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Table 3 Research model multiple regression analysis results

From: Promote citizen engagement with warnings ― an empirical examination of government social media accounts during public health crises

Variables

Model

M1

M2

M3

Time of post

-0.132**(-2.414)

-0.174***(-4.017)

-0.183**(-6.028)

Time interval

0.251***(6.425)

0.282***(4.916)

0.304*** (7.903)

Sentiment tendency (ST)

 

0.138***(4.832)

0.201***(7.143)

Warning elements (WE)

 

0.264***(5.034)

0.319***(8.125)

Message length (ML)

 

0.254**(2.671)

0.267**(2.865)

Message length2(ML2)

 

-0.199**(-2.177)

-0.203**(-2.261)

Dialogic loop (DL)

 

-0.074(-0.907)

-0.082(-1.362)

Media richness (MR)

 

0.296***(9.733)

0.319***(9.697)

Information style variety (SV)

 

0.376***(11.247)

0.410***(12.101)

Source influence (SI)

 

0.347***(9.024)

0.365***(10.223)

Source activeness (SA)

 

0.249***(4.812)

0.234***(4.946)

Disease type * ST

  

0.093(1.143)

Disease type * WE

  

0.087(1.121)

Disease type * ML

  

0.058(0.892)

Disease type * ML2

  

-0.042(-0.757)

Disease type * DL

  

-0.076(-1.147)

Disease type * MR

  

0.283***(5.522)

Disease type * SV

  

0.381***(10.436)

Disease type * SI

  

0.175**(3.572)

Disease type * SA

  

0.072(0.906)

VIF

2.045

3.088

2.761

F

25.147

30.324

34.921

R2

0.104

0.261

0.326

Adj R2

0.098

0.274

0.313

  1. t statistics in parentheses. *P < 0.10, **P < 0.05, ***P < 0.01