ACTA WASAENSIA 553 Khaled Abed Alghani Beyond the Nest Navigating Strategic Challenges in Industry Platform Management Copyright © Vaasan yliopisto and copyright holders. Compilation dissertation's summary section is licensed under Creative Commons Attribution 4.0 International . ISBN 978-952-395-188-4 (print) 978-952-395-189-1 (online) ISSN 0355-2667 (Acta Wasaensia 553, print) 2323-9123 (Acta Wasaensia 553, online) URN https://urn.fi/URN:ISBN:978-952-395-189-1 PunaMusta Oy, Joensuu, 2025. https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1 https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1 https://urn.fi/URN:ISBN:978-952-395-189-1 ACADEMIC DISSERTATION To be presented, with the permission of the Board of the School of Management of the University of Vaasa, for public examination on the 28th of March, 2025, at noon. Article-based dissertation, School of Management, Strategic Management Author Khaled Abed Alghani https://orcid.org/0000-0003-1141-202X Supervisor(s) Professor Marko Kohtamäki University of Vaasa. School of Management, Strategic Management. Associate Professor Tuomas Huikkola University of Vaasa. School of Management, Strategic Management. Custos Professor Marko Kohtamäki University of Vaasa. School of Management, Strategic Management. Reviewers Professor Andreas Schroeder Lancaster University. Management School. Associate Professor Marin Jovanovic Copenhagen Business School. Department of Operations Management. Opponent Associate Professor Marin Jovanovic Copenhagen Business School. Department of Operations Management. https://orcid.org/0000-0003-1141-202X V Tiivistelmä Alustojen (industry platform) yleistyminen on herättänyt huomiota sekä teollisuuden ammattilaisten että akateemisten tutkijoiden keskuudessa. Yritykset ovat pyrkineet kehittämään alustoja taloudellisten verkostovaikutusten hyödyn- tämiseksi. Samanaikaisesti tutkijat ovat suunnanneet huomionsa tämän liiketoimintamallin tutkimiseen ymmärtääkseen sen monimutkaisia dynamiikkoja. Kaikki alustoja perustaneet yritykset eivät kuitenkaan ole onnistuneet hankkeissaan, ja tutkimukset, jotka ovat tarkastelleet alustanomistajien kohtaamia strategisia haasteita, eivät ole vielä tarjonneet kattavaa ymmärrystä näistä kysymyksistä tai strategioista, jotka ovat tarpeen alustan menestyksekkäälle perustamiselle. Tämä saattaa johtua siitä, että suurin osa keskusteluista on keskittynyt niin kutsuttuun muna-kana-dilemmaan, eli siihen, mitä toimijoita pitäisi houkutella alustalle ensin, esittäen muna-kana -dilemman pääasiallisena strategisena haasteena alustaa luotaessa. Tämä väitöskirja pyrkii paljastamaan alustojen hallinnan strategiset haasteet, etenkin alustan luomisprosessin aikana, ja strategiat näiden haasteiden voittamiseksi. Ottaen huomioon tämän väitöskirjan tavoitteet ja alustojen dynaamisen, moni- mutkaisen luonteen, tutkimuksessa hyödynnetään seuraavanlaista lähestymistapaa sille asetettujen tavoitteiden saavuttamiseksi. Aluksi väitöskirja kartoittaa teollisuusalustojen kirjallisuutta tunnistamalla klusterit, jotka tutkivat alustailmiötä erilaisista näkökulmista. Kirjallisuuden rakenteen ymmärtämisen myötä tunnistetaan myös keskeiset prosessit teollisuusalustojen hallinnassa: luominen, integrointi, orkestrointi, navigointi ja evoluutio, joista muna-kana -dilemma sijoittuu harmaalle alueelle luomisen ja integroinnin prosessien välille. Empiirisen aineiston ja käsitteellisen analyysin avulla tämä väitöskirja väittää, että muna-kana -dilemma liittyy integrointiprosessiin, ei luomisprosessiin. Huolimatta niiden keskinäisestä riippuvuudesta, luomisprosessiin sisältyy strategisia haasteita, jotka edeltävät muna- kana -dilemmaa. Nämä haasteet ovat yhtä haastavia tai jopa haastavampia kuin muna-kana -dilemma, mutta ne on suurelta osin sivuutettu kirjallisuudessa. Lisäksi väitöskirja hahmottelee keskeisiä strategioita, joita alustan omistajat voivat sisällyttää käsitelläkseen tehokkaasti sekä luomisen että integroinnin prosessien haasteita. Näin ollen tämä väitöskirja edistää jatkuvia strategisen johtamisen keskusteluja alustoista, etenkin niitä keskusteluja, jotka käsittelevät luomisen ja integroinnin prosesseja. Asiasanat: alustat, digitaaliset alustat, verkostovaikutukset, muna-kana-dilemma, alustojen johtaminen, strateginen johtaminen, liiketoimintamallit VI Abstract The proliferation of industry platforms has captured significant attention from both industry professionals and academic researchers. Practitioners have embraced industry platforms to harness the economic benefits of network externalities, while scholars have focused on examining this blockbuster business model to understand the intricate dynamics of managing industry platforms. Not all firms that have embraced industry platforms have succeeded, and studies examining the challenges faced by platform owners have not yet provided a comprehensive understanding of these challenges or the strategies required to successfully manage industry platforms. This might stem from the fact that the majority of academic discourse has extensively focused on the chicken-and-egg dilemma, or deciding whom to attract first, ultimately portraying it as the primary strategic challenge in managing industry platforms. Consequently, this dissertation aims to uncover the strategic challenges of managing industry platforms, as well as the strategies employed to overcome them. Considering the objectives of this dissertation on one hand, and the dynamic and complex nature of industry platforms on the other, a structurationist approach is adopted to achieve its goals. Initially, this dissertation maps the landscape of the literature by exploring the diverse perspectives (clusters) that examine industry platforms. With a grasp of the field’s structure and an aim to systematically identify the challenges, this dissertation outlines the processes central to managing industry platforms, namely creation, integration, orchestration, navigation, and evolution, and highlights the chicken-and-egg dilemma as falling in a gray area between the creation and integration processes. Drawing on empirical evidence, and supported by conceptual analysis, this dissertation examines the creation and integration processes, arguing that the chicken-and-egg dilemma is primarily associated with the integration process rather than the creation process. Despite their interdependence, the creation process involves strategic challenges that precede the chicken-and-egg dilemma and are at least as challenging, if not more, yet remain largely overlooked in the literature. Additionally, this dissertation outlines key strategies that platform owners can adopt to effectively address the challenges associated with the creation and integration processes. Therefore, this dissertation contributes to the ongoing strategic management discussions on industry platforms, particularly those addressing platform owners’ management of industry platforms. Keywords: Industry platforms, digital platforms, network externalities, chicken-and- egg dilemma, industry platform management, strategic management, business models VII ACKNOWLEDGEMENT I am grateful to be writing this section two years and eight months after beginning my PhD journey. This milestone would not have been possible without the support of many, both from the University of Vaasa and beyond. First of all, this journey would not have been possible without a great mentor and supervisor, and I was fortunate enough to have one. I cannot thank Mark Kohtamäki enough for his invaluable supervision and guidance throughout my PhD journey. THANK YOU, Marko! I would like to thank Rodrigo Rabetino for his constant support and valuable advice whenever needed, and Adam Smale for continuously empowering our career development and growth. I also extend my gratitude to all members of the School of Management, particularly those in the Strategic Business Development (SBD) Research Group (I decided not to mention names here to avoid forgetting anyone). Further, I would also like to sincerely thank the Finnish Ministry of Education and Culture and the MIDAS Project for their financial support throughout this journey. I am glad to have met great people here in Vaasa. At the top of the list is Nahil, who is much more than a friend, and of course, Hussein, Hassan, Omar, and Mohammad. Thank you guys for all the support! I also want to express my deep appreciation to Tayyab and Rimsha, who are more than just colleagues; they are like family to me here in Finland. I am also grateful to my childhood friends in my beloved home country, Lebanon. For over 20 years, our friendship has been a constant source of support and strength; you know who you are! Even though they are mentioned last, my family will always come first. This journey would not have been possible without the support of Jamal, Mariam, Nour, and my beloved Rawan. You mean the world to me! Khaled Abed Alghani 07/02/2025 IX Contents TIIVISTELMÄ ............................................................................. V ABSTRACT ............................................................................... VI ACKNOWLEDGEMENT ............................................................... VII 1 INTRODUCTION .................................................................... 1 1.1 Background .................................................................. 1 1.2 Research objectives ....................................................... 3 1.3 Research questions and theoretical gaps .......................... 4 1.4 Dissertation’s positioning and contributions ...................... 5 1.5 Dissertation’s structure .................................................. 7 2 THEORETICAL BACKGROUND .................................................10 2.1 Network externalities ....................................................10 2.1.1 Emergence .....................................................10 2.1.2 Evolution ........................................................12 2.1.3 Current state of affairs .....................................15 2.2 Industry platforms .......................................................18 2.2.1 Emergence .....................................................18 2.2.2 Evolution ........................................................21 2.2.3 Current state of affairs .....................................24 3 METHODOLOGY ....................................................................27 3.1 Philosophical assumptions .............................................27 3.1.1 Ontological choices ..........................................28 3.1.2 Epistemological choices ....................................30 3.1.3 Methodological choices .....................................31 3.2 Research design ..........................................................33 3.2.1 Research process ............................................35 3.2.2 Data collection ................................................37 3.2.3 Research quality ..............................................39 4 ARTICLE SUMMARIES ............................................................42 4.1 Article 1: Mapping the landscape: unveiling the structural dynamics of industry platforms ......................................42 4.2 Article 2: Unveiling the processes of industry platform management: A systematic literature review and research agenda .......................................................................44 4.3 Article 3: The Dynamics of Organizational Boundaries in Creating a B2B Industry Platform: Interplay and Repositioning Practices .................................................46 4.4 Article 4: The New Space Ecosystem: Insights from the Architecture of Digital Platforms .....................................51 X 5 DISCUSSION AND CONCLUSIONS .......................................... 54 5.1 Theoretical contributions .............................................. 54 5.2 Managerial contributions .............................................. 57 5.3 Limitations and future research directions ....................... 59 5.4 Conclusions ................................................................ 62 REFERENCES ........................................................................... 67 ARTICLES ................................................................................ 82 Figures Figure 1. Research design: Funnel-based approach ................. 35 Figure 2. The five main clusters ............................................ 43 Figure 3. The dynamics of industry platform management ........ 45 Figure 4. The interplay among the diverse boundary lenses ...... 48 Figure 5. The development of organizational boundaries across the two main transitions ........................................ 50 Figure 6. The layered structure of the New Space Ecosystem .... 51 Tables Table 1. Summary of the four articles .................................... 8 Table 2. The research process ............................................ 36 Table 3. Comparison of roles across the diverse layers: Digital platform ecosystem versus New Space Ecosystem ..... 52 XI Articles [1] Abed Alghani, K., Kohtamäki, M., & Kraus, S. (2024). Mapping the landscape: Unveiling the structural dynamics of industry platforms. European Journal of Innovation Management, 27(9). https://doi.org/10.1108/EJIM-09-2023-0748. © Khaled Abed Alghani, Marko Kohtamäki and Sascha Kraus. CC BY 4.0. [2] Abed Alghani, K., & Kohtamäki, M. Unveiling the processes of industry platform management: A systematic literature review and research agenda.1 [3] Abed Alghani, K., Rabetino, R., & Kohtamäki, M. The Dynamics of Organizational Boundaries in Creating a B2B Industry Platform: Interplay and Repositioning Practices.2 [4] Abed Alghani, K., Kohtamäki, M., Kuusniemi, H. (2024). The New Space Ecosystem: Insights from the Architecture of Digital Platforms. In: Ojala, A., Baber, W.W. (eds) Space Business. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978- 981-97-3430-6_3. © 2024 The Author(s). CC BY 4.0. 1 Reprinted with permission from the International Journal of Management Reviews. The manuscript is currently under revision and is scheduled to be submitted in June 2025 for the third round of review. 2 Reprinted with permission from the Journal of Product Innovation Management. The manuscript is currently under revision and is scheduled to be submitted in April 2025 for the second round of review. https://doi.org/10.1108/EJIM-09-2023-0748 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/978-981-97-3430-6_3 https://doi.org/10.1007/978-981-97-3430-6_3 https://creativecommons.org/licenses/by/4.0/ 1 INTRODUCTION 1.1 Background “For anyone who follows the world of business, it is now common knowledge that the most valuable firms on the planet and the first companies to surpass the trillion- dollar mark in value (albeit temporarily) are platforms. If we look at market values in late 2018, the top firms were Microsoft, Apple, Amazon, and Alphabet (the holding- company parent of Google since 2015). Also among the leaders were Facebook, Alibaba, and Tencent. Together, these seven companies at their peak represented close to $5 trillion in market value” (Cusumano et al., 2019, p. 4). The advent of the platform business model has captured significant attention, likely because many of the most valuable companies by market capitalization operate on this model (Constantinides et al., 2018; Cusumano et al., 2019; Parker et al., 2016). Forbes has reported that in 2024, five of the top ten most valuable companies by market capitalization operate on a platform business model, collectively representing over USD 11 trillion in market value. These companies include Microsoft ($3.342 trillion), Apple ($3.160 trillion), Alphabet ($2.065 trillion), Amazon ($1.899 trillion), and Meta Platforms ($1.181 trillion). This significant attention is reflected not only in the academic field, with increasing research on this topic (Rietveld & Schilling, 2020), but also on the practical side, where several companies have begun the shift toward this blockbuster model to reap the benefits of strong network effects in specific markets and industries (Trabucchi & Buganza, 2023; Wortmann et al., 2024). However, not all companies that attempted to adopt this model succeeded, and many failed (Parker et al., 2016). It could be assumed that the failures in adopting this model are attributable to a range of factors; however, surprisingly, most academic discussions focus on a single aspect: overcoming the chicken-and-egg dilemma, or in other words, whether the platform should first attract buyers or sellers. This dilemma is inherently linked to network effects (Eisenmann et al., 2006; Eisenmann & Hagiu, 2007), which is the primary distinguishing factor between an industry platform and other types of platforms (Gawer, 2014; Gawer & Cusumano, 2014). Industry platforms are defined as “products, services, or technologies developed by one or more firms, and which serve as foundations upon which a larger number of firms can build further complementary innovations and potentially generate network effects” (Gawer & Cusumano, 2014, p. 420). The majority of the research on industry platforms agrees that overcoming the chicken-and-egg dilemma is the most challenging process in managing an industry 1 platform (Caillaud & Jullien, 2003; Eisenmann et al., 2006; Rochet & Tirole, 2003). For instance, Parker et al. (2016, p. 26) argue that “platforms need to solve a chicken-or- egg problem that pipeline businesses don’t suffer from: users won’t come to a platform unless it has value, and a platform won’t have value unless users use it. Most platforms fail simply because they never overcome this problem.” The identification of the chicken-and-egg dilemma as a strategic challenge stems from the origins of the literature on industry platforms (Gawer, 2014), which bridged economics literature focused on competition (Rochet & Tirole, 2003, 2006) and engineering literature focused on innovation (Baldwin & Woodard, 2009). Specifically, the chicken-and-egg dilemma stems from the economics literature, notably Rochet and Tirole (2003), a central contributor to the industry platform literature. Rochet and Tirole’s (2003, 2006) work primarily focuses on two-sided markets characterized by network effects. It draws extensively on the literature on the economics of network externalities (Katz & Shapiro, 1985) and discussions centered around the chicken- and-egg dilemma (Caillaud & Jullien, 2003). Subsequent discussions have attempted to determine whether it is most advantageous to attract the buyer or the seller side to the platform first (Armstrong, 2006; Bolt & Tieman, 2008; Eisenmann et al., 2006; Rochet & Tirole, 2006). The chicken-and-egg dilemma is then portrayed as a significant strategic challenge, to the extent that one might assume overcoming this dilemma is the starting point for establishing an industry platform (Eisenmann et al., 2006; Eisenmann & Hagiu, 2007). While the chicken-and-egg dilemma has been widely discussed (Caillaud & Jullien, 2003), scholars have begun to emphasize that the process of creating industry platforms is an underexplored area that warrants more attention (de Reuver et al., 2018; Gawer & Cusumano, 2014; Shi et al., 2021; B. Tan et al., 2015; Teece, 2017). This shift does not diminish the significance of the chicken-and-egg dilemma as a strategic challenge; instead, it suggests that the existing literature on industry platforms has primarily focused on post-creation issues, such as identifying which participants to attract first (Bolt & Tieman, 2008; Economides & Katsamakas, 2006; Rochet & Tirole, 2003) or how to orchestrate the actors within the platform ecosystem (Eaton et al., 2015; Foerderer et al., 2021; Ghazawneh & Henfridsson, 2013; Zhang et al., 2022), rather than on the initial creation process itself. Despite this renewed focus, the topic of creating industry platforms is not entirely new. It was initially addressed by Gawer and Cusumano (2008) but has not received the necessary attention in subsequent research. Gawer and Cusumano (2008, p. 32) identified two main strategic options for becoming a platform leader: (1) coring, or “how to create a new platform where none existed before,” and (2) tipping, or “how to win platform wars by building market momentum.” The chicken-and-egg dilemma specifically pertains to the tipping strategy (Caillaud & Jullien, 2003; Gawer & Cusumano, 2008), which is where discussions on industry platforms began and 2 Acta Wasaensia evolved from, thereby almost entirely neglecting the coring phase, or the process of creating an industry platform (de Reuver et al., 2018; Gawer & Cusumano, 2014; Shi et al., 2021; B. Tan et al., 2015). This neglect of the coring phase may be due to the extensive theoretical discussions in the industry platform literature and the distancing from the practical aspects of creating an industry platform. A case in point is an interview with a Director of Digital Product Development from a Finnish-based multinational firm, who managed a five- year project to assess the feasibility of developing an industry platform. He mentioned, “I would say that the company began moving in this direction around 2016, and we received evidence that it was not working approximately five years later. This took a significant amount of time and money, as well as considerable effort, particularly as all companies began investing heavily in the platform business model.” This evidence indicates that there are indeed phases preceding the chicken-and-egg dilemma that merit significant attention (de Reuver et al., 2018; Gawer & Cusumano, 2014; Shi et al., 2021; B. Tan et al., 2015). Simultaneously, this does not imply that no processes occur subsequent to the chicken-and-egg dilemma (Eaton et al., 2015; Foerderer et al., 2021; Ghazawneh & Henfridsson, 2013; Zhang et al., 2022). 1.2 Research objectives The main objective of this dissertation is to explore the strategic challenges that platform owners face when managing their industry platforms and the approaches utilized to overcome these challenges. However, the complexity of this task is heightened by the various and contradictory terminologies, definitions, and classifications associated with the literature on technology platforms characterized by network effects, that is, industry platforms (Gawer & Cusumano, 2014). Consequently, Article 1 aims to map the structural landscape of the field and investigate the diverse scholarly approaches to studying these platforms. During the research process, the researcher reviewed most, if not all, definitions and classifications in the existing literature. The terminologies, definitions, and classifications proposed by Cusumano et al. (2019) and Gawer and Cusumano (2014) emerged as the most coherent and were subsequently adopted for this dissertation. Furthermore, building on the comprehensive review of the industry platform literature provided in Article 1, the subsequent focus shifted to uncovering the various processes that platform owners engage in to manage their platforms, with the aim of identifying the strategic challenges in a structured and systematic approach. Article 2 thus reveals the five main processes that constitute the management of industry platforms: creation, integration, orchestration, navigation, and evolution, 3Acta Wasaensia with the chicken-and-egg dilemma ambiguously situated in a gray area between the creation and integration processes. To thoroughly investigate what is presumed to be the strategic challenge in managing industry platforms, Article 3 centers on an empirical study that examines the process of creating an industry platform and integrating the different actors into it. This study blends the theoretical insights from Article 2 with empirical evidence from an in-depth case study explored in Article 3 to closely examine the presumed strategic challenge. Further, Article 3 presents a novel approach to overcoming the strategic challenges that are associated with the processes of creation and integration. Finally, Article 4 delineates the architecture of the New Space Ecosystem by drawing parallels with industry platforms and conceptually examines the feasibility of creating industry platforms in new contexts, such as that of the New Space Ecosystem. The main aim of this article is to support the insights gained from Article 3 and to identify further challenges encountered by platform owners, thereby achieving the objectives of the dissertation. 1.3 Research questions and theoretical gaps The primary research question of this dissertation is driven by an interest in exploring how platform owners manage their industry platforms. Specifically, it targets a comprehensive understanding of the processes that platform owners follow while managing industry platforms, the strategic challenges they encounter, and the approaches they employ to overcome them. These insights may clarify, though partially, why not all firms transition to or integrate industry platforms into their core business activities. Consequently, the overarching research question of this study is: RQ: What are the strategic challenges in managing industry platforms, and how can platform owners address these challenges? First, the diversity and fragmentation of the literature on industry platforms can be overwhelming, hindering obtaining a comprehensive understanding of such platforms. However, this diversity can be harnessed to explore the structure of the field and understand the various scholarly approaches to industry platforms. Consequently, as an initial step toward addressing the dissertation’s main research question, it is imperative to familiarize oneself with the phenomenon under examination and to map the landscape of the field. Accordingly, the research question for Article 1 (RQ1) is: What are technological platforms associated with network effects, and how do different scholarly groups conceptualize and approach this phenomenon? Having obtained an understanding of the field’s structure, the next step toward addressing the dissertation’s main research question was to explore the different processes that platform owners face in managing their industry platforms. 4 Acta Wasaensia Three main challenges hinder a comprehensive understanding of these different processes: examining these processes in an isolated manner, adopting a narrow perspective when examining each of the individual processes, and neglecting specific processes. Accordingly, the main research question of Article 2 (RQ2) is: What are the different processes a platform owner engages in to manage their industry platforms? With a comprehensive theoretical understanding of the diverse processes and acknowledging that the chicken-and-egg dilemma represents a strategic challenge, particularly falling within a gray area between the creation and integration processes, Article 3 aims to examine these two processes empirically. This examination focuses on how a firm develops its organizational boundaries when shifting toward an industry platform, particularly emphasizing the creation and integration processes. Therefore, the main research question of Article 3 (RQ3) is: How does a platform owner develop its organizational boundaries when transitioning toward an industry platform? Bridging the theoretical knowledge gained from Articles 1 and 2 with the hands-on experience developed in Article 3, Article 4 contributes to the dissertation’s main research question by conceptually examining the possibility of creating an industry platform in new contexts. This examination stems from the empirical findings in Article 3, which show that the chicken-and-egg dilemma is not the sole, and perhaps not the primary or most significant, strategic challenge platform owners face when creating an industry platform. Thus, in addition to uncovering the architecture of the New Space Ecosystem through parallels drawn with that of industry platforms, Article 4 contributes to the dissertation’s overarching research question by providing insights into the following research question (RQ4): What challenges arise when creating an industry platform in new contexts? 1.4 Dissertation’s positioning and contributions The literature on industry platforms emerged from the intersection of two distinct fields (Gawer, 2014): economics and engineering (Baldwin & Woodard, 2009; Rochet & Tirole, 2003, 2006). Over the past 20 years, this literature evolved, with new streams emerging to address novel aspects of the industry platform phenomenon (Boudreau, 2010; Eisenmann et al., 2011). Accordingly, it is vital to position each of the four articles concerning the different streams of literature examining industry platforms, thereby properly positioning the entire dissertation in relation to these scholarly contexts. To elaborate, Article 1 contributes to the diverse discussions on industry platforms by conducting a bibliometric analysis of a broad spectrum of articles across different literature streams. Its primary objective is to map the landscape of the field and examine how various scholars engage with the topic. Unlike Article 1, which covers a broad range of discussions on industry platforms, Article 2 5Acta Wasaensia specifically examines the processes that platform owners undergo in managing their platforms. Consequently, this article has a narrower scope, focusing on the platform owner’s perspective (Van Alstyne et al., 2016) and contributing to strategic discussions on industry platforms (Cusumano et al., 2019; Gawer, 2014; Gawer & Cusumano, 2014), primarily by bridging both the initial pillars of engineering and economics and the subsequent developments in the field. Article 3 empirically examines the process of creating an industry platform and integrating various actors into the platform ecosystem, maintaining a focus on the platform owner’s perspective (Van Alstyne et al., 2016), as is the case in Article 2. The process of creating an industry platform remains an underexplored area (de Reuver et al., 2018; Gawer & Cusumano, 2014; Shi et al., 2021; B. Tan et al., 2015; Teece, 2017), with academic exploration progressing slowly. Nevertheless, this topic is predominantly examined by strategic management scholars who started to address the subject, as demonstrated by several special issues that concentrate extensively on this aspect, e.g., Teece et al. (2022). Furthermore, given that the integration of various actors into the platform ecosystem has been primarily the focus of economic scholars (Armstrong, 2006; Economides & Katsamakas, 2006; Kaiser & Wright, 2006; Rochet & Tirole, 2003), this article makes a significant contribution to debates challenging early economic theories. Specifically, Article 3 highlights discussions that have favored non-pricing strategies as a means to attract and integrate diverse actors into the platform ecosystem. These strategies are viewed as less risky and more cost- effective than traditional pricing approaches (Eisenmann & Hagiu, 2007). This stream of research was initiated by Eisenmann and Hagiu (2007, p. 1), who were among the first to introduce a non-pricing-related strategy, namely the “vendor to two-sided platform strategy.” Consequently, similar to Article 2, Article 3 primarily contributes to the strategic discussions on industry platforms. Lastly, similar to Articles 2 and 3, Article 4 primarily contributes to the perspective of the platform owner (Van Alstyne et al., 2016), particularly through its focused examination in the second-to-last chapter on assessing the strength of network effects and exploring the potential for creating industry platforms in uncharted territories. As the main research question of this dissertation is addressed through four distinct sub-questions, each primarily answered in one of the four articles comprising this dissertation, the dissertation as a whole makes a significant contribution to strategic discussions on the management of industry platforms. It particularly focuses on the initial processes of platform creation and the integration of various actors into the platform ecosystem, which is the gray area where the chicken-and-egg dilemma arises (Caillaud & Jullien, 2003). Furthermore, although these processes are somewhat distant from the orchestration process that begins directly after onboarding the various actors into the platform ecosystem (Eaton et al., 2015; 6 Acta Wasaensia Foerderer et al., 2018; Ghazawneh & Henfridsson, 2013), this dissertation remains closely aligned with discussions of interest to information systems scholars, especially as pioneers in the field are advocating for further exploration of industry platform creation, such as de Reuver et al. (2018). In brief, while this dissertation is more closely aligned with the discussions of strategic management scholars, it is not disconnected from other streams of literature, whether those that were bridged to form the foundation of industry platform studies or those that emerged later. This is particularly relevant because the foundations of strategic management discussions on industry platforms emerged from bridging diverse perspectives (Gawer, 2014), making this multidisciplinary approach inherent to this stream of literature since its commencement. Another important aspect is that most discussions on industry platforms focus on B2C or C2C contexts, leaving the B2B context relatively underexplored (Jovanovic et al., 2021). However, this does not imply that this dissertation does not contribute to the literature on B2B platforms. Instead, it serves as a foundation for further developing strategic management discussions on industry platforms within B2B contexts. 1.5 Dissertation’s structure The dissertation is divided into two main parts: the first serves as the introduction to the entire dissertation and comprises five distinct chapters, while the second consists of four different articles that constitute the dissertation. The first part begins with an introductory chapter, to which this section belongs, and serves primarily as a roadmap for the entire dissertation. Specifically, it highlights the research objectives, the central research question, and positions the study within the broader field. The second chapter introduces two main streams of literature: network externalities and industry platforms. Both subsections are organized similarly, with each divided into three parts: emergence, evolution, and current state of affairs. Following these, the dissertation presents three additional chapters: the third chapter discusses the methodology used throughout the dissertation; the fourth summarizes the four articles included; and the fifth and final chapter presents the discussion and conclusion. That concludes the first part. The second part of the dissertation comprises four distinct articles, as outlined in Table 1. Article 1, a published journal article, is co-authored by Abed Alghani, Kohtamäki, and Kraus. Article 2, an unpublished article, is co-authored by Abed Alghani and Kohtamäki and is currently in its third round of revision. Article 3, another unpublished article, is co-authored by Abed Alghani, Rabetino, and Kohtamäki and is in its first round of revision. Finally, Article 4 is a chapter of a published book co-authored by Abed Alghani, Kohtamäki, and Kuusniemi. Further, it is important to note that the researcher served as the lead author, primarily responsible for defining the scope of the study, conducting data 7Acta Wasaensia collection and analysis, writing the manuscript, and managing the review processes required for publication. Table 1. Summary of the four articles Article 1 Article 2 Article 3 Article 4 Key contributions to the dissertation’s main research question Mapping the landscape of literature on industry platforms and identifying five different clusters, each with its own terminologies, definitions, classifications, and approaches to examining industry platforms Identifying five different processes that platform owners engage in to manage their industry platforms, with the chicken-and- egg dilemma emerging as a strategic challenge between the initial processes of creation and integration Thoroughly exploring the creation and integration processes, particularly through an empirical examination of how a platform owner develops its organizational boundaries as it transitions toward an industry platform Uncovering the architecture of the New Space Ecosystem by drawing parallels with that of industry platforms, with a particular focus on the challenges of creating industry platforms in new contexts, such as that of the New Space Research context Industry platforms Industry platforms – platform owner perspective Industry platform in the manufacturing sector – platform owner perspective New Space Ecosystem – platform owner perspective Research method Bibliometric methods and systematic review techniques Systematic literature review Qualitative case study Systematic literature review Unit of analysis ≥ CABS3 journal articles Industry platform management processes Organizational boundaries The architecture of digital infrastructures (industry platforms) Sample 458 journal articles 359 journal articles A Finnish firm operating in the manufacturing industry 51 academic articles Key data sources Scopus Scopus 13 in-depth interviews + comprehensive secondary data Scopus 8 Acta Wasaensia Article 1 Article 2 Article 3 Article 4 The role of the PhD candidate The researcher served as the lead author, primarily responsible for defining the scope of the study, conducting data collection and analysis, writing the manuscript, and managing the review processes required for publication The researcher served as the lead author, primarily responsible for defining the scope of the study, conducting data collection and analysis, writing the manuscript, and managing the review processes required for publication The researcher served as the lead author, primarily responsible for defining the scope of the study, conducting data collection and analysis, writing the manuscript, and managing the review processes required for publication The researcher served as the lead author, primarily responsible for defining the scope of the study, conducting data collection and analysis, writing the manuscript, and managing the review processes required for publication 9Acta Wasaensia 2 THEORETICAL BACKGROUND This chapter presents the theoretical background of the dissertation, particularly focusing on network externalities and industry platforms. The discussions on each of these two theoretical underpinnings, Sections 2.1 and 2.2, are divided into three different subsections: (1) emergence, (2) evolution, and (3) current state of affairs. As the name suggests, the emergence subsections provide a brief overview of the discussions that paved the way for the emergence of the two literatures, including the initial discussions on the topics. Following that, the evolution subsections examine the discussions that evolved after the boundaries of the literature were defined, specifically after Katz and Shapiro’s (1985) study relating to network externalities and Gawer’s (2014) work on industry platforms. Finally, the current state of affairs subsections address the recent literature on these topics. 2.1 Network externalities 2.1.1 Emergence When examining the network externality literature, the majority of studies refer to Katz and Shapiro’s seminal work Network Externalities, Competition, and Compatibility (1985). However, the roots of the network externality literature can be traced back to studies that have challenged the demand theory, particularly the seminal work of the founder of neoclassical economics, Marshall (2013). The conventional idea of demand is that consumers tend to buy more when the prices of a certain product or service fall. In simpler terms, the demand curve slopes downward from left to right, with the price (P) on the y-axis and the quantity of the product or service sold (Q) on the x-axis. Consequently, the collective or aggregate demand is the sum of the quantities (Q) consumers buy at specific prices (P) in a certain period. This concept is known as additivity, where the total demand for a specific product or service is the sum of the demands of all individual consumers in the market. Morgenstern (1948) argues that the concept of additivity holds only if the demand functions of different individuals who purchase a certain quantity (Q) at a specific price (P) are independent of each other. He argues that “even if everything were completely known for additive aggregate demand curves and found to be in order, that knowledge would only apply to a small part of aggregate demand; in the majority of empirical cases, non-additivity seems to prevail” (Morgenstern, 1948, p. 175). The initial empirical examples that were given were in the fashion industry, where a person tends to buy a specific piece of clothing because another person has done so rather than due to a decrease in price as the quantity sold increases. Accordingly, 10 Acta Wasaensia Morgenstern’s (1948) main aim was to examine how variations in market prices can be addressed in the analysis of demand. Yet, to a certain extent, Morgenstern’s (1948) work inherently criticized existing theories without offering practical solutions. Building on the early work of Morgenstern (1948), Leibenstein (1950) examined the fashion industry and coined the term “bandwagon effect” (Leibenstein, 1950, p. 183), which is defined as “the extent to which the demand for a commodity is increased due to the fact that others are also consuming the same commodity. It represents the desire of people to purchase a commodity in order to get into “the swim of things” in order to conform with the people they wish to be associated with; in order to be fashionable or stylish; or, in order to appear to be “one of the boys”” (Leibenstein, 1950, p. 189). However, the “bandwagon effect” did not receive much attention, and the ideas discussed by Leibenstein (1950, p. 183) remained dormant until the advent of studies that shifted their focus from the fashion industry to that of communication services. For instance, Rohlfs (1974) relied on the previously examined concept of interdependent demand to examine communication services, referring to the phenomenon that describes how the utility of a communication service user increases as more users adopt the same service as a “classic case of external economies in consumption” (Rohlfs, 1974, p. 16); and Oren and Smith (1981) examined communication services, labeling the phenomenon under examination as “demand externality” (Oren & Smith, 1981, p. 467). Therefore, although the initially discussed concepts, such as non-additivity (Morgenstern, 1948) and bandwagon effects (Leibenstein, 1950), remained dormant for some time, they were revived through the examination of the communication service sector (see, e.g., Rohlfs (1974) and Oren and Smith (1981)), thereby paving the way for the seminal work of Katz and Shapiro (1985). Katz and Shapiro (1985) argue that network externalities occur when the “utility that a user derives from consumption of the good increases with the number of other agents consuming the good” (Katz & Shapiro, 1985, p. 424). They identified different sources of network externalities; however, these sources can be classified into (1) direct network externalities, such as telephone, data networks, and other communication technologies, and (2) indirect network externalities, such as personal computers, video games, video players and recorders, phonograph equipment, and the automobile market. The examination of the previously mentioned industries gave rise to the concept of a “relevant network” (Katz & Shapiro, 1985, p. 424), which is defined as a “set of users who have compatible brands of hardware” (Katz & Shapiro, 1985, p. 425). Put simply, identifying users within a specific relevant network depends on whether the products they use, which are developed by different firms, can work together or not. Accordingly, the concept of “compatibility” (Katz & Shapiro, 1985, p. 424) began to emerge in the literature, with early scholars arguing that 11Acta Wasaensia compatibility serves as a driver of demand-side economies of scale (Katz & Shapiro, 1985). For instance, if two systems are not compatible in the telecommunications industry, the network size is limited to the capacity of each system. However, when the two systems are compatible, the network size expands to include the combined capacity of both systems. The examination of “compatibility” (Katz & Shapiro, 1985, p. 424) brought the topic of “standardization” (Farrell & Saloner, 1985, p. 70) to the forefront, particularly in early discussions on the topic of network externality. Farrell and Saloner (1986, p. 940) argue that “the benefits from compatibility create demand-side economies of scale: there are benefits to doing what others do. These benefits make standardization a central issue in many important industries,” which aligns smoothly with Katz and Shapiro’s (1985) arguments. However, unlike Katz and Shapiro (1985), who argue that a certain product or service is more valuable as the number of users increases, Farrell and Saloner (1986) argue that a product or service is more valuable when other users are more willing to use compatible goods. Perhaps the divergence in perspectives stems from the diverse examples that these scholars have drawn upon, particularly the communications industry examined by Katz and Shapiro (1985) and the QWERTY keyboard case discussed by Farrell and Saloner (1986), which builds on David (1985). Farrell and Saloner (1986) further argue that compatibility might hinder innovation and cause excess inertia, defined as “a socially excessive reluctance to switch to a superior new standard when important network externalities are present in the current one” (Farrell & Saloner, 1986, p. 940), primarily relying on the keyboard case as the phenomenon under examination (David, 1985). Accordingly, early scholars attributed market inefficiencies and failures to network externalities (Arthur, 1989), particularly in the examination of the Video Home System (VHS) versus Beta (Katz & Shapiro, 1986) and the QWERTY keyboard versus the Dvorak keyboard (David, 1985; Farrell & Saloner, 1986). 2.1.2 Evolution Liebowitz and Margolis (1994) argue that the previously examined cases, namely the keyboard case (David, 1985; Farrell & Saloner, 1986) and the Video Cassette Recorder (VCR) (Katz & Shapiro, 1986), are often “a combination of anecdotes and speculation” (Liebowitz & Margolis, 1994, p. 146). They strongly criticize the idea that network externalities lead to market failures and advocate for a distinction between network externalities and network effects (Liebowitz & Margolis, 1994, p. 135). They argue that network externalities and network effects should not be used interchangeably unless the actors in a certain market fail to internalize these effects. Essentially, if adding more users makes something better for everyone and the 12 Acta Wasaensia benefits are internalized, the outcome should be referred to as network effects. However, if adding more users affects only certain users, whether positively or negatively, and those effects are not fully internalized, the outcome should be referred to as network externalities (Liebowitz & Margolis, 1994). Moreover, in addition to distinguishing between network effects and network externalities, Liebowitz and Margolis (1994) differentiated not only between direct and indirect network externalities but also between the various economic implications of direct and indirect network effects, a topic that was somewhat broadly addressed in early discussions on network externalities by Katz and Shapiro (1985). Direct network effects occur when the value of a product increases due to the growing number of users. At the same time, indirect network effects arise from the increased availability or reduced prices of complementary goods as the user base expands. Moreover, Liebowitz and Margolis (1994) argue that network externalities do not always lead to positive effects; they can also have negative consequences. For example, if a communication network becomes overloaded by users, the effect on each individual user will be negative due to network congestion. In the 1990s, the distinction between direct and indirect effects became clearer (Katz & Shapiro, 1994), and concurrently, some attention was given to positive and negative externalities (Arthur, 1996). Moreover, there was a notable shift in focus toward the examination of software, e.g., Arthur (1996), Church and Gandal (1992), and Katz and Shapiro (1994), rather than the cases previously examined (Farrell & Saloner, 1986; Katz & Shapiro, 1986). This shift was mainly driven by the observation that “indirect network effects are perhaps easiest to see when many firms offer differentiated software” (Katz & Shapiro, 1994, p. 99). Besides, between the 1990s and early 2000s, particularly with the proliferation of personal computers and the internet, a stock market bubble was inflating before it burst in the early 2000s, incentivizing practitioners and scholars to realize that transforming network externalities into profits is easier said than done (C.-C. Wu et al., 2013). Accordingly, the examination of network externalities began to focus more on the different business models that could be adopted to generate profits from these externalities, with pricing emerging as a central topic in these discussions (Caillaud & Jullien, 2001b; Parker & Van Alstyne, 2005; Rochet & Tirole, 2002). Therefore, in addition to the previously mentioned issues and in line with Katz and Shapiro’s (1994) argument that indirect network externalities are clearer in software, different scholars started examining software to understand network externalities, particularly focusing on strategies to generate profits in competitive environments characterized by network externalities (Caillaud & Jullien, 2001b, 2003; Rochet & Tirole, 2002, 2003). 13Acta Wasaensia Caillaud and Jullien (2001b) examined price competition between two software intermediaries, such as online dating services or search engines that match two different sides of the market. The research considered intermediation a source of indirect network externalities. The primary motivation for examining cybermediaries stems from the fact that they represented one of the four most revenue-generating activities associated with the internet by that time (Caillaud & Jullien, 2001b). Therefore, such software emerged as the perfect context for examining network externalities, as the value of the software for the buyer side depends on the number of goods sold and the number of sellers on the seller side; simultaneously, the value of the software for sellers depends on the size of the demand, that is the number of buyers on the buyer side (Armstrong & Wright, 2007; Eisenmann et al., 2006). Accordingly, in the same way that discussions on compatibility (Katz & Shapiro, 1985) paved the way for discussions on standardization (Farrell & Saloner, 1985) in the 1980s, discussions on various business models, particularly in terms of pricing, paved the way for discussions on (cross-) subsidizing the different users of the software to incentivize their participation (Armstrong & Wright, 2007; Economides & Katsamakas, 2006). Although their study was not the first to examine intermediaries, Caillaud and Jullien’s (2001b) work was, as far as the researcher is aware, among the first to explore the “potential rationing of demand” (Caillaud & Jullien, 2001b, p. 799) within the context of intermediaries. In short, due to the possibility of restricted demand, companies compete to attract more users to their software, often through cross-subsidization. That involves raising the prices for one side while reducing, or even eliminating, them for the other (Rochet & Tirole, 2003), where the subsidized side is the one more valued by the opposite side of the platform (Economides & Katsamakas, 2006). However, attracting the different actors to the software is, again, easier said than done, as there are a variety of actors on the different sides of the market. Consequently, Caillaud and Jullien (2003) argue that indirect network externalities give rise to the chicken-and-egg dilemma. Simply put, “to attract buyers, an intermediary should have a large base of registered sellers, but these will be willing to register only if they expect many buyers to show up” (Caillaud & Jullien, 2003, p. 310). “Despite much theoretical progress made in the last two decades on the economics of network externalities and widespread strategy discussions of the chicken-and-egg problem, two-sided markets have received scant attention” (Rochet & Tirole, 2003, p. 990). Accordingly, the discussions on network externalities (Farrell & Saloner, 1985, 1986; Katz & Shapiro, 1985, 1986) as well as the discussions on the chicken-and-egg dilemma (Caillaud & Jullien, 2003) were the two main pillars for subsequent discussions on markets with network externalities, currently referred to as “two- sided markets” (Rochet & Tirole, 2003, p. 990). Rochet and Tirole (2003) argue that many, if not all, of the markets associated with network externalities are two-sided, 14 Acta Wasaensia where two different sides mainly benefit from interacting on a common platform, thereby advancing the observation provided by Caillaud and Jullien (2001b) regarding intermediation as a source of indirect network externalities. Accordingly, the owners of these platforms must address the chicken-and-egg dilemma to “get both sides on board” (Rochet & Tirole, 2003, p. 990). However, to overcome this chicken-and-egg dilemma, pricing emerged as a key tool to attract the different sides of the market, echoing what was previously mentioned by Caillaud and Jullien (2001b). After the seminal work by Rochet and Tirole (2003), discussions shifted from purely focusing on network externalities to emphasizing two-sided markets. This shift was notable, particularly after Rochet and Tirole (2003, p. 1020) argued that “markets with network externalities are predominantly two-sided markets,” ultimately defining them as “A market with network externalities is a two-sided market its platforms can effectively cross-subsidize between different categories of end users that are parties to a transaction. That is, the volume of transactions on and the profit of a platform depend not only on the total price charged to the parties to the transaction but also on its decomposition” (Rochet & Tirole, 2003, p. 1017). Therefore, to a certain extent, these two bodies of literature— network externalities and two-sided markets—collided to the point where it became difficult to differentiate between them as the mainstream of the network externality literature began examining this phenomenon in the context of two-sided markets (Rysman, 2009) or multi-sided markets (Hagiu & Wright, 2015). These two bodies of literature collided not only in terms of their definitions but also over the necessary conditions for the emergence of such markets. Evans (2003) argues that there are three essential conditions necessary for the emergence of two- or multi-sided markets: (1) the existence of at least two or more groups of users, (2) the existence of externalities associated between the different groups of users, and (3) the existence of an intermediary capable of internalizing the externalities created by one group for the other. Accordingly, this blended the majority of the key concepts examined in the early literature on network externalities, namely those by Farrell and Saloner (1985, 1986), Katz and Shapiro (1985, 1986), and Liebowitz and Margolis (1994), along with insights from the literature on two-sided markets, specifically those by Caillaud and Jullien (2001b, 2003) and Rochet and Tirole (2003, 2006). 2.1.3 Current state of affairs More recently, the topic of network externalities not only overlapped with that of two-sided markets but also with other related topics, forming a nested web of inherently interrelated subjects. For instance, Armstrong (2006) identified three 15Acta Wasaensia main factors that impact the pricing structure in two-sided markets: (1) the size of the cross-group externality, (2) single-homing or multi-homing, and (3) fixed fees or per-transaction charges. Furthermore, Eisenmann et al. (2006) argue that for a firm to succeed in managing a two-sided market, it should (1) establish the right pricing, (2) avoid envelopment, and (3) cope with winner-take-all competition, where a networked market is more likely to be served by one platform when “(1) multi- homing costs are high for at least one user side, (2) network effects are positive and strong at least for the users on the side of the network with high multi-homing costs, (3) neither side’s users have a strong preference for special features” (Eisenmann et al., 2006, p. 7). Therefore, the evolution of network externality literature paved the way for the discussion of diverse topics that were addressed, albeit indirectly, in the early literature (Arthur, 1989; Katz & Shapiro, 1986). These topics particularly relate to multi-homing (Armstrong & Wright, 2007; Bakos & Halaburda, 2020; Belleflamme & Peitz, 2019; Doganoglu & Wright, 2006, 2010; Eisenmann et al., 2006; Jeitschko & Tremblay, 2020; Wiegand et al., 2022), winner-take-all competition (Cennamo & Santalo, 2013; Eisenmann et al., 2006), and envelopment (Eisenmann et al., 2006, 2011), among others. Other scholars have continued to explore network externalities in both two-sided and multi-sided markets. These studies include examining the competitive implications of customer loyalty when network effects are present (Chen & Xie, 2007), analyzing the interplay between consumer adoption and merchant card acceptance in the presence of network effects (Bounie et al., 2017), and discussing piracy in the presence of network externalities (Rasch & Wenzel, 2013). Furthermore, other scholars have examined the dynamics between network effects and various factors, such as the interplay between network effects and platform dominance (Srinivasan & Venkatraman, 2010), balancing network effects and differentiation in mergers (Farronato et al., 2024), and the interplay between network effects and decision rules (Varga et al., 2023). Some researchers have also examined how network effects impact other factors, like the influence of network effects on profit and market efficiency (Choi et al., 2012), while others have examined the impact of specific factors on network effects, such as the impact of geographical locality (Kim et al., 2022). However, the examination of pricing strategies in the presence of network externalities remains a central topic in discussions of network externalities (Chou et al., 2012; G. Dou et al., 2016; Kaiser & Wright, 2006; Rochet & Tirole, 2006; H. Tan & Wright, 2021; Tong et al., 2020; W. Wu et al., 2022). In addition to the previously mentioned discussions, the estimation of network effects also attracted attention. Empirical studies on network effects began in the early 1990s and evolved similarly to the literature described above (Caillaud & Jullien, 2003; Katz & Shapiro, 1985, 1994; Liebowitz & Margolis, 1994; Rochet & 16 Acta Wasaensia Tirole, 2003). Initially, these studies emerged in random contexts involving software and the internet (Caillaud & Jullien, 2001a; Katz & Shapiro, 1994) and then progressed to focus particularly on two-sided markets (Rochet & Tirole, 2003, 2006), thereby bypassing the early period when studies on network effects focused on examples like QWERTY keyboards (David, 1985), telecommunications (Farrell & Saloner, 1986; Katz & Shapiro, 1985), and VCRs (Katz & Shapiro, 1986). One of the first studies was that of Greenstein (1993), who examined the impact of the installed base on federal agencies’ acquisition of commercial mainframe computer systems. Subsequently, Gandal (1994) investigated computer spreadsheet programs to empirically test the presence of network effects, particularly by assessing consumers’ willingness to pay premium prices; Saloner and Shepard (1995) examined the adoption of Automated Teller Machines (ATMs) by banks, with a particular emphasis on analyzing the impact of the number of a bank’s branches on its ATM adoption rates; Brynjolfsson and Kemerer (1996) conducted a longitudinal case study analyzing microcomputer software to examine the role of network effects, specifically focusing on the size of the product’s installed base and its impact on pricing dynamics; and Gandal et al. (2000) focused on the CD industry to explore the diffusion of hardware/software systems, where they examined the impact of changes in CD player prices and the variety of CD titles on the overall adoption of CDs. Thereafter, mirroring the evolution of the network externality literature and its intersection with that of two-sided markets (Rochet & Tirole, 2003, 2006), studies focusing on estimating or quantifying network effects shifted their focus to these markets, with Rysman’s (2004) study being the first to examine network effects in two-sided markets. Rysman (2004) focused on the Yellow Pages industry and examined the impact of indirect network effects on consumer surplus and, consequently, on the market structure, whether it be monopoly, oligopoly, or competition. They mainly emphasized three key elements in their examination of network effects: (1) consumer demand for usage of a directory, (2) advertiser demand for advertising, and (3) a publisher’s first-order condition (Rysman, 2004). In a similar vein, Clements and Ohashi (2005) examined indirect network effects by analyzing the interaction between hardware adoption decisions and software supply decisions on the product cycle, specifically focusing on pricing and software variety in the US video game industry from 1994 to 2002; and Chao and Derdenger (2013) focused on the same industry to examine the impact of mixed bundling on indirect network effects, particularly on the participation rates of the two different sides of the market. Tucker and Zhang (2010) were among the first to diverge from the approach of the majority of the studies that focused on indirect network effects and to address both direct and indirect network effects simultaneously. Tucker and Zhang (2010) examined the impact of advertising the user base on the number of buyers, the number of sellers, and the combined participation of buyers and sellers in two- 17Acta Wasaensia sided exchange networks, particularly focusing on a B2B website. More recently, an increasing number of studies have empirically examined both direct and indirect network effects, such as those by Voigt and Hinz (2015), Chu and Manchanda (2016), and Hinz et al. (2020). However, most studies have mainly focused on estimating indirect network effects, specifically in established markets or industries. 2.2 Industry platforms 2.2.1 Emergence Industry platforms are defined as “products, services, or technologies developed by one or more firms, and which serve as foundations upon which a larger number of firms can build further complementary innovations and potentially generate network effects” (Gawer & Cusumano, 2014, p. 420). The term “industry platforms” initially appeared in Gawer and Cusumano’s (2008, p. 28) paper, “How Companies Become Platform Leaders.” Despite using the term industry platform, it lacked a clear definition at that time. To explain the concept, Gawer and Cusumano (2008) relied mainly on examples such as the Google Search Engine, Qualcomm wireless technology, EMC Corp.’s WideSky, and the Linux operating system, among others. By that time, the majority of studies relied extensively on case studies of Intel, Microsoft, and Cisco, e.g., Cusumano and Gawer (2002), Gawer and Cusumano (2008), Gawer and Henderson (2007), and Gawer and Phillips (2013), where the “base platform” (Cusumano & Gawer, 2002, p. 52) was the hardware and the applications were the software. Meanwhile, other scholars took a different approach to studying these platforms. For example, Boudreau (2010) considered the software, the platform, and the hardware to be the complementor. However, both research and practical experience have shown that network effects are not significantly present in the context of hardware (Boudreau, 2012). Therefore, Boudreau (2012) reverted to considering the hardware as the platform and the software (i.e., the applications) as the complementor. Subsequently, different scholars tried to examine and understand this new phenomenon, each from their own perspective. Gawer (2014) bridged two dominant perspectives that examine technological platforms, each with its own terminologies, classifications, and level of analysis. The first perspective is the economics perspective, which views platforms as markets and focuses mainly on competition (Rochet & Tirole, 2003, 2006), while the other is the engineering perspective, which views platforms as technological architectures consisting of a core and a periphery and focuses mainly on innovation (Baldwin & Woodard, 2009). Accordingly, Gawer (2014) clearly distinguished between three 18 Acta Wasaensia different types of technological platforms: (1) internal platforms, which exist at the level of the firm and include the firm and its subunits; (2) supply-chain platforms, which operate at the level of the supply chain and include the platform and its various suppliers; and (3) industry platforms, which function at the level of the industry ecosystem and include a firm acting as a platform owner and its complementors. This classification paved the way for a clear definition of industry platforms by Gawer and Cusumano (2014, p. 420), as mentioned in the first paragraph of the Introduction Section. Furthermore, not only did the definition of industry platforms become clear in the literature, but it also paved the way for a clear classification of these platforms. Cusumano et al. (2019) identified two main types of industry platforms based on their primary function: (1) transaction platforms, which facilitate transactions between different actors in the platform ecosystem, e.g., Google Play; and (2) innovation platforms, which allow third-party developers to develop complementary applications on top of the platform, e.g., Google Android. Furthermore, (3) hybrid platforms lie between these two types and share functionalities from each, e.g., Google. Throughout this work, the researcher adopts the term “industry platforms” (Gawer & Cusumano, 2014, p. 420) to refer to technological platforms associated with network effects and adopts the classifications presented by Cusumano et al. (2019). However, industry platform is not the only term used to refer to platforms associated with network effects; other scholars use different terminologies to refer to such platforms. Economics scholars, for instance, refer to them as two-sided markets (Rochet & Tirole, 2006), two-sided platforms (Evans & Schmalensee, 2008), multi- sided platforms (Hagiu, 2014), or even platforms (Hagiu & Wright, 2018). Strategic management scholars, on the other hand, use terms such as industry platforms (Gawer & Cusumano, 2014), double-sided markets (Gawer, 2014), platforms (Cennamo et al., 2018), or digital platforms (Gawer, 2021). Information systems scholars often refer to them as software-based platforms (Tiwana et al., 2010), digital infrastructures (Tilson et al., 2010), platforms (Ghazawneh & Henfridsson, 2015), or digital and non-digital platforms (de Reuver et al., 2018). Additionally, other scholars use terms like innovation platforms (Baldwin & von Hippel, 2011) or two-sided networks (Parker & Van Alstyne, 2005). The issue also extends to the definition of these platforms, as different scholars provide varying definitions associated with each term. Further, different scholars offer distinct classifications for categorizing these platforms that are associated with the presence of network effects. Despite the various terminologies, definitions, and classifications used to refer to the phenomenon under examination, the dynamics of industry platforms started to become clear, particularly in terms of what distinguishes them from other types of platforms (Gawer, 2014, 2021; Gawer & Cusumano, 2014) and what constitutes the 19Acta Wasaensia industry platform ecosystem (Van Alstyne et al., 2016). As mentioned previously, Gawer (2014) developed the concept of industry platforms by bridging two different literatures, one of which was economics (Evans, 2003; Rochet & Tirole, 2003, 2006; Rysman, 2009). Accordingly, drawing on the literature of two-sided markets (Rochet & Tirole, 2003, 2006), Gawer (2014) argues that network effects are the main distinguishing factor between industry platforms and other types of platforms. The presence of network effects as a differentiating factor is an issue that the majority of academic scholars agree on, and it is embedded in discussions about these platforms (Bakos & Halaburda, 2020; Hagiu & Wright, 2015; Rochet & Tirole, 2003), regardless of the terminology used. However, it turned out that the presence of network effects alone is not sufficient for distinguishing industry platforms from other types of platforms, particularly after Hagiu and Wright (2015) argued that two main features differentiate these platforms beyond indirect network effects or other requirements: (1) “They enable direct interactions between two or more distinct sides,” and (2) “each side is affiliated with the platform” (Hagiu & Wright, 2015, p. 63). Accordingly, these three main factors represent the criteria for assessing whether a certain platform qualifies as an industry platform (Gawer, 2014; Gawer & Cusumano, 2014; Hagiu & Wright, 2015). The three previously mentioned factors are more applicable to transaction platforms than to innovation platforms (Gawer, 2014; Gawer & Cusumano, 2014; Hagiu & Wright, 2015). Those factors alone are not sufficient to differentiate innovation platforms from other types of platforms, particularly transaction platforms (Cusumano et al., 2019). Somehow, this is linked to the ongoing debate of which industry platforms are considered digital platforms and which are not (de Reuver et al., 2018; Gawer, 2014; Gawer & Cusumano, 2014; Thomas et al., 2014). To be more specific, the definition of digital platforms aligns with the definition of innovation platforms, particularly in terms of allowing third-party complementors to develop complementary applications on top of the platform (Cusumano et al., 2019). de Reuver et al. (2018, p. 126) defined digital platforms as “purely technical artefacts where the platform is an extensible codebase, and the ecosystem comprises third- party modules complementing this codebase.” Further, de Reuver et al. (2018) criticized the broad treatment of technological platforms. They suggested that some scholars, such as Gawer (2014) and Thomas et al. (2014), had not considered the technological aspect, particularly digitality, when examining platforms. Accordingly, the presence of an extensible codebase emerges as a fourth factor that sets an innovation platform apart from a transaction platform (Cusumano et al., 2019), both of which are industry platforms (Gawer, 2014; Gawer & Cusumano, 2014). As for the different actors that are part of the platform ecosystem, Van Alstyne et al. (2016) argue that there are four main actors in a platform ecosystem: (1) the platform 20 Acta Wasaensia owner, (2) the platform provider, which are at the core of the platform (Modol & Eaton, 2021), and (3) producers, and (4) consumers, which are at the periphery of the platform (Modol & Eaton, 2021). Despite the simplicity of this classification, it aligns more closely with discussions related to two-sided markets (Armstrong, 2006; Rochet & Tirole, 2003, 2006; Rysman, 2009). Due to the complexity of industry platforms (Hanseth & Lyytinen, 2010), it is almost impossible to have a single framework that encompasses all possible actors in the ecosystem, as these actors vary greatly from one platform to another and from one industry to another (Gawer & Cusumano, 2014). For that reason, to understand industry platforms, the majority of the literature starts with a two-sided market conceptualization to simplify this inherently complex phenomenon (Hanseth & Lyytinen, 2010). That is not something irrelevant to the literature, as it has been argued since the very early days of examining these platforms, where Rochet and Tirole (2004, p. 2) clearly state, “We focus on two-sided markets for expositional simplicity. Many markets or platforms are multisided, though”. 2.2.2 Evolution As mentioned earlier, scholars have adopted different terminology to refer to technological platforms associated with the presence of network effects, that is, industry platforms. While Gawer (2014) focused on industry platforms, particularly on bridging the economics (Rochet & Tirole, 2003, 2006) and engineering perspectives (Baldwin & Woodard, 2009), other scholars were deeply involved in discussing specific aspects of this phenomenon. To begin with, and to avoid repetition of the discussion on two-sided markets from the previous section, economics scholars inherently focused on platform competition, particularly on pricing strategies to overcome the chicken-and-egg dilemma and attract various actors to the platform ecosystem (Armstrong, 2006; Caillaud & Jullien, 2003; Economides & Katsamakas, 2006; Kaiser & Wright, 2006). These discussions gave rise to a vast array of interrelated topics, namely, winner-takes-all competition (Cennamo & Santalo, 2013; Eisenmann et al., 2006), multi-homing (Armstrong & Wright, 2007; Doganoglu & Wright, 2006, 2010), and envelopment (Eisenmann et al., 2006, 2011), among others. For instance, Eisenmann et al. (2006) argue that for an industry platform to succeed, it must adapt to the winner-take-all competition. Essentially, the increasing rates of returns can lead to a winner-take-all scenario (Arthur, 1996; Eisenmann et al., 2006), and platform owners should assess whether the market or industry they target can be served by a single platform (Eisenmann et al., 2006). Accordingly, several scholars started examining this topic further and exploring its intricacies (Belleflamme & Peitz, 2019; Wiegand et al., 2022), while others focused on challenging it, e.g., Anderson et al. (2014), with some even claiming that the winner 21Acta Wasaensia does not take all (Huotari et al., 2017). Additionally, as the topic of winner-take-all competition is directly linked to that of multi-homing (Eisenmann et al., 2006), several studies focused on either of the topics or both (Huotari et al., 2017; Xie et al., 2021), with different scholars examining these topics from different perspectives. In brief, most of these studies primarily focused on topics directly related to competition between platforms in a particular market or industry. Similarly to how economics scholars primarily focused on competition and its various related topics (Eisenmann et al., 2006), information systems scholars shifted their attention to the ecosystem of industry platforms, particularly focusing on platform governance and its different related topics. For instance, Ghazawneh and Henfridsson (2010) relied on the innovation networks perspective, which focuses on platform governance regarding control versus coordination, and the boundary objects perspective, which examines the utilization and rationale behind the use of boundary resources. They utilized these perspectives to examine the governance of third-party developers in a platform ecosystem through the use of boundary resources (Ghazawneh & Henfridsson, 2010), which are defined as “the software tools and regulations that serve as the interface for the arm’s-length relationship between the platform owner and the application developer” (Ghazawneh & Henfridsson, 2013: 175). Thereafter, different scholars started examining different types of boundary resources (Ghazawneh & Henfridsson, 2010), specifically application programming interfaces (Ghazawneh & Henfridsson, 2013), software libraries (Fink et al., 2020), and standardized development tools (Miric et al., 2022), among others. Further, other scholars started examining different routes to attain governance within an industry platform ecosystem (Foerderer et al., 2018; Parker & Van Alstyne, 2018). However, the common denominator among these studies is that all the governance mechanisms examined are directly related either to the use of control, such as control versus autonomy (Wareham et al., 2014), or to the offering of resources, such as knowledge boundary resources (Foerderer et al., 2019, p. 20), with the ultimate goal of orchestrating the behavior of the different actors in the platform ecosystem. In other words, most studies that initially examined ecosystem governance mainly emphasized hard governance mechanisms (Foerderer et al., 2021). Other scholars have built on the discussions of platform governance to explore further aspects of industry platforms. For instance, some scholars have focused on the evolution of industry platforms, particularly that driven by the platform’s architecture and the governance mechanisms employed (Baldwin & Woodard, 2009; Gawer & Cusumano, 2014; Tiwana et al., 2010). These discussions on evolution arise from the inherent link between the evolution of a platform and its ecosystem, and the platform’s interfaces (Baldwin & Woodard, 2009; Constantinides et al., 2018; Tiwana et al., 2010). These interfaces are defined as “specifications and design rules that 22 Acta Wasaensia describe how the platform and modules interact and exchange information” (Tiwana et al., 2010, p. 676). Simply put, controlling the platform’s interfaces effectively means controlling the platform itself (Baldwin & Woodard, 2009). Baldwin and Woodard (2009) were not alone in linking platform evolution to interfaces. Tiwana et al. (2010) extended that perspective by relating the evolution of industry platforms not only to their architecture and the employed governance mechanisms but also to environmental dynamics. Similarly, Gawer and Cusumano (2014) linked the architecture of the platform and the employed governance mechanisms to the platform’s evolution over time and its progression toward a leadership position in a specific market and industry. Accordingly, various scholars started examining the topic of platform evolution more closely, particularly through the lenses of architecture and governance (Baldwin & Woodard, 2009; Constantinides et al., 2018; Tiwana et al., 2010), with some even focusing on how governance mechanisms themselves evolve during the evolutionary process of an industry platform (Huber et al., 2017). The previously mentioned streams are not the only ones to have examined industry platforms (Gawer, 2014; Gawer & Cusumano, 2014; Ghazawneh & Henfridsson, 2013; Rochet & Tirole, 2003, 2006; Tiwana et al., 2010); however, these were among the major streams to have explored the phenomenon. Other scholars have focused on topics related to the interplay between competition and innovation and were able to create their own niches in discussions on industry platforms (Gawer, 2014), such as Boudreau (2010) and Eisenmann et al. (2011). For instance, Boudreau (2010) focused on the open strategies of a platform and their interplay with innovation. Specifically, Boudreau (2010) examined handheld computer systems to identify possible approaches to opening up an industry platform, granting access, e.g., Apple, versus devolving control, e.g., Linux, and their impact on the firm’s rate of innovation. Furthermore, Boudreau and Lakhani (2009) examined whether firms should manage external innovation through collaborative communities or competitive markets and consequently identified three factors that affect such a decision: the type of innovation, the motivation of external innovators, and the nature of the business model. Accordingly, this gave rise to discussions focusing on industry platforms not from the perspective of the platform owner, as was prevalent in previous studies, but from the perspective of third-party complementors (Miric et al., 2019; Nambisan et al., 2018; Srinivasan & Venkatraman, 2018), who are integral actors in the platform ecosystem (Van Alstyne et al., 2016). Accordingly, a new stream of research emerged that focuses on the innovation aspect of industry platforms, mainly viewing industry platforms as “innovation platforms” (Baldwin & von Hippel, 2011, p. 1412), yet different from the “innovation platforms” defined by (Cusumano et al., 2019, p. 20). They mainly considered any industry platform that allows the various actors within its ecosystem to participate in creating value for the platform and its ecosystem as an 23Acta Wasaensia “innovation platform” (Baldwin & von Hippel, 2011, p. 1412). A case in point is Facebook, which has “features of both single-user innovation (each person designs her page) and open collaborative innovation (she and her friends contribute content to each others’ pages)” (Baldwin & von Hippel, 2011, p. 1411). According to Baldwin and von Hippel (2011), Facebook is an innovation platform; however, Cusumano et al. ’s (2019) classification would have it as a transaction platform. Therefore, the varying terminology and classifications complicate understanding industry platforms. 2.2.3 Current state of affairs As mentioned previously, most discussions among economics scholars have focused on competition (Armstrong, 2006; Eisenmann et al., 2006; Evans, 2003; Rochet & Tirole, 2003, 2006). Specifically, the discourse has centered on the (cross-subsidized) pricing strategy that aims to overcome the chicken-and-egg dilemma and attract the different actors to the platform ecosystem (Caillaud & Jullien, 2003). However, some scholars argue that pricing strategies can be both risky and costly for firms (Eisenmann & Hagiu, 2007). For that reason, several scholars started deviating from pricing strategies and began exploring non-pricing ones as a means to overcome the chicken-and-egg dilemma (Eisenmann & Hagiu, 2007; Hagiu & Spulber, 2013). For instance, Hagiu and Spulber (2013) argue that providing first-party content plays dual strategic roles, one of which aids the platform owner in overcoming the chicken- and-egg dilemma. Other scholars have examined different strategies to attract various actors to the platform ecosystem, such as introducing tokens (Cong et al., 2021), piggybacking (Y. Dou & Wu, 2021), and signaling output control (Adam et al., 2022), among others. Further, scholars not only shifted away from pricing strategies, which are inherently related to platform competition (Rochet & Tirole, 2003, 2006), to non-pricing strategies but also transitioned from competition-related topics to non-competition-related ones. Generally speaking, a platform owner must navigate not only the competitive environment (Eisenmann et al., 2006) but also the external factors that are not directly related to competition, such as technological advancements (Tiwana et al., 2010), piracy (Ishihara & Muller, 2020; Miric & Jeppesen, 2020), and trade policies (McCalman, 2022), among others. In a similar vein to the observed shift from competition-related topics (Rochet & Tirole, 2003, 2006) to non-competition-related ones (Eisenmann & Hagiu, 2007; Hagiu & Spulber, 2013), namely the shift from examining pricing strategies to non- pricing strategies, there was also a notable shift in the examined governance mechanisms. As mentioned before, initial studies extensively focused on hard governance mechanisms (Ghazawneh & Henfridsson, 2013; Wareham et al., 2014), which involve some form of control or provision of resources (Foerderer et al., 2021). 24 Acta Wasaensia However, these are not the only means of controlling the behavior of ecosystem actors. More recently, scholars started examining soft governance mechanisms (Foerderer et al., 2021), which, in simple terms, do not involve direct control or the provision of resources. For instance, Foerderer et al. (2021) examined the role of granting awards to complementors as a means to influence their behavior; Chan et al. (2022) examined the role of ratings and reviews on the behavior of ecosystem actors, namely buyers and suppliers; and Reuber and Fischer (2022) highlighted the roles of likes, endorsements, and hashtags, among others, in influencing the behavior of social media platform users. Besides, since the evolution of industry platforms is closely tied to governance mechanisms (Tiwana et al., 2010), particularly hard governance mechanisms (Foerderer et al., 2021), the observed shifts in examining various aspects of industry platforms have broadened scholars’ perspectives, thereby influencing discussions on platform evolution. Accordingly, various scholars started exploring the evolution of industry platforms from perspectives other than governance and architecture. For instance, some scholars have related the evolution of industry platforms to changing the leverage logics (Thomas et al., 2014) or to the capabilities of the platform owner, namely innovative, environmental scanning and sensing, and integrative (Helfat & Raubitschek, 2018), or even to the design of the business model, as well as to entrepreneurial actions such as business model innovation and imitation (Y. Zhao et al., 2020). The preceding sections, specifically 2.1.2, 2.1.3, and 2.2.1, demonstrate that discussions of industry platforms have been built upon the foundations of two distinct literatures (Gawer, 2014), namely economics and engineering (Baldwin & Woodard, 2009; Rochet & Tirole, 2003). However, the literature examining these platforms, including that addressing strategic management (Cennamo et al., 2018; Gawer, 2014; Gawer & Cusumano, 2014), has, to some extent, taken the presence of the platform for granted. That is evident in the early discussions, where the examination of these platforms started with a focus on attracting the different actors to the platform (Caillaud & Jullien, 2003; Rochet & Tirole, 2003, 2006) or on orchestrating the behavior of the actors already present in the platform ecosystem (Eaton et al., 2015; Ghazawneh & Henfridsson, 2013; Parker & Van Alstyne, 2018; Wareham et al., 2014). Accordingly, the issue of how these platforms are created did not receive sufficient attention in the literature (de Reuver et al., 2018; Gawer, 2014; Shi et al., 2021; Tan et al., 2015). Indeed, this topic was primarily addressed in the early discussions of Gawer and Cusumano (2008, p. 32), particularly the “coring phase,” but subsequently, it remained somewhat dormant. More recently, several scholars noticed that the topic of platform creation deserves more attention, which prompted various journal special issues focusing on the creation process of industry platforms, such as that by Teece et al. (2022), as well as numerous studies empirically 25Acta Wasaensia examining the creation process, such as those by Cennamo et al. (2022) and Trabucchi and Buganza (2022). 26 Acta Wasaensia 3 METHODOLOGY This chapter focuses on the dissertation’s methodology, which is divided into two main sections: philosophical assumptions and research design. The first section outlines the dissertation’s philosophical assumptions, positioning it with respect to the various paradigms utilized in social theory analysis. It identifies the ontological, epistemological, and methodological choices guiding the research. The second section outlines the research design, specifically addressing the research process, methods of data collection, and considerations regarding the validity and reliability of the research. 3.1 Philosophical assumptions The nature of science can be approached from a subjective or an objective perspective. Further, the nature of society can be examined through two perspectives, either regulation or change (Burrell & Morgan, 1979). The combination of the two dimensions, the nature of science with its two ends of subjectivity and objectivity, and the nature of society with its two ends of regulation and change, gives rise to four different paradigms adopted in the analysis of social theory, radical humanist, radical structuralist, interpretive, and functionalist (Burrell & Morgan, 1979), each grounded in distinct ontological, epistemological, and methodological assumptions. Although researchers often adhere to a single research paradigm, this approach often results in a narrow focus that restricts researchers and the broader community from developing a comprehensive understanding of organizational reality (Gioia & Petre, 1990). For that reason, multiparadigmatic approaches emerged as a viable method for theorizing by bridging different paradigms (Gioia & Petre, 1990), such as the structurationist paradigm (Barley, 1986; Giddens, 1979; Riley, 1983). Considering the primary objective of this dissertation and acknowledging the dynamic and complex nature of industry platforms, this research adopts a structurationist perspective (Giddens, 1979). The structurationist paradigm occupies the middle ground of the debate on the nature of social science and leans toward the regulation side regarding the nature of society, thereby focusing on maintaining and preserving the social order. Therefore, it lies in the “transition zone” between the interpretive and functionalist paradigms (Gioia & Petre, 1990, p. 592). The interpretive paradigm in philosophy and sociology centers on understanding the social world through the perspectives of individuals who are actively involved in social processes, particularly from a subjective standpoint (Burrell & Morgan, 1979). In contrast, the origins of the functionalist paradigm can be traced back to the sociology of regulation and the objective 27Acta Wasaensia perspective (Burrell & Morgan, 1979). Therefore, the structurationist paradigm primarily bridges the subjective and objective perspectives (Gioia & Petre, 1990), or in other words, “cuts through the action::structure paradox” (Poole & van de Ven, 1989, p. 575). Due to the complexity of industry platforms (Hanseth & Lyytinen, 2010), their dynamics can be better understood through diverse paradigmatic lenses that integrate both subjective and objective choices. This approach is particularly valuable because previous purely subjective or objective-oriented studies have limitations despite providing valuable insights (Gawer, 2021). Accordingly, the structurationist paradigm is ideal for achieving the dissertation’s objectives, given its strength in bridging multiple perspectives (Gioia & Petre, 1990). From a structurationist perspective, human action is not isolated from structures, particularly organizational structures, as argued by Giddens (1979, 1982, 1984). Instead, structures are shaped by human actions while simultaneously constraining and enabling them. This dual role makes structures both the medium and the outcome of social practices (Giddens, 1979). This is precisely the case with industry platforms. For instance, the different ecosystem actors can influence the structure of an industry platform by driving changes in its value propositions and business model configurations (Trabucchi & Buganza, 2022). Conversely, the structure of the industry platform, including its architecture and associated governance mechanisms (Tiwana et al., 2010), can shape the actions of the ecosystem actors by controlling or influencing their behavior (Eaton et al., 2015; Foerderer et al., 2021; Ghazawneh & Henfridsson, 2013). This continuous interplay between agency and structure, which is fundamental to the structurationist perspective (Giddens, 1979, 1982, 1984), can lead to the creation and evolution of platforms, as seen with industry platforms like Friendz (Trabucchi & Buganza, 2022). The following sections will elaborate on how this philosophical framework, the structurationist paradigm (Giddens, 1979), influences ontological, epistemological, and methodological choices. 3.1.1 Ontological choices As mentioned at the beginning of this section, the nature of science can be approached either subjectively or objectively. Burrell and Morgan (1979) elaborated on this debate by identifying dimensions that illustrate the subjective-objective spectrum, one of which is ontological choices. Ontological choices concern what exists in the world, that is, the nature of reality, falling on a continuum between two extremes: nominalism and realism (Burrell & Morgan, 1979). Nominalism, on the subjective approach to social science, argues that anything beyond individual cognition is simply labels and names that people use to organize their perception of the real world (Burrell & Morgan, 1979). Conversely, realism, on the objective approach to social 28 Acta Wasaensia science, argues that the social world external to an individual’s cognition is real, characterized by tangible and immutable structures that can be empirically explorable. Further, it is worth noting that combining different ontological choices is possible (Weick, 1995), even though Burrell and Morgan (1979) do not support it. Weick (1995, p. 35) argues that “that very mixing of ontologies is what drives Burrell and Morgan nuts. But it shouldn’t. People who study sensemaking oscillate ontologically because that is what helps them understand the actions of people in everyday life who could care less about ontology”. In line with the philosophical assumptions discussed in the previous section, particularly the structurationist paradigm (Giddens, 1979; Gioia & Petre, 1990), the dissertation’s ontological choices also occupy the middle ground between nominalism and realism. This balanced approach recognizes that a comprehensive understanding of reality emerges from the interplay between objective and subjective elements. To clarify this positioning, it is essential to briefly reflect on the researcher’s conceptualization of the examined phenomenon. The researcher asserts that the phenomenon under examination is not exclusively referred to as “industry platforms” (Gawer & Cusumano, 2014, p. 417). Different scholars and bodies of literature adopt various terminologies to refer to it, such as digital platforms (de Reuver et al., 2018), innovation platforms (Baldwin & von Hippel, 2011), two-sided markets (Armstrong, 2006), and multi-sided platforms (Hagiu, 2014), among others. Further, these diverse terminologies are often associated with different definitions and classifications (Boudreau & Lakhani, 2009; Cusumano et al., 2019; Eisenmann et al., 2006; Hagiu, 2014). However, the researcher argues that certain key characteristics set this phenomenon apart regardless of the terminologies, definitions, and classifications used. More specifically, this type of platform is distinct from others due to the potential generation of network effects or network externalities (Gawer & Cusumano, 2014), which are often used interchangeably in the literature despite the differences between the two terms (Liebowitz & Margolis, 1994). Additionally, other scholars identified further characteristics that set these platforms apart, namely the presence of interactions between the different sides of the platform and the affiliation with the platform (Hagiu & Wright, 2015). Accordingly, the dissertation’s ontological positioning (Giddens, 1979), which occupies a middle ground between nominalism and realism, acknowledges that different labels, definitions, and classifications are applied to refer to the examined phenomenon, combined with the belief that it possesses distinct characteristics. 29Acta Wasaensia 3.1.2 Epistemological choices Another dimension on the continuum of the subjective-objective approach to the nature of science is epistemological choices (Burrell & Morgan, 1979), a branch of philosophy concerned with the nature of knowledge and how it is acquired (Fleetwood, 2005). Similar to ontological choices, epistemological choices also fall on a continuum between the subjective and objective approaches to social science, respectively, anti-positivism and positivism (Burrell & Morgan, 1979). Anti- positivism argues that knowledge is obtained through subjective experiences, meanings, and interpretations rather than relying solely on empirical evidence and scientific methods (Burrell & Morgan, 1979). In contrast, positivism is a philosophical approach that argues that knowledge can only be obtained through empirical evidence, scientific methods, and the causal relationships among the different components of the social world. Echoing the positioning initially explained in terms of the philosophical assumptions, namely the structurationist paradigm (Giddens, 1979; Gioia & Petre, 1990), the epistemological choices of this dissertation occupy a middle ground, falling on a continuum between anti-positivism and positivism To further clarify the dissertation’s epistemological choices, it is essential to briefly elaborate on how the researcher acquired knowledge about industry platforms. In other words, it involves exploring the origins and foundational concepts of industry platforms. The literature on industry platforms emerged by bridging two distinct bodies of literature that have examined this phenomenon from an objective perspective (Gawer, 2014): the economics literature focusing on competition (Rochet & Tirole, 2003, 2006) and the engineering literature focusing on innovation (Baldwin & Woodard, 2009). The literature on industry platforms has evolved over the past two decades, with new streams of research emerging that blend the topics of innovation and competition (Boudreau, 2010; Eisenmann et al., 2