Employee engagement is becoming a critical component of an organization's success. For many companies, increases in engagement mean increases in productivity. While organizations have long used traditional questionnaires to measure engagement, these leave room for interpretation; and trying to get clarity makes the questionnaires longer, which decreases participation. On the other hand, user-generated content can often act as a barometer on employee engagement, but does not lend itself easily to engagement prediction. We present a model leveraging machine-learning techniques to analyze and predict employee engagement, using both user-generated social analytics and text content from organization-wide surveys, to calculate a real-time engagement level for employees. Our method helped identify specific words and phrases used by individual employees that play an important role in engagement prediction. This study was able to uncover critical themes driving employee engagement and behavior at the enterprise level. Our model for textual analysis cannot only predict engagement, but also provide highly valuable, and actionable, data for business leaders about the health of their organizations.

Behavioral statescan be transferred to others, leading people to behave in ways similar to those around them. Can this phenomenon of behavioral contagion be seen in the workplace? Using employees' organizational social media data and their workplace hierarchical network structure, we studied contagion across a large multinational corporation, focusing on an important workplace behavior - employee engagement. We measured employees' engagement based on their word choice in organizational social media, and we applied a longitudinal statistical technique which controls for homophily, employees' traits and prior expressions of engagement. We found that engagement and disengagement spread from one employee to another with direct peers exerting the strongest influence. While engagement-spread was more powerful laterally among people at the same organizational level, disengagement-spread followed the vertical managerial chain. Further, we found that disengaged co-workers exerted a stronger influence on employee's future engagement compared to the engaged co-workers. Our results suggest the need for organizations to sense and address workplace disengagement promptly. Moreover, our findings offer opportunities for using workplace interventions to promote engagement and mitigate disengagement.

Employee engagement is a reflection of an employee's experience of work. Previous research has analyzed each employee's experience in terms of individual factors. We provide the first report of the influence on engagement of peers (who report to the same manager) and friends (who share social ties in an internal social network), using linear regression to model employee engagement in a sample of more than 44,000 employees. We show that an employee's engagement is associated with the engagement of her/his peers, friends, and manager. Our results contribute to analyses of social factors at work, and argue for revisions to existing theories of employee engagement.

Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.

Under contribution is an important problem in online social production communities: important tasks don't get done, and only a small minority of participants are active contributors. How can we remedy this situation? We explore the feasibility of using the act of consuming information as a gateway to contributing information; specifically, we investigate semi-automated means to extract useful information from standard types of user feedback. We explore this approach in the context of a geographic wiki and route-planning system for bicyclists. We analyzed naturally occurring textual route feedback, finding that the feedback was rich in information such as bikeability ratings, tags and notes that are useful to improve the system's route finding and navigational assistance capabilities. We also present a technique to extract such information by engaging users in dialogue immediately after they obtain a route. We believe that our results and ideas are applicable to a broad class of social production systems.

In open content communities like Wikipedia and StackOverflow and in open source software projects, a small proportion of users produce a majority of the content and take on much of the required community maintenance work. Understanding this class of users is crucial to creating and sustaining healthy communities. We carried out a mixed-method study of core contributors to the Cyclopath geographic wiki and bicycle routing web site. We present our findings and organize our discussion using concepts from activity theory. We found that the Cyclopath core contributors aren't the dedicated cyclists and that the characteristics of the community shape the site, the rules, and the tools for contributing. Additionally, we found that numerous aspects about the surrounding ecology of related systems and communities may help to shape how the site functions and views itself. We draw implications for future research and design from these findings.

Social psychology offers several theories of potential use for designing techniques to increase user contributions to online communities. Some of these techniques follow the "compliance without pressure" approach, where users are led to comply with a request without being subjected to any obvious external pressure. We evaluated two such techniques -- foot-in-the-door and low-ball -- in the context of Cyclopath, a geographic wiki. We found that while both techniques succeeded, low-ball elicited more work than foot-in-the-door. We discuss design and research implications of applying these (and other such techniques) in online communities.

We present the system design and rational for a novel social microcalendar called Timely. Our system has been inspired by previous research on calendaring and popular social network applications, in particular microblogging. Timely provides an open, social space for enterprise users to share their events, socialize, and discover what else is going on in their network and beyond. A detailed analysis of the events shared by users during the site's first 47 days reveals that users willingly share their time commitments despite an existing culture of restricted calendars.

Existing enterprise calendaring systems have suffered from problems like rigidity, lack of transparency, and poor integration with social networks. We present the system design and rationale for a novel social coordination mechanism, called "Suggestions," that addresses these issues. Our system integrates ideas drawn from designs of lightweight polling systems and one's social network into an open calendar tool, providing a space for users to coordinate, socialize around, or negotiate the "what" and the "when" of their events. Suggestions was released inside a large enterprise setting, where initial interviews revealed users' thoughts on transparent scheduling, reaching wider audiences and task appropriateness, and suggested ways to improve our design.

Open content communities such as wikis derive their value from the work done by users. However, a key challenge is to elicit work that is sufficient and focused where needed. We address this challenge in a geographic open content commu-nity, the Cyclopath bicycle route finding system. We devised two techniques to elicit and focus user work, one using fa-miliarity to direct work opportunities and another visually highlighting them. We conducted a field experiment, find-ing that (a) the techniques succeeded in eliciting user work, (b) the distribution of work across users was highly unequal, and (c) user work benefitted the community (reducing the length of the average computed route by 1 kilometer). Author Keywords Wiki, geowiki, open content, geographic volunteer work, volunteered geographic information ACM Classification Keywords H.5.3 [Group and Organization Interfaces]: Collaborative computing, computer-supported cooperative work, web-based interaction.