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Figure 4
Variable Description_
| Dimension | Variable | Definition |
|---|---|---|
| Consequent variable | Pay_Numi | The increase in the number of paid questions knowledge contributor i has answered within one month |
| Antecedent variables in the systematic processing route | Effective_RatingScorei | The effective average rating score that knowledge contributor i got during one month |
| AvgLikes_Numi | The average number of likes for each public answer knowledge contributor i shared for free during one month | |
| Antecedent variables in the heuristic processing route | Consulting_Numi | The number of consultations that knowledge contributor i has answered at the start of observation period |
| Network_Centralityi | The network centrality (sum up out-degree and in-degree) of knowledge contributor i at the start of observation period | |
| Info_Integrityi | The personal information integrity of knowledge contributor i | |
| Honor_Labeli | The number of honor labels that knowledge contributor i owns | |
| Public antecedent variable | Pricei | The consulting fee that knowledge contributor i asks for |
Analysis of necessary conditions for the presence of payment decision_
| Conditions | Consistency | Coverage |
|---|---|---|
| Effective_RatingScore+AvgLikes_Num | 0.868 | 0.452 |
| Consulting_Num+Network_Centrality+Info_Integrity+Honor_Labels | 0.994 | 0.455 |
| Effective_RatingScore | 0.752 | 0.474 |
| AvgLikes_Num | 0.643 | 0.538 |
| Consulting_Num | 0.823 | 0.768 |
| Network_Centrality | 0.664 | 0.575 |
| Info_Integrity | 0.742 | 0.464 |
| Honor_Labels | 0.706 | 0.487 |
| Price | 0.633 | 0.575 |
| Outcome variable: Pay_Num |
Calibration of variables_
| Variable | full membership (fuzzy score=0.95) | cross-over point (fuzzy score=0.5) | Full non-membership (fuzzy score=0.05) |
|---|---|---|---|
| Pay_Num | 101.000 | 3.000 | 1.000 |
| Effective_RatingScore | 5.000 | 4.875 | 3.857 |
| AvgLikes_Num | 1652.340 | 186.378 | 15.725 |
| Consulting_Num | 1446.000 | 94.000 | 13.000 |
| Network_Centrality | 475946.000 | 81021.000 | 7288.000 |
| Info_Integrity | 7.000 | 6.000 | 4.000 |
| Honor_Labels | 5.000 | 1.000 | 0.000 |
| Price | 199.000 | 48.000 | 5.000 |
Configurations for achieving low/medium intention in payment decision_
| Condition | Configuration | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Perceived Usefulness | Effective_RatingScore | • | ⊗ | ⊗ | ||
| AvgLikes_Num | • | • | ⊗ | |||
| Perceived Crebitility | Consulting_Num | ⊗ | ||||
| Network_Centrality | ||||||
| Info_Integrity | • | ⊗ | • | ⊗ | • | |
| Honor_Labels | • | • | ⊗ | |||
| Knowledge Information | Price | ⊗ | ⊗ | • | • | • |
| Raw Coverage | 0.284 | 0.138 | 0.153 | 0.166 | 0.213 | |
| Unique Coverage | 0.090 | 0.021 | 0.047 | 0.024 | 0.045 | |
| Consistency | 0.993 | 0.999 | 0.994 | 0.999 | 0.996 | |
| Solution Coverage | 0.444 | |||||
| Solution Consistency | 0.993 | |||||
Selected studies on payment decision in paid Q&A_
| Scholar | Method | Conclusion |
|---|---|---|
| 1. Perspective: knowledge contributors’ ability and credibility | ||
| Zhao, Zhao, Yuan, & Zhou (2018) | Negative binomial panel regression | Knowledge contributors’ reputation, ability and personal information integrity play a positive role on askers’ willingness to pay while price plays a positive regulatory role. |
| Yan, Leidner, Benbya, & Zou (2019) | Granger causality test | Knowledge contributors’ structural capital and relational capital, such as personal information integrity and followers, have a positive influence on askers’ payment decision. |
| 2. Perspective: askers’ perception about answers | ||
| Morris (2010) | Survey study | Answering speed and quality of answers can be valued as influencing factors when making payment decision. |
| Zhang, Hu, & Fang (2019) | Semi-structured interviews | Askers participate in paid Q&A for answerers’ heterogeneous resources, credible answers and cognition of questions. |
| 3. Perspective: price | ||
| Harper et al. (2008) | Field study | Higher price will lead to askers’ trust in answer quality, which will encourage their payment intention. |
| Zhang, Zhang, & Zhang (2019) | Text mining; Hierarchical OLS regression | The influence of price on askers’ motivation in making payment decision might differ according to their knowledge levels. Expert askers are less sensitive to price. |
Correlations of variables_
| Pay_Num | Effective_RatingScore | AvgLikes_Num | Consulting_Num | Network_Centrality | Info_Integrity | Honor_Labels | Price | |
|---|---|---|---|---|---|---|---|---|
| Pay_Num | 1.000 | |||||||
| Effective_RatingScore | −0.014 | 1.000 | ||||||
| AvgLikes_Num | 0.060 | 0.034 | 1.000 | |||||
| Consulting_Num | 0.542 | −0.024 | 0.097 | 1.000 | ||||
| Network_Centrality | −0.027 | 0.032 | 0.477 | 0.022 | 1.000 | |||
| Info_Integrity | −0.089 | 0.131 | −0.044 | −0.014 | 0.054 | 1.000 | ||
| Honor_Labels | −0.158 | −0.003 | −0.024 | −0.148 | 0.238 | 0.073 | 1.000 | |
| Price | −0.115 | 0.098 | 0.132 | −0.025 | 0.396 | 0.151 | −0.140 | .000 |
Complex configurations indicating high intention in payment decision for the subsample_
| Models from Subsample for High Intention in Payment Decision | Raw Coverage | Unique Coverage | Consistency |
|---|---|---|---|
| 1. ~Effective_RatingScore*~AvgLikes_Num*Consulting_Num*~Network_Centrality*~Info_Integrity*~Honor_Lables | 0.230 | 0.058 | 0.800 |
| 2. ~Effective_RatingScore*Consulting_Num*~AvgLikes_Num*~Network_Centrality*~Honor_Lables*Price | 0.218 | 0.034 | 0.825 |
| 3. ~Effective_RatingScore*AvgLikes_Num*Consulting_Num*~Network_Centrality*Info_Integrity*~Honor_Lables*Price | 0.175 | 0.046 | 0.904 |
| 4. Effective_RatingScore*~AvgLikes_Num*Consulting_Num*~Network_Centrality*Info_Integrity*Honor_Lables*~Price | 0.265 | 0.130 | 0.852 |
| solution coverage | 0.468 | ||
| solution consistency | 0.837 |
Summary statistics of variables_
| Variable | Count | Mean | Std. | Min | Max |
|---|---|---|---|---|---|
| Pay_Num | 95 | 22.074 | 77.262 | 1.000 | 696.000 |
| Effective_RatingScore | 95 | 4.745 | 0.427 | 2.000 | 5.000 |
| AvgLikes_Num | 95 | 486.745 | 689.375 | 3.950 | 3899.620 |
| Consulting_Num | 95 | 379.358 | 1171.699 | 3.000 | 10673.000 |
| Network_Centrality | 95 | 134364.421 | 163643.105 | 387.000 | 805492.000 |
| Info_Integrity | 95 | 6.032 | 1.165 | 2.000 | 7.000 |
| Honor_Labels | 95 | 1.726 | 1.469 | 0.000 | 6.000 |
| Price | 95 | 57.105 | 53.393 | 1.000 | 268.000 |
Configurations for achieving high intention in payment decision_
| Condition | Configuration | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Perceived Usefulness | Effective_RatingScore | ⊗ | • | ⊗ | ⊗ | ⊗ |
| AvgLikes_Num | ⊗ | • | • | |||
| Perceived Crebitility | Consulting_Num | |||||
| Network_Centrality | ⊗ | ⊗ | • | |||
| Info_Integrity | ⊗ | ⊗ | ||||
| Honor_Labels | • | • | ||||
| Knowledge Information | Price | • | ⊗ | • | ||
| Raw Coverage | 0.250 | 0.301 | 0.293 | 0.220 | 0.212 | |
| Unique Coverage | 0.061 | 0.064 | 0.031 | 0.026 | 0.019 | |
| Consistency | 0.829 | 0.855 | 0.861 | 0.903 | 0.901 | |
| Solution Coverage | 0.515 | |||||
| Solution Consistency | 0.823 | |||||