OVERCOMING THE CHALLENGES OF FORMAL ORGANIZATIONAL STRUCTURE: INDIVIDUALS’ DESIRE FOR REDUCING THEIR WORKFLOW DEPENDENCIES

In a field social network study of 141 employees in an international organization, I examined individuals’ future desires to either collaborate more intensely with existing network partners or seek out new partners based on the latent value of these social ties – the potential social capital that will be generated from strengthening or building a tie in terms of reducing their formal workflow dependencies on others. Employees tended to desire more intense collaboration with a constraining existing tie (i.e., a bottleneck in their existing workflow network) when they trusted the person, suggesting they believed that the partner would provide high-quality work inputs in a reliable manner once a stronger relationship was built, thus increasing the tie’s latent relational value. Building new ties was more likely to happen when it would reduce one’s workflow dependencies by detouring around the bottlenecking person and closing disadvantageous structural holes, suggesting those new potential ties had greater latent structural value as they allow the focal individual to reach out to other workers further upstream in the workflow network. When comparing the intentions to use both approaches, the bypassing, structural approach was more prevalent than the tie strengthening approach for reducing workflow dependencies, in spite of the inherent additional costs of searching and building a new tie. The study illustrates how informal networks are used intentionally to ameliorate the deficiencies of the formal organizational workflow network and suggests the relative prominence of the latent structural value of ties as compared to their relational value

Impact of stakeholder type and collaboration on issue resolution time in OSS Projects

Born as a movement of collective contribution of volunteer developers, Open source software (OSS) attracts an increasing involvement of commercial firms. Many OSS projects are composed of a mix group of firm- paid and volunteer developers, with different motivation, collaboration practices and working styles. As OSS is collaborative work in nature, it is important to know whether these differences have an impact on project outcomes. In this paper, we empirically investigate the firm-paid participation in resolving OSS evolution issues, the stakeholder collaboration and its impact on OSS issue resolution time. The results suggest that though a firm-paid developer resolves much more issues than a volunteer developer does, there is no difference in issue resolution time between firm-paid and volunteer developers. Besides, the more important factor that influences the issue resolution time comes from the collaboration among stakeholders rather than from measures of individual characteristics.Peer ReviewedPostprint (author’s final draft

Design Architecture, Developer Networks and Performance of Open Source Software Projects

In this study we seek to understand the factors differentiating successful from unsuccessful software projects. This article develops and tests a model measuring the impact on software project performance of (1) software products ’ design architectures and (2) developers ’ positions within collaborative networks. Two indicators of project success are used: product quality and project velocity. Two dimensions of design architecture – degree of decomposition and coupling – and one characteristic of developer network structures – degree centrality – are investigated for their impact on project performance. Using data gathered from SourceForge.net and its monthly dumps, we empirically test hypotheses on the top 100 projects according to project rankings. These rankings are generated from the traffic, communication, and development statistics collected for each project hosted on SourceForge.net. Besides the top 100 projects, we also randomly choose another 100 projects to form the data sample. The main findings are that (1) the degree of decomposition has an inverted U-shaped relationship with project performance, (2) when tested on the sample of top 100 projects, average degree centrality of a project team has a positive and significant effect on project performance and (3) the effects of network metrics o

Mining and untangling change genealogies

Developers change source code to add new functionality, fix bugs, or refactor their code. Many of these changes have immediate impact on quality or stability. However, some impact of changes may become evident only in the long term. This thesis makes use of change genealogy dependency graphs modeling dependencies between code changes capturing how earlier changes enable and cause later ones. Using change genealogies, it is possible to: (a) applyformalmethodslikemodelcheckingonversionarchivestorevealtemporal process patterns. Such patterns encode key features of the software process and can be validated automatically: In an evaluation of four open source histories, our prototype would recommend pending activities with a precision of 60—72%. (b) classify the purpose of code changes. Analyzing the change dependencies on change genealogies shows that change genealogy network metrics can be used to automatically separate bug fixing from feature implementing code changes. (c) build competitive defect prediction models. Defect prediction models based on change genealogy network metrics show competitive prediction accuracy when compared to state-of-the-art defect prediction models. As many other approaches mining version archives, change genealogies and their applications rely on two basic assumptions: code changes are considered to be atomic and bug reports are considered to refer to corrective maintenance tasks. In a manual examination of more than 7,000 issue reports and code changes from bug databases and version control systems of open- source projects, we found 34% of all issue reports to be misclassified and that up to 15% of all applied issue fixes consist of multiple combined code changes serving multiple developer maintenance tasks. This introduces bias in bug prediction models confusing bugs and features. To partially solve these issues we present an approach to untangle such combined changes with a mean success rate of 58—90% after the fact.Softwareentwickler ändern Source-Code um neue Funktionalität hinzuzufügen, Bugs zu beheben oder um ihren Code zu restrukturieren. Viele dieser Änderungen haben einen direkten Einfluss auf Qualität und Stabilität des Softwareprodukts. Jedoch kommen einige dieser Einflüsse erst zu einem späteren Zeitpunkt zur Geltung. Diese Arbeit verwendet Genealogien zwischen Code-Änderungen um zu erfassen, wie frühere Änderungen spätere Änderungen erfordern oder ermöglichen. Die Verwendung von Änderungs-Genealogien ermöglicht: (a) die Anwendung formaler Methoden wie Model-Checking auf Versionsarchive um temporäre Prozessmuster zu erkennen. Solche Prozessmuster verdeutlichen Hauptmerkmale eines Softwareentwicklungsprozesses: In einer Evaluation auf vier Open-Source Projekten war unser Prototyp im Stande noch ausstehende Änderungen mit einer Präzision von 60–72% vorherzusagen. (b) die Absicht einer Code-Änderung zu bestimmen. Analysen von Änderungsabhängigkeiten zeigen, dass Netzwerkmetriken auf Änderungsgenealogien geeignet sind um fehlerbehebende Änderungen von Änderungen die eine Funktionalität hinzufügen zu trennen. (c) konkurrenzfähige Fehlervorhersagen zu erstellen. Fehlervorhersagen basierend auf Genealogie-Metriken können sich mit anerkannten Fehlervorhersagemodellen messen. Änderungs-Genealogien und deren Anwendungen basieren, wie andere Data-Mining Ansätze auch, auf zwei fundamentalen Annahmen: Code-Änderungen beabsichtigen die Lösung nur eines Problems und Bug-Reports weisen auf Fehler korrigierende Tätigkeiten hin. Eine manuelle Inspektion von mehr als 7.000 Issue-Reports und Code-Änderungen hat ergeben, dass 34% aller Issue-Reports falsch klassifiziert sind und dass bis zu 15% aller fehlerbehebender Änderungen mehr als nur einem Entwicklungs-Task dienen. Dies wirkt sich negativ auf Vorhersagemodelle aus, die nicht mehr klar zwischen Bug-Fixes und anderen Änderungen unterscheiden können. Als Lösungsansatz stellen wir einen Algorithmus vor, der solche nicht eindeutigen Änderungen mit einer Erfolgsrate von 58–90% entwirrt

A Coat of Many Colours - New Concepts and Metrics of Economic Power in Competition Law and Economics

The digital economy has brought new business models that rely on zero-price markets and multi-sided platforms nested in business ecosystems. The traditional concept of market power used by competition authorities cannot engage with this new reality in which (economic) power manifests beyond price and output within a relevant market. These developments have culminated in multiple recent calls for a more multidimensional concept of power. Consequently, suggestions over new concepts of power triggering antitrust/regulatory intervention, such as ‘strategic market status’, ‘conglomerate market power’, ‘intermediation power’, ‘structuring digital platforms’, or ‘gatekeepers’ have proliferated to complete, or even substitute, the archetypical concept of market or monopoly power in competition law. However, a theoretical framework for this multidimensional concept of power that can set the basis for new metrics is missing. This article makes three contributions in that direction. First, we conceptualize different forms of (economic) power that go beyond competition within a single relevant market in terms of competition law and economics. Second, we propose new metrics to measure two forms of power: panopticon power and power based on differential dependency between value co-creators. Third, we test the latter and show how they could reduce false positives and false negatives when assessing dominance

Productivity Effects of Information Diffusion in Networks

We examine the drivers of diffusion of information through organizations and the effects on performance. In particular, we ask: What predicts the likelihood of an individual becoming aware of a strategic piece of information, or becoming aware of it sooner? Do different types of information exhibit different diffusion patterns, and do different characteristics of social structure, relationships and individuals in turn affect access to different kinds of information? Does better access to information predict an individual’s ability to complete projects or generate revenue? We hypothesize that the dual effects of content and structure jointly predict the diffusion path of information, and ultimately performance. To test our hypotheses, we characterize the social network of a medium sized executive recruiting firm using accounting data on project co-work relationships and ten months of email traffic observed over two five month periods. We identify two distinct types of information diffusing over this network – ‘event news’ and ‘discussion topics’ – by their usage characteristics, and observe several thousand diffusion processes of each type of information from their original first use to their varied recipients over time. We then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it more quickly. Our results demonstrate that the diffusion of news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is influenced by demographic and network factors but not by functional relationships (e.g. prior co-work, authority) or the strength of ties. In contrast, diffusion of discussion topics, which exhibit more shallow diffusion characterized by ‘back-and-forth’ conversation, is heavily influenced by functional relationships and the strength of ties, as well as demographic and network factors. Discussion topics are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties, while news is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. Furthermore, we find that access to information strongly predicts the number of projects completed by each individual and the amount of revenue that person generates. The effects are economically significant, with each additional “word seen” correlated with about $70 of additional revenue generated. Our findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies and provide some of the first evidence on the economic importance of information diffusion in networks.The National Science Foundation, Cisco Systems, France Telecom and the MIT Center for Digital Busines

Understanding The Impact Of Virtual-Mirroring Based Learning On Collaboration In A Data And Analytics Function: A Resilience Perspective

Large multinational organizations are struggling to adapt and innovate in the face of increasing turbulence, uncertainty, and complexity. The lack of adaptive capacity is one of the major risks facing such organizations as the rapid change in technology, urbanization, socio-economic trends, and regulations continues to accelerate and outpace their ability to adapt. This is a resilience problem that organizations are addressing by investing in Data and Analytics to improve their innovation and competitive capabilities. However, Data and Analytics projects are more likely to fail than to succeed. Competing on data and analytics is not only a technical challenge but also a challenge in promoting collaborative innovation networks that are based on two key characteristics of resilient systems. One characteristic is the ability to learn while the second is the ability to foster diversity. In this study, we examine how a newly-established Data and Analytics function has evolved over a one-year period. First, we conduct a baseline survey with two sections. The first section captures the structure of Innovation, Expertise, and Projects networks using network science techniques. In the second section we extract four resilience-based workstyles that provide a behavioral representation of each phase of the Adaptive Cycle Theory. Following the survey, we conduct a controlled experiment where the Data and Analytics population is divided into four groups. One group acts as control mechanism while the remaining three groups are exposed to three different Virtual-Mirroring-Based Learning (VMBL) interventions. A virtual-mirror, which is a visualization of an employee’s own social network that provides a self-reflection as a learning process. The premise is that exposure to such self-insights leads to a change in collaborative behavior. After a period of nine months, the baseline survey is repeated and then the effects of the interventions are analyzed. The findings provided original insights into the evolution of the Data and Analytics function, the characteristics of an effective VMBL design, and the relationship between resilience-based workstyles and brokerage roles in social networks. The applied and theoretical contributions of this research provide a template for practitioners while advancing the theory and measurement of resilience