How the Internet of Things reshapes the organization of innovation and entrepreneurship

ISSN:0166-497

Expertise Diversity, Informal Leadership Hierarchy, and Team Knowledge Creation: A study of pharmaceutical research collaborations

Knowledge creation increasingly requires experts from diverse domains to collaborate in teams, yet the effect of expertise diversity on team knowledge creation is inconclusive. We focus on task uncertainty and informal leadership hierarchies - the disparity in team members' engagement in leadership activities (task- and relationship-oriented) - to answer the questions when and why expertise diversity may hinder team knowledge creation. We develop a model in which informal leadership hierarchy mediates the conditional indirect effect of the team's expertise diversity on its knowledge creation under different levels of task uncertainty. We test this moderated mediation model using multi-source data from self-managing project teams comprising collaborators from a pharmaceutical company and its research partners. We find that when task uncertainty is low, the indirect effect of expertise diversity on team knowledge creation is positive, whereas when task uncertainty is high, it is negative. This conditional indirect effect occurs via task-oriented but not relationship-oriented leadership hierarchy. Our findings provide insights into the mechanisms and boundary conditions for expertise diversity to hinder, rather than facilitate, knowledge creation in collaborations.ISSN:0170-8406ISSN:1741-304

Designing human resource management systems in the age of AI

The increasing adoption of artificial intelligence (AI) is reshaping the practices of human resource management (HRM). We propose a typology of HR–AI collaboration systems across the dimensions of task characteristics (routine vs. non-routine; low vs. high cognitive complexity) and social acceptability of such systems among organizational members. We discuss how organizations should design HR–AI collaboration systems in light of issues of AI explainability, high stakes contexts, and threat to employees’ professional identities. We point out important design considerations that may affect employees' perceptions of organizational fairness and emphasize HR professionals' role in the design process. We conclude by discussing how our Point of View article contributes to literatures on organization design and human–AI collaboration and suggesting potential avenues for future research.ISSN:2245-408

Asymmetries between partners and the success of university-industry research collaborations

Despite the common belief that orientation asymmetry—fundamental differences in the goals and expectations between partners—constitute a major barrier to successful collaboration, empirical evidence on how orientation asymmetry impacts university-industry collaboration is rare. We seek to understand the nature and impact of orientation asymmetry by conducting a mixed-method study of the research collaborations between a Big Pharma and its academic partners. Our interviews reveal critical asymmetries between partners, concerning not only different orientations, but also different perceptions of conflict. Building on these qualitative findings, we conduct a multi-wave, multi-source survey study to unpack the relationships between orientation asymmetry, conflict within collaboration teams, conflict perception asymmetry, and different types of collaboration success. We contribute to the literature on university-industry collaborations by providing a much-needed comparison of the perspectives from both sides of the collaboration and developing a nuanced understanding of the dynamics within collaboration project teams. We discuss the implications of our findings for researchers, managers, and policymakers.ISSN:0048-7333ISSN:1873-762

Resolving governance disputes in communities: A study of software license decisions

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

Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?

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.ISSN:1047-7039ISSN:1526-545

Leader Emergence in Nascent Venture Teams: The Critical Roles of Individual Emotion Regulation and Team Emotions

This 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.ISSN:1467-6486ISSN:0022-2380ISSN:0022-239