Three essays on problem-solving in collaborative open productions

The term “open production” is frequently used to describe production systems that rely on volunteer participants who are willing to participate, produce, and bear private costs in order to provide a public good. Examples of open production are becoming increasingly common in many industries. What make these productions possible? How may they be sustained in a world of organizations in which the evolutionary products of economic selection are elaborate hierarchical forms of organization? One way to address these questions is to look at how open productions solve problems that are common to all production organizations such as, for example, problems in the division of labor, allocation of tasks, collaboration, coordination, and maintaining balance between inducement and contributions. Under the conditions of extreme decentralization that are the defining feature of open productions, this approach implies a detailed observation of individual problem solving practices. This is the approach I develop in my dissertation. Unlike much of the prior literature on open productions, I deemphasize motivational elements, status-seeking motives, and allocation of property rights issues. I focus instead on actual work practices as revealed by the day-by-day problem solving activities that qualify open productions projects as production organizations despite the absence of formal contractual arrangements to regulate principal-agent relations. What my work adds to the extensive, informative, and well-developed discipline-based explanations that are currently available, is a focus on the emergence of micro-organizational mechanisms through which problem assignment (Chapter 2), problem resolution (Chapter 3), and sustained participation (Chapter 4) are obtained in open productions. In my essays, I draw from organizational sociology and the behavioral theory of the firm to specify models that relate individual problem-solving activities to structured patterns of action through emergent work practices. In the models that I specify and test, I emphasize processes of attention allocation (Chapter 2), repeated collaboration and group diversity (Chapter 3) and identity construction (Chapter 4) as central to our understanding of the dynamics of problem-solving in organizations. One element of novelty in my study is that my research design makes these work practices directly observable at a level of detail, completeness, and precision that was inaccessible in the past. To illustrate the empirical value of the view that I develop I examine problem-solving activities – i.e., bug fixing and code production – within two Free/Open Source Software (F/OSS) projects during their entire life span. Readers of my work will know more about how organizational micro-mechanisms emerge in open productions

Wild birds as carriers of antimicrobial-resistant and ESBL-producing Enterobacteriaceae

open6noopenDotto, G.; Menandro, M.L.; Mondin, A.; Martini, M.; Tonellato, F.R.; Pasotto, D.Dotto, Giorgia; Menandro, MARIA LUISA; Mondin, Alessandra; Martini, Marco; Tonellato, F. R.; Pasotto, Daniel

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics

Jumping on the bandwagon? A longitudinal study on collaboration networks and decision to participate

In the context of self-managing teams, whether a member decides to voluntarily take action in a required task may depend on how many fellow members have already done so. In this kind of binary decisions with externalities bandwagon or 'herding' effects play a crucial role in individual decisions to undertake a specific course of action. Such effects have been linked to a broad set of phenomena including diffusion of innovation, segregation, and success of fads. Building on these general results, in this paper we conjecture that individual decisions to take on a task (i.e., the matching between individuals and jobs) are influenced by network relations generated by collaboration among team members. In order to explore our conjecture we collected data on a Free/Open Source Software (F/OSS) project team consisting of 227 volunteer developers committed since 2002 to the development of a web browser. We reconstructed 2-mode co-collaboration networks (software developer by bug) in which a tie represents a voluntary action taken by a developer in order to solve a specific bug. Co-collaboration networks were collected for several six-month development cycles of the software. We report and discuss results of longitudinal actor-based modelling that we specify to test for the influence of local network structures on developer’s decision to take action on a specific bug. The study controls for bug-specific and developer-specific characteristics that may also affect developers’ decisions exogenously. We also control for priority and severity levels assigned by the team to bugs in an attempt to manage voluntary contribution

Organized Anarchies and the Network Dynamics of Decision Opportunities in an Open Source Software Project

When considered as organized solutions to problems of provision of public goods, Free/Open Source Software (F/OSS) productions share a number of their defining features with the organized anarchies described by Cohen, March and Olsen in their “Garbage Can Model” (GCM). The open and voluntary contribution of software developers creates constant fluctuations in levels of attention and an extremely fluid participation. The lack of predefined hierarchical access to organizational problems determines a fundamental uncertainty about how collective goals may be linked to individual activities, and in how responsibilities and tasks may be allocated efficiently within the project. Finally, the complexity involved in the collective production of tens of thousands of lines of computer code without explicit coordination creates a situation of technological ambiguity supported by a radically decentralized activity of organizational problem finding and problem solving. In this paper we take these broad similarities as point of departure to specify an empirical model that captures some of the garbage can properties of organizational problem-solving activities in the context of a specific F/OSS project followed throughout a complete release cycle. We examine the interconnected system of individual decisions emerging from problem-solving activities performed by the 135 contributors involved in the F/OSS project on the 719 software bugs reported during the period of observation. We treat the evolving two-mode network produced by encounters between carriers of organizational solutions (contributors) and organizational problems (software bugs) as a dynamic opportunity structure that constrains and enables organizational decision making. We document how stable local configurations linking problems and solutions are induced by – and at the same time sustain – decentralized problem-solving activities with meaningful self-organizing properties

A longitudinal study on collaboration networks and decision to participate in a FLOSS community.

In this paper we conjecture that individual decisions of FLOSS (Free/Libre Open Source Software) developers to take on a task are influenced by network relations generated by collaboration among project members. In order to explore our conjecture we collected data on a FLOSS project team consisting of 227 developers committed since 2002 to the development of a web browser. We reconstructed 2-mode co-collaboration networks (software developer by bug) in which a tie represents an action taken by a developer in order to solve a specific bug. Co-collaboration networks were collected at five points in time during a six-month development cycle of the software. We report and discuss results of longitudinal actor-based modeling that we specify to test for the influence of local network structures on developer's decision to take action on a specific bug. The study controls for bug-specific and developer-specific characteristics that may also affect developers' decisions exogenously. We also control for priority and severity levels assigned by the team to bugs in an attempt to manage voluntary contribution

Time heterogeneity in stochastic actor-oriented models: an empirical test on problem-solving dynamics in an open source software project

How do organizations induce and coordinate individual action toward collective goals? The interest of organizational and sociological scholars in this question has been renewed by the emergence of communities of production composed by volunteers who can choose which task to take on, why and when. Relatively little is known about the social micro-mechanisms that drive individual action and ensure coordination between interdependent tasks when traditional market-based and hierarchy-based mechanisms of coordination are unavailable. In previous research we applied stochastic actor-oriented models to analyze one of such communities. We showed that decisions of individuals to take on specific tasks are influenced by their local network neighborhood through feedback mechanisms, which shape the different levels of engagement across individuals, and balancing feedback mechanisms, which affect the different popularity of tasks. In this study we investigate whether the effect of these local network structures on the association of individuals and tasks varies according to when it unfolds in the life-cycle of the project. In order to address this question we analyze the same Free/Open Source Software (F/OSS) community of our previous study. We reconstruct the two-mode network generated by problem-solving activities undertaken by 135 software developers on 719 software bugs during an entire release cycle of the software. We estimate new stochastic actor-oriented models by allowing the effects of local networks structures to be heterogeneous over time. We discuss the differences with previous restricted models and their implications for the understanding of social micro-mechanisms underpinning the emergence of endogenous coordination in communities of production