Effects of Goal Orientations on Innovation Implementation Behaviors 계량심리학 세미나...

Effects of Goal Orientations on In-novation Implementation Behaviors

계량심리학 세미나경영학과 인사조직 전공

최세연

Introduction

Overview : 혁신 상황에서 각기 다른 목표지향성이 혁신 도입 행동에 미치는 영향과 , 이 관계를 조절하는 혁신의 특성 및 도입분위기 탐색 .

• Innovation implementation behavior ( 혁신 도입 행동 ; e.g., Klein et al., 2001)• Active implementation ( 적극적 도입 )• Passive implementation ( 수동적 도입 )

• Goal orientation( 목표지향성 ; e.g., VandeWalle et al., 2001)• Learning goal orientation (LGO; 학습목표지향성 )

- 도전적인 상황에 숙달함으로써 자신의 능력을 증진시키는 것을 통해 경쟁력을 발전시키려는 경향

• Performance approach goal orientation (PGO; 성과추구적 목표지향성 )

- 우호적인 평가를 좇음으로써 자신의 능력을 보여주고 증명하려는 경향• Performance avoidance goal orientation (AGO; 성과회피적

목표지향성 )- 부정적인 평가를 피함으로써 자신의 능력을 보여주고 증명하려는 성향

Conceptual Framework

LGO

PGO

AGO

Passive Imple-mentation

Active Implementation

PEU

Imp. Au-tonomy

H1(+)

H2(+)

H3(-)

H4a(+)

H4b(+)

H5a(+)

H5b(-)

Hypotheses

Main effect

• Goal orientation• Learning goal orientation (LGO)• Performance approach goal orientation (PGO)• Performance avoidance goal orientation (AGO)

• Innovation implementation• Active implementation• Passive implementation

H1 LGO (+) Active implementation H2 PGO (+) Passive implementation H3 AGO (-) Passive implementation

Hypotheses

Moderation effect• Implementation Climate

• Innovation Implementation Autonomy (Imp.Auton-omy; Spreitzer, 1995)

H4a Imp. Autonomy (+) LGO – Active implementa-tion

H4b Imp. Autonomy (+) PGO – Active implementa-tion

• Innovation characteristics• Perceived Ease of Use (PEU; Davis, 1989)

H5a PEU (+) PGO – Passive implementation H5b PEU (-) AGO – Passive implementation

The Design of the study

• Participants• 중국 제조업 기업의 26 개 팀에 속한 134 명의 직원들 대상

(nested data)• ERP(Enterprise resource planning) 혁신시행 중

• Variables• Outcome variables: Implementation behaviors (Active,

Passive)- 상사 평정 .

• Covariates (controls) : age, gender, education, tenure• Predictors: Goal orientations (learning, performance

approach, performance avoidance)• Moderators: Perceived ease of use, Implementation au-

tonomy Control variable 을 제외한 모든 변인은 5 점 리커트 척도로 측정 .

• Analysis: HLM 6.01, fixed effect

Descriptive Statistics

VariablesVariable Name

Mean sd

Active Implementation Y(ACTIV)ij 3.37 .66Passive Implementation Y(PASSV)ij 4.35 .63Gender (1 = Male; 0 = Female) (GENDER)ij .43 .50Age (years) (AGE)ij 31.82 8.75Tenure (years) (TENURE)ij 4.86 4.20Education (1 = Middle school; 2 = High school; 3 = College; 4 = University; 5 = Graduate school)

(EDUCATIO)ij 2.60 1.03

Learning Goal Orientation (LGO)ij 4.30 .62Performance Approach Goal Orienta-tion

(PGO)ij 3.21 .82

Performance Avoidance Goal Orien-tation

(AGO)ij 2.63 .91

Perceived Ease of Use (PEU)ij 3.29 .75Implementation Autonomy (IMPATO)ij 3.08 .88

N = 134, * p <.05, ** p <.01.

Correlations among Variables

Variables 1 2 3 4 5 6 7 8 9 10 111. Gender 1 　　　　　　　　　　2. Age (years) .11 13. Tenure (years) .16 .55** 14. Education .04 -.56*

*-.30** 1

5. Learning Goal Orienta-tion -.11 -.04 -.02 .02 1

6. Performance Approach Goal Orientation .07 -.09 .09 .30** .25** 1

7. Performance Avoidance Goal Orientation .14 .08 .05 -.02 -.08 .23** 1

8. Perceived Ease of Use .13 .00 .18* -.08 .20* .18 .19* 19. Implementation Auton-omy .11 .06 .11 -.07 .13 .20* .20* .40** 1

10. Active Implementation -.15 .19* -.05 -.18* .13 -.06 -.01 -.08 .06 111. Passive Implementa-tion

-.26** .07 .11 .09 .06 .12 -.05 -.01 .04 .33** 1

N = 134, * p <.05, ** p <.01.

Unconditional Model (Model 1)

• Level-1 model

Yij = β0j + rij , rij ~ N(0, σ2)

• Yij : j 팀에 속한 i 직원의 implementation behavior

• β0j : j 팀 직원들의 평균 혁신 도입 정도 .

• σ2 : within-employee variance

• Level-2 modelβ0j = γ00 + u0j , u0j ~ N(0, τ00)

Conditional Model (Control; Model 2)

• Level-1 model

Yij = β0j + β1j(Gender)ij + β2j (AGE)ij + β3j(TENURE)ij

+ β4j (EDUCATION)ij + rij , rij ~ N(0, σ2)

• All level-1 predictors are grand-mean centered• Var(rij) = σ2

• Level-2 modelβ0j = γ00 + u0j

β1j = γ10

β2j = γ20

β3j = γ30

β4j = γ40

Conditional Model (Main Effects; Model 3)

• Level-1 modelYij = β0j + β1j(Gender)ij + β2j(AGE)ij + β3j(TENURE)ij

+ β4j (EDUCATION)ij + β5j(LGO)ij + β6j(PGO)ij +

β7j(AGO)ij + rij


• Level-2 modelβ0j = γ00 + u0j β4j = γ50

β1j = γ10 β5j = γ60

β2j = γ20 β6j = γ70

β3j = γ30 β7j = γ40

Conditional Model (Moderation Effects; Model 4)

• Level-1 modelYij = β0j + β1j(Gender)ij + β2j(AGE)ij + β3j(TENURE)ij

+ β4j (EDUCATION)ij + β5j(LGO)ij + β6j(PGO)ij + β7j(AGO)ij

+ β8j(PEU)ij + β9j(L*PEU)ij + β10j(P*PEU)ij + β11j(A*PEU)ij

+ rij



β1j = γ10 β7j = γ70

β2j = γ20 β8j = γ80

β3j = γ30 β9j = γ90

β4j = γ40 β10j = γ100

β5j = γ50 β11j = γ110

Conditional Model (Moderation Effects; Model 5)

• Level-1 modelYij = β0j + β1j(Gender)ij + β2j(AGE)ij + β3j(TENURE)ij + β4j

(EDUCATION)ij + β5j(LGO)ij + β6j(PGO)ij + β7j(AGO)ij +

β8j(IMPATO)ij + β9j(L*IMPATO)ij + β10j(P*IMPATO)ij +

β11j(A*IMPATO)ij + rij

• rij ~ N(0, σ2) • All level-1 predictors are grand-mean centered• Var(rij) = σ2


β1j = γ10 β7j = γ70

β2j = γ20 β8j = γ80

β3j = γ30 β9j = γ90

β4j = γ40 β10j = γ100

β5j = γ50 β11j = γ110

Results (Active Implementation)　 Model 1 Model 2 Model 3(a) Fixed Effects Unconditional Model Controls Main EffectsPredictor Coeff. se t Ratio Coeff. se t Ratio Coeff. se t RatioINTERCEPT 3.377 0.103 32.84 3.371 0.1 33.65 3.368 0.103 32.75GENDER -0.058 0.103 -0.56 -0.037 0.103 -0.36

AGE 0.005 0.008 0.67 0.005 0.007 0.67

TENURE -0.018 0.013 -1.35 -0.019 0.013 -1.4

EDUCATION -0.016 0.063 -0.26 -0.027 0.064 -0.42

LGO 0.179 0.076 2.34

PGO 0.029 0.06 0.48

AGO 　　　　　　 0.008 0.05 0.16

(b) Variance Components Pa-rameter

Estimate Estimate Estimate

Team Level (Level-2) Variance0.223 0.207 0.224 Var (β0j) = τ00

Change in Variance Δτ 0.016 0.017Proportion of Explained Vari-ance(%) 7.01 8.21Employee Level (Level-1) Vari-ance

0.22 0.226 0.215 Var (rij) = σ²Change in Variance Δσ² 0.006 0.011Proportion of Explained Vari-ance(%) 2.63 4.99Model df 25 128 125Note: N=134

H1

Results (Active Implementation)　 Model 4 Model 5 　(a) Fixed Effects Moderation (PEU) Moderation (ImpAutonomy)Predictor Coeff. se t Ratio Coeff. se t Ratio 　INTERCEPT 3.401 0.106 31.97 3.388 0.104 32.47GENDER 0.004 0.102 0.04 -0.022 0.104 -0.22AGE -0.001 0.008 -0.11 0.005 0.007 0.62TENURE -0.011 0.013 -0.85 -0.021 0.014 -1.56EDUCATIO -0.050 0.064 -0.79 0.002 0.067 0.03LGO 0.201 0.077 2.61 0.160 0.077 2.07　PGO 0.058 0.060 0.97 0.015 0.061 0.24AGO 0.041 0.053 0.78 0.029 0.052 0.56PEU -0.071 0.065 -1.08LGO * PEU -0.104 0.070 -1.49PGO * PEU 0.081 0.071 1.15AGO * PEU -0.160 0.074 -2.18

ImpAto 0.035 0.056 0.62LGO * ImpAto -0.062 0.058 -1.08PGO * ImpAto 0.131 0.064 2.05　AGO * ImpAto 　　　 -0.172 0.068 -2.51　

(b) Variance Components Parame-ter

Estimate Estimate 　

Team Level (Level-2) Variance Var (β0j) = τ00 0.239 0.228Change in Variance Δτ 0.015 0.004Proportion of Explained Variance(%) 6.83 1.84Employee Level (Level-1) Variance Var (rij) = σ² 0.205 0.206Change in Variance Δσ² 0.021 0.009Proportion of Explained Variance(%) 9.33 3.99Model df 121 121 　Note: N=134.

H4b

Results (Passive Implementation)

　 Model 1 Model 2 Model 3 Model 4(a) Fixed Effects Unconditional

ModelControls Main Effects Moderation(PEU)

Predictor Co-eff.

se t Ra-tio

Coeff. se t Ra-tio

Coeff. se t Ra-tio

Coeff. se t Ra-tio

INTERCEPT 4.359 0.100 43.70 4.354 0.097 44.94 4.353 0.097 44.99 4.386 0.094 46.77GENDER -

0.2150.088 -2.45

-0.210

0.090 -2.34-

0.2040.088 -2.33

AGE -0.001

0.007 -0.18-

0.0010.007 -0.15

-0.005

0.007 -0.81

TENURE 0.019 0.011 1.65 0.017 0.012 1.42 0.018 0.012 1.59EDUCATIO 0.102 0.054 1.87 0.088 0.056 1.58 0.068 0.055 1.25LGO 0.015 0.067 0.22 0.019 0.066 0.28PGO 0.054 0.053 1.03 0.084 0.052 1.62AGO -

0.0170.044 -0.38 0.014 0.045 0.30

PEU 0.070 0.056 1.24LGO * PEU -

0.0930.060 -1.55

PGO * PEU 0.132

0.060

2.18

AGO * PEU -0.23

4

0.063

-3.72

(b) Variance Components Pa-rameter

Estimate Estimate Estimate Estimate

Team Level (Level-2) Variance Var (β0j) = τ00 0.219 0.205 0.204 0.188Change in Variance Δτ 0.013 0.001 0.016Proportion of Explained Variance(%) 6.17 0.61 7.63Employee Level (Level-1) Vari-ance Var (rij) = σ² 0.166 0.160 0.163 0.150Change in Variance Δσ² 0.006 0.003 0.013Proportion of Explained Variance(%) 3.82 1.61 8.02Model df 25 128 125 121Note: N=134.

H5a

Results (Simple slope)

Interaction between PGO and Imp Autonomy in predicting Active imple-mentation

Low PGO High PGO3.0

3.1

3.2

3.3

3.4

3.5

3.6

Low ImpAto

High ImpAto

Act

ive I

mp

lem

en

tati

on

(β = .13, p < .10)

(β = -.10, ns)

Results (Simple slope)

Low PGO High PGO3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

Low PEU

High PEU

Pass

ive I

mp

lem

en

tati

on

Interaction between PGO and PEU in predicting Passive imple-mentation

(β = -.02, ns)

(β = .13, p < .05)

Results

LGO

PGO

AGO



PEU

Imp. Au-tonomy

H1(+)

H2(+)

H3(-)

H4a(+)

H4b(+)

H5a(+)

H5b(-)

LGO

PGO

AGO



PEU

Imp. Au-tonomy

H1(+)

H2(+)

H3(-)

H4a(+)

H4b(+)

H5a(+)

H5b(-)

Discussion

• Results ( 종합 ) • LGO: 조절변인의 영향과 무관하게 Active implementation 에

정적으로 관련• PGO: 그 자체로는 implementation 에 유의미한 영향이 없으나 ,

조절변인과 상호작용하여 각각 Active, passive implementation과 정적으로 관련 .

• AGO: 그 자체로는 implementation 에 유의미한 영향이 없으나 , 조절변인과 상호작용하면 implementation 에 부적으로 관련

• Limitation• Team 수가 적어서 (26 팀 ) level-2 분석은 어려움

• 향후 계획• 각각의 implementation behavior 가 궁극적으로 innovation ef-

fectiveness 에 미치는 영향을 탐색 .• Multilevel moderated mediation

Thank you!

Effects of Goal Orientations on Innovation Implementation Behaviors 계량심리학 세미나...

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