Voluntary contributions are crucial to the success of open source software (OSS) projects. Firms sponsoring OSS projects may face substantial challenges in soliciting such contributions, since volunteer participants are neither regulated by an employment contract nor offered financial incentives. Although prior work has shown the positive impact of motivation on the effort expended by volunteer participants, there is limited understanding of how specific firm attributes shape volunteers’ intrinsic motivation. We offer a theoretical model of how the perceived community-based credibility and openness of the sponsoring firm have a positive impact on the intrinsic motivation of volunteer participants. The model is explored using survey data on volunteer participants from two sponsored OSS projects. Results show that a sponsoring firm’s community-based credibility (OSS developers’ perception of its expertise and trustworthiness) and openness (its mutual knowledge exchange with the community) strengthen the volunteer participants’ social identification with the firm-sponsored community, which in turn reinforces their intrinsic motivation to participate. Moreover, the perceived community-based credibility of a sponsoring firm directly enhances volunteer participants’ intrinsic motivation, whereas perceived openness fails to affect motivation without the mediating mechanism of social identification. Implications for firms seeking voluntary contributions for their sponsored OSS projects are discussed.

This study investigates the operations of a novel service model, mobile charging, in which an e-commerce platform operator dispatches trucks equipped with charging piles to provide charging services for customers’ electric vehicles with low battery levels. We model the platform-customer interaction with a Stackelberg game and explicitly characterize customers’ optimal charging decisions under the platform’s various service plans. In a general scenario that involves charging requests within a transportation network, we develop a joint optimization model for the platform’s pricing and service fulfillment, utilizing an elaborately constructed augmented network. In addition, we explore a localized subproblem where multiple orders are concentrated within a specific region. With a simplified model structure, we propose an approximation algorithm with provable performance guarantees and further theoretically evaluate the resource consumption and associated platform benefits for serving the orders in each region. The results enable the batching of neighboring demands within the general scenario as a specialized node, resulting in a streamlined network with fewer nodes. Additionally, we can enhance the algorithm’s efficiency within the general framework through strategic prioritization of node visitations, leveraging the analytical findings. Furthermore, the insights derived can offer recommendations for the deployment of mobile charging and the selection of target areas in the initial stages. Overall, our study provides comprehensive guidelines and valuable insights for mobile charging operations.

AbstractThis study advances a theory of how different aspects of emotion regulation influence individual leader emergence in the intensely emotional context of nascent venture teams. Despite the growing amount of research on the role of leadership in the entrepreneurial process, the emergence of leaders in nascent venture teams has rarely been explored. Drawing on theories and research on leadership emergence and emotion regulation, we argue that the two aspects of emotion regulation (i.e., reappraisal and suppression) exert opposite effects on the degree to which nascent venture team members come to perceive an individual as a leader. We also theorize that team emotions arising from affective events moderate the relationship between reappraisal and leader emergence in such teams. Data from 103 nascent venture teams without prior leaders show a negative relationship between individuals’ trait disposition to suppress emotions and their emergence as leaders, and a positive relationship between their trait disposition to reappraise emotions and their emergence as leaders. Moreover, we find that negative team emotions magnify the positive association between reappraisal and leader emergence, while positive team emotions mitigate it. We discuss the implications of our findings for the literature on entrepreneurial leadership, entrepreneurial emotions, and leadership in general.

Abstract Research summary Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, we employ hybrid inductive methods to study discussions around initiation and change of software licenses—a fundamental and potentially contentious governance issue. First, we apply machine learning algorithms to identify robust patterns in data: resolution is more likely in larger discussion groups and in projects without a license compared to those with a license. Second, we analyze textual data to explain the causal mechanisms underpinning these patterns. The resulting theory highlights the group process (reflective agency switches disputes from bargaining to problem solving) and group property (preference alignment over attributes) that are both necessary for the resolution of governance disputes, contributing to the literature on community governance. Managerial summary Online communities play an increasingly important role in how companies innovate across organizational boundaries and attract talent across geographic locations. However, online communities are no Utopia; disputes abound even (more) when we collaborate virtually. In particular, governance disputes can threaten the functioning and existence of online communities. Our study suggests that governance disputes in online communities either unfold as bargaining over which solution is better or searching for a satisfactory solution. The latter is more likely to reach a resolution, when there is common ground. Companies interested in leveraging the power of online communities should (a) identify or train certain participants to transform endless bargaining into collective problem solving and (b) foster shared knowledge and value basis among participants through recruitment and strong organizational culture.

Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.