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Crowdsourcing refers to a new business model in which enterprises or individuals publish tasks or problems, attracting freelancers or contributors to participate in solving tasks, submitting bids and allowing task seekers to choose the final solution. How to attract more quantity and quality of contributors to submit their solutions through a crowdsourcing platform has become a vital question.

In this study, the authors use web crawling to obtain 43,265 effective tasks in EPWK website (www.epwk.com) to probe how to elevate the quantity and quality of contributors via task reward design. This study uses the hierarchical linear model to probe the research questions.

Results show that, with the increase of task reward, the quantity of contributors goes up first and then goes down (inverted U shape), whereas the quality of contributors goes down first and then goes up (U sharp). Moreover, the authors investigate the moderating effects of another task design attribute, task duration. This study finds that task duration weakens the effect of task reward on the quantity of contributors while strengthening the effects of task reward on the quality of contributors.

First, this study theoretically probes two key aspects of task performance, namely, the quantity and quality of contributors, which expand the scope of task performance evaluation. Second, this study reconciles previous concern about the relationship of task reward and performance, which is different from previous studies that have paid more attention to the single perspective of their relationship. Finally, the authors investigate the moderating effects of task duration, which further uncover the mechanism behind task reward and performance, that is, the quantity and quality of task contributors.

Although online brand communities (OBCs) are extensively demonstrated to be an important social media tool in building brand equity, they may have backfire effects under certain conditions. Drawing from the self–brand connection theory, the purpose of this study is to investigate the effect of group heterogeneity on brand commitment. The mediation effect of self–brand connection and moderation effect of brand symbolism has also been examined.

Data were collected using a survey of 498 users from a range of OBCs. Hierarchical regression and bootstrapping method were used to test the research model.

The findings indicate that group heterogeneity negatively affects brand commitment in which self–brand connection plays a role of mediation. Further, the negative effect is more pronounced for high-symbolic brands than low-symbolic ones.

Brand managers are advised to note the dark side of OBCs in general and alleviate the adverse effects of group heterogeneity in particular, especially for high-symbolic brands.

Previous research pays little attention to the adverse effect of OBCs. This study enriches the literature by revealing that the backfire effect of OBCs arises when users become heterogeneous and uncovering in what situations the negative effect is stronger.

Previous studies on social influence and virtual product adoption have mainly taken users’ purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the prevalence of competing versions (basic vs upgraded) of a virtual product in online communities, this paper investigated the differences in the effect of social influence on users’ adoption of basic and upgraded choices of a virtual product. It also examined how the effect varies with users’ social status and user-level network density.

A natural experiment was conducted in an online game community. Two competing versions (basic vs upgraded) of a virtual product were provided for in-game purchase while a random set of users selected from 897,765 players received the notification of their friends’ adoption information. A competing-risk model was used to test the hypotheses.

Social influence exerts a stronger positive effect on users’ adoption of the upgraded virtual product than of the basic virtual product. Middle-status users have the greatest (least) susceptibility to social influence in adopting the upgraded (basic) virtual product than low- and high-status users. User’s network density enhances the effect of social influence on adoption of both virtual products, even more for the upgraded one.

This research contributes to the social influence and product adoption literature by disentangling the different effects of social influence on basic and upgraded versions of a virtual product. It also identifies the boundary conditions that social influence works for each version of the virtual product.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.