1. Effects of Goal Orientations on Innovation Implementation Behaviors 계량심리학 세미나 경영학과 인사조직 전공 최세연

2. 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

3. Conceptual Framework LGO PGO AGO Passive Implementation Active Implementation PEU Imp. Autonomy H1(+) H2(+) H3(-) H4a(+) H4b(+) H5a(+) H5b(-)

4. 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

5. Hypotheses Moderation effect Implementation Climate Innovation Implementation Autonomy (Imp.Autonomy; Spreitzer, 1995)  H4a Imp. Autonomy  (+) LGO – Active implementation  H4b Imp. Autonomy  (+) PGO – Active implementation Innovation characteristics Perceived Ease of Use (PEU; Davis, 1989)  H5a PEU  (+) PGO – Passive implementation  H5b PEU  (-) AGO – Passive implementation

6. 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 autonomy  Control variable 을 제외한 모든 변인은 5 점 리커트 척도로 측정. Analysis : HLM 6.01, fixed effect

7. Descriptive Statistics VariablesVariable NameMeansd Active ImplementationY(ACTIV) ij 3.37.66 Passive ImplementationY(PASSV) ij 4.35.63 Gender (1 = Male; 0 = Female)(GENDER) ij.43.50 Age (years)(AGE) ij 31.828.75 Tenure (years)(TENURE) ij 4.864.20 Education (1 = Middle school; 2 = High school; 3 = College; 4 = University; 5 = Graduate school) (EDUCATIO) ij 2.601.03 Learning Goal Orientation(LGO) ij 4.30.62 Performance Approach Goal Orientation(PGO) ij 3.21.82 Performance Avoidance Goal Orientation(AGO) ij 2.63.91 Perceived Ease of Use(PEU) ij 3.29.75 Implementation Autonomy(IMPATO) ij 3.08.88 N = 134, * p <.05, ** p <.01.

8. Correlations among Variables Variables 1234567891011 1. Gender 1 2. Age (years).111 3. Tenure (years).16.55**1 4. Education.04-.56**-.30**1 5. Learning Goal Orientation -.11-.04-.02.021 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*1 9. Implementation Autonomy.11.06.11-.07.13.20*.40**1 10. Active Implementation -.15.19*-.05-.18*.13-.06-.01-.08.061 11. Passive Implementation -.26**.07.11.09.06.12-.05-.01.04.33**1 N = 134, * p <.05, ** p <.01.

9. Unconditional Model (Model 1) Level-1 model Y ij = β 0j + r ij, r ij ~ N(0, σ 2 ) Y ij : j팀에 속한 i직원의 implementation behavior β 0j : j팀 직원들의 평균 혁신 도입 정도. σ 2 : within-employee variance Level-2 model β 0j = γ 00 + u 0j, u 0j ~ N(0, τ 00 )

10. Conditional Model (Control; Model 2) Level-1 model Y ij = β 0j + β 1j (Gender) ij + β 2j (AGE) ij + β 3j (TENURE) ij + β 4j (EDUCATION) ij + r ij, r ij ~ N(0, σ 2 ) All level-1 predictors are grand-mean centered Var( r ij ) = σ 2 Level-2 model β 0j = γ 00 + u 0j β 1j = γ 10 β 2j = γ 20 β 3j = γ 30 β 4j = γ 40

11. Conditional Model (Main Effects; Model 3) Level-1 model Y ij = β 0j + β 1j (Gender) ij + β 2j (AGE) ij + β 3j (TENURE) ij + β 4j (EDUCATION) ij + β 5j (LGO) ij + β 6j (PGO) ij + β 7j (AGO) ij + r ij All level-1 predictors are grand-mean centered Var( r ij ) = σ 2 Level-2 model β 0j = γ 00 + u 0j β 4j = γ 50 β 1j = γ 10 β 5j = γ 60 β 2j = γ 20 β 6j = γ 70 β 3j = γ 30 β 7j = γ 40

12. Conditional Model (Moderation Effects; Model 4) Level-1 model Y ij = β 0j + β 1j (Gender) ij + β 2j (AGE) ij + β 3j (TENURE) ij + β 4j (EDUCATION) ij + β 5j (LGO) ij + β 6j (PGO) ij + β 7j (AGO) ij + β 8j (PEU) ij + β 9j (L*PEU) ij + β 10j (P*PEU) ij + β 11j (A*PEU) ij + r ij All level-1 predictors are grand-mean centered Var( r ij ) = σ 2 Level-2 model β 0j = γ 00 + u 0j β 6j = γ 60 β 1j = γ 10 β 7j = γ 70 β 2j = γ 20 β 8j = γ 80 β 3j = γ 30 β 9j = γ 90 β 4j = γ 40 β 10j = γ 100 β 5j = γ 50 β 11j = γ 110

13. Conditional Model (Moderation Effects; Model 5) Level-1 model Y ij = β 0j + β 1j (Gender) ij + β 2j (AGE) ij + β 3j (TENURE) ij + β 4j (EDUCATION) ij + β 5j (LGO) ij + β 6j (PGO) ij + β 7j (AGO) ij + β 8j (IMPATO) ij + β 9j (L*IMPATO) ij + β 10j (P*IMPATO) ij + β 11j (A*IMPATO) ij + r ij r ij ~ N(0, σ 2 ) All level-1 predictors are grand-mean centered Var( r ij ) = σ 2 Level-2 model β 0j = γ 00 + u 0j β 6j = γ 60 β 1j = γ 10 β 7j = γ 70 β 2j = γ 20 β 8j = γ 80 β 3j = γ 30 β 9j = γ 90 β 4j = γ 40 β 10j = γ 100 β 5j = γ 50 β 11j = γ 110

14. Results (Active Implementation) Model 1Model 2Model 3 (a) Fixed EffectsUnconditional ModelControlsMain Effects PredictorCoeff.set RatioCoeff.set RatioCoeff.set Ratio INTERCEPT 3.3770.10332.843.3710.133.653.3680.10332.75 GENDER -0.0580.103-0.56-0.0370.103-0.36 AGE 0.0050.0080.670.0050.0070.67 TENURE -0.0180.013-1.35-0.0190.013-1.4 EDUCATION -0.0160.063-0.26-0.0270.064-0.42 LGO 0.1790.0762.34 PGO 0.0290.060.48 AGO 0.0080.050.16 (b) Variance Components ParameterEstimate Team Level (Level-2) Variance 0.2230.2070.224 Var ( β 0j ) = τ 00 Change in Variance Δ τ 0.0160.017 Proportion of Explained Variance(%) 7.018.21 Employee Level (Level-1) Variance 0.220.2260.215 Var (r ij ) = σ² Change in Variance Δ σ² 0.0060.011 Proportion of Explained Variance(%) 2.634.99 Model df25128125 Note: N=134 H1

15. Results (Active Implementation) Model 4Model 5 (a) Fixed EffectsModeration (PEU)Moderation (ImpAutonomy) PredictorCoeff.set RatioCoeff.set Ratio INTERCEPT 3.4010.10631.973.3880.10432.47 GENDER 0.0040.1020.04-0.0220.104-0.22 AGE -0.0010.008-0.110.0050.0070.62 TENURE -0.0110.013-0.85-0.0210.014-1.56 EDUCATIO -0.0500.064-0.790.0020.0670.03 LGO 0.2010.0772.610.1600.0772.07 PGO 0.0580.0600.970.0150.0610.24 AGO 0.0410.0530.780.0290.0520.56 PEU -0.0710.065-1.08 LGO * PEU -0.1040.070-1.49 PGO * PEU 0.0810.0711.15 AGO * PEU -0.1600.074-2.18 ImpAto 0.0350.0560.62 LGO * ImpAto -0.0620.058-1.08 PGO * ImpAto 0.1310.0642.05 AGO * ImpAto -0.1720.068-2.51 (b) Variance Components Parameter Estimate Team Level (Level-2) Variance Var ( β 0j ) = τ 00 0.2390.228 Change in Variance Δ τ 0.0150.004 Proportion of Explained Variance(%) 6.831.84 Employee Level (Level-1) Variance Var (r ij ) = σ² 0.2050.206 Change in Variance Δ σ² 0.0210.009 Proportion of Explained Variance(%) 9.333.99 Model df 121 Note: N=134. H4b

16. Results (Passive Implementation) Model 1Model 2Model 3Model 4 (a) Fixed EffectsUnconditional ModelControlsMain EffectsModeration(PEU) PredictorCoeff.set RatioCoeff.set RatioCoeff.set RatioCoeff.set Ratio INTERCEPT 4.3590.10043.704.3540.09744.944.3530.09744.994.3860.09446.77 GENDER -0.2150.088-2.45-0.2100.090-2.34-0.2040.088-2.33 AGE -0.0010.007-0.18-0.0010.007-0.15-0.0050.007-0.81 TENURE 0.0190.0111.650.0170.0121.420.0180.0121.59 EDUCATIO 0.1020.0541.870.0880.0561.580.0680.0551.25 LGO 0.0150.0670.220.0190.0660.28 PGO 0.0540.0531.030.0840.0521.62 AGO -0.0170.044-0.380.0140.0450.30 PEU 0.0700.0561.24 LGO * PEU -0.0930.060-1.55 PGO * PEU 0.1320.0602.18 AGO * PEU -0.2340.063-3.72 (b) Variance Components ParameterEstimate Team Level (Level-2) Variance Var ( β 0j ) = τ 00 0.2190.2050.2040.188 Change in Variance Δ τ 0.0130.0010.016 Proportion of Explained Variance(%) 6.170.617.63 Employee Level (Level-1) Variance Var (r ij ) = σ² 0.1660.1600.1630.150 Change in Variance Δ σ² 0.0060.0030.013 Proportion of Explained Variance(%) 3.821.618.02 Model df 25128125121 Note: N=134. H5a

17. Results (Simple slope) Interaction between PGO and Imp Autonomy in predicting Active implementation ( β =.13, p <.10) ( β = -.10, ns)

18. Results (Simple slope) Interaction between PGO and PEU in predicting Passive implementation ( β = -.02, ns) ( β =.13, p <.05)

19. Results LGO PGO AGO Passive Implementation Active Implementation PEU Imp. Autonomy H1(+) H2(+) H3(-) H4a(+) H4b(+) H5a(+) H5b(-) LGO PGO AGO Passive Implementation Active Implementation PEU Imp. Autonomy H1(+) H2(+) H3(-) H4a(+) H4b(+) H5a(+) H5b(-)

20. Discussion Results ( 종합 ) LGO: 조절변인의 영향과 무관하게 Active implementation 에 정적으로 관련 PGO: 그 자체로는 implementation 에 유의미한 영향이 없으나, 조절변 인과 상호작용하여 각각 Active, passive implementation 과 정적으로 관 련. AGO: 그 자체로는 implementation 에 유의미한 영향이 없으나, 조절변 인과 상호작용하면 implementation 에 부적으로 관련 Limitation Team 수가 적어서 (26 팀 ) level-2 분석은 어려움 향후 계획 각각의 implementation behavior 가 궁극적으로 innovation effectiveness 에 미치는 영향을 탐색. Multilevel moderated mediation

21. Thank you! Thank you!