Peer-Reviewed Publications
It’s about Showing Good Faith, Not Avoiding Shows of Weakness: Reworking Leifer’s ‘Local Action’ to Build a Robust Theory of Reciprocity. Advances in Group Processes, 2023.
Simon Friis and Ezra W. Zuckerman Sivan.
The Variety of Beliefs about the Causes of Safety among Safety Practitioners. Safety Science, 2022.
John S. Carroll, Yvonne Pfeiffer, Hans Nowak, and Simon Friis.
Other Publications
How Your Business Should Tap into the Creator Economy. Harvard Business Review, 2024.
Rebecca Karp, Carolyn Fu, and Simon Friis.
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI. Harvard Business Review, 2023.
Simon Friis and James Riley.
Working papers
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Abstract: From genetically modified foods to autonomous vehicles, society often resists otherwise beneficial technologies. Resistance can arise from performance-based concerns, which fade as technology improves, or from principle-based objections, which persist regardless of capability. Using a large-scale U.S. survey quota-matched to census demographics and assessing 940 occupations (N = 23,570 occupation ratings), we disentangle these sources in the context of artificial intelligence (AI). Despite cultural anxiety about artificial intelligence displacing human workers, we find that Americans show surprising willingness to cede most occupations to machines. Given current AI capabilities, the public already supports automating 30% of occupations. When AI is described as outperforming humans at lower cost, support for automation nearly doubles to 58% of occupations. Yet a narrow subset (12%)—including caregiving, therapy, and spiritual leadership—remains categorically off-limits because such automation is seen as morally repugnant. This shift reveals that for most occupations, resistance to AI is rooted in performance concerns that fade as AI capabilities improve, rather than principled objections about what work must remain human. Occupations facing public resistance to the use of AI tend to provide higher wages and disproportionately employ White and female workers. Thus, public resistance to AI risks reinforcing economic and racial inequality even as it partially mitigates gender inequality. These findings clarify the “moral economy of work,” in which society shields certain roles not due to technical limits but to enduring beliefs about dignity, care, and meaning. By distinguishing performance- from principle-based objections, we provide a framework for anticipating and navigating resistance to technology adoption across domains.
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[Previous version: SSRN working paper]
Abstract: Organizations routinely rely on professional judgment to evaluate candidates, asking professionals to assess several dimensions of merit at once. Multi-criteria evaluative instruments make such judgment comparable and actionable by giving each criterion its own named place, ostensibly preserving the plurality of merit by creating room for differentiated professional judgment. We find, paradoxically, that they can make professional judgment less differentiated. We explain this reversal through evaluative hierarchy: because evaluation is itself professional work, criteria differ in professional status. Some criteria more fully enact the profession’s valued identity, competence, and jurisdiction; others remain necessary but more operational. When organizations bundle criteria of unequal status, higher-status judgments can organize lower-status ones, a process we call hierarchical coupling. We test this argument in a natural field experiment embedded in a live scientific grant competition. When reviewers assessed feasibility alongside the higher-status criteria of novelty and impact, feasibility became coupled to novelty and decoupled from whether the proposed work could realistically be carried out and thus the underlying quality it was meant to capture. Although organizations turn to professionals because expertise promises reliable distinctions of merit, our findings suggest that a named criterion does not enter professional evaluation with a fixed meaning: what it comes to capture depends in part on how organizations place it in relation to other professional judgments.
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Abstract: A well-established constraint on diversification comes from the demand side: audiences often penalize producers that span multiple categories: audiences often penalize producers that span multiple categories. A prominent explanation attributes this multicategory discount to the classification difficulties created by offerings that depart from familiar category prototypes. This account helps explain why atypical combinations often struggle but leaves open why the same combination may be valuable to one audience yet not another. Building on the theory-of-value approach to categorization, I argue that audiences evaluate category combinations not only by their typicality but also by what I call theoretical coherence: the extent to which a combination fits an audience’s understanding of how value is created. I test this argument in videogame livestreaming by comparing eSports and speedrunning, two communities that evaluate the same game categories on the same platform and prize elite skill but hold different theories of what skill requires. Using word embeddings trained on more than 119 million community posts, I recover these theories of skill and construct audience-specific measures of coherence, which I link to panel data on Twitch viewership. Viewership is lower when streamers play games that are incoherent with the theory of skill relevant to their audience; this association is stronger than for incoherence under the comparison audience’s theory and persists after accounting for typicality. These findings demonstrate how demand-side theories of value constrain strategic choices in markets and help explain why some atypical combinations can be successful while typical combinations may fail. They shift the question from whether audiences prefer specialists or generalists to which combinations they regard as value creating, thereby specifying the audience-specific content of demand-side limits to diversification. For strategy research, this complements recent theory-based accounts of how novel combinations are conceived with an account of how they are received.
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Abstract: Entrepreneurial experiments are traditionally thought to improve outcomes because they avoid commitment and create real options. Yet, an underappreciated challenge in conducting experiments is that they often are costly for participants, who must endure crude prototypes and invest time and effort in providing the rich feedback entrepreneurs need to learn. In this paper, I document a setting where these costs of participation lead participants to demand commitment from the entrepreneur before they will tolerate experimentation. To this end, I develop and test theory about the tradeoff faced by entrepreneurs who wish to use experiments to test the viability of their ideas when their participants demand commitment. On the one hand, an entrepreneur who enacts a strong commitment to their participants finds that participants are more tolerant of experimentation and willing to provide feedback that yields learning. On the other hand, the entrepreneur is obligated to heed participant feedback, resulting in a trajectory that reinforces appeal to the current participants while constraining them from pivoting to more attractive opportunities. I validate two key implications of my theory using data from a large livestreaming platform, Twitch. I find that streamers who enact a strong commitment strategy experience less viewer drop-off when experimenting with new additions to their repertoire but that each addition leads to less growth in viewership than streamers who enact a weak commitment strategy. Together, my theory and results highlight that entrepreneurial experimentation is, at least in some contexts, a relational process. Reconsidering entrepreneurial experimentation as a relational process helps us understand the limits to entrepreneurial experimentation and provides a basis for understanding why experimentation results in better outcomes for some entrepreneurs than others.
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Abstract: Since Gouldner (1960), social scientists have seen the generalized norm of reciprocity (NOR) as an internalized norm that provides a key “starting mechanism” for exchange among strangers. However, we observe that the NOR is more frequently invoked among strong ties—i.e., where it is presumably extraneous and even off-putting. To explain this difference and provide a stronger foundation for understanding how the NOR facilitates social exchange, we rework Leifer’s (1988) theory of “local action” so that it encompasses a broader array of strategic motives. Whereas Leifer explained why strangers will avoid invoking the NOR in a bid to limit risky claims to status, our theory entails that such avoidance can be expected whenever instrumental goals are salient and mutual commitment is low. And since the costs of imbalance are high precisely when mutual commitment is high (as the parties may be “stuck” with the imbalance “forever”), parties to strong ties should be more likely to invoke the NOR. Two online vignette experiments provide preliminary support for the theory.
The graveyard 🪦
This section showcases papers and projects I've set aside for various reasons—whether due to null results, shifting priorities, or new insights that led me in different directions. I believe in the value of transparency in research, so I'm sharing these to provide a fuller picture of my journey. Who knows? Some of these ideas might get resurrected 🧟♂️
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Content creators face a fundamental tension: they need to monetize their work to sustain their efforts, but doing so can undermine their perceived authenticity. Audiences often view overt commercialization as a sign that a creator has "sold out" or compromised their artistic integrity.
What really drew me to this project was the idea that platforms could resolve this tension in a counterintuitive way: by taking more control, not less. If platforms manage monetization policies, they could potentially shield creators from accusations of "selling out" while still enabling them to earn from their work. This raises intriguing questions about the nature of authenticity in digital markets and suggests that voluntary loss of control might sometimes benefit creators.
One thing I really liked about this project was how it highlights how sociology can inform market design and platform policy.
I ran an experiment to test these ideas (admittedly, I thought the setup for these experiments was pretty cool! Basically, it was disguised as a user experience study). However, it yielded null results, and I had to focus on my dissertation, which led me to put this project on hold.
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I was intrigued by a seeming paradox in entrepreneurial finance: existing theory on relational embeddedness suggests that entrepreneurs should prefer to seek financing from friends and family ties. These close relationships should offer preferential terms, especially given their enhanced information access and control benefits that mitigate risks in economic transactions. Close ties should have better insight into the entrepreneur's character and capabilities, reducing information asymmetry. They also have social leverage to discourage opportunistic behavior, lowering the risk of default or misuse of funds. Yet, there's often a strong aversion to mixing business with personal relationships. This contradiction puzzled me. Why would entrepreneurs avoid tapping into these seemingly advantageous networks? My goal was to explore the factors behind this reluctance, challenging the dominant transaction cost logic found in theories of relational embeddedness.
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Description coming soon...
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Description coming soon...