Param Vir Singh

The small-world effect: The influence of macro-level properties of developer collaboration networks on open-source project success

In this study we investigate the impact of community-level networks—relationships that exist among developers in an OSS community—on the productivity of member developers. Specifically, we argue that OSS community networks characterized by small-world properties would positively influence the productivity of the member developers by providing them with speedy and reliable access to more quantity and variety of information and knowledge resources. Specific hypotheses are developed and tested using longitudinal data on a large panel of 4,279 projects from 15 different OSS communities hosted at Sourceforge. Our results suggest that significant variation exists in small-world properties of OSS communities at Sourceforge. After accounting for project, foundry, and time-specific observed and unobserved effects, we found a statistically significant relationship between small-world properties of a community and the technical and commercial success of the software produced by its members. In contrast to the findings of prior research, we also found the lack of a significant relationship between closeness and betweenness centralities of the project teams and their success. These results were robust to a number of controls and model specifications.

Crowdsourcing New Product Ideas Under Consumer Learning

We propose a dynamic structural model that illuminates the economic mechanisms shaping individual behavior and outcomes on crowdsourced ideation platforms. We estimate the model using a rich data set obtained from IdeaStorm.com, a crowdsourced ideation initiative affiliated with Dell. We find that, on IdeaStorm.com, individuals tend to significantly underestimate the costs to the firm for implementing their ideas but overestimate the potential of their ideas in the initial stages of the crowdsourcing process. Therefore, the “idea market” is initially overcrowded with ideas that are less likely to be implemented. However, individuals learn about both their abilities to come up with high-potential ideas as well as the cost structure of the firm from peer voting on their ideas and the firms response to contributed ideas. We find that individuals learn rather quickly about their abilities to come up with high-potential ideas, but the learning regarding the firms cost structure is quite slow. Contributors of low-potential ideas eventually become inactive, whereas the high-potential idea contributors remain active. As a result, over time, the average potential of generated ideas increases while the number of ideas contributed decreases. Hence, the decrease in the number of ideas generated represents market efficiency through self-selection rather than its failure. Through counterfactuals, we show that providing more precise cost signals to individuals can accelerate the filtering process. Increasing the total number of ideas to respond to and improving the response speed will lead to more idea contributions. However, failure to distinguish between high-and low-potential ideas and between high-and low-ability idea generators leads to the overall potential of the ideas generated to drop significantly. This paper was accepted by Sandra Slaughter, information systems.

Blog, Blogger, and the Firm: Can Negative Employee Posts Lead to Positive Outcomes?

Consumer-generated media, particularly blogs, can help companies increase the visibility of their products without spending millions of dollars in advertising. Although a number of companies realize the potential of blogs and encourage their employees to blog, a good chunk of them are skeptical about losing control over this new media. Companies fear that employees may write negative things about them and that this may bring significant reputation loss. Overall, companies show mixed response toward negative posts on employee blogs---some companies show complete aversion; others allow some negative posts. Such mixed reactions toward negative posts motivated us to probe for any positive aspects of negative posts. In particular, we investigate the relationship between negative posts and readership of an employee blog. In contrast to the popular perception, our results reveal a potential positive aspect of negative posts. Our analysis suggests that negative posts act as catalyst and can exponentially increase the readership of employee blogs, suggesting that companies should permit employees to make negative posts. Because employees typically write few negative posts and largely write positive posts, the increase in readership of employee blogs generally should be enough to offset the negative effect of few negative posts. Therefore, not restraining negative posts to increase readership should be a good strategy. This raises a logical question: what should a firms policy be regarding employee blogging? For exposition, we suggest an analytical framework using our empirical model.

A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects

This study develops a stochastic model to capture developer learning dynamics in open source software projects (OSS). A hidden Markov model (HMM) is proposed that allows us to investigate (1) the extent to which individuals learn from their own experience and from interactions with peers, (2) whether an individuals ability to learn from these activities varies as she evolves/learns over time, and (3) to what extent individual learning persists over time. We calibrate the model based on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM, three latent learning states (high, medium, and low) are identified, and the marginal impact of learning activities on moving the developer between these states is estimated. Our findings reveal different patterns of learning in different learning states. Learning from peers appears to be the most important source of learning for developers across the three states. Developers in the medium learning state benefit the most through discussions that they initiate. On the other hand, developers in the low and the high states benefit the most by participating in discussions started by others. While in the low state, developers depend entirely upon their peers to learn, whereas in the medium or high state, they can also draw upon their own experiences. Explanations for these varying impacts of learning activities on the transitions of developers between the three learning states are provided. The HMM is shown to outperform the classical learning curve model. The HMM modeling of this study contributes to the development of a theoretically grounded understanding of learning behavior of individuals. Such a theory and associated findings have important managerial and operational implications for devising interventions to promote learning in a variety of settings.

Developer Heterogeneity and Formation of Communication Networks in Open Source Software Projects

Over the past few years, open source software (OSS) development has gained a huge popularity and has attracted a large variety of developers. According to software engineering folklore, the architecture and the organization of software depend on the communication patterns of the contributors. Communication patterns among developers influence knowledge sharing among them. Unlike in a formal organization, the communication network structures in an OSS project evolve unrestricted and unplanned. We develop a non-cooperative game-theoretic model to investigate the network formation in an OSS team and to characterize the stable and efficient structures. Developer heterogeneity in the network is incorporated based on their informative value. We find that there may exist several stable structures that are inefficient and there may not always exist a stable structure that is efficient. The tension between the stability and efficiency of structures results from developers acting in their self-interest rather than the group interest. Whenever there is such tension, the stable structure is either underconnected across types or overconnected within type of developers from an efficiency perspective. We further discuss how an administrator can help evolve a stable network into an efficient one. Empirically, we use the latent class model and analyze two real-world OSS projects hosted at SourceForge. For each project, different types of developers and a stable structure are identified, which fits well with the predictions of our model. Overall, our study sheds light on how developer abilities and incentives affect communication network formation in OSS projects.

Board Networks and Merger Performance

We compare the post-merger financial performance of acquiring firms that have well-connected (central) boards with the performance of less-connected (non-central) boards and find that central boards are associated with better performing acquisitions as evidenced by larger post-merger buy-and-hold abnormal returns, stronger improvements in the ROA, and a 7-12% annual abnormal return based on calendar time portfolios. Central firms are more likely to use cash, to make an acquisition, and to be acquired. Our results suggest that board networks affect the decision to acquire, the choice of target, the method of payment, and ultimately the financial performance of the firm around the merger.

Knowledge Sharing in Online Communities: Learning to Cross Geographic and Hierarchical Boundaries

Many organizations have launched online knowledge-exchanging communities to promote knowledge sharing among their employees. We empirically examine the dynamics of knowledge sharing in an organization-hosted knowledge forum. Although previous researchers have suggested that geographic and social boundaries disappear online, we hypothesize that they remain because participants prefer to share knowledge with others who share similar attributes, as a result of the challenges involved in knowledge sharing in an online community. Further, we propose that as participants acquire experience in exchanging knowledge, they learn to rely more on expertise similarity and less on categorical similarities, such as location or hierarchical status. As a result, boundaries based on categorical attributes are expected to weaken, and boundaries based on expertise are expected to strengthen, as participants gain experience in the online community. Empirical support for this argument is obtained from analyzing a longitudinal data set of an internal online knowledge community at a large multinational information technology consulting firm.

How to Attract and Retain Readers in Enterprise Blogging

We investigate the dynamics of blog reading behavior of employees in an enterprise blogosphere. A dynamic model is developed and calibrated using longitudinal data from a Fortune 1,000 IT services firm. Our modeling framework allows us to segregate the impact of textual characteristics sentiment and quality of a post on attracting readers from retaining them. We find that the textual characteristics that appeal to the sentiment of the reader affect both reader attraction and retention. However, textual characteristics that reflect only the quality of the posts affect only reader retention. We identify a variety-seeking behavior of blog readers where they dynamically switch from reading on one set of topics to another. The modeling framework and findings of this study highlight opportunities for the firm to influence blog-reading behavior of its employees to align it with its goals. Overall, this study contributes to improved understanding of reading behavior of individuals in communities formed around user generated content.

A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing

Accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. Recently, online social platforms have produced an unparalleled amount of data on consumer behavior. However, two challenges have limited the use of these data in obtaining meaningful business marketing insights. First, the data are typically in an unstructured format, such as texts, images, audio, and video. Second, the sheer volume of the data makes standard analysis procedures computationally unworkable. In this study, we combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting. We conduct our analysis on a significant volume of nearly two billion Tweets and 400 billion Wikipedia pages. Our main findings emphasize that, by contrast to basic surface-level measures such as the volume of or sentiments in Tweets, the information content of Tweets and their timeliness significantly improve forecasting accuracy. Our method endogenously summarizes the information in Tweets. The advantage of our method is that the classification of the Tweets is based on what is in the Tweets rather than preconceived topics that may not be relevant. We also find that, by contrast to Twitter, other online data (e.g., Google Trends, Wikipedia views, IMDB reviews, and Huffington Post news) are very weak predictors of TV show demand because users tweet about TV shows before, during, and after a TV show, whereas Google searches, Wikipedia views, IMDB reviews, and news posts typically lag behind the show.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0972 .

Learning Curves of Agents with Diverse Skills in Information Technology-Enabled Physician Referral Systems

To improve operational efficiencies while providing state of the art healthcare services, hospitals rely on information technology enabled physician referral systems (IT-PRS). This study examines learning curves in an IT-PRS setting to determine whether agents achieve performance improvements from cumulative experience at different rates and how information technologies transform the learning dynamics in this setting. We present a hierarchical Bayes model that accounts for different agent skills (domain and system) and estimate learning rates for three types of referral requests: emergency (EM), nonemergency (NE), and nonemergency out of network (NO). Furthermore, the model accounts for learning spillovers among the three referral request types and the impact of system upgrade on learning rates. We estimate this model using data from more than 80,000 referral requests to a large IT-PRS. We find that: (1) The IT-PRS exhibits a learning rate of 4.5% for EM referrals, 7.2% for NE referrals, and 12.3% for NO referrals. This is slower than the learning rate of manufacturing (on average 20%) and more comparable to other service settings (on average, 8%). (2) Domain and system experts are found to exhibit significantly different learning behaviors. (3) Significant and varying learning spillovers among the three referral request types are also observed. (4) The performance of domain experts is affected more adversely in comparison to system experts immediately after system upgrade. (5) Finally, the learning rate change subsequent to system upgrade is also higher for system experts in comparison to domain experts. Overall, system upgrades are found to have a long-term positive impact on the performance of all agents. This study contributes to the development of theoretically grounded understanding of learning behaviors of domain and system experts in an IT-enabled critical healthcare service setting.