The Decision Sciences Journal of Innovative Education

Converting Data to Information for Case Study Analysis

J. David Osborne, DBA

Campbell School of Business

Berry College

Mt. Berry, GA 30149-5024

Phone: (706) 290-2688

FAX: (706) 238-7854

Email: dosborne@berry.edu

Case study analysis; SWOT analysis; problem solving; qualitative data management

INTRODUCTION

            Students who receive case studies containing a mixture of narrative, graphic, and tabular data, face a sizeable challenge in making sense of it all (Stubbart, 1989; Thomas, Clark & Gioia, 1993).  By using the first two steps of the normative decision making model, and readily available spreadsheet technology, small qualitative data points, or factoids, can be converted to high-value information for use in SWOT (strengths, weaknesses, opportunities, and threats) analysis (Archer, 1980; Simon, 1960; Daft, 2003).  This teaching brief describes this process in detail, along with lessons learned from both undergraduate and graduate management students.  Feedback from students, from outside business-persons, and from academic colleagues, suggest that this application of readily available technology to complex problem solving, produces clear business briefings (and write-ups) that identify both macro-presenting problems and their underlying causal factors.  This method also gives confidence to students who can defend their recommendations by using the micro-factors available, but often overlooked, in case studies.  The narrative first describes the details of this systematic integration of classical decision-making theory with readily available computer spreadsheet technology, and, then along the way, it provides helpful implementation comments from a professor’s point of view.  Finally, it closes with a summary of lessons learned in five years of college experience with this approach.

This approach can be applied to any form of written analysis, whether long or short, but in practice, my students have been most willing to use it when overwhelmed by the size and complexity of a large case study.  For shorter problem descriptions, my students often want to jump on the first issue they find.  Even when I point out that their first impressions are usually tied to presenting problems, rather than to causal factors (or at least antecedents in a cause-effect chain), the student’s tendency is to fix the obvious problem.  This quick analysis approach is often successful since shorter case studies are more focused on one or two teaching points, and the data is slanted to arrive at a quick diagnosis.  So, while the longer analytic technique presented here works in all settings, students who want to conserve time and mental energy often seek to avoid the extra critical thinking effort it entails.  Only when presented with a big, complex challenge, will students normally choose to use this step-by-step method of analysis.

STARTING AN ANALYSIS 

            To begin case analysis, students are first instructed to scan, and then re-read, their assigned case studies.  Once familiar with the case, I have them work in small groups of four to seven, and ask them to find four to six issues coming from their case.  To initiate this discussion, they are given a shopping list of potential problem areas (Appendix A-1).  While students are often puzzled as to the nature of an issue, they readily understand the potential problem area list, and begin discussing the case.  Usually, too many issues surface and students must practice prioritization methods to arrive at a “biggest issues” list.  This first step, identifying the problem(s), was often overlooked or ignored before I pointed out the utility of using these issues to value later bits of information.  This step has turned out to be very productive, since student discussions bring everyone into the analytic process, solidify common understandings of the case, and produce the measures used in evaluating qualitative data points. 

FACTOIDS

            Students are then asked to identify and give a generic example of a “factoid.”  A factoid is a very short, single fact that a well-known television network (CNN) uses to entertain viewers for a moment while they are adjusting their program timing.  For example, “The average time to produce a new generation of PCs is nine months.”  This is a fact, but without context, it is of little use.  Factoids originate in the narrative, tables, and graphs of the case under analysis, and are easy to spot.  It is important to let students know that factoids must be brief, a phrase or meaningful word group, and limited to a single topic.  This is a controlled variation of brainstorming and students who compete among themselves to find more factoids often enjoy it.  At this stage, all factoids are of equal value and none is excluded.  As a practical matter, having students sit around a laptop or PC while a group member makes the entries seems to work better than writing and transcribing results.  It is essential that students use a spreadsheet program (Excel, for example), and that they enter each factoid on a separate row (Example at Appendix A-2).  From experience, I have come to expect eight to twelve pages of factoids from twenty to thirty page case studies.  When students have finished entering data, they add further value by determining which factoids can make meaningful contributions to a SWOT analysis.

CLASSIFICATION AND VALUATION

            As with brainstorming, categorization and valuation lead to a subset of critical SWOT factors.  This process involves two steps, and provides opportunities to review other lessons from management and statistics.  First, students receive a short talk about open and closed systems, and corporate boundaries (Kast and Rosensweig, 1972).  With this lesson in mind, they are asked to insert a new column to the left of the factoids column, and then to classify each one as internal ( I ) or external ( E ).  Usually this leads to discussions about “boundary spanning” in sales, executive leadership, and logistics.  Students also see that even if they make an error in classification on one or two items, the effect, when spread across so many items (as many as 1,000 factoids), is minimal.  This process goes very quickly.

            Continuing to parallel brainstorming, students next value each item.  While arbitrary valuation systems are introduced, I suggest they use a scale from negative ten, through zero, to positive ten (Appendix A-3).  Negative tens are strong factors having a detrimental effect with respect to one of the issues identified in the first step of the case analysis.  Positive tens are those factors having a good effect on those same issues.  The zero point is neutral, and applies to those factoids having no relevance to the study issues.  The positive and negative values on either side of zero allow for different degrees of uncertainty, and often defuse potential conflicts between students who may be defending “their” factoids.  With the scale established, students value each factoid independently, discuss their recommendations with their group members, and arrive at a decision about the value of each factoid.  At this point, insert another column into the spreadsheet so the group’s laptop operator can record the value assigned to each factoid.  Because of the high number of factoids, and the group interaction, student gain skill in valuing different types of qualitative and quantitative case study data.  They also can use the spreadsheet’s sorting technology to make sense of many pages of randomly entered factoids.

SPREADSHEET USE

            If the I/E classification is in column A, the assigned value is in column B, and the factoid is in column C, primary and secondary sorts are done on columns A and B, respectively.  The result groups internal and external factoids into internal and external groups whose values range from +10, through 0, to -10.  This sort is helpful, but students are still overwhelmed with the volume of factoids, and this provides another teaching opportunity leading to discussion of the concept of a “zone of indifference (Appendix A-2).”  Factors in this zone are discarded because they are not strong enough to affect a decision.  As a practical matter, the zone of indifference using the +10 to -10 scale includes values between +9 and -9 (this condition may be eased).  Students ignore the factoids in the zone, and some even erase them from a copy of the worksheet.  While this causes initial concern, students see that the remaining high-value items are important with respect to the case’s issues, and they quickly spot the implications for SWOT analysis.

SWOT ANALYSIS

            No more than twelve factoids (a low, but workable number) enter a SWOT analysis.  Those factoids having high, positive, internal values are STRENGTHS; those with high, negative, internal values are WEAKNESSES; those with high, positive, external values are OPPORTUNITIES, and those with high, negative, external values are THREATS.  At this point, students have a set of factors to use in SWOT analysis that are derived from the  qualitative and quantitative data in the case study.  They have information, and are ready to continue their case analysis based on a strong, empirical foundation.

METHOD OUTCOMES

            At our college, case studies are used in all our business courses, and professors and local business-persons often sit in as active critics of discussions and presentations.  Comments from these sources suggest there is a significant difference in the presentations based on this analytic technique, and those that use a hasty or impressionistic approach.  Students who use the hasty approach are often embarrassed when asked to defend their choices.  In contrast, those who use this analytic technique have viable macro-problem statements, and they are better able to defend their statements at the micro-analytic level.  They also do a much better job of spotting and avoiding the false trails that are sometimes a distracting part of analysis.  Better accuracy, more depth, and avoidance of pitfalls all mark, and distinguish, the student analyses that use this systematic approach to solving case studies.  

CONCLUSION

By using this systematic approach to data gathering, classification, and valuing, and by employing the strengths of computer spreadsheets, students can make good sense of otherwise complex case studies.  Since beginning to use this approach, I have been very pleased with the increase in rigor of case write ups and presentations.  Informally, student work has become more consistent with the solutions provided by the authors of case studies, and students are more comfortable and confident when explaining the case to outside evaluators.  Overall, it has improved the quality of case analyses in my management courses, and it may do so in yours, too.

REFERENCES

Archer, E. R. (1980).  How to make a business decision:  An analysis of theory and practice. 

            Management Review 69: 54-61

Daft, R. L. (2003).  Management, 6^th Ed. Mason, OH:  South-Western.

Kast, R. E., & Rosenzweig, J. E. (1972).  General systems theory:  Applications for organization

            and management.  Academy of Management Journal: 447-465.

Simon, H. A. (1960).  The new science of management decision.  New York:  Harper-Row.

Stubbart, C. I.  1989. Managerial cognition: A missing link in strategic management research.

            Journal of Management Studies 26: 325-347.

Thomas, H., Clark, S. M., & Gioia, D. A.  1993. Strategic sense making and organizational

            performance: Linkages among scanning, interpretation, action, and outcomes.  Academy

            of Management Journal 36: 239-270.

A-1.  Generic case study problem list

Ethics, planning, organizing, directing, leading, structure, marketing, information systems, accounting, finance, technology, economics, social, political, legal, R&D, logistics, manufacturing, communications, human resources, facilities, others.

A-2.  Sample Spreadsheet

A-3.  Recommended Valuation Scale

  -10 |-------------------- -5 ------------------------  0  ---------------------- +5 ------------------------| + 10

   VERY              NEGATIVE                 NEUTRAL                POSITIVE                  VERY  

NEGATIVE                                                                                                                   POSITIVE

Appendix A