Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders

Third parties play a prominent role in network-based explanations for successful knowledge transfer. Third parties can be either shared or unshared. Shared third parties signal insider status and have a predictable positive effect on knowledge transfer. Unshared third parties, however, signal outsider status and are believed to undermine knowledge transfer. Surprisingly, unshared third parties have been ignored in empirical analysis, and so we do not know if or how much unshared third parties contribute to the process. Using knowledge transfer data from an online technical forum, we illustrate how unshared third parties affect the rate at which individuals initiate and sustain knowledge transfer relationships. Empirical results indicate that unshared third parties undermine knowledge sharing, and they also indicate that the magnitude of the negative unshared-third-party effect declines the more unshared third parties overlap in what they know. Our results provide a more complete view of how third parties contribute to knowledge sharing. The results also advance our understanding of network-based dynamics defined more broadly. By documenting how knowledge overlap among unshared third parties moderates their negative influence, our results show when the benefits provided by third parties and by bridges (i.e., relationships with outsiders) will be opposed versus when both can be enjoyed

SHOW ME THE INCENTIVES: A DYNAMIC STRUCTURAL MODEL OF EMPLOYEE BLOGGING BEHAVIOR

Many firms believe that enterprise blogging forums can be used to help build the structured platform required for an environment that supports emergent innovation. While some employees tend to be \u27consumers\u27 of content created by others, some others contribute by acting as \u27creators\u27. In this paper, we build and estimate a dynamic structural model towards understanding the mechanisms that incentivize users to contribute to blog forums that are consumed by employees across the organization. We find strong evidence of competitive dynamics in our enterprise wide data setting. Our results demonstrate why employees contribute to blog forums. We find that employees derive higher utility from readership of their work related posts than their leisurerelated posts. Employees compete with their peers to attract more readerships for their posts. Further, results indicate positive spillover effect from readership of leisure posts to work posts of a employee. We also find that knowledge-based benefits are higher for work related knowledge than leisure related knowledge. Our results suggest that enterprises would benefit more from feedback systems that provide a picture of how knowledge workers in the organization are interacting with the tools are made available to them. We discuss implications for implementing feedback systems that quantify the reputation of the content creator and incentivize employees to engage in this practice

A Structural Model of Employee Behavioral Dynamics in Enterprise Social Media

We develop and estimate a dynamic structural framework to analyze the social-media content creation and consumption behavior of employees within an enterprise. We focus, in particular, on employees’ blogging behavior. The model incorporates two key features that are ubiquitous in blogging forums: users face (1) a trade-off between blog posting and blog reading; and (2) a trade-off between work-related and leisure-related content. We apply the model to a unique data set comprising the complete details of the blog posting and reading behavior of employees over a 15-month period at a Fortune 1000 IT services and consulting firm. Despite getting a higher utility from work-related blogging, employees nevertheless publish a significant number of leisure posts. This is partially because the creation of leisure posts has a significant positive spillover effect on the readership of work posts. Counterfactual experiments demonstrate that leisure-related blogging has positive spillovers for work-related blogging, and hence a policy of abolishing leisure-related content creation can inadvertently have adverse consequences on work-related content creation in an enterprise setting. When organizations restrict leisure blogging, the sharing of online work-related knowledge decreases and this in turn can also reduce employee performance rating. Overall, blogging within enterprises by employees during their work day can have positive long-term benefits for organizations

How Much Is An Image Worth? An Empirical Analysis of Property’s Image Aesthetic Quality on Demand at AirBNB

Consumers using sharing economy platforms such as Airbnb are challenged with high product uncertainty and search cost. To ameliorate these issues, Airbnb has implemented many strategies such as professionally taking high quality photos for hosts and calling them verified. In this paper we study the impact of having unit list\u27s photos verified. To assess the aesthetic quality of images, we use machine learning techniques. Employing Difference-in-Difference analysis, we find that on average, rooms with verified photos are 9% more frequently booked. We further separate the effect of photo verification from photo quality and room reviews and find an extra $2,455 in yearly earnings brought by high photo quality. Lastly, we look at the properties in the same neighborhood and find asymmetric spillover effects. On the neighborhood level, the results suggest higher overall demand if more rooms have verified photos

How do companies collaborate in open source ecosystems? An empirical study of OpenStack

OpenSourceSoftware (OSS) has come to play a critical role in the software industry. Some large ecosystems enjoy the participation of large numbers of companies, each of which has its own focus and goals. Indeed, companies that otherwise compete, may become collaborators within the OSS ecosystem they participate in. Prior research has largely focused on commercial involvement in OSS projects, but there is a scarcity of research focusing on company collaborations within OSS ecosystems. Some of these ecosystems have become critical building blocks for organizations worldwide; hence, a clear understanding of how companies collaborate within large ecosystems is essential. This paper presents the results of an empirical study of the Open Stack ecosystem, in which hundreds of companies collaborate on thousands of project repositories to deliver cloud distributions. Based on a detailed analysis, we identify clusters of collaborations, and identify four strategies that companies adopt to engage with the Open Stack ecosystem. We also find that companies may engage in intentional or passive collaborations, or may work in an isolated fashion. Further, we find that a company’s position in the collaboration network is positively associated with its productivity in Open Stack. Our study sheds light on how large OSS ecosystems work, and in particular on the patterns of collaboration within one such large ecosystem

Using Generative Art from Brain Signals for Enabling Self Expression in the Differently Abled

The ability to express one’s emotions is a fundamental human need. However, people with disabilities may be unable to partake in even this most fundamental of human needs. This can lead to bottling up of emotions and adverse mental health effects. Recent developments in neuroscience and brain-computer-interfaces are now making it possible to detect emotional states from brain signals. In this study, we use these advances in emotion detection techniques to design and develop a system for enabling emotional expression by the disabled using abstract art generated from EEG brain signals

A Hidden Markov Model for Collaborative Filtering

In this paper, we present a method to make personalized recommendations when user preferences change over time. Most of the works in the recommender systems literature have been developed under the assumption that user preference has a static pattern. However, this is a strong assumption especially when the user is observed over a long period of time. With the help of a data set on employees’ blog reading behavior, we show that users’ product selection behaviors change over time. We propose a hidden Markov model to correctly interpret the users’ product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A variable number of product selections of different types by each user in each time period requires a novel observation model. We propose a negative binomial mixture of multinomial to model such observations. This allows us to identify stable global preferences of users and to track individual users through these preferences. We evaluate our model using three real-world data sets with different characteristics. They include data on employee blog reading behavior inside a firm, users’ movie rating behavior at Netflix, and users’ music listening behavior collected through last.fm. We compare the recommendation performance of the proposed model with that of a number of collaborative filtering algorithms and a recently proposed temporal link prediction algorithm. We find that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static. However, it outperforms the existing algorithms when the data is less sparse and the user preference is changing. We further examine the performances of the algorithms using simulated data with different characteristics and highlight the scenarios where it is beneficial to use a dynamic model to generate product recommendation