Designing Effective Performance Feedback Notification Systems to Stimulate Content Contribution: Evidence from a Crowdsourcing Recipe Platform

This study investigates whether and how a platform’s provision of performance feedback to users about their prior content contributions can help to stimulate users’ subsequent contributions. We draw on social value orientation theory to hypothesize how different framings may impact users’ likelihood of producing additional content. We partnered with a major mobile crowdsourcing recipe platform based in China to conduct a randomized field experiment involving the delivery of feedback messages with randomly determined framings, via mobile push notifications. We find that feedback framed either pro-socially or pro-self has a positive effect on content contributions, whereas feedback framed competitively has no such effect. Additionally, we observe differences across genders, such that the positive effects of pro-socially framed feedback are significantly stronger for female users. In contrast, competitively framed feedback is only effective for male users. Our findings provide implications for the design of platform-provided performance feedback to stimulate users\u27 content contribution

A new model integrating short- and long-term aging of copper added to soils

<div><p>Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t^1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature.</p></div

Development of a multi-species biotic ligand model predicting the toxicity of trivalent chromium to barley root elongation in solution culture.

Little knowledge is available about the influence of cation competition and metal speciation on trivalent chromium (Cr(III)) toxicity. In the present study, the effects of pH and selected cations on the toxicity of trivalent chromium (Cr(III)) to barley (Hordeum vulgare) root elongation were investigated to develop an appropriate biotic ligand model (BLM). Results showed that the toxicity of Cr(III) decreased with increasing activity of Ca(2+) and Mg(2+) but not with K(+) and Na(+). The effect of pH on Cr(III) toxicity to barley root elongation could be explained by H(+) competition with Cr(3+) bound to a biotic ligand (BL) as well as by the concomitant toxicity of CrOH(2+) in solution culture. Stability constants were obtained for the binding of Cr(3+), CrOH(2+), Ca(2+), Mg(2+) and H(+) with binding ligand: log KCrBL 7.34, log KCrOHBL 5.35, log KCaBL 2.64, log KMgBL 2.98, and log KHBL 4.74. On the basis of those estimated parameters, a BLM was successfully developed to predict Cr(III) toxicity to barley root elongation as a function of solution characteristics

The measured E values (E_m) versus the E values predicted by erfc model (E_p) for short-term data.

<p>The measured E values (E_m) versus the E values predicted by erfc model (E_p) for short-term data.</p

The estimated values of parameters, R² and RMSE in erfc model.

<p>The estimated values of parameters, R² and RMSE in erfc model.</p

The measured E values (E_m) versus the predicted E values of the erfc model (E_p).

<p>The measured E values (E_m) versus the predicted E values of the erfc model (E_p).</p

Average Cu labile pool (E value as fraction of total added Cu) in 17 short-term soil samples as a function of incubation time and temperature (vertical lines represent the standard errors).

<p>Average Cu labile pool (E value as fraction of total added Cu) in 17 short-term soil samples as a function of incubation time and temperature (vertical lines represent the standard errors).</p