Collective expectations and the process of platform ecosystem creation: the case of Baidu in autonomous driving

Keywords:

Abstract:

COLLECTIVE EXPECTATIONS AND THE PROCESS OF PLATFORM ECOSYSTEM CREATION: THE CASE OF BAIDU IN AUTONOMOUS DRIVING

In this paper, we explore how collective expectations dynamics about an emerging technology influences the process of platform ecosystem creation by a diversifying entrant in the nascent industry stage. We study the case of Baidu (2013-2019), which diversified into autonomous driving (AD) technology and engaged in creating an open platform ecosystem in China. Collective expectations dynamics about the value and commercialization potential of the AD technologies influenced the cognition and behavior of other ecosystem actors, especially the incumbent automakers, creating unique challenges for Baidu at each stage. We analyse Baidu’s strategic response to these challenges and propose a process model. We contribute to the literature on platform ecosystem creation in nascent industries by underlining the collective expectations dynamics and the evolving entrant-incumbent relationship as salient factors in the process.

Purpose

AD is a most revolutionary technology. It may fundamentally transform the idea of mobility, shifting the focus from product to service, and alter the business landscapes of auto industry and transportation sector (Cavazza, Gandia, Antonialli, & Zambalde, 2019). The advent of AD technology not only triggered the emergence and transformation of industries (Agarwal, Moeen, & Shah, 2017), but have also opened up new opportunities for platform ecosystem creation (Gawer, Yoffie, & Cusumano, 2019). Entrants with capabilities in the AD software and hardware massively enter into the field, the industry incumbents— automakers and tier ones also increase commitments on it. AD technology, like any other AI technologies, is empirical in nature. It creating strong network effects and could create a winner-takes-all situation in the platform competition (Gawer et al., 2019; Parker, Van Alstyne, & Choudary, 2016)– a firm which succeeds in attracting complementors and deploying its technology has an advantage to further improve its algorithms through generating user data. Hence, software entrants aiming to dominate this industry, like Baidu, are racing against time to create platform ecosystems to develop their core technologies and attract complementors, especially automakers.

Scholars have studied the process of platform ecosystem creation in mature industries (Gawer & Cusumano, 2002; Zhu & Iansiti, 2012); there are few studies in the context of nascent industries when the platform entrants need to persuade incumbent complementors to join in (e.g., Ansari, Garud, & Kumaraswamy (2016)). Furthermore, there is no work explore the effects of industry-level environmental changes on the platform creation processes, which happens frequently during industry emergence. We aim to address these gaps in the literature. Nascent industries are characterized by high uncertainty and ambiguity, and fleeting cognition of actors (Kaplan & Tripsas, 2008; Santos & Eisenhardt, 2009). Scholars have established the role of expectations and visions in the emergence of technical fields and studied the effects of collective expectations dynamics on the strategic behavior of industry actors and institutions to the technology development (Borup, Brown, Konrad, & Van Lente, 2006; Fenn & Raskino, 2008; Konrad, 2006; Konrad, Markard, Ruef, & Truffer, 2012; Melton, Axsen, & Sperling, 2016). It becomes even more salient when an entrant needs the incumbents to join the platform as complementors but the latter may be constrained by inertia to recognize the opportunity. Thus, our study integrates the research on collective expectations dynamics to the literature on platform ecosystem.

Design

Given limited prior theory, we adopted an inductive case study research design (Yin, 2013). We followed a theoretical sampling approach (Eisenhardt, 1989) and studied how Baidu – a software & AI entrant, created a platform ecosystem for autonomous driving in China between 2013 and 2019. Baidu, founded in 2000, is one of the foremost Internet firms and a major player in the AD technology in China since 2013. Baidu takes a platform strategy and focuses on developing the “brains” of autonomous vehicles. It creates a large ecosystem and collaborated with as many partners as possible. In 2017, it launched Apollo Open Platform, striving to become “Android of the AD industry” (Qi Lu, COO of Baidu, 2017-18). As of January 2020, Apollo platform has gathered more than two hundred participants, including 31 automakers like BMW, Ford, Toyota, FAW, Geely, BAIC. Yet, the nascent AD industry in China underwent cycles of hype and disappointment between 2013 and 2019. Therefore, how Baidu created the platform ecosystem and addressed the challenges stemming from changes in the collective expectations about AD technologies represents a “revelatory case” for our study (Eisenhardt & Graebner, 2007).

Our study relies on both primary data and secondary data from Baidu, its complementors, and other industry stakeholders. We conducted 39 in-depth semi-structured interviews and collected about 60 hours of data. Additionally, we accessed 18 archival interviews and 10 keynote speeches. Therefore, we have in total 67 interviews. We also used archival materials in Chinese and English. Qualitative data from multiple sources helped us not only reconstruct the events but also capture a variety of actions, perceptions, and meanings of key actors involved in strategy making across organizations. Furthermore, we follow the works of expectations (e.g., Konrad et al., 2012; Melton, Axsen, & Sperling, 2016) to measure collective expectation through the mainstream newspaper data and verified the trends with our informants.

Findings and contributions

The Chinese AD industry underwent three evolutionary stages of collective expectations between 2013 and 2019 – incubation (2013-16), hype (2017-mid 2018) and disappointment (mid 2018-19). Expectation dynamics significantly influenced the cognitions, decisions and behavior of various industry actors, and threw distinct challenges to Baidu. In the incubation stage, incumbent automakers were reluctant to commit resources to AD technology and co-innovate with Baidu, thus, creating a “dual clocks” problem between the platform entrant and incumbent complementors. In the hype stage, industry witnessed new entries, intensifying competition between and within ecosystems. Automakers entered into the field aggressively yet wishing to expand their firm boundary. Therefore, Baidu’s ecosystem experienced strong coopetitive tensions over the issues of value-creation and value capture. In the disappointment stage, automakers became pessimistic about AD, which threatened collapse of Baidu’s ecosystem. The challenge was to conserve the ecosystem and continue the development efforts. We further unravel strategic actions Baidu took to address those challenges and create a thriving ecosystem.

Practical and theoretical implications

Our study contributes to the literature in several ways. First, we propose a process model of how a diversifying entrant creates a new platform ecosystem in a nascent industry, characterized by ups and downs in the collective expectations about value, reliability and commercialization of the new technology. Second, we contribute to the literature on platform ecosystems by underlining collective expectation dynamics and evolving entrant-incumbent relationship as salient factors in the process of ecosystem development in nascent industry.

This study also contributes to practice. In particular, there is a massive surge of interest today in “disrupting” auto industry. This project will extend our understanding of the challenges the new entrants face at the ecosystem level — no matter they are start-ups or tech giants— and how they, especially the diversifying ones, might navigate these challenges.

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