First Look

Abstract—Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new insights about firm heterogeneity and its effects on performance in empirical settings in strategy. RCMs allow testing for and predicting firm-specific coefficients, thereby distinguishing between effects that have a significant mean versus significant variance. RCMs may also be used to explore the sources of firm heterogeneous effects. We develop a simulation testbed using synthetic datasets to show that RCMs are more precise at allocating firm heterogeneous effects to model slopes and intercepts than standard regression models. We also discuss RCMs’ possible limitations due to sample size requirements, nonconvergence problems, and potentially restrictive assumptions. Overall, RCMs allow strategy researchers to test and build new theories at a more granular level.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=55151

Abstract—We conduct laboratory experiments that explore how gender stereotypes shape beliefs about ability of oneself and others in different categories of knowledge. The data reveal two patterns. First, men’s and women’s beliefs about both oneself and others exceed observed ability on average, particularly in difficult tasks. Second, overestimation of ability by both men and women varies across categories. To understand these patterns, we develop a model that separates gender stereotypes from misestimation of ability related to the difficulty of the task. We find that stereotypes contribute to gender gaps in self-confidence, assessments of others, and behavior in a cooperative game.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=55101

Abstract—Using retail chain data, we study the effects of a tournament incentive plan based primarily on objective performance, but incorporating managerial discretion in the selection of winners. In principle, such plans could motivate employees to perform both at a high level, based on objective criteria, and in accordance with company values, considered via managerial discretion. However, such plans could be counterproductive if enough participants (especially those who don’t win) perceive that subjectivity (introduced via discretion) adds unfairness. We show that, on average, the tournament incentive plan was associated with improved store sales. We also find that such plans can be more beneficial for geographically distant participants, where the potential for improving alignment is greater. Lastly, we find some evidence that participants’ resource constraints (potentially affecting unfairness concerns) can impact outcomes under the plan.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=55144

Abstract—Many of the largest and most successful businesses today rely on providing services at no charge to at least a portion of their users. For consumers, it is easy to celebrate free service. At least in the short term, free services are often high quality, and users find a zero price virtually irresistible. But long-term assessments could differ, particularly if the free service reduces quality and consumer choice. In this short paper, we examine these concerns.

Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=55137

Abstract—The purpose of this chapter is to present analytic tools based on functional maps that can be used to identify investment opportunities and to formulate strategy in large, evolving technical systems. I argue that the points of value creation and value capture in a technical system are the system’s bottlenecks. Bottlenecks arise first as important technical problems to be solved. Once the problem is solved, the solution in combination module boundaries and property rights can be used to capture a stream of rents. In this chapter I extend the functional mapping techniques developed in the last chapter to locate technical and strategic bottlenecks, modules, and property rights. I then show how these analytic tools can be used to construct narratives explaining the dynamics of three nascent technical systems: early aircraft, high-speed steel in machine tools, and container shipping.

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Abstract—CEO activism refers to corporate leaders speaking out on social and environmental policy issues not directly related to their company’s core business. Distinct from nonmarket strategy and traditional corporate social responsibility, the recent wave of CEO activism focuses on social issues unrelated to their core business, ranging from environmental issues to LGBT rights and race relations. In the first study of this phenomenon, we implement two field experiments to provide evidence on how CEO activism can influence public opinions about government policies and consumer attitudes about the CEO’s company.

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Abstract—We study the effect of staggered boards (SBs) on managers' behavior and on long-run firm value using a natural experiment: a 1990 law that imposed a SB on all firms incorporated in Massachusetts. We find that the law led to an increase in Tobin's Q, increased investment in capital expenditure and R&D, more patents, less earnings management, and higher ROA. These effects are concentrated at innovating firms—those firms that are early-life-cycle or engage in R&D spending—and especially at those facing Wall Street scrutiny. Collectively, the evidence suggests that early-life-cycle firms facing high information asymmetries benefit from staggered boards, in part because managers make more valuable long-term investments and reduce myopic behavior.

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Abstract—This study sheds new light on first- and early-mover advantages. Research on this classic topic often assumes that each firm participates in the entirety of the innovation process and that all firms aim to monetize their innovations on product markets. However, a division of labor between innovative new entrants and industry incumbents, endowed with complementary assets, is common in many industries. Such settings are distinct because new entrants have the additional option to sell their innovation in a “market for technology” and may, therefore, seek acquisition rather than shepherding an innovation through the entire commercialization process. We argue that this binary outcome—i.e., success via acquisition—creates different opportunities and threats for new entrants and has important and novel implications for the following question: is it advantageous to be early to market? Using data from the U.S. medical device industry, we find that pioneer (dis-)advantages in a market for technology setting are similar to those typically seen in product markets but different in some important respects. In particular, pioneers must pave the way for a new product type in order to reduce the technological and market risks, where reducing technological risk is of paramount importance. As a reward, pioneers ultimately realize a higher likelihood of acquisition, but among acquired firms, early entrants wait longer for acquisition to happen. To a certain extent, therefore, later movers can free ride on early-movers’ efforts: although they are less likely to experience acquisition, acquisitions of later entrants happen more quickly.

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Abstract—Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces quantile forecasts for the number of passengers arriving at the immigration and security areas. Academic/Practical relevance: Airports and airlines have been challenged to improve decision-making by producing accurate forecasts in real time. Our work is the first to apply machine learning for predicting real-time quantile forecasts in the airport. We focus on passengers’ connecting journeys, which have only been studied by few researchers. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive model developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict complete distributions, moving beyond point forecasts. To derive insights from the tree, we introduce the concept of a stable tree that can be summarized by its key variables’ splits. Results: We identify seven key factors that impact passengers’ connection times, dividing passengers into 16 passenger segments. We find that adding correlations among the connection times of passengers arriving on the same flight can improve the forecasts of arrivals at the immigration and security areas. When compared to several benchmarks, our model is shown to be more accurate in both point forecasting and quantile forecasting. Managerial implications: Our predictive system can produce accurate forecasts, frequently, and in real-time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late connecting passengers and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger arrivals. Our approach can be generalized to other domains, such as rail or hospital passenger flow.

Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=55098

Abstract—Many organizations face critical decisions that rely on forecasts of binary events. In these situations, organizations often gather forecasts from multiple experts or models and average those forecasts to produce a single aggregate forecast. Because the average forecast is known to be under-confident, methods have been proposed that create an aggregate forecast more extreme than the average forecast. But is it always appropriate to extremize the average forecast? And if not, when is it appropriate to anti-extremize (i.e., to make the aggregate forecast less extreme)? To answer these questions, we introduce a class of optimal aggregators. These aggregators are Bayesian ensembles because they follow from a Bayesian model of the underlying information experts have. Each ensemble is a generalized additive model of experts' probabilities that first transforms the experts' probabilities into their corresponding information states, then linearly combines these information states, and finally transforms the combined information states back into the probability space. Analytically, we find that these optimal aggregators do not always extremize the average forecast, and when they do, they can run counter to existing methods. On two publicly available datasets, we demonstrate that these new ensembles are easily fit to real forecast data and are more accurate than existing methods.

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Abstract—We examine whether and to what extent managers are evaluated, in their relative-performance contracts, on the basis of systematic performance. Focusing on relative total shareholder returns (rTSR), the predominant metric specified in these contracts and used by market participants to evaluate managers, we document that 60% of firms—those that choose specific peers—do a remarkable job of capturing the systematic component of returns in adherence to the informativeness principle. However, 40% of firms—those that choose index-based benchmarks—retain substantial systematic noise in their rTSR metrics, which they could have substantially corrected by using their self-chosen compensation-benchmarking peers. The selection of noisy benchmarks is economically important, is associated with lower ROA, and can be explained by compensation consultants' tendencies and firms' governance weaknesses.

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Abstract—Firms do not typically disclose information on their costs to produce a good to consumers. However, we provide evidence of when and why doing so can increase consumers’ purchase interest. Specifically, building on the psychology of disclosure and trust, we posit that cost transparency, insofar as it represents an act of intimate disclosure, fosters trust. In turn, this heightened trust enhances consumers’ willingness to purchase from that firm. This account was supported in four studies, conducted in the field and in the lab. A pre-registered field experiment indicated that diners were 21.1% more likely to buy a bowl of chicken noodle soup when a sign revealing the ingredients of the soup also included the cafeteria’s costs to make the soup (Study 1). Three subsequent lab experiments replicated and extended this basic effect, providing evidence of when and why it occurs (Studies 2–4). Taken together, these studies imply that the proactive revelation of costs can improve a firm's bottom line.

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Abstract—Combining corporate sustainability performance scores based on environmental, social, and governance (ESG) data with big data measuring public sentiment about a company’s sustainability performance, I find that the valuation premium paid for companies with strong sustainability performance has increased over time and that the premium is increasing as a function of positive public sentiment momentum. An ESG factor going long on firms with superior or increasing sustainability performance and negative sentiment momentum and short on firms with inferior or decreasing sustainability performance and positive sentiment momentum delivers significant positive alpha. This low sentiment ESG factor is uncorrelated with other factors, such as value, momentum, size, profitability, and investment. In contrast, the high sentiment ESG factor delivers insignificant alpha and is strongly negatively correlated with the value factor. The evidence suggests that public sentiment influences investor views about the value of corporate sustainability activities and thereby both the price paid for corporate sustainability and the investment returns of portfolios that consider ESG data.

Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=55162

Abstract—The use and aggregation of probability forecasts in practice is on the rise. In this position piece, we explore some recent, and not so recent, developments concerning the use of probability forecasts in decision-making. Despite these advances, challenges still exist. We expand on some important challenges such as miscalibration, dependence among forecasters, and selecting an appropriate evaluation measure, while connecting the processes of aggregating and evaluating forecasts to decision-making. Through three important applications from the domains of meteorology, economics, and political science, we illustrate state-of-the-art usage of probability forecasts: how they are aggregated, evaluated, and communicated to stakeholders. We expect to see greater use and aggregation of probability forecasts, especially given developments in statistical modeling, machine learning, and expert forecasting; the popularity of forecasting competitions; and the increased reporting of probabilities in the media. Our vision is that increased exposure to and improved visualizations of probability forecasts will enhance the public’s understanding of probabilities and how they can contribute to better decisions.

Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=55099

Silkroad—a cross-cultural music collaboration that world-famous cellist Yo-Yo Ma had spearheaded since 1998, was preparing to celebrate its 20th anniversary. In parallel, Ma was stepping back from his role as the organization’s Artistic Director. Silkroad had come of age, gaining international recognition for its uniquely global music, which blended musical traditions from across the world—especially Asia, the Middle East, Europe, and America. The Ensemble had also forged strong partnerships with universities, local governments, and civic groups and developed an array of outreach programs to help people understand and express themselves through music. Ma was confident that there could be no better time to hand over leadership. But the transition was bittersweet: He felt a deep personal connection both to the Ensemble as a whole and to every one of its performers and staff members. And he wondered how reducing his involvement would affect the organization’s sustainability.

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This case follows Enfoca, Peru’s largest local private equity firm and its portfolio company Maestro, a leading player in Peru’s hardware retail market. Peru’s GDP growth between 2008 and 2014 was the highest of any Latin American country. Growth of the Peruvian middle class led to a wide array of investment opportunities, particularly in industries such as housing, construction, and home improvement. While the post-recession years were very good for Maestro, due to the market’s small size, Enfoca could not escape some of the longstanding specificities of the Peruvian private capital market, namely, limited exit and new investment opportunities. As Enfoca faced an aging fund, its leadership team began considering alternatives to the traditional private equity fund model. This case presents an example of a local private equity firm operating in a small emerging market with relatively low market capitalization. It also evaluates the costs and benefits of the traditional finite-life fund structure and introduces the concept of a secondary transaction as a means to transition fund shares between existing and new limited partners while maintaining the original fund investments.

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As a middle manager at a biotechnology company, Sandra Brown harnessed digital tools and social media to engage others and build campaigns for change in the company. This case follows her career at the company and describes the challenges she faced as a change agent, working to promote gender equality within the company and a new drug developed to fight cancer externally.

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Sandra Brown, a middle manager at a biotech company who has led internal and external movements for change over the last few years, faces a decision. Whether to continue to work for change at the company or move on to pursue new opportunities elsewhere, where her new ideas and innovative approach might be better received.

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Using digital and social media tools and lessons learned from prior change campaigns as a middle manager in a large biotech company, Sandra Brown continued in a new role in the quality division, engaging staff in a quality movement at the company. She had found a new executive who valued her ability to engage and develop new ideas, and together they pushed efforts that encouraged participation and grassroots idea development to further quality improvement.

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Jonah Peretti, CEO of digital publishing company BuzzFeed, needs to decide how to respond to Facebook’s announcement that it would prioritize posts from friends over content from publishers.

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No abstract available.

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