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2018/2019  KAN-CINTO1010U  Evidence Based Management

English Title
Evidence Based Management

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Qiqi Jiang - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Management
  • Methodology and philosophy of science
Teaching methods
  • Face-to-face teaching
Last updated on 27-06-2018

Relevant links

Learning objectives
By the end of the course the students should demonstrate:
  • Ability to account for the required course reading and to illustrate points from the literature in the exam case.
  • Ability to account for the theoretical-empirical relationships, that is, ability to develop descriptive conceptual models based on theory and the data in the exam case.
  • Ability to design evidence based study that can inform decision-making.
  • Ability to critically evaluate the validity, generalizability and applicability of both quantitative and qualitative data.
  • Ability to perform credible analysis of both quantitative and qualitative data.
  • Ability to communicate in writing evidence based recommendations.
Course prerequisites
**In the first class, students are instructed to install software, e.g. SPSS, STATA, R Studio, UCINET, Nvivo etc., in their personal computer and get familiar with the basic operation. Some software are available for download at https:/​/​studentcbs.sharepoint.com/​itandtools/​pages/​free-programmes.aspx
Evidence Based Management:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

Decision-making is one of the most fundamental activities that define the role of managers. Every manager has to make decisions which can determine the fate of people, projects, and organizations. Evidence-based management (EBM) entails managerial decisions and work practices informed by valid evidence, which were obtained through scientific-grade methods. The scientific origin of evidence based management provides not only a grounded approach for effective decision making, but also a solid skill set that can be applied in leading let alone assessing any research project from master thesis to market research. The course Evidence Bases Management offers a foundation for students who aspire to play a pivotal role in data driven organizations.


This course is designed to provide practical knowledge about evidence and its application to decision making and management in general. In lieu of using generic 'best practice' as a benchmark, evidence-based practice seeks to engage managers in critical evaluation of the validity, generalizability and applicability of the available evidence. Specially, student will learn to frame organizational challenges and knowledge requirements, identify sources of evidence, gather the required data, qualify its validity, detect recurring patterns, and communicate the findings in an effective fashion. In particular, the students will learn how to leverage quantitative and qualitative methods to conduct evidence-based management decision making. In particular, survey, experiment, case study, and empirical modeling will be covered. The course provides a balanced mix of formal theory, critical thinking, and hands-on experience.

Description of the teaching methods
Thematic lectures, in-class exercises, tool training, and student presentations.
Feedback during the teaching period
Online exercises and project topic feedback in class.
Student workload
Class lectures and exercises 30 hours
Class preparation: readings + home assignments 100 hours
Exam and exam preparation 76 hours
TOTAL 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on LEARN before they buy the books.



Joseph F. Hair, Jr William C. Black Barry J. Babin Rolph E. Anderson (2010). Multivariate data analysis 7th Edition, Upper Saddle River, NJ: Prentice hall. (ISBN-10: 0138132631; ISBN-13: 978-0138132637)


Cooper, D. R., Schindler, P. S., & Sun, J. (2013). Business research methods, New York: McGraw-Hill Irwin.


Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.


Seleted Research Articles

Rousseau, D. M. (2006). Is there such a thing as “evidence-based management”?

Academy of Management Review, 31(2), 256-269.


Briner, R. B., Denyer, D., & Rousseau, D. M. (2009). Evidence-based management: concept cleanup time? The Academy of Management Perspectives, 23(4), 19-32.


Hevner, Alan R., Salvatore T. March, Jinsoo Park, and Sudha Ram. "Design science in information systems research." MIS Quarterly 28, no. 1 (2004): 75-105.


Gregor, Shirley, and Alan R. Hevner. "Positioning and presenting design science research for maximum impact." MIS quarterly 37, no. 2 (2013).


Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1), 15-31.


Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management review14(4), 532-550.


Davison, R., Martinsons, M. G., & Kock, N. (2004). Principles of canonical action research. Information systems journal14(1), 65-86.


Myers, M. D. (1997). Qualitative research in information systems. Management Information Systems Quarterly21(2), 241-242.


Wodarski, J. S., & Hopson, L. M. (2011). Research methods for evidence-based practice. Sage. (Chapter 1), pp. 1-17


Pfeffer, J., & Sutton, R. I. (2006). Evidence-based management. Harvard business review, 84(1), 62-74.


Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii-xxiii.


Lee, A. S. (1991). Integrating positivist and interpretive approaches to organizational research. Organization science, 2(4), 342-365.


Kankanhalli, A., Tan, B. C., & Wei, K. K. (2005). Contributing knowledge to electronic knowledge repositories: an empirical investigation. MIS quarterly, 113-143.


Tan, C. H., Teo, H. H., & Benbasat, I. (2010). Assessing screening and evaluation decision support systems: A resource-matching approach. Information Systems Research, 21(2), 305-326.


Liu, Q. B., & Karahanna, E. (2017). The dark side of reviews: The swaying effects of online product reviews on attribute preference construction. MIS Quarterly, 41(2), 427-448.


Holsapple, C. W., & Wu, J. (2011). An elusive antecedent of superior firm performance: The knowledge management factor. Decision Support Systems, 52(1), 271-283.


Sutanto, J., Palme, E., Tan, C. H., & Phang, C. W. (2014). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. MIS Quarterly, 37(4), 1141-1164.


Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2013). Mobile targeting. Management Science, 60(7), 1738-1756.


Tan, C. H., Sutanto, J., Phang, C. W., & Gasimov, A. (2014). Using personal communication technologies for commercial communications: A cross-country investigation of email and SMS. Information Systems Research, 25(2), 307-327.


Zhang, X. M., & Zhu, F. (2011). Group size and incentives to contribute: A natural experiment at Chinese Wikipedia. American Economic Review, 101(4), 1601-15.


Mithas, S., & Rust, R. T. (2016). How information technology strategy and investments influence firm performance: conjecture and empirical evidence. MIS Quarterly, 40(1), 223-245.


Chen, J., Heng, C. S., Tan, B. C., & Lin, Z. (2018). The distinct signaling effects of R&D subsidy and non-R&D subsidy on IPO performance of IT entrepreneurial firms in China. Research Policy, 47(1), 108-120.


Goh, K. Y., Heng, C. S., & Lin, Z. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information Systems Research, 24(1), 88-107.


Huang, P., Lurie, N. H., & Mitra, S. (2009). Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods. Journal of marketing73(2), 55-69.


Vir Singh, P., Tan, Y., & Mookerjee, V. M. (2011). Network Effects: The Influence of Structural Capital on Open Source Project Success. Management Information Systems Quarterly35(4), 813-829.


Joseph, D., Boh, W. F., Ang, S., & Slaughter, S. A. (2012). The career paths less (or more) traveled: A sequence analysis of IT career histories, mobility patterns, and career success. MIS Quarterly, 427-452.


Li, M., Jiang, Q., Tan, C. H., & Wei, K. K. (2014). Enhancing user-game engagement through software gaming elements. Journal of Management Information Systems, 30(4), 115-150.


Last updated on 27-06-2018