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

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Presentation on theme: "Effects of Goal Orientations on Innovation Implementation Behaviors 계량심리학 세미나 경영학과 인사조직 전공 최세연."— Presentation transcript:

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 Passive ImplementationY(PASSV) ij Gender (1 = Male; 0 = Female)(GENDER) ij Age (years)(AGE) ij Tenure (years)(TENURE) ij Education (1 = Middle school; 2 = High school; 3 = College; 4 = University; 5 = Graduate school) (EDUCATIO) ij Learning Goal Orientation(LGO) ij Performance Approach Goal Orientation(PGO) ij Performance Avoidance Goal Orientation(AGO) ij Perceived Ease of Use(PEU) ij Implementation Autonomy(IMPATO) ij N = 134, * p <.05, ** p <.01.

8 Correlations among Variables Variables Gender 1 2. Age (years) Tenure (years).16.55**1 4. Education **-.30**1 5. Learning Goal Orientation Performance Approach Goal Orientation **.25**1 7. Performance Avoidance Goal Orientation **1 8. Perceived Ease of Use * *.18.19*1 9. Implementation Autonomy *.40**1 10. Active Implementation * * Passive Implementation -.26** **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 GENDER AGE TENURE EDUCATION LGO PGO AGO (b) Variance Components ParameterEstimate Team Level (Level-2) Variance Var ( β 0j ) = τ 00 Change in Variance Δ τ Proportion of Explained Variance(%) Employee Level (Level-1) Variance Var (r ij ) = σ² Change in Variance Δ σ² Proportion of Explained Variance(%) Model df Note: N=134 H1

15 Results (Active Implementation) Model 4Model 5 (a) Fixed EffectsModeration (PEU)Moderation (ImpAutonomy) PredictorCoeff.set RatioCoeff.set Ratio INTERCEPT GENDER AGE TENURE EDUCATIO LGO PGO AGO PEU LGO * PEU PGO * PEU AGO * PEU ImpAto LGO * ImpAto PGO * ImpAto AGO * ImpAto (b) Variance Components Parameter Estimate Team Level (Level-2) Variance Var ( β 0j ) = τ Change in Variance Δ τ Proportion of Explained Variance(%) Employee Level (Level-1) Variance Var (r ij ) = σ² Change in Variance Δ σ² Proportion of Explained Variance(%) 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 GENDER AGE TENURE EDUCATIO LGO PGO AGO PEU LGO * PEU PGO * PEU AGO * PEU (b) Variance Components ParameterEstimate Team Level (Level-2) Variance Var ( β 0j ) = τ Change in Variance Δ τ Proportion of Explained Variance(%) Employee Level (Level-1) Variance Var (r ij ) = σ² Change in Variance Δ σ² Proportion of Explained Variance(%) Model df 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!


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