, 12 p., 3824.Research output: Journal contributions › Journal articles › Research › peer-review