When and Who Do Platform Companies Acquire? Understanding the Role of Acquisitions in the Growth of Platform Companies

Miric, Milan; Pagani, Margherita; Sawy, Omar A. El

doi:10.25300/misq/2022/16374

Cited by 26 publications

(18 citation statements)

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“…For instance, Choudhury et al (2019) studied CEO communication styles, a domain in which it may be difficult to ex ante specify the keywords associated with any particular style. Similarly, Miric et al (2021) distinguish between platform companies and non‐platform companies, which lack a common dictionary of keywords. In these situations, it is straightforward to manually classify a small number of observations to construct training data, as it may be easy for an individual to “know it when [they] see it” (Polanyi, 1966) but difficult for that same individual to abstract intuitively to construct a list of keywords that reliably and comprehensively distinguishes the two groups.…”

Section: Overview Of Machine Learning Methods In Strategic Management...mentioning

confidence: 99%

“…While this approach is effective when the theoretical constructs and language used to describe them are clear, well‐established, and commonly accepted, in many other contexts it is challenging to construct and validate a dictionary of keywords. To address this issue, some strategy researchers have recently begun using machine learning (ML) techniques to construct theoretically grounded variables from text data (e.g., Choudhury & Kim, 2019; Leyden, 2018; Tidhar & Eisenhardt, 2020; Miric, Pagani, & El Sawy, 2021; Shrestha, He, Puranam and von Krogh, 2021). 1 Although these approaches are beginning to be implemented, best practices regarding the use of supervised ML tools to classify text data are not widely known among strategy researchers so those unfamiliar with these methods may find it challenging to apply them in their own research.…”

Section: Introductionmentioning

confidence: 99%

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Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Miric

Jia

Huang

2022

Strategic Management Journal

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Research Summary Researchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development. Managerial Summary Text‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research.

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Section: Overview Of Machine Learning Methods In Strategic Management...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Miric

Jia

Huang

2022

Strategic Management Journal

Self Cite

View full text Add to dashboard Cite

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“…Nevertheless, its variability makes CE malleable and thus manageable for platform owners via their governance. Essentially, platform governance allows platform owners to influence CE antecedents and steer engagement: Platform owners may govern strategic adjustments of resources and rules (Ghazawneh & Henfridsson, 2013; Song et al, 2018), the platform value proposition (Miric et al, 2021) or of platform agents (Huber et al, 2017). In addition, they may impact relations among complementors by, for example, steering intra‐platform competition (Tiwana, 2015) and customer needs by entering complementary markets (Foerderer et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Sustaining complementor engagement in digital platform ecosystems: Antecedents, behaviours and engagement trajectories

Engert

Evers

Hein

et al. 2023

Information Systems Journal

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Digital platform ecosystems increasingly dominate the enterprise software domain, and the persistence of platforms depends on the sustained engagement of complementors. However, there is a limited understanding of its antecedents, complementors' evaluation of antecedents and the manifestations and dynamic changes of complementors' engagement. Therefore, we investigate complementors' engagement within platform ecosystems over time. We draw on actor and stakeholder engagement from service research to conceptualise complementor engagement (CE) and create an integrated empirical understanding of CE and its dynamics in digital platform ecosystems. Our embedded case study builds on 30 interviews with complementors in Anubis and Osiris enterprise software platform ecosystems. Inductive data analysis reveals five CE antecedents: platform resources and rules, platform value proposition, platform agents, customer needs and other complementors' value propositions. The antecedents are associated with three CE behaviours: generating, networking and synchronising. Further analysis of CE over time resulted in 26 different sequences representing stable and changing engagement trajectories, the latter comprising selective, growing and abating engagement as subcategories. We show how complementors' evaluations of antecedents lead to behaviour changes, providing a novel perspective on the dynamics underlying CE. Finally, we link complementors' evaluation outcomes to their (dis)satisfaction, contributing to the discussion on what drives and impedes CE. The findings implicate the debate on dynamic platform governance and inform platform owners about using cooperative and competitive approaches in the short and long term.

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“…Studies have explored how platform owners manage platform growth, adoption, complementors' incentives, and how platform owners can improve customer discovery by highlighting underappreciated products (Boudreau, 2010; Cennamo & Santalo, 2013; Cennamo & Santaló, 2019; Chu & Wu, 2021; Kapoor & Lee, 2013; Rietveld et al, 2019; Schilling, 2009; Zhang et al, 2020; Zhu & Furr, 2016). Recently, scholars have also started focusing on the distribution of value between platform owners and complementors (Ceccagnoli, Forman, Huang, & Wu, 2012; Chatain & Plaksenkova, 2020; Huang, Ceccagnoli, Forman, & Wu, 2013; Pagani, 2013; Wang & Miller, 2020) and management of competition with other platforms (Miric, Pagani, & El Sawy, 2021).…”

Section: Introductionmentioning

confidence: 99%

Growing platforms within platforms: How platforms manage the adoption of complementor products in the presence of network effects?

Agarwal

Miller

Ganco

2023

Strategic Management Journal

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Research Summary Platform owners often use endorsements to actively manage complementor firms. We argue that the direct network effects of complementors' products play a central role in the platform management by its owner. We test our predictions using data on the Apple's promotion of apps. We find that apps with network effects are more likely to receive an award. This likelihood increases when the app is introduced by a developer with a larger market share but declines when introduced in a concentrated segment. The likelihood decreases further if the app is introduced in a concentrated segment by a developer that holds a larger market share. Further, we observe that in concentrated segments, the “challenger” developer has a higher likelihood of receiving the award relative to the leader. Managerial Summary Many products offered through platforms have their own direct network effects. The value of the product for each user grows with the number of other consumers using the product. Many platform owners also actively manage their platforms to ensure platform growth and often use less traditional tools such as product endorsements to achieve this goal. In the context of video games listed in the iOS App Store and the Editors' Choice Awards as a form of product endorsement, we examine the tradeoffs that products with direct network effects (i.e., multiplayer video games) present to the platform owner (Apple). On the one hand, the platform owner may want to promote apps with direct network effects to help them achieve a momentum in terms of user growth. On the other hand, networked apps may become dominant in the segment with negative implications for future growth of the segment and the platform owner's profitability. We find evidence consistent with this tension and find that it critically depends on the complementor strength and market segment concentration. We also observe that in concentrated segments, the “challenger” developer has a higher likelihood of receiving the award relative to the leader.

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When and Who Do Platform Companies Acquire? Understanding the Role of Acquisitions in the Growth of Platform Companies

Cited by 26 publications

References 0 publications

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

Sustaining complementor engagement in digital platform ecosystems: Antecedents, behaviours and engagement trajectories

Growing platforms within platforms: How platforms manage the adoption of complementor products in the presence of network effects?

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