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Although the importance of network embeddedness attracted much attention in recent years, the interpretations of the underlying mechanism almost focus on the positive effects and neglect the potential negative aspects. This paper aims to use Chinese listed manufacturing enterprises, in the perspective of network embeddedness, to analyze whether resource curse effects exist in strategic networks of enterprises.

This paper examines the resource curse effect that exists in enterprise networks through analysis of enterprise’s total factors productivity compared with those of its industry peers with different degrees of embeddedness. This paper then uses several different methods, such as clustered fixed and random effect model, core and peripheral model, and generalized method of moments estimation in endogenous checks, to detect network curse effects and reveal three potential mechanisms, including overinvestment, extra maintaining cost and innovation extrusion.

This paper finds that excessive network embeddedness hinders continuous improvement of productivity, which is derived from three mechanisms: excessive network embeddedness makes information redundancy among enterprises, and the imitate-follow effect makes excessive investment seriously; enterprises have to encounter more problems and difficulties, such as information coordination and benefit negotiation, to maintain network relationship, which leads to extra cost; enterprises with abundant network resources may tend to more emphasis on marketable operational abilities, resulting in resource decentralization and less investment on innovation, which is finally not beneficial to productivity improvement in the long run.

Compared with previous literatures, this paper not only enriches the understanding of negative mechanisms in enterprise`s network embeddedness, but also unfolds the inconsistency of network embeddedness effects in practice and finds new evidence that the two types of network embeddedness are consistent in certain circumstances.

The purpose of this paper is to examine how Olympic audiences utilized Twitter to follow American National Governing Bodies (NGBs) during the 2016 Rio Olympic Games.

Guided by economic demand theory, the researchers sought to explore whether factors such as the content of social media messages, athlete’s performance, event presentation, scheduling, and TV broadcasting contribute to enhancing fans’ interests in following NGBs on Twitter during the Olympic Games. In total, 33 American NGB Twitter accounts formed the data set for this study. Each of NGBs’ Twitter data was collected every night at midnight from August 7 to 23, 2016. Data collected from each NGB account included number of followers, number of accounts followed, number of tweets, and number of “likes.”

Results of this study revealed that team’s performance and the number of tweets had direct and positive relationships with increasing the number of NGB’s Twitter followers on each competition day. The number of “likes,” however, had a significant negative relationship with fans’ interests in following NGBs’ Twitter.

The results of the study are expected to help Governing Bodies in the Olympic sports have a better understanding of fans’ social media usage.

In recent years, the updating speed of products has been significantly accelerated, which not only provides diversified styles for consumers to select from but also makes consumers face selection problems sometimes. In addition, a large number of online reviews for products emerge on many e-commerce websites and influence consumers’ purchasing decisions. The purpose of this study is to propose a method for product selection considering consumer’s expectations and online reviews to support consumers’ purchasing decisions.

The product attributes are divided into two categories, i.e. demand attributes and word-of-mouth (WOM) attributes. For the demand attributes, for which the consumers can give specific quantified expectations, the value function of prospect theory is used to determine the consumer’s perceived values to the alternative products according to consumers’ expectations for these attributes and products’ specifications. For the WOM attributes, for which the consumers cannot give specific quantified expectations, the sentiment analysis method is used to identify the sentiment strengths for these attributes in the online reviews, and then the consumer’s perceived values to the alternative products are determined. On this basis, the product selection methods for single consumers and group consumers are given respectively.

Finally, taking the data of JD.com (https://www.jd.com/) as an example, the practicability and rationality of the method proposed in this paper is validated.

First, a new product selection problem considering consumer’s expectations and online reviews is extracted. Second, the product attributes are considered more comprehensively and are classified into two main categories. Third, the bounded rationality of the consumers in the decision-making process is described more reasonably. Fourth, the sentiment dictionaries for each WOM attribute are constructed and the algorithm step of identifying the sentiment strengths is designed, which can help to identify the sentiment strengths in the online reviews more accurately. Fifth, the situation that a group plans to purchase the same products and the members have inconsistent expectations for the product attributes is considered.

Social commerce sites offer fertile ground for users to communicate product information. Given that such sites have the potential to transform the way of doing business, it is clearly important for academics to understand user information sharing on social commerce sites. Existing research has considered motivations as core elements of user information sharing in online communities. The purpose of this paper is to advance the theoretical understanding of user information sharing by investigating differences in motivations between the different user types of social commerce sites: sellers and buyers, and the impact on social commerce.

The authors conducted an empirical study by analyzing data from a social commerce site in China, including panel data (n=892) and survey data (n=913).

This research showed that user type (i.e. sellers and buyers) plays an important moderating role in user information sharing: sellers exert a positive moderating effect on utilitarian and social motivations, while buyers are found to have a positive moderating effect on hedonic motivation.

This study contributes to existing literature, not only by exploring the antecedents of user information sharing on social commerce sites from utilitarian, hedonic and social dimensions, but also by providing an evaluation of user types (i.e. sellers and buyers). The authors believe that the results of this study offer important and interesting insights for IS research and practice.

This study will enhance social commerce site managers’ understanding of better features for information sharing and differences in motivation between sellers and buyers. This could improve the effectiveness of encouraging strategies and help social commerce sites be more sustainable in the highly competitive contemporary environment.

Based on social exchange theory and motivation theory, this paper takes user types into account, and postulate that user type (i.e. sellers and buyers) plays a moderating role in the relationships between motivations (i.e. utilitarian, hedonic and social motivation) and user information sharing intention on social commerce sites.

The purpose of this paper is to focus on investigating the impact of crowd participation on degree of project success, which is defined as the total amount of funds a project can obtain after it reaches its initial funding goal threshold.

Drawing on the theory of crowd capital, this study develops six hypotheses about the impact of crowd capability of a fundraiser (i.e. project updates, goal setting, reward levels and social media usage) and crowd participation (i.e. namely, funds pledge and on-site communication) on degree of project success. The hypotheses are tested using data sets of successful projects collected from two popular crowdfunding websites.

This study finds that funds pledge has an inverse U-shaped relationship with degree of project success. Project updates, reward levels and on-site communication positively influence degree of project success, while funding goal negatively affects degree of project success.

This study contributes to prior literature by investigating the degree of project success determinants using the perspectives of both fundraisers and crowds, which provides a more comprehensive understanding of what makes a crowdfunded project a success.

The empirical results of this study provide fundraisers with guidelines about how to access more funds after achieving the initial funding goals.

This work is one of the first to investigate the degree of project success and its determinants from the perspectives of both fundraisers and crowds.

The purposes of this paper are to review the progress of and conclude the trend for paint thickness simulation for painting robot trajectory planning.

This paper compares the explicit function-based method and computational fluid dynamics (CFD)-based method used for paint thickness simulation. Previous research is considered, and conclusions with the outlook are drawn.

The CFD-based paint deposition simulation is the trend for paint thickness simulation for painting robot trajectory planning. However, the calculation of paint thickness resulting from dynamically painting complex surface remains to be researched, which needs to build an appropriate CFD model, study approaches to dynamic painting simulation and investigate the simulation with continuously changing painting parameters.

This paper illustrates that the CFD-based method is the trend for the paint thickness simulation for painting robot trajectory planning. Current studies have been analyzed, and techniques of CFD modeling have also been summarized, which is vital for future study.