Incentives & Governance

The Laboratory for Innovation Science at Harvard (LISH) is changing the way we look at incentives and governance in industry and academia through experimentation and analysis with a focus on behavioral economics.

Key Questions

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What are the motives for creative workers engaged in innovation activities?

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How do extrinsic, intrinsic, and pro-social motives interact in driving effort and performance for creative workers?

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When are contests vs. community most effective?

 

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What is the optimal balance between autonomy and managerial control for innovation activities?

 

These, and related questions, drive the research conducted by LISH and provide a clear pathway to achieve LISH’s mission. Research projects span a diverse array of areas of application, from the development of crowdsourcing best practices to exploring the science of science by examining the value of scientific citations. See below for more information on each of the individual projects in this research track.

Related Publications

Philip Brookins, Dmitry Ryvkin, and Andrew Smyth. 3/8/2021. “Indefinitely repeated contests: An experimental study.” Experimental Economics . Publisher's VersionAbstract
We experimentally explore indefinitely repeated contests. Theory predicts more cooperation, in the form of lower expenditures, in indefinitely repeated contests with a longer expected time horizon. Our data support this prediction, although this result attenuates with contest experience. Theory also predicts more cooperation in indefinitely repeated contests compared to finitely repeated contests of the same expected length, and we find empirical support for this. Finally, theory predicts no difference in cooperation across indefinitely repeated winner-take-all and proportional-prize contests, yet we find evidence of less cooperation in the latter, though only in longer treatments with more contests played. Our paper extends the experimental literature on indefinitely repeated games to contests and, more generally, contributes to an infant empirical literature on behavior in indefinitely repeated games with “large” strategy spaces.
Philip Brookins and Paan Jindapon. 2/20/2021. “Risk preference heterogeneity in group contests.” Journal of Mathematical Economics. Publisher's VersionAbstract
We analyze the first model of a group contest with players that are heterogeneous in their risk preferences. In our model, individuals’ preferences are represented by a utility function exhibiting a generalized form of constant absolute risk aversion, allowing us to consider any combination of risk-averse, risk-neutral, and risk-loving players. We begin by proving equilibrium existence and uniqueness under both linear and convex investment costs. Then, we explore how the sorting of a compatible set of players by their risk attitudes into competing groups affects aggregate investment. With linear costs, a balanced sorting (i.e., minimizing the variance in risk attitudes across groups) always produces an aggregate investment level that is at least as high as an unbalanced sorting (i.e., maximizing the variance in risk attitudes across groups). Under convex costs, however, identifying which sorting is optimal is more nuanced and depends on preference and cost parameters.
Marco Iansiti and Karim R. Lakhani. 3/3/2020. “From Disruption to Collision: The New Competitive Dynamics.” MIT Sloan Management Review.Abstract
In the age of AI, traditional businesses across the economy are being attacked by highly scalable data-driven companies whose operating models leverage network effects to deliver value.
Roberto Verganti, Luca Vendraminelli, and Marco Iansiti. 3/19/2020. “Innovation and Design in the Age of Artificial Intelligence”. Publisher's VersionAbstract

At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This “decision making” side of innovation is what scholars and practitioners refer to as “design”. Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture?

This article proposes a framework for understanding design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem solving is significantly conducted by algorithms, human design increasingly becomes an activity of sense making, i.e. understanding which problems should or could be addressed. This shift in focus calls for new theories and brings design closer to leadership, which is, inherently, an activity of sense making.

Our insights are derived from and illustrated with two cases at the frontier of AI ‐‐ Netflix and AirBnB (complemented with analyses in Microsoft and Tesla) ‐‐, which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human‐intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people‐centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user‐centered than human‐based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle.

In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This article discusses the implications of these insights for design and innovation management scholars and practitioners.

Andrea Blasco, Olivia S. Jung, Karim R. Lakhani, and Michael E. Menietti. 4/2019. “Incentives for Public Goods Inside Organizations: Field Experimental Evidence.” Journal of Economic Behavior & Organization, 160, Pp. 214-229. Publisher's VersionAbstract

We report results of a natural field experiment conducted at a medical organization that sought contribution of public goods (i.e., projects for organizational improvement) from its 1200 employees. Offering a prize for winning submissions boosted participation by 85 percent without affecting the quality of the submissions. The effect was consistent across gender and job type. We posit that the allure of a prize, in combination with mission-oriented preferences, drove participation. Using a simple model, we estimate that these preferences explain about a third of the magnitude of the effect. We also find that these results were sensitive to the solicited person’s gender.

Teppo Felin, Karim R. Lakhani, and Michael L. Tushman. 2017. “Firms, Crowds, and Innovation.” Strategic Organization, 15:2, Special Issue on Organizing Crowds and Innovation, Pp. 119-140. Publisher's VersionAbstract

The purpose of this article is to suggest a (preliminary) taxonomy and research agenda for the topic of “firms, crowds, and innovation” and to provide an introduction to the associated special issue. We specifically discuss how various crowd-related phenomena and practices—for example, crowdsourcing, crowdfunding, user innovation, and peer production—relate to theories of the firm, with particular attention on “sociality” in firms and markets. We first briefly review extant theories of the firm and then discuss three theoretical aspects of sociality related to crowds in the context of strategy, organizations, and innovation: (1) the functions of sociality (sociality as extension of rationality, sociality as sensing and signaling, sociality as matching and identity); (2) the forms of sociality (independent/aggregate and interacting/emergent forms of sociality); and (3) the failures of sociality (misattribution and misapplication). We conclude with an outline of future research directions and introduce the special issue papers and essays.

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