Innovation Strategy and Entry Deterrence

Ozge  Turut; Elie  Ofek

Article | Journal of Economics & Management Strategy | Fall 2012

Innovation Strategy and Entry Deterrence

by Ozge Turut and Elie Ofek

Abstract

We model an incumbent's decision to pursue radical or incremental innovation when facing a rival entrant. The radical innovation may yield lucrative financial returns but entails significant technological and market-related uncertainties. It is also particularly attractive to the rival entrant: if the market for it pans out, such an innovation obsoletes the existing technology and any incremental improvements to it. Each firm has its own assessment of the market potential for the radical innovation, and the reliability of these market forecasts can differ. We show that when the entrant's market-assessment capability is weak, the incumbent will pursue incremental innovation and postpone its plans to develop radical innovation even when it thinks highly of the market potential for the radical innovation. The incumbent does so to avoid validating the high market potential to the entrant, who may otherwise be encouraged to invest aggressively. The incumbent thus prefers to look 'soft' with respect to its innovation strategy in order to discourage entry. Even if its innovation strategy is not observable, we show that an incumbent that assesses the commercial potential for a radical innovation favorably may pursue an incremental path and communicate its plans publicly; this strategy serves to reduce entry by affecting the entrant's beliefs about the market potential of the innovation. Finally, we extend the model to investigate the entrant's decision to communicate its innovation intentions. We find that the entrant communicates its plans to aggressively pursue radical innovation only if the incumbent's market-assessment capabilities are strong. In doing so, the entrant acts preemptively to discourage the incumbent from pursuing the radical innovation and is less concerned with validating market potential.

Keywords: Competition; Innovation Strategy; Risk and Uncertainty; Markets; Mathematical Methods;

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Citation:

Turut, Ozge, and Elie Ofek. "Innovation Strategy and Entry Deterrence." Journal of Economics & Management Strategy 12, no. 3 (Fall 2012).

In 2019, Chinese smartphone maker and telecommunications empire Huawei was preparing to launch its new flagship smartphone series, the Mate 30. After years of explosive growth, the previous 18 months had been a challenge for the company. In early 2018, Huawei’s planned smartphone launch in the U.S. had failed due to pressure from regulators who cited concerns about the company facilitating Chinese spying. Over the following months, a trade war between the U.S. and China had broken out, with Huawei at its epicenter. By mid-2019, after months of U.S. pressure and restrictions, Huawei found its supply chain for computer parts and software under siege and several governments around the world threatened to ban its 5G network equipment from their countries. In this unpredictable climate, Huawei founder Ren Zhengfei and his management team had to figure out not only how to successfully launch Mate 30 phones in the upcoming fall, but how to ensure the company’s long term viability.

Launching its first products in the fall of 2016 in New York, insurtech startup Lemonade was on a mission to disrupt the insurance market by using AI and behavioral economics principles. The company offered renters, homeowners and condo insurance and mainly targeted millennial, tech-savvy individuals with relatively few assets— customers who were less attractive to traditional insurers who had costly, labor-intensive, operations. By offering a delightful, bot-driven consumer experience, embracing a model that removed any incentive to deny claims and that let customers pick causes to donate leftover money to, building a loveable brand, and employing creative digital advertising – the company was able to grow at an impressive pace. By the end of 2018, Lemonade was active in 24 U.S. states, had insured 425,000 customers and was expecting sales (gross premiums) of over $57 million.
In an effort to sustain the spectacular growth momentum, the co-founders sought to pursue ambitious plans in 2019, including the launch of new insurance product lines as well as international expansion. But limited resources and scarce management bandwidth suggested that taking on both moves simultaneously could be highly risky. In deciding which path to take, the leadership team had to consider whether it was more important at this stage to scale globally with the current product lineup or to cover a breadth of insurance categories and stay in the U.S. for now. Assessing the relevance of the business model and the role of AI for each of these moves, understanding the nature of consumers in different markets, and foreseeing competitive pressures from both incumbents and startups were key in making the right call.

Article | Management Science | August 2019

When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation

Yicheng Song, Nachiketa Sahoo and Elie Ofek

Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer’s preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts by selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% more articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site.

Keywords: recommender systems; personalization; recommendation diversity; variety seeking; collaborative filtering; consumer utility models; digital media; clickstream analysis; learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods;

Citation:

Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757. View Details

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