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Matching Case Identification Hypotheses and Case-Level Data Analysis

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Accurate Case Outcome Modeling

Abstract

The traditional and still dominant logic among nearly all empirical positivist researchers in schools of management is to write symmetric (two directional) variable hypotheses (SVH), even though the same researchers formulate their behavioral theories at the case (typology) identification level. The behavioral theory of the firm (Cyert and March, A behavioral theory of the firm. Prentice-Hall, Englewood Cliffs, 1963), the theory of buyer behavior (Howard and Sheth, The theory of Buyer behavior. Wiley, New York, 1969)), and Miles and Snow’s (Organizational strategy, structure, and process. McGraw Hill, New York, 1978) typologies of organizations’ strategy configurations (e.g., “prospectors, analyzers, and defenders”) are iconic examples of formulating theory at the case identification level. When testing such theories, most researchers automatically, nonconsciously, switch from building theory of beliefs, attitudes, and behavior at the case identification level to empirically testing of two-directional relationships and additive net-effect influences of variables. Formulating theory focusing on creating case identification hypotheses (CIH) to describe, explain, and predict behavior and then empirically testing at SVH is a mismatch and results in shallow data analysis and frequently inaccurate contributions to theory. This chapter describes the mismatch and resulting unattractive outcomes as well as the pervasive practice of examining only fit validity in empirical studies using symmetric tests. The chapter reviews studies in the literature showing how matching both case-based theory and empirical positivist research of CIH is possible and produces findings that advance useful theory and critical thinking by executives and researchers.

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Author information

Authors and Affiliations

  1. Yonsei University, Yonsei Frontier Lab, Seoul, Republic of Korea

    Arch G. Woodside

Authors
  1. Arch G. Woodside
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Corresponding author

Correspondence to Arch G. Woodside .

Editor information

Editors and Affiliations

  1. Yonsei Frontier Lab, Yonsei University, Seoul, Korea (Republic of)

    Arch G. Woodside

Appendices

Appendix 1: Data for Cases 23–45

Cases

Income

Education

Gender

Beer

inc_c

edu_c

beer_c

not_edu_c

gen_inc_notedu_c

Liz

12,500

10

0

26

0.95

0.23

0.97

0.77

0

Chuck

12,500

12

1

23

0.95

0.5

0.9

0.5

0.5

George

12,500

14

1

24

0.95

0.95

0.93

0.05

0.05

Sarah

12,500

16

0

17

0.95

1

0.5

0

0

Doug

18,000

6

1

37

1

0.03

1

0.97

0.97

Betty

18,000

10

0

13

1

0.23

0.18

0.77

0

Albert

18,000

12

1

28

1

0.5

0.98

0.5

0.5

Nigel

18,000

14

1

34

1

0.95

1

0.05

0.05

Shirley

18,000

16

0

18

1

1

0.59

0

0

Judy

1500

6

0

14

0.01

0.03

0.25

0.97

0

Luke

1500

10

1

39

0.01

0.23

1

0.77

0.01

Adel

1500

12

0

11

0.01

0.5

0.1

0.5

0

Mark

1500

14

1

3

0.01

0.95

0.01

0.05

0.01

Roger

1500

16

1

9

0.01

1

0.05

0

0

Ann

4000

6

0

5

0.05

0.03

0.01

0.97

0

Kane

4000

10

1

11

0.05

0.23

0.1

0.77

0.05

Able

4000

12

1

4

0.05

0.5

0.01

0.5

0.05

Julia

4000

14

0

0

0.05

0.95

0

0.05

0

Peggy

4000

16

0

0

0.05

1

0

0

0

Don

6500

6

1

13

0.27

0.03

0.18

0.97

0.27

Meg

6500

10

0

5

0.27

0.23

0.01

0.77

0

Virginia

6500

12

0

2

0.27

0.5

0

0.5

0

Tim

6500

14

1

21

0.27

0.95

0.82

0.05

0.05

Appendix 2: Data for Cases 46–60

Case

Income

Education

Gender

Beer

inc_c

edu_c

beer_c

not_edu_c

gen_inc_notedu_c

Hugh

6500

16

1

18

0.27

1

0.59

0

0

Arch

9000

6

1

19

0.69

0.03

0.68

0.97

0.69

Christine

9000

10

0

16

0.69

0.23

0.41

0.77

0

Dilbert

9000

12

1

9

0.69

0.5

0.05

0.5

0.5

Audrey

9000

14

0

3

0.69

0.95

0.01

0.05

0

Vivian

9000

16

0

8

0.69

1

0.03

0

0

Aaron

12,500

6

1

33

0.95

0.03

1

0.97

0.95

Olivia

12,500

10

0

5

0.95

0.23

0.01

0.77

0

Nick

12,500

12

1

7

0.95

0.5

0.02

0.5

0.5

Kent

12,500

14

1

12

0.95

0.95

0.13

0.05

0.05

Kim

12,500

16

0

0

0.95

1

0

0

0

Graham

18,000

6

1

31

1

0.03

0.99

0.97

0.97

Ruby

18,000

10

0

5

1

0.23

0.01

0.77

0

Brad

18,000

12

1

36

1

0.5

1

0.5

0.5

Clark

18,000

14

1

3

1

0.95

0.01

0.05

0.05

Amber

18,000

16

0

6

1

1

0.02

0

0

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Woodside, A.G. (2019). Matching Case Identification Hypotheses and Case-Level Data Analysis. In: Woodside, A. (eds) Accurate Case Outcome Modeling. Springer, Cham. https://doi.org/10.1007/978-3-030-26818-3_1

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