After Online Innovators Receive Performance-Contingent Material Rewards: A Study Based on an Open Innovation Platform
Abstract
:1. Introduction
2. Theoretical Framework
2.1. Related Literature
2.2. Cognitive Evaluation Theory
3. Hypothesis Development
3.1. Impact on Subsequent Idea Generation Quantity
3.2. Impact on Subsequent Idea Quality
3.3. Impact on Subsequent Idea Novelty
3.4. Differential Impact on Innovators with Varying Tenures
4. Research Design
4.1. Research Context
4.2. Variable Measurement
- Calculate Term Frequency (TF): Determine the frequency of each word within an idea, normalized by the idea’s length to mitigate biases stemming from varying idea lengths.
- Calculate Inverse Document Frequency (IDF): Compute the logarithm of the inverse frequency of each word across all ideas, thereby assigning lower weights to common words and higher weights to rare words.
- Compute TF-IDF Values: Multiply the TF of a word in a specific idea by its IDF, yielding the word’s TF-IDF value for that idea. Higher TF-IDF values indicate greater uniqueness and rarity within the entire corpus.
- Novelty Score: Sum the TF-IDF values of all words in an idea to derive its novelty score. A higher total score signifies greater uniqueness and the use of less common terms, which is indicative of higher novelty.
4.3. Matching
5. Empirical Analysis
5.1. Model-Free Evidence
5.2. DID Estimation
5.2.1. Econometric Framework
5.2.2. Hypothesis Testing
5.2.3. Relative Time Model
5.3. Robustness Checks
6. Discussion
6.1. Theoretical Contributions
6.2. Practical Implications
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Focal Point of the Study | |||
---|---|---|---|
The Impact of Introducing the Material Incentive Policy | The Impact of Receiving Material Rewards | ||
Incentive Mechanism | Completion-Contingent | Online Review Platforms: (Khern-am-nuai et al. 2018) [10]; (Burtch et al. 2018) [11] Online Q&A Platforms: (Kuang et al. 2019) [4] Online Crowdsourcing Platforms: (Ikeda and Bernstein 2016) [12] | Online Review Platforms: (Qiao et al. 2017) [13] Online Q&A Platforms: (Hsieh et al. 2010) [14] |
Performance-Contingent | Online Review Platforms: (Wang et al. 2012) [15] Online Crowdsourcing Platforms: (Ho et al. 2015) [16] | Online Review Platforms: (Yu et al. 2022) [5] |
Variables | Mean (Treatment) | Mean (Control) | p-Value |
---|---|---|---|
20.60 | 17.22 | 0.107 | |
0.37 | 0.36 | 0.513 | |
1.86 | 1.89 | 0.655 | |
0.48 | 0.49 | 0.644 | |
0.41 | 0.42 | 0.681 | |
0.54 | 0.52 | 0.417 | |
3.05 | 2.96 | 0.345 | |
1466.90 | 1389.45 | 0.186 |
Variables | Panel A: Pre-Treatment Period (6 Months) | Panel B: Post-Treatment Period (6 Months) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Std. Dev. | Min | Max | Obs | Mean | Std. Dev. | Min | Max | Obs | ||
1 | 20.60 | 36.869 | 1 | 547 | 448 | 11.898 | 21.363 | 1 | 315 | 400 | |
2 | 17.22 | 25.346 | 1 | 187 | 461 | 13.58 | 22.282 | 1 | 223 | 461 | |
1 | 0.37 | 0.265 | 0 | 1 | 448 | 0.42 | 0.292 | 0 | 1 | 400 | |
2 | 0.36 | 0.310 | 0 | 1 | 461 | 0.36 | 0.273 | 0 | 1 | 461 | |
1 | 1.86 | 1.050 | 0 | 7 | 448 | 2.15 | 1.420 | 0 | 13 | 400 | |
2 | 1.89 | 1.287 | 0 | 6 | 461 | 1.78 | 1.009 | 0 | 5 | 461 | |
1 | 0.48 | 0.169 | 0.04 | 0.89 | 448 | 0.44 | 0.182 | 0.02 | 0.87 | 400 | |
2 | 0.49 | 0.199 | 0.01 | 0.89 | 461 | 0.48 | 0.185 | 0.001 | 0.87 | 461 | |
1 | 0.41 | 0.261 | 0 | 1 | 448 | 0.37 | 0.264 | 0 | 1 | 400 | |
2 | 0.42 | 0.324 | 0 | 1 | 461 | 0.36 | 0.254 | 0 | 1 | 461 | |
1 | 0.54 | 0.270 | 0 | 1.13 | 448 | 0.47 | 0.283 | 0 | 2 | 400 | |
2 | 0.52 | 0.320 | 0 | 1.12 | 461 | 0.48 | 0.256 | 0 | 1 | 461 | |
1 | 48.54 | 29.409 | 2 | 89 | 448 | 54.84 | 29.409 | 8 | 95 | 400 | |
2 | 45.40 | 29.808 | 1 | 89 | 461 | 51.40 | 35.808 | 7 | 95 | 461 |
Variables | ||||
---|---|---|---|---|
−1.121 ** (0.454) | 0.064 *** (0.017) | 0.381 *** (0.090) | −0.044 *** (0.016) | |
0.583 *** (0.189) | 0.558 *** (0.012) | 0.288 *** (0.078) | 0.024 ** (0.011) | |
0.135 *** (0.201) | 0.354 *** (0.013) | 0.205 *** (0.071) | 0.020 ** (0.010) | |
Fixed Effects | Yes | Yes | Yes | Yes |
Obs | 5378 | 5378 | 5378 | 5378 |
Adj. R2 | 0.086 | 0.467 | 0.027 | 0.009 |
Variables | ||||
---|---|---|---|---|
−0.434 (0.569) | 0.060 *** (0.023) | 0.352 *** (0.129) | −0.028 (0.023) | |
−1.992 *** | 0.074 *** (0.024) | 0.439 *** (0.128) | −0.061 *** (0.022) | |
Yes | Yes | Yes | Yes | |
Yes | Yes | Yes | Yes | |
Fixed Effects | Yes | Yes | Yes | Yes |
Obs | 5378 | 5378 | 5378 | 5378 |
Adj. R2 | 0.096 | 0.396 | 0.030 | 0.012 |
Variables | ||||
---|---|---|---|---|
Relative time −6 | −0.582 (1.154) | 0.014 (0.040) | 0.421 (0.200) | −0.060 (0.037) |
Relative time −5 | −0.489 (0.852) | −0.003 (0.033) | 0.214 (0.172) | −0.025 (0.032) |
Relative time −4 | −0.695 (0.792) | 0.017 (0.031) | 0.175 (0.157) | −0.026 (0.030) |
Relative time −3 | −0.039 (0.723) | 0.022 (0.028) | 0.205 (0.153) | −0.010 (0.028) |
Relative time −2 | −0.242 (0.546) | −0.035 (0.026) | 0.110 (0.139) | −0.013 (0.025) |
Relative time −1 | The baseline | |||
Relative time +1 | −1.616 *** (0.561) | 0.094 *** (0.029) | 0.550 *** (0.154) | −0.070 ** (0.027) |
Relative time +2 | −1.534 *** (0.565) | 0.095 *** (0.030) | 0.528 *** (0.169) | −0.076 ** (0.030) |
Relative time +3 | −1.599 *** (0.596) | 0.067 ** (0.031) | 0.644 *** (0.167) | −0.057 * (0.030) |
Relative time +4 | −1.361 ** (0.561) | 0.034 (0.034) | 0.321 * (0.199) | 0.041 (0.031) |
Relative time +5 | −1.054 (0.717) | 0.043 (0.034) | 0.575 *** (0.202) | −0.046 (0.034) |
Relative time +6 | −0.513 (0.886) | 0.016 (0.035) | 0.504 ** (0.241) | −0.050 (0.037) |
Controls | Yes | Yes | Yes | Yes |
Fixed Effects | Yes | Yes | Yes | Yes |
Obs | 5378 | 5378 | 5378 | 5378 |
Adj. R2 | 0.087 | 0.474 | 0.028 | 0.011 |
Description | |
---|---|
Test1 | We considered an alternative control group selection mechanism by using less strict matching criteria (i.e., fewer matching covariates) to obtain more matching pairs. |
Test2 | We used coarsened exact matching (CEM), an alternative matching algorithm, instead of PSM in the matching process. |
Test3 | We included innovators who received rewards multiple times during the study period. |
Test4 | We conducted a cross-validation-treating user tenure as a continuous moderating variable. |
Test5 | We performed placebo tests using bootstrap simulations to randomly reassign treatment. |
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Chu, Y.; Qi, G.; Wang, K.; Xu, F. After Online Innovators Receive Performance-Contingent Material Rewards: A Study Based on an Open Innovation Platform. Behav. Sci. 2024, 14, 723. https://doi.org/10.3390/bs14080723
Chu Y, Qi G, Wang K, Xu F. After Online Innovators Receive Performance-Contingent Material Rewards: A Study Based on an Open Innovation Platform. Behavioral Sciences. 2024; 14(8):723. https://doi.org/10.3390/bs14080723
Chicago/Turabian StyleChu, Ying, Guijie Qi, Kaiping Wang, and Feng Xu. 2024. "After Online Innovators Receive Performance-Contingent Material Rewards: A Study Based on an Open Innovation Platform" Behavioral Sciences 14, no. 8: 723. https://doi.org/10.3390/bs14080723