Is the Psychological Safety a matter in the Manufacturing industry? 

(An Empirical Study of large Korean Auto Part Manufacturing industry, n = 331)

One sentence summary: 

While the Psychological safety is highly correlated with performance in the ICT industry, the construct has a limited influence on job performance in the manufacturing industry. 

Definition of Psychological Safety: 

Psychological safety: is defined as the “individuals’ perceptions about the consequence of interpersonal risks in their work environment” (Edmondson, 2004, p. 239). Empirical studies supported the idea that employees who perceive high psychological safety commonly end up with higher self-directed behavior that drives better creativity and performance (Hülsheger, Anderson, & Salgado, 2009; Kim, 2007).

Measurement of Psychological Safety: 

Psychological safety was measured with seven items that were introduced by Edmondson (1999). A sample item for team psychological safety is “It is safe to take a risk in this unit”. The reliability of the psychological safety was reported as a measure of Cronbach’s alpha = .82 (Edmondson, 1999). Another empirical study in the U.S. that used the psychological safety measure also showed strong reliability (Cronbach’s alpha = .82) (Kim, 2007). Subordinates and supervisors got the same instrument to measure psychological safety because the instrument assesses the personal perception of a team environment.

For this study, the researcher used a Korean version of the team psychological safety measure that was developed and validated by Zhang (2011). The reliability of this Korean version was acceptable (Cronbach’s alpha = 0.74) (Zhang, 2011).

Participants (n=331)

The researcher recruited six large Korean automotive parts manufacturing companies through his personal network and snowball sampling techniques. 489 surveys out of 679 distributed surveys (73.0% response rate) were collected. Collected surveys from subordinates and supervisors from the six organizations were matched and screened prior to data analyses. After matching and screening data, a total of 331 (49.4%) surveys were selected for further quantitative data analysis. The selected dataset was composed of 43 surveys from supervisors and 288 surveys from subordinates.

Psychological Safety Data Validation

psychological safety measurement was assessed. CFA results of psychological safety indicated that the model was poorly fit with the measurement model. Three items (1, 6, 7) had poor factor loadings that were less than .50.

After constraining items 3 and 5 of psychological safety, the CFA results indicated that the collected data had a generally good model fit with the proposed measurement model. However, one item (sr3) was less than the factor loading criteria and was removed from the final list of items.

Correlating Psychological Safety with a few constructs

*Remark: This result comes from the study of Dr. Jeonghwan Choi's dissertation (2014) at the University of Illinois at Urbana-Champaign. 

The effects of the autonomous work environment and positive psychological capital on self-directed employee behavior: evidence from Korea

Choi, Jeong-Hwan


Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative science quarterly, 44(2), 350-383.

Edmondson, A. C. (2004). Psychological safety, trust, and learning in organizations: A group- level lens. In R. M. Kramer & K. S. Cook (Eds.), Trust and distrust in organizations: Dilemmas and approaches (pp. 239-272): Russell Sage Foundation.

Hülsheger, Ute R., Anderson, Neil, & Salgado, Jesus F. (2009). Team-level predictors of innovation at work: A comprehensive meta-analysis spanning three decades of research. Journal of Applied Psychology, 94(5), 1128-1145. doi: 10.1037/0003-066x.34.10.932

Kim, D. M. (2007). Predicting psychological safety and its outcome in the workplace. (Master), San Jose State University.

Zhang, L.I. (2011). The Effects of Transformational Leaership on Subordinate's psychologicla Safety and Innovation Behavior. (Master), Honam University, Gwangju.

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PLS-SEM Guideline and Compliance Summary

A study of Diffusion of Legal Software Use in a Global Campus: Action Research at a Global Campus in China. 

by Jeonghwan (Jerry) Choi, Aug. 2017

Partial Least Square Structural Equation Modeling (PLS-SEM)

PLS-SEM method gets a special attention from business and social science researchers for its robustness in psychometric model analysis. This critical advantage makes the PLS-SEM as an alternative technique for SEM and it has become a key research method in recent years (J.F. Hair & Hult, 2016). For example, PLS-SEM gives a more robust structural equation model convergence over CB-SEM in many situations especially when a research model has many indicators, paths, and relationships among key variables and constructs (Chin, 2010; J.F. Hair & Hult, 2016; Henseler et al., 2014). For this benefit of PLS-SEM, it gets popular in management disciplines such as marketing management, strategic management, human resources management, and particularly management information system field of studies (J.F. Hair & Hult, 2016; J.F. Hair, Ringle, & Sarstedt, 2011; Williams et al., 2015).

Considering the rules of thumb for selecting CB-SEM or PLS-SEM (J.F. Hair et al., 2011), we judged that our study was appropriate for applying the PLS-SEM technique because this study’s main goal was to explore new knowledge and to extend an existing structural theory of the complex UTAUT. Prior to conducting the PLS-SEM, we summarized prerequisite or necessary conditions and recommendations through synthesizing a few foundational literature  (Chin, 2010; J.F. Hair & Hult, 2016; Latan & Ramli, 2013; Richter, Sinkovics, Ringle, & Schlaegel, 2016). By using our data, we validated these seven categories of PLS-SEM guidelines:

·      Data and sampling characteristics

·      PLS-SEM algorithm

·      Outer model evaluation: reflective (mode A)

·      Out model evaluation: formative (mode B)

·      Inner model evaluation: recursive model

·      Model fit

·      Multi-group analysis

Results of validation of PLS-SEM guideline and compliance are described in the following Table.


Overall, our data and research model showed good compliances to the proposed PLS-SEM guidelines. It should be noted that we use the Smart PLS [ SmartPLS (v. 3.2.6).  Ringle, Wende, and Becker, 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH,] as the PLS-SEM analysis tool.


PLS-SEM guideline and compliance 





Data and sampling characteristics

Sample size

Ten times rule: the minimum sample size should be equal to the larger of 10x largest number of 1) formative indicator and structural paths directed at a particular latent construct (Wong, 2013)


Alternatively, Cohen’s sample size recommendation of statistical power and effect sizes takes into accounts. Cohen’s sample size recommendation (In this study, 5% significance level, 0.10 minimum R2, and maximum number of arrows pointing at a construct (5) = 205

·  10x formative indicator (N/A)

·  10x structural paths directed a a latent construct (10 x 5 = 50)


·  Cohen’s sample size recommendation = 205

Samples size 

n = 215 > 205


30% of original sample (Hair Jr & Hult, 2016)

> 30%

215/218 = 98.6%

Missing data

Less than 5% of missing or screening out data

< 5%

3 / 218 data were screened out = 1.38%


Robust when applied to highly skewed data, but skewness and − kurtosis should be reported (Richter, Sinkovics, Ringle, & Schlaegel, 2016)



PLS-SEM Algorithm

Weighting scheme

In general, the path weighting scheme is strongly recommended because it provides the highest R2 value for endogenous latent variable (Vinzi, Chin, Henseler, & Wang, 2010)

path weighting

path weighting

Data metric

The standardized value setting

Mean 0, Var 1

Mean 0, Var 1

Total maximum iteration

The standard maximum iteration is 300



Abort criterion

The recommended number is 1.0E-5



Starting value

Initial outer weight can be set as 1.0



Algorithm to handle missing data

Missing value treatment options are mean replacement, EM (expectation-maximization algorithm), and nearest neighbor (Hair, Ringle, & Sarstedt, 2013)

Mean replacement

Mean replacement

Bootstrap subsample size

The number of bootstrap samples should be high but must be at least equal to the number of valid observations. As a rule, 5,000 bootstrap samples is recommended (Hair Jr & Hult, 2016, p. 132)

2000 - 4000


Bootstrap sign change

No sigh change option is recommendable because it results in the most conservative outcome (Hair Jr & Hult, 2016, p. 135)

No sign change

No sign change

Significance level 

Generally, 5% significance level is widely used in social science field of study

5%, Two-tailed

5%, Two-tailed

Predictive relevance

In the process of blindfolding, omission distance (D) can be set between 5 and 10 (Hair Jr & Hult, 2016, p. 179)

£ D £ 10

D = 7

Software feature

SmartPLS (v. 3.2.6).  Ringle, C. M., Wende, S., and Becker, J.-M. 2015. "SmartPLS 3." Boenningstedt: SmartPLS GmbH,


Outer model evaluation: Reflective (mode A)

Indicator reliability

Recommended > 0.6 for exploratory research and > 0.7 for confirmatory research (Chin, 2010)

> 0.7

All indicators (factor loadings) are higher than 0.7 [0.737 ~ 0.939] 

Internal consistency reliability

The cut-off value for composite reliability is > 0.6 for exploratory research and > 0.7 for confirmatory research. The Cronbach’s alpha is not suggested for distinguishing 

> 0.7

All composite reliabilities are higher than 0.7 [0.904 ~ 0.952]

Convergent validity

The Average Variance Extracted (AVE) is > 0.5

AVE > 0.5

All AVE is higher than 0.5 [0.662 ~ 0.869]

Discriminant validity

Fornell and Larcker (1981) criterion: Each construct’s AVE should be higher than its squared correlation with any other construct (Fornell & Larcker, 1981)

Square root AVE > Correlation

All square root AVE is larger than any other correlations with other constructs


Cross-loading: Each indicator should load highest on the construct it is intended to measure (Chin, 2010)

Highest loading on the construct

Each indicator loaded highest on the intended construct


Heterotrait-Monotrait Ratio (HTMT) should under 0.85 for each outer model indicators: (Henseler, Hubona, & Ray, 2016)

HTMT ratio < 0.85

All HTMT ratio is under 0.85 [0.358 ~ 0.824]  except ‘Effort expectation and Facilitation’ [0.852] 

Item removal

If some item has been dropped to achieve a model fit, give additional information

No removed item

Outer model evaluation: Formative


According to Confirmatory Tetrad Analysis (CTA), all constructs in the model are not formative constructs in 1% level of significance (Hair Jr & Hult, 2016, pp. 46-47)




The cut-off value for VIF should be smaller than 0.5. A stabilized estimation is suggested as ranging 2.5 ~ 3.3 (Hair et al., 2013)

VIF < 0.5

Most of outer VIF values are under 0.5 [1.000 ~ 4.014]. However, the VIF values of UP1 (6.070) and UP2 (6.425) are higher than the criterion.    

Construct removal

If a construct has been dropped due to collinearity, the problem should be reported. 


No removed construct

Inner model evaluation: Recursive model

Path estimates


1) Path coefficient

2) significance and confidence interval from bootstrapping

Bootstrapping is applied for the significant of the path coefficient with two-tails of 5% = 1.96

Bootstrapping is applied for the significant of the path coefficient with two-tails of 5% = 1.96  


(Adjusted R2)

R2 acceptable level is context-dependent. (Hair Jr & Hult, 2016; Latan & Ramli, 2013)

0.25: Weak

0.50: Moderate

0.75: Strong

R(Adj- R2) of Behavioral intention: 0.584 (0.578)


Rof Use Behavior 0.180 (0.172)

Effect size f2

Cohen’s statistical power analysis of effect size (Cohen, 1992)

0.02: Weak

0.15: Moderate

0.35: Strong

PE: 0.169

EE: 0.053

SI: 0.044

BI: 0.040

FA: 0.016

BI: 0.040

Predictive relevance

The cross validated redundancy as a measure of Q2 is recommended because it includes the key element of the path model, the structural model, to predict eliminated data points (Chin, 2010; Hair Jr & Hult, 2016, pp. 183-184)

Q2 > 0

Behavioral intention Q2 = 0.471

Use behavior Q2 = 0.151

Model fit

Standardized Root Mean Square Residual


< .08


Squared Euclidean Distance


< .95


Geodesic Distance


< .95


Incremental fit measure


> 0.9


rms Theta








Multi-Group Analysis

Bootstrapping for MGA

The number of bootstrap samples for Multi Group Analysis should be high but must be at least equal to the number of valid observations. As a rule, 5,000 bootstrap samples is recommended (Hair Jr & Hult, 2016, p. 132)

2000 - 4000


Path coefficient difference between groups

PLS-MGA (Henseler’s MGA) with 5% of significance level. 

t-value > 1.96

p-value < .05 or > .95

The path between Facilitation -> Use behavior has a marginal difference (p = .055 





Note: It is important to note that these model fit assessment criteria often not be useful for PLS SEM and must be used with caution. These criteria are in their very early stage of research and not fully understood. However, these fit statistics give researchers to estimate the quality of the model when it is a reflective model (Hair Jr & Hult, 2016). In more detail, please see this Note of Caution (


Chin, W. W. (2010). How to write up and report PLS analyses. Handbook of partial least squares, 655-690.

Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 155.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39-50.

Hair, J. F., & Hult, G. T. M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46, 1-12. doi:

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., . . . Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182-209.

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.

Latan, H., & Ramli, N. A. (2013). The Results of Partial Least Squares-Structural Equation Modelling Analyses (PLS-SEM). doi:10.2139/ssrn.2364191

Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlaegel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376-404.

Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares: Concepts, methods and applications: Springer Science & Business Media.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The Unified Theory of Acceptance and Use of Technology (UTAUT): A Literature Review. Journal of Enterprise Information Management, 28(3), 443-488.

Wong, K. K.-K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.

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Five Levels of Questions Needed in Case Study and Program Evaluation

1. Topical Questions
- Upgrading the curriculum?
- The cost of accreditation
- Staff development
- Advocacy for the client
- The inadequacy of measurement

2. Basic Research Questions
- What is the nature of community support for child-oriented strategies?
- How can reading be taught more effectively?
- Are the concepts of "pluralism" and "mainstreaming" fundamentally opposed?
- How are authority and decision-making distributed in Athletics department?

3. Issue (Case Study) Questions
- Is the fact that teaching loads increased from 4 classes to 5 affecting the quality of teaching?
- Is the increased emphasis on student competence in this school an obstacle to the teachers fostering the students' own conceptualizations and tacit knowledge?
- Are staff members who reside outside the district taking less than their fair share of the work load?
- Do conditions facilitate or even allow the department head to be an instructional leader?

4. Information questions
- How effective is the Superintendent?
- Do the students understand "conservations of energy?"
- What portion of class time is primarily instruction time?
- Is there a correlation between teacher ratings and whether or not they live in the community?
- How have case loads changed in the last tow years?

5. Immediate problems
- What new reading series to buy?
- How will the business manager's work get done if that position is eliminated?
- Should intelligence testing in the fourth grade be ended?
- Should the issue of nepotism be raised regarding the appointment of the superintendent's cousin as director of counseling?
- Is is time to change team leaders?

Good Examples of Case (Issue) Questions

Foreshadowing Questions


Good Issue Questions for organizing evaluation studies. They are rhetorical questions, not expecting an answer


These issues have been gathered here to stimulate the thinking of evaluation specialists as they are getting newly acquainted with youth programs, to help them expand their scope and draw in their attentions, to help them give priority to questions and ways of spending their time. It is recognized that there are a great many additional observations they will have to make to get the picture of the programs, and to come to understand what are seen to be the more important questions at the sites.


1.        Is there good communication and working relationship between community and program, also among governmental, ethnic, industrial and school entities?

2.        Is there undesirable interference or redundancy of service created by new efforts to provide youth assistance?

3.        Are youth services conceptually in tune with services for the mid-age unemployed, the soon to retire, and the retired?

4.        Do youth services of this sort – in effect- relieve governments and industries of their proper responsibility to provide employment and training opportunities?

5.        Are the youth activities integrated into school offerings or considered adjunct and peripheral? What does the grand plan say?

6.        Are the youth services in fact as good as the community’s other social services?

7.        Do youth get better access to information about interests and abilities, about job requirements and opportunities?

8.        Do youngsters learn more about the difference between craft and opportunistic entrepreneurship?

9.        Are youth taught responsibilities and opportunities for job redesign? For collective (union) action? Are they taught the personal and societal consequences of work?

10.    Are separate needs of boys and girls adequately realized? How about handicapped youngsters? What about migrant youngsters from different cultural backgrounds?

11.    Are staff members responsible for youth services personally experienced with a diversity of living and working conditions? Is the experience sufficiently recent? Is there exchange of school and business personnel? What is done to increase such an experience base?

12.    Do staff and volunteers share in the responsibility for the services? What preparation have they for taking responsibility?

13.    Are youth workers teachers or civil servants or neither?

14.    Do the persons in charge exploit the variety of roles that parents and family play in helping the youngster toward social and economic maturity?

15.    Do these services emphasize the modem dependency of workers on job created by business and industry or is there an exploration of the possibilities of youngsters singly or collectively creating their won income opportunities? Do they encourage exploration of entrepreneurial lines? Do they encourage young ‘inventors’?

16.    Is there realization of the increasing period that youngsters in technical societies experience, now beyond age 25 in the United States, alternating among post-secondary schooling, working, and unemployment, without strong commitment to what will be a life-time work? Is this period treated as a period of irresponsibility or opportunity?

17.    Are pan-tiem cooperative work programs organized to benefit the youngster, the parents, the employer, the school? Are decisions on what knowledge the project will provide based on a proper compromise in these interests?

18.    Are cooperative work programs coordinated with other youth services?

19.    Are these programs having the effect of teaching most youngsters that they are unsuited for work in technological, professional or entrepreneurial occupations and thus unnecessarily perpetuating a socially immobile lower working class? Are only lower class students involved? Are only lower class occupations involved?

20.    Are credits toward graduation given for successful participation in youth programs? Do such credits violate the practice and the various expectations people have as to what should earn credit toward graduation?

21.    Is the emphasis in these youth services local, national, or international, such that the youngster entertains ideas of working both close to home and far from home? How is the idea of “worker mobility” treated?

22.    Are disproportionate resources spent for information services while present information is underused?

23.    Do these services “imply” that national manpower estimates (or state or local) are the proper indication of what the work force should be and that youngsters should submit their own aspirations to the “official” view?

24.    Some writers distinguish between “helping youth over common obstacles to work-entry” and “preparing youth for the lifelong eventualities of uncertainty, changing demands, having to start over, etc.” Does this distinction lie at the root of major disagreements (at the site) about youth services?

Source: Dr. Bob Stake (Class of Fall 2008 , Case Study) in University of Illinois at Urbana-Champaign

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Integral Innovation Leadership Research

Education Study

  • 氣 Cognitive (Knowledge, Comprehension, Application, Analysis, Synthesis, Evaluation)
    • 思 Knowledge
      • Multiple Intelligence (incl. Emotional Intelligence)
        • Bodily-Kinesthetic
        • Interpersonal
        • Verbal-Linguistic
        • Logical-Mathematical
        • Intrapersonal
        • Spatial
        • Musical
        • Naturalistic
        • Other intelligences
    • Learning style
      • Gregorc Style
        • CS
        • AS
        • AR
        • CR
  • 神 Affective (Receiving, Responding, Valuing, Organizing, Characterizing)
    • 心 Mind
      • Leading minds (Gardner's)
        • 1. Disciplinary
        • 2. Synthesizing
        • 3. Creating
        • 4. Respectful
        • 5. Ethical
      • Technology Acceptance (UTAUT)
    • Skills
  • 精 Psychomotor (Reflexes, Perception, Physical abilities, Skilled movement, Nondiscursive)
    • 言行 Body
    • Attitude
  • Organizational Learning
    • Teaching organization (Tichy's Leadership Engine)
      • Energy
      • Energize
      • Edge
      • Execution
    • Learning organization (Senge's Fifth discipines)
      • 1. Personal Mastery
      • 2. Mental Models
      • 3. Shared Vision
      • 4. Team Learning
      • 5. Systems Thinking


  • Ken Wilber: AQAL (All quadrant, All Level)
    • 4 Quadrant
      • Upper-Left "I" Interior-Individual Intentional e.g. Freud
      • Upper-Right "It" Exterior-Individual Behavioral e.g. Skinner
      • Lower-Left "We" Interior-Collective Cultural e.g. Gadamer
      • Lower-Right "Its" Exterior-Collective Social e.g. Marx
    • Level of Consciousness
      • Egocentric (similar to Carol Gilligan's 'Selfish' stage)
      • Ethnocentric or Sociocentric (Gilligan's 'Care' stage)
      • Worldcentric (Gilligan's 'Universal Care' stage)
      • Being-centric (Gilligan's 'Integrated' stage)
  • Don Beck: vMEME
    • Turquoise: Holistic
    • Yellow: Integrative
    • Green: Communitarian/Egalitarian
    • Organe: Achievist/Strategic
    • Blue: Purposeful/Authoritarian
    • Red: Impulisve/Egocentric
    • Purple: Magical/Animistic
    • Beige: Instinctive/Survivalistic
  • Bierly: Wisdom

Innovation Schools

  • Economics
  • People centric
  • Science & Technology
  • Knowledge
  • Social system (STS)
  • Strategy
  • Learning organization
  • Psychology
  • Integrative


  • Leadership study
    • 1. Trait Approach
    • 2. Skills Approach
    • 3. Style Approach
    • 4. Situational Approach
    • 5. Contingency Theory
    • 6. Path-Goal Theory
    • 7. Leader-Member Exchange Theory
    • 8, Transformational Leadership Theory
    • 9. Team Leadership Theory
    • 10. Psychodynamic Approach
    • 11. Women Leadership
    • 12. Culture and Leadership
    • 13. Leadership Ethics
    • 14. Integrative (Holistic) Leadership Theory
  • Entrepreneurship Education
    • Business
      • MBA
      • Non MBA
    • Non business
      • Scientists & Engineers
      • Education

Educational Technology

  • Conventional learning (Cognitivism)
  • Action learning (Constructivism)
  • Online learning
  • Blended learning


  • Target
    • International
      • University students
        • S&E students
        • Non S&E
      • Professionals
        • KSEA
        • Entrepreneurs
    • Domestic
  • Method
    • Quantitative
      • Scale Development
      • IRB
      • Survey
        • Online
        • Offline
      • Statistical Analysis
    • Qualitative
    • Mixed
  • Publication
    • AOM
    • AHRD
    • Educational Tech.
  • Time Line
  • Networks

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