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Transparency—the observability of activities, behaviors, and performance—is often treated as a panaceafor modern management. Yet there is a conundrum in the literature, with some studies suggesting thattransparency may benefit group creativity and others suggesting that privacy may do so. A similarconundrum exists regarding the effects of different social capital types—structural holes vs. networkcohesion—on group creativity. Enterprise social media (ESM) provide a unique opportunity to solve theseconundrums by allowing groups to be “transparent” (non-group members can observe and/or participatein group activities) or “private” (group members and activities are hidden from the community) andenabling groups to develop distinct social capital structures. Using data from 28,083 written interactionsproduced by 109 transparent and 106 private groups in an ESM of a multinational design firm, we foundstrong support for our contingency hypotheses that both transparent and private groups may produce highlevels of creative dialogues, yet in different forms. Specifically, expansion-focused creative dialogues—those focused on combining or expanding existing concepts—emerge in transparent groups, but onlywhen the group’s social capital is characterized by structural holes. Conversely, we found that reframingfocused dialogues—those focused on challenging and rethinking—emerge in private groups but onlywhen the group’s social capital is characterized by network cohesion. Theoretically, these findings canhelp to solve the conundrums in the literature on group creativity and shed light on the role of ESM usein this context. Practically, our findings offer a critical reflection o contemporary initiatives for increasingtransparency, whether through physical design or digital transformation.

 

Teamwork is at the heart of most organizations today. Given increased pressures for organizations to be flexible, and adaptable, teams are organizing in novel ways, using novel technologies to be increasingly agile. One of these technologies that are increasingly used by distributed teams is Enterprise Social Media (ESM): web-based applications utilized by organizations for enabling communication and collaboration between distributed employees. ESM feature unique affordances that facilitate collaboration, including interactions that are generative: group conversations that entail the creation of innovative concepts and resolutions. These types of interactions are an important attraction for companies deciding to implement ESM. There is a unique opportunity offered for researchers in the field of HCI to study such generative interactions, as all contributions to an ESM platform are made visible, and therefore are available for analysis. Our goal in this preliminary study is to understand the nature of group generative interactions through their linguistic indicators. In this study, we utilize data from an ESM platform used by a multinational organization. Using a 1% sub sample of all logged group interactions, we apply machine-learning to classify text as generative or non-generative and extract the linguistic antecedents for the classified generative content. Our results show a promising method for investigating the linguistic indicators of generative content and provide a proof of concept for investigating group interactions in unobtrusive ways. Additionally, our results would also be able to provide an analytics tool for managers to measure the extent to which text-based tools, such as ESM, effectively nudge employees towards generative behaviors. 

Companies hold particular interest in group generative interactions - the conception of novel ideas and solutions through group exchanges. They are a root-cause of innovation and thus are important to companies’ survival. Enterprise Social Media (ESM) offer a unique opportunity to study generative group interactions, due to the transparent nature of activities on these platforms. In this research-in-progress paper, we conduct a preliminary analysis to develop a method that could identify the instances of ESM-based generative group interactions, where we focus on distinguishing generative versus non-generative group interactions. To do this, we used the text from all group interactions from an ESM platform of a multinational organization. We implemented machine learning models to learn and classify the text as generative or non-generative. As a result, we produced the top important term features from the best performing model. These features will help us understand the nature of discussions that occur in these interactions in future studies.