Organizing Entrepreneurial Teams: A Field Experiment on Autonomy over Choosing Teams and Ideas

2023 | Organization Science | Citations: 1

Authors: Boss, Viktoria; Dahlander, Linus; Ihl, Christoph; Jayaraman, Rajshri

Abstract: Scholars have suggested that autonomy can lead to better entrepreneurial team pe ... Expand

Abstract: Scholars have suggested that autonomy can lead to better entrepreneurial team performance. Yet, there are different types of autonomy, and they come at a cost. We shed light on whether two fundamental organizational design choices—granting teams autonomy to (1) choose project ideas to work on and (2) choose team members to work with—affect performance. We run a field experiment involving 939 students in a lean startup entrepreneurship course over 11 weeks. The aim is to disentangle the separate and joint effects of granting autonomy over choosing teams and choosing ideas compared with a baseline treatment with preassigned ideas and team members. We find that teams with autonomy over choosing either ideas or team members outperform teams in the baseline treatment as measured by pitch deck performance. The effect of choosing ideas is significantly stronger than the effect of choosing teams. However, the performance gains vanish for teams that are granted full autonomy over choosing both ideas and teams. This suggests the two forms of autonomy are substitutes. Causal mediation analysis reveals that the main effects of choosing ideas or teams can be partly explained by a better match of ideas with team members’ interests and prior network contacts among team members, respectively. Although homophily and lack of team diversity cannot explain the performance drop among teams with full autonomy, our results suggest that self-selected teams fall prey to overconfidence and complacency too early to fully exploit the potential of their chosen idea. We discuss the implications of these findings for research on organizational design, autonomy, and innovation.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors are grateful for funding from the Innovation Growth Laboratory, the Peter Curtius Foundation, and the Deutsche Forschungsgemeinschaft [Grant CRC TRR 190].Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1520. Collapse

Topics: team productivity entrepreneurship missing data intrinsic motivation evaluation criteria

Methods: experiment experimental group survey causal mediation analysis field experiment

HARKing and P-Hacking: A Call for More Transparent Reporting of Studies in the Information Systems Field

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Hu, Han-fen; Moody, Gregory; Galletta, Dennis

Abstract: While researchers are expected to look for significant results to confirm their ... Expand

Abstract: While researchers are expected to look for significant results to confirm their hypotheses, some engage in intentional or unintentional HARKing (Hypothesizing After Results are Known) and p-hacking (repeated tinkering with data and retesting). If these practices are widespread, one possible result is field-wide exaggerated (inflated) results reported in Information Systems (IS) publications. In this paper, we summarize the literature on HARKing and p-hacking across different disciplines. We offer an illustrative example of how an IS study could involve HARKing and p-hacking in various stages of the project to generate a more "publishable" result. We also report on a survey targeted at IS researchers to explore their experiences and awareness of this issue. Finally, we provide recommendations and suggestions based on the review of practices in other fields and advocate for more transparency in reporting research projects, so that study results can be interpreted properly, and reproducibility and replicability can be increased. Collapse

Topics: website web page

Methods: p hacking descriptive statistic survey parametric test statistical hypothesis test

Theories: protection motivation theory

Filtering Survey Responses from Crowdsourcing Platforms: Current Heuristics and Alternative Approaches

2019 | International Conference on Information Systems | Citations: 0

Authors: Schmidt, Lennard; Dost, Florian; Maier, Erik

Abstract: Information Systems research continues to rely on survey participants from crowd ... Expand

Abstract: Information Systems research continues to rely on survey participants from crowdsourcing platforms (e.g., Amazon MTurk). Satisficing behavior of these survey participants may reduce attention and threaten validity. To address this, the current research paradigm mandates excluding participants through filtering heuristics (e.g., time, instructional manipulation checks). Yet, both the selection of the filter and the filtering threshold are not standardized. This flexibility may lead to suboptimal filtering and potentially “p-hacking”, as researchers can pick the most “successful” filter. This research is the first to tests a comprehensive set of established and new filters against key metrics (validity, reliability, effect size, power). Additionally, we introduce a multivariate machine learning approach to identify inattentive participants. We find that while filtering heuristics require high filter levels (33% or 66% of participants), machine learning filters are often superior, especially at lower filter levels. Their “black box” character may also help prevent strategic filtering. Collapse

Topics: logistics management information privacy concern website privacy shapley additive explanation

Methods: survey machine learning artificial neural network regression analysis method computational algorithm