Corporate Social Responsibility and the Local Community Effect

The objective of the chapter is to take a multilevel perspective to better understand how CSR practices are developed inside a cluster. The authors aim to describe how CSR practices are developed comparing both firm's characteristics (e.g., innovative capacity, marketing innovation, international activities) and local interactions inside the cluster. In particular, they evaluate how internal dynamics of the cluster, defined by the networks of relationships that are developed with supporting organizations, along with leading firms in the cluster, shape a new institutional framework that locally legitimates CSR practices.

Global community effect: large-scale cooperation yields collective survival of differentiating embryonic stem cells

SUMMARY“Community effect” conventionally describes differentiation occurring only when enough cells help their local (micrometers-scale) neighbors differentiate. Although new community effects are being uncovered for myriad differentiations, macroscopic-scale community effects - fates of millions of cells all entangled across centimeters - remain elusive. We found that differentiating mouse Embryonic Stem (ES) cells that are scattered as individuals over many centimeters form one macroscopic entity via long-range communications. The macroscopic population avoids extinction only if its centimeter-scale density is above a threshold value. Single-cell-level measurements, transcriptomics, and mathematical modeling revealed that this “global community effect” occurs because differentiating ES-cell populations secrete, accumulate, and sense survival-promoting factors, including FGF4, that diffuse over many millimeters and activate Yap1-induced survival mechanisms. Only above-threshold-density populations accumulate above-threshold-concentrations of factors required to survive. We thus uncovered a previously overlooked, large-scale cooperation that underlies ES-cell differentiation. Tuning such large-scale cooperation may enable constructions of macroscopic, synthetic multicellular structures.

Mobbability

This chapter discusses mobbing as a predictable institutional disorder with significant community effect. Academic departments are particularly vulnerable as contexts where conflicting motivations and tacit power differentials may allow undetectable and infectious incivility, and while there are research tools to measure experience, there are few effective practical campus-based strategies to monitor these issues. The authors explore mobbing through the lenses of epidemiology, public health, and organizational psychology. As part of this exploration the terms “mobbable” and “mobbability” are proposed, connoting the degree of incivility tolerated in the workplace climate, people's and institution's vulnerabilities, and the potential for improved capacity surrounding mobbing prevention. Outlining a story of academic mobbing, the chapter highlights contributing factors at both personal and organizational levels. The authors close with practical suggestions for recognizing symptoms and opportunities.

Hashtag Recommendation for Photo Sharing Services

Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy.