Research
Research contributions
Research methods
| Method | Criteria and reporting guideline |
|---|---|
| Literature review | Paré et al. (2015), Paré et al. (2024), Page et al. (2021), Templier & Paré (2018), Okoli (2015), PRISMA1 |
| Theory development | Gregor (2006) |
| Design science | Hevner et al. (2004), Prat et al. (2015), Peffers et al. (2007), Journal of Open Source Software (n.d.) |
| Machine learning | Heil et al. (2021), Kapoor et al. (2023), Stevens et al. (2020), Walsh et al. (2021) |
| Experiments | Frank et al. (2024) |
| Qualitative surveys and interviews | O’Brien et al. (2014), Tong et al. (2007) |
Support for statistical analyses (BACES)
For students conducting research for their thesis, support from the Bamberger Centrum für Empirische Studien (BACES) is available. BACES provides free statistical consultation for students working on seminar papers, thesis projects, and for university members involved in publications. This service is particularly useful for students needing assistance with statistical analysis and data interpretation.
The BACES team, which consists of professors, researchers, and student assistants, offers:
- Free methodological consultation for students and university members on their research projects (seminar and thesis papers, publications).
- Training sessions on various statistical topics.
- Immediate support on urgent statistical issues every Wednesday between 10 and 11 AM.
For further details on their services and team, you can visit the BACES website or directly reach out for statistical consultation via the BACES consultation page.
Please note that the consultations are provided with the consent of thesis advisors. We highly encourage students to take advantage of this valuable resource during their research phase.
References
Ågerfalk, P. J., & Karlsson, F. (2020). Artefactual and empirical contributions in information systems research. European Journal of Information Systems, 29(2), 109–113. https://doi.org/10.1080/0960085X.2020.1743051
Frank, M. C., Braginsky, M., Cachia, J., Coles, N. A., Hardwicke, T. E., Hawkins, R. D., Mathur, M. B., & Williams, R. (2024). Experimentology: An open science approach to experimental psychology methods. MIT Press. https://doi.org/10.7551/mitpress/14810.001.0001
Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642. https://doi.org/10.2307/25148742
Heil, B. J., Hoffman, M. M., Markowetz, F., Lee, S.-I., Greene, C. S., & Hicks, S. C. (2021). Reproducibility standards for machine learning in the life sciences. Nature Methods, 18(10), 1132–1135. https://doi.org/10.1038/S41592-021-01256-7
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–106. https://doi.org/10.2307/25148625
Journal of Open Source Software. (n.d.). JOSS review criteria and checklist. https://joss.readthedocs.io/en/latest/review_checklist.html.
Kapoor, S., Cantrell, E., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., & Narayanan, A. (2023). REFORMS: Reporting standards for machine learning based science. arXiv Preprint.
Leidner, D. E. (2020). What’s in a contribution? Journal of the Association for Information Systems, 21(1), 2. https://doi.org/10.17705/1JAIS.00598
O’Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research. Academic Medicine, 89(9), 1245–1251. https://doi.org/10.1097/ACM.0000000000000388
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1CAIS.03743
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, 105906. https://doi.org/10.1016/J.IJSU.2021.105906
Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199. https://doi.org/10.1016/J.IM.2014.08.008
Paré, G., Wagner, G., & Prester, J. (2024). How to develop and frame impactful review articles: Key recommendations. Journal of Decision Systems, 33(4), 566–582. https://doi.org/10.1080/12460125.2023.2197701
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
Prat, N., Comyn-Wattiau, I., & Akoka, J. (2015). A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32(3), 229–267. https://doi.org/10.1080/07421222.2015.1099390
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P. (2020). Recommendations for reporting machine learning analyses in clinical research. Circulation: Cardiovascular Quality and Outcomes, 13(10), e006556. https://doi.org/10.1161/CIRCOUTCOMES.120.006556
Templier, M., & Paré, G. (2018). Transparency in literature reviews: An assessment of reporting practices across review types and genres in top IS journals. European Journal of Information Systems, 27(5), 503–550. https://doi.org/10.1080/0960085X.2017.1398880
Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357. https://doi.org/10.1093/INTQHC/MZM042
Walsh, I., Fishman, D., Garcia-Gasulla, D., Titma, T., Pollastri, G., Harrow, J., & Tosatto, S. C. E. (2021). DOME: Recommendations for supervised machine learning validation in biology. Nature Methods, 18(10), 1122–1127. https://doi.org/10.1038/s41592-021-01205-4