16 Chapter Critical Thinking and Continuous Learning Organizational Behavior 1 2 3 4 Critical Thinking and the Scientific Process Causal Inferences Generalizing

16Chapter Critical Thinking and Continuous Learning

Organizational Behavior

1

2

3

4

Critical Thinking and the Scientific Process

Causal Inferences

Generalizing Research Results

Purpose of This Chapter



1 Purpose of This Chapter

It is very interesting that many of the most successful CEOs do not hold MBAs. GE’s Jack Welch, for example, is formally trained in the “hard” sciences.

Although the specialized skills learned in an MBAs program are useful for gaining entry-level positions, managers who rise to the top of the organization are often those who generate, test, and implement new ideas and discoveries. In deed, some have begun to question whether the convergent thinking skills associated with a traditional MBA degree are the most relevant in changing world, and many employers now look in nonbusiness programs for successful leaders.

What is the most essential factor to be a successful manager




“It is no longer cost-effective for the Perks Company to continue offering its employees a generous package of benefits and incentive year after year. In periods when national unemployment rates are low, Perks may need to offer such a package in order to attract and keep good employees, but since national unemployment rates are now high, Perks does not need to offer the same benefits and incentives. The money thus saved could be better used to replace the existing plant machinery with more technologically sophisticated equipment, or even to build an additional plant.”

This case is an argument question for the GMAT.

Case: The following is part of a business plan being discussed at a

board meeting of the Perks Company.

To avoid the “quick-fix” mentality, managers need to take several steps:

• Keep current with the literature in the field of management.

• Be skeptical when simple solutions are offered and analyze such solution thoroughly.

• Ensure that the concept they apply are based on science rather than advocacy; and experiment with new solutions themselves whenever

possible.




ChapterChapter 1 Chapter 1

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ChapterChapter

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Content of Management

Thinking Process



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Causal Inferences





2Critical Thinking and the Scientific Process

Traditional Way of Knowing:

Rationalism

Personal Experience

Reliance on Authorities

Scientific Method:

Characteristic of Scientific Method:

Objectivity Repeatable Self-correcting Cumulative

The Purpose of Science:

Public

Understandthe World

Description Prediction Controlthe situation

Explanation

Charles Sanders Peirce: “To satisfy our doubt… it is necessary that a method should be found by which our

beliefs may be determined by nothing human, but by some external permanency….The method must be such that the ultimate conclusion of every man should be the Same. Such is the method of

science.”



2 Critical Thinking and the Scientific Process

The Nature of Scientific Process

Verification Hypotheses

Data

Theory

is expressed as

which is quantified withwhich allow us test the theory through

which refutes or modifies the

A set of interrelated constructs,

definitions and propositions that

present a systematic view of phenomenon by

specifying relations among variables.

A specific, testable

prediction, deprived

typically from a theory, about the

relationship between two

variables.

Characteristics of Good Data ：

i. Reliabilityii. Validityiii. standardization



Stages in the Scientific Method

Mark W. Roosa, Scientific Method, Arizona University: http://www.public.asu.edu/~atmwr/fas500/documents/scifig.gif

Case



Case

Correlates of Intellectual Property Violation

Introduction:

Ilkka A. Ronkainen and Jose-Luis Guerrero-Cusumano, “Correlates of Intellectual Property Violation “, Best Practices in International Marketing: 61-69

The occurrence of intellectual property violation varies significantly across countries.

Whereas industrialized countries provide the strongest support to the protection of intellectual property, developing countries are at the end of the spectrum.



CaseCorrelates of Intellectual Property Violation

1. Identify Research Problem

Market Factors Involvement Factors

1. GNP per capita 7. Signatory to intellectual property treaty

2. Degree of enforcement of the intellectual property law

8. Level of trade dependence

3. Power distance 9. Level of trade with advanced economies

4. Avoidance of uncertainty

5. Degree of individualism

6. Masculinity

This study focuses on two sets of factors as correlates of intellectual property violation:

Market factors and involvement factors.



2. Formulate Hypotheses

H1: The lower GNP per capita in a country, he higher the incidence of intellectual property violation.

H2: The lower the degree of enforcement of law and regulations governing the protection of intellectual property, the higher the incidence of its violation.

H3: The higher the power distance in a country, the higher the occurrence of intellectual property violation....

H8: The higher the level of trade dependence, the lower the level of intellectual property violation.

H9: The higher the level of trade with advanced economies, the lower the level of intellectual property violation.



3.Choose Research Design/Develop Models to Measures Variables

The relationships were tested using regression with the following model: IPV = ß0 + ß1 GNP+ … ß9 TRADEADV + ε.

The measures of the factors as correlates of intellectual property violation are as follows:

Factors Measures

1. GNP per capita Purchasing Power Parities (PPP)

2. Degree of enforcement of the intellectual property law

Corruption Perception Index (CPI)

3. Power distance Power Distance Index (PDI)

4. Avoidance of uncertainty Uncertainty Avoidance Index (UAI)

5. Degree of individualism Individualism Index (II)

6. Masculinity Masculinity Index (MI)

7. Signatory to intellectual property treaty Membership in Berne Convention

8. Level of trade dependence Imports/GNP

9. Level of trade with advanced economies U.S Share in Imports for a Nation



4. Gather Data

The data for the study were obtained from public sources. The software industry was chosen as the focus for the testing of the hypothesis with the rate of piracy per market as estimated by the industry serving as the dependent variable. The data base consisted of 50 countries for which all of the data in this study could be secured (1997 Global Software Piracy Report, 1998) .



5. Analyze Data / Test Hypotheses

MODEL CONSTANT X1 X2 X3 X4 X5 X6 X7 X8 X9 R2 S

Proposed

Results

1 92.3(0.000)

23456789

99.6(0.000)

39.2(0.000)

48.7(0.000)

90.9(0.000)

80.3(0.000)

73.2(0.000)

67.9(0.000)

73.1(0.000)109.6

(0.000)

-0.0021(0.000)

-5.66(0.000)

+0.502(0.000)

+0.281(0.005)

-0.539(0.000)

-0.124(0.365)

-0.257(0.000)

+0.137(0.038)

-0.0003(0.0008)

-0.0008(0.019)

-2.45(0.027)

-0.219(0.005)

-0.172(0.032)

72.8

62.9

36.913.6

60.0

0.029.76.2

11.1

79.2

9.7

10.7

14.216.6

11.3

17.915.116.7

16.3

8.7



6. Analyze Data / Test Hypotheses

HYP r2 p Test Hypotheses

1 0.73 P<0.000 The result is in line with the Hypotheses.



4 0.14 P<0.005 The result is contrary to the Hypotheses.


6 0.00 P<0.365 The result fail to receive support.






7. Prepare Research Report

Report Preparation i. Report Formatii. Report Writingiii. Guidelines for Tablesiv. Guidelines for Graphs

i. Timely ii. Objectiveiii. Professionaliv. Informative v. Clear

Basic Principle of Report Writing: TOPIC



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Causal Inferences





3 Causal Inferences

Criteria for Inferring Cause (John Stuart Mill)

I. Temporal Precedence : the cause must come before the effect

时间顺序：原因发生在结果之前

II. Covariation : the cause is varied the effect varied

共变（存在相关关系）：原因变化结果变化

III. Elimination of Alternative Explanation

排除其他可能的原因性因素



Case The Cause of Absenteeism

I. Establishing Covariation:

Test of mean difference （均值差异）

Employee

Number of AbsencesPlant A

(With Day Care)Plant B

(Without Day Care)

123456789

10Average

10118

11343215

1211138

161410423

5.8 9.3

Number of Absences at Plant A

Employee (Before Day Care) (After Day Care)

123456789

10Average

12141012682116

10118

11343215

7.5 9.3




I. Establishing Covariation:

Test of correlation coefficient （相关系数）

Age

Abse

nce

s

+1.0A perfect

Positive Relationship

+.50

.00

No Relationship

-.50

Positive Relationship Negative Relationship

(The absolute value of the correlation reveals the magnitude of the relationship.)

Plots Depicting Various Level of Correlation between Variables




II. Elimination of Alternative Explanation :

Selection threat: the group selected for comparison were not the same initially 选择因素威胁：指测试群体与总体有系统误差

Employee Number of Absences

Plant A(With Day Care)

Plant B(Without Day Care)

123456789

10Average

10118

11343215

1211138

161410

423

5.8 9.3

Age

27313026406152474641

34.6

Age

27343125333525405246

Number of Absences

40.1



II. Elimination of Alternative Explanation :

Selection threat: the group selected for comparison were not the same initially 选择因素威胁：指测试群体与总体有系统误差

History threat: the real cause is not the change you made but rather something else that happened at the same time 历史因素威胁：指不受研究人员控制发生在试验期间的事件



3 Causal Inferences

Designing Observation to Infer Cause

The Symbol of Research Design:X: Chang situation for this groupO: Score for this group

Two Faulty Research Designs 预先实验设计A. One Group Before-After 一组前后设计

O1 X O2

实验组 X O1

控制组 O2

B. After Only with Unequal Group 静态组比较设计

Collect data at Time 1

Change situation

Collect data at Time 2

Score for Group 1in Situation A

Score for Group 2without Situation A

We use (O2-O1) to estimate the result of this experiment.




3 Causal Inferences

Designing Observation to Infer Cause

Two Improved Research Designs 真实实验设计A. Two Groups Before-After 前后测量控制组设计

实验组 X O1

控制组 O2

B. Tow Groups After-Only with Randomization 后期测量控制设计

O1 X O2

O3 O4

We use (O2-O1)- (O4-O3) to estimate the

result of this experiment.


Score at Time 1for Group 1

Change SituationFor Group 1



No Change in SituationFor Group 2


Score for Group 1in Situation A

Score for Group 2without Situation A

Randomly Assignment Of people to groupsA and B



CaseHow can we rule out age as the alternative explanation for our results?

I. Homogeneous Group ： select workers who are 25to 35 years old.




123456789

10Average

10118

11343215

1211138

161410

4

23

5.8 9.3

Age

27313026406152474641

34.6

Age

27343125333525405246

Number of Absences

Average 10 12 30

40.1

28.5

Conclusion ： With this sample, we still found lower absenteeism in Plant A than in Plan B, so we could attribute the difference to age.




II. Matching Subject ： study only the subject in Plant A for whom there are corresponding subjects in Plant B .




123456789

10Average

10118

11343215

1211138

161410

4

23

5.8 9.3

Age

27313026406152474641

34.6

Age

27343125333525405246

Number of Absences

Average 5.6 6.8 39.2

40.1

39.2

Conclusion ： With this sample, we still found lower absenteeism in Plant A than in Plan B, so we could attribute the difference to age.




III. Build the Threat into the Design ： simply treat age as another possible factor affecting the rate of absenteeism and examine its effectat the some time that we study the effect of day care.

Conclusion ： The relationship between day care and absenteeism depends on the factor of age.

Plant B（ No day care)

Plant A（ day care)

Avera

ge N

um

ber

of

Abse

nce

s

3

30

(35-65-year-old)

(25-35-year-old)



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Causal Inferences





4 Generalizing Research Results

Results of research may not generalize across all samples.Results of research must be examined across:

- Samples - Settings - Time

Research is generally conducted with one sample, in one setting, in one time period 。 However, we often wish to know the generalizability of the results.

Generalizability is defined as the extent to which results obtained in one sample-setting-time configuration can be repeated in a different configuration.



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16 Chapter Critical Thinking and Continuous Learning Organizational Behavior 1 2 3 4 Critical Thinking and the Scientific Process Causal Inferences Generalizing