Quantitative research methods in business studies
(doctoral course; 7,5 ECTS credits)
Course director
Mandar Dabhilkar, associate professor of operations management, email: mda@fek.su.se
Learning objectives
The aim of this course is to introduce new doctoral students to quantitative research methods in business studies. On successful completion of the course students are expected to be able to:
Describe basic ideas, underlying assumptions, and elements of design and implementation of different research approaches, data collection, and analysis techniques available to a quantitative researcher in the field of business studies, e.g., surveys, patent data, hospital and patient data and event studies
Reflect over contemporary methodological problems and possible solutions
Understand how to report and read quantitative research in a critical and publishable way
Be familiar with basic central concepts associated with quantitative methods, such as statistical inference, statistical association and causation among variables
Be familiar with and able to use basic multivariate data analysis techniques, using computer based statistical packages such as SPSS
Content
The course deals with the following topics:
Differences between qualitative and quantitative research
The quantitative research process: models, constructs, measurement, design
Use of primary data sources such as in survey research
Use of secondary data sources such as in patent data-, health care hospital and patient data-, bibliometrical- and event studies
Validity and reliability
Pooling data across transparently different groups of key informants
Techniques for improving response rates
Introduction to SPSS
Basic statistical analysis: summarizing and describing samples of data
Statistical inference (from samples to populations): probability; estimation; hypothesis testing for relationships between variables and comparing groups.
Statistical association and causation among variables
Key statistical tests of hypothesis: t-tests; Chi-Square, F-test
Analysis of Variance (ANOVA)
Regression analysis
Multiple regression analysis
Factor analysis
Cluster analysis
Teaching and learning activities
The course consists of seminars, lectures and exercises. The emphasis is on business studies which for example will be reflected in the reading list for each seminar and the statistical exercises.
There are six parts:
1. Seminar on quantitative research methodology in business studies 2. Seminar on survey research
3. Seminar on patent data- and hospital and patient data studies 4. Seminar on event study methods
5. Lecture and exercise on descriptive statistics
Preliminary reading list (subject to change) Seminar on survey research
Forza, C. (2002). Survey research in operations management: A process based
perspective. International Journal of Operations & Production Management, 22(2), 152-194.
Rungtusanatham, M., Ng, C. H., Zhao, X., & Lee, T. S. (2008). Pooling Data Across Transparently Different Groups of Key Informants: Measurement Equivalence and Survey Research*. Decision Sciences, 39(1), 115-145. doi:
10.1111/j.1540-5915.2008.00184.x
Frohlich, M. T. (2002). Techniques for improving response rates in OM survey research. Journal of Operations Management, 20(1), 53-62.
Boyer, K. K., & Pagell, M. (2000). Measurement issues in empirical research: improving measures of operations strategy and advanced manufacturing technology. Journal of Operations Management, 18(3), 361-374. doi: 10.1016/s0272-6963(99)00029-7 Boyer, K. K., & Lewis, M. W. (2002). COMPETITIVE PRIORITIES:
INVESTIGATING THE NEED FOR TRADE-OFFS IN OPERATIONS STRATEGY. Production and Operations Management, 11(1), 9-20. doi: 10.1111/j.1937-5956.2002.tb00181.x
Ferdows, K., & De Meyer, A. (1990). Lasting Improvements in Manufacturing
Performance: In Search of a New Theory. Journal of Operations Management, 9(2), 168-184.
Dabhilkar, M. (2011). Trade-offs in make-buy decisions. Journal of Purchasing and Supply Management, 17(3), 158-166. doi: 10.1016/j.pursup.2011.04.002
González-Benito, J. (2007). A theory of purchasing's contribution to business performance. Journal of Operations Management, 25(4), 901-917.
Seminar on patent data- and hospital and patient data studies:
Bergek, A, Tell, F, Berggren, C & Watson, J. (2008) Technological capabilities and late shakeouts: industrial dynamics in the advanced gas turbine industry, 1987–2002, Industrial and Corporate Change, 17(2): 335-392
Capkun, V., Messner, M., & Rissbacher, C. (2012). Service specialization and operational performance in hospitals. International Journal of Operations & Production Management, 32(4), 468-495.
McDermott, C. M., & Stock, G. N. (2011). Focus as emphasis: conceptual and
performance implications for hospitals. Journal of Operations Management, 29(6), 616-626.
Clark, J. R., & Huckman, R. S. (2012). Broadening focus: Spillovers, complementarities, and specialization in the hospital industry. Management Science, 58(4), 708-722.
Clark, J. R. (2012). Comorbidity and the Limitations of Volume and Focus as Organizing Principles. Medical Care Research and Review, 69(1), 83-102.
KC, D. S., & Terwiesch, C. (2011). The effects of focus on performance: Evidence from California hospitals. Management Science, 57(11), 1897-1912.
Seminar on event study methods
Hendricks, K. B., V. R. Singhal. 2003. "The effect of supply chain glitches on
shareholder value”. Journal of Operations Management, 21, 5, December 2003, Pages 501-522.
Hendricks, K. B., V. R. Singhal, V. R. 2001. "The Long-Run Stock Price performance of Firms with Effective TQM Programs as Proxied by Quality Award Winners".
Management Science, 47, 359-368.
Hendricks, K. B. and Singhal, V. R. 2008. The effect of product introduction delays on operating performance. Management Science, 54, 878-892.
Brown, S., J. Warner. 1985. "Using daily stock returns: The case of event studies". Journal of Financial Economics, 14, 3-31.
MacKinlay, C. A. 1997. “The event studies in economics and finance”. Journal of Economic Literature, 35, 13-39.
Barber, B. M., J. D. Lyon. 1997. “Detecting Long-Run Abnormal Stock Returns: The Empirical Power and Specification of Test-Statistics”. Journal of Financial Economics, 43, 341-372.
Kothari, S. P., J. B. Warner. 1997. “Measuring Long-Horizon Security Price Performance”. Journal of Financial Economics, 43, 301-339.
Reference literature for statistical exercises
Hair, J. P., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New Jersey: Prentice Hall.
Examination
The course is examined through active participation in seminars and exercises and through one final assignment. The final assignment consists of three parts:
1. Discussion of how one or more of the approaches to data collection possibly could be adopted in the doctoral student’s own research, eg survey, patent- or patient data and/or event studies. Reflect on advantages and disadvantages with the selected approaches.
2. Selection of a few articles that are based on quantitative research methods in the doctoral student’s dissertation area (eg marketing, finance, accounting, operations management or organization studies) and reflect on key particularities for their field.
3. Using and trying out statistical techniques taught in the course, eg descriptive statistics, regression analysis and factor analysis. A dataset and instructions will be provided by the course director.
The student’s performance in relation to the learning objectives for the course is assessed according to a pass/fail grading scale. Further guidelines and criteria will be distributed in due course. Schedule April 22: 09-13 April 29: 09-13 May 6: 09-13 May 13: 09-13 May 20: 09-13 May 27: 09-13
