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A Systems View Across Time and Space

Table 8 PLS path model’s predictive accuracy

From: Assessing the impact of corporate entrepreneurship in the financial performance of subsidiaries of Colombian business groups: under environmental dynamism moderation

Exogenous construct

SSOb

SSEc

Q2 (=1-SSE/SSO)d

AU a

3,480,000

2,226,190

0.3603

AG

1,740,000

1,265,015

0.2730

CV

1,305,000

1,305,000

 

BC

4,350,000

2,610,852

0.3998

EO

1,479,000

1,479,000

 

ED

2,610,000

2,610,000

 

EG

3,480,000

2,083,007

0.4014

IN

3,480,000

2,064,864

0.4066

PR

3,480,000

1,598,949

0.5405

PI

3,480,000

1,628,330

0.5321

RT

2,610,000

1,207,631

0.5373

TE

1,740,000

1,217,996

0.3000

  1. Source: computed by the author using Smart PLS 3
  2. aThe first column includes principal latent variables that are used in this study: autonomy (AU), aggressiveness (AG), corporate venturing (CV), business creation (BC), entrepreneurial orientation (EO), environmental dynamism (ED), expansion and growth (EG), innovativeness (IN), proactiveness (PR), product innovation (PI), risk-taking (RT), and technological entrepreneurship (TE)
  3. bSSO sum of the squared observations
  4. cSSE sum of the squared prediction errors
  5. dQ2>0 exogenous constructs have predictive relevance for endogenous ones