Introduction
The concept of platform businesses, referring to businesses connecting those that produce goods and services with those that consume them, has sparked enthusiasm due to the popularity and global success of gigantic platforms including Uber, Facebook, Amazon, and Alibaba, among others. The platform business model does not cut out the “middle-man,” but rather attempts to provide an alternative pathway that is less costly and more efficient. Platform businesses predominantly rely on information technologies and resemble digital warehouses (with some brick-and-mortar ones) where informational signals about businesses’ products and services, and consumer preferences and experiences related to them, are exchanged and stored. The network ecosystems created by platform businesses often reduce informational asymmetries, by providing better management of, and greater access to, informational signals from network participants, which in turn allows for increased global market participation (Lehdonvirta, Kässi, Hjorth, Barnard, & Graham, 2019). Such platforms may also, paradoxically, fuel network effects that create winner-take-all marketplaces (Knee, 2018) and confound information signals used to precisely attribute the shared reputational value created.
Albeit a very popular organizational form in the digital economy, platform businesses have not been the subject of much inquiry in the international business (IB) field despite the fact that they often represent a globally dispersed ecosystem that could be very complex to manage due to the cultural, economic, political, geographic differences that exist among countries. In addition, the success (broadly defined) of a platform business, consisting of a network of individual actors, often depends on the behaviors of other actors in the ecosystem in which they operate (Zhu & Iansiti, 2019). This can certainly lead to synergetic effects, but also simultaneously engender informational asymmetries or other challenges to reputational value creation. In this paper, we delve into such duality by focusing on an intangible dimension of firm success, namely corporate reputation, and introduce the concept of the platform paradox, highlighting the complexity of global reputation management in the platform business model.
While a large body of work has explored firm reputation given its importance for firm success (e.g., Roberts & Dowling, 2002), there is not much research on platform business models and shared reputation. To address the interplay between the platform business model and reputation, we borrow insights from signaling theory, one of the most prominent theoretical lenses used to examine reputation. This theory builds on the premise that relevant constituents seek to reduce informational asymmetries by generating signals that serve as the foundation of their reputation (Connelly, Certo, Ireland, & Reutzel, 2011; Spence, 1974). Naturally, signaling is costly in terms of time and money, but building a good reputation is an expense that, when paid, creates an intangible asset used to offset information acquisition costs of interested parties. Platform businesses create such value by lowering the costs of information acquisition; they are global information wholesalers crowdsourcing and distributing bulk information throughout their global network ecosystem. Online retailers such as Amazon or Overstock depend on user-generated reviews to reduce transaction hesitancy. Similarly, ride-share companies such as Uber and Lyft, and home-share companies including Airbnb and VRBO, use their wholesale legitimacy and review system to encourage people to get in a stranger’s car or their home.
Platforms rely heavily on the co-creation of signals and value by globally networked constituents throughout the reputational value chain. Paradoxically, as many informational asymmetries are reduced allowing for more global participants (Lehdonvirta, Kässi, Hjorth, Barnard, & Graham, 2019), signal attribution, and the corresponding shared reputational value allocation, becomes less clear. Stated another way, the global value chain creates signals related to the quality of products and services based on consumer experiences within the platform, but it may be unclear to the consumer what caused the experience, or they may be unclear when providing feedback such as reviews, that create reputational value. So, how that value is allocated, and whether the good or bad reputation signals go to the party responsible, is asymmetric and ambiguous, especially if the signals are not managed well. Because platform businesses often rely on third-party producers, physical and digital warehousing for retail distribution and logistics, and information shared by producers and consumer generated reviews, informational asymmetries that are reduced or created rely heavily on signal management capabilities that affect corporate reputation (Kelley & Alvarado-Vargas, 2019). Informational asymmetries are affected by signal generation capabilities for product and service quality (qualitative efficiency), the ease and cost of acquiring, comparing, and using informational signals for consumption choices (productive efficiency), and consumer satisfaction tied to meeting expectations (expectation efficiency) - and of course the signal management capabilities of platform businesses. Figure 1 below models a simple platform ecosystem with the platform business (green) connecting a network of constituents and supporting infrastructure (light blue) that all help to generate information signals (dark gray), which are then managed in such a way as to reduce, or sometimes create, informational asymmetries.
The Platform Paradox in global Reputation Management is that the very systems and business models that help bridge informational asymmetries, by providing information acquisition at a wholesale discount, also may make it difficult to know who precisely is responsible for successes and failures in the shared reputational value chain. The extent of the paradox will be related to the diversity and quantity of information generating constituents in the platform ecosystem, likely higher in ecosystems that cross national boundaries, and of course how effective, or efficient, these signals are managed. The remainder of this article explores various platform businesses and the extent to which these business models present challenges for signal generation and management in relation to shared reputational value. Applying the CAGE framework (Ghemawat, 2001), we illustrate potential areas of global signal generation and management concern (GSGC and GSMC) and then conclude with a few recommendations for managers.
Platform Businesses
Many would consider a platform as a “digital business with a lot of developers and followers latching onto it, both from within and without” (McKendrick, 2019). A platform business is looking for a network effect to attract massive web-traffic to enhance commerce and information creation and distribution - i.e., a business built around high-tech marketing that shares information about products and services. Information signals are generated by the producers of goods and services and consumers that access them through the platform and then co-create value using Web 2.0 technologies (i.e., customer reviews) and they are all managed by the platform itself. Co-created value stems from signals not entirely controlled by providers of products and services (Kelley & Alvarado-Vargas, 2019), in this case also consumers and the platforms providing the experience of acquisition and consumption by also managing the information needed to broker the transaction. This information is often fed into sophisticated algorithms helping improve (manage) the platform user experience by lowering information acquisition or search costs. Indeed, some platform businesses may be simply based on algorithms that produce relevant search results (Google, Yahoo, Bing, Baidu, Yandex) or perhaps those appealing to social media content and trending searches around the world (Facebook, Twitter, TikTok, LinkedIn, Reddit).
Platform retailers like Amazon or Aliexpress work with producers, ranging in size from large scale retail manufacturers to micro-providers, around the globe using various supply chain and logistical approaches. Yet, while larger platforms have more global scope, some platforms such as food delivery (GrubHub, DoorDash, Uber Eats) or grocery delivery (Instacart, Shipt, and Amazon Fresh) connect local consumers and retailers with minimal regional adaptations to value creation necessary. Sharing economy platforms such as ride-share businesses (Uber, Lyft, Gett, Grab, Bolt) or home-share companies (Airbnb, Vrbo, HomeAway, Wimbdu, 9Flats) rely on individual service providers, crowdsourced evaluation signals, and technological and legal infrastructure adapted to regional specifications, a mix of local and cross-national.
Platform businesses with more localized supply chains and homogeneous network constituents (e.g., similar consumer segments without diverse preferences and expectations) should have low Global Signal Generation Concerns (GSGCs) and low Global Signal Management Concerns (GSMCs). Conversely, platform businesses with highly-globalized supply chains and a heterogeneous scope of network constituents will have high GSGCs and GSMCs. It is more difficult to align signals and create shared reputational value in a global setting than it is a regional or local one (Kelley & Thams, 2019). Table 1 provides a list of platform business types, descriptions, and levels of GSGCs and GSMCs.
Signal Generation & Management in a Global Context
Another point highlighted in this article is the implications of globalization on the management of signals and reputational assessment in the context of platform businesses. While globalization adds an important layer of complexity to the notion of signal management and interpretation by relevant constituents, its implications are underexplored, making it more difficult for practitioners to assess its ramifications. Indeed, a large majority of platform businesses have expanded their realm across borders and have gained a sizable market share in a large number of countries (e.g., AliExpress, Airbnb, Uber, Amazon).
For example, in Germany, Amazon’s sales accounted for more than 56% of online shopping dollars spent in 2019; this figure was 47% in France or 48% in Spain for the same year. Research has recognized that signal effects vary across institutional and cultural settings (Connelly et al., 2011; Deephouse, Newburry, & Soleimani, 2016) (as well as task and industry environments). The interpretation of a signal sent by a firm differs based on the institutional milieu in which it is embedded (Connelly et al., 2011) since constituents are likely influenced by different institutional norms and standards. Globalization itself is not the concern when generating or managing signals, it is the various differences, or distances, embedded within it that create ambiguity in the allocation of shared reputational value by constituents.
To help practitioners ascertain the impact of the scope of a global supply chain and its network constituents on a platform business’ reputation, we employ the CAGE framework developed by Ghemawat (2001). The framework is composed of 4 dimensions: cultural, administrative (or political), geographic, and economic. We provide below a few illustrations and examples of how each factor may influence signal management and reputation that could be built upon for a more rigorous analysis.
Starting with culture, referring to “the collective programming of the mind which distinguishes the members of one human group from another” (Hofstede, 1980: 260), there exist many ways in which global cultural differences may affect the reputational assessment of platform businesses. One example is the complexity of signal management due to cross-national variations related to consumer reviews and product/service descriptions. Indeed, signals generated by certain constituents such as consumers (e.g., in the form of reviews) may be impacted by cultural biases, leading to difficulty in interpretations when aggregated at the global level (Newlands, Lutz, & Fieseler, 2018). Unsurprisingly, many scholars who conduct cross-national research have identified the threats to the validity of inferences emanating from measurement instruments employed across different cultures (e.g., Sireci, 2011). Advances in communication technology make it easy for consumers to conduct international comparisons of products and services by accessing reviews made by consumers all over the globe. Consumers are unlikely to take into account cultural biases when reading reviews made by individuals from other countries leading to informational asymmetries and reputational distortion. For example, consumers from one culture may be more critical in their reviews on products/services compared to those from another. This also relates to how platform businesses are managing the various information signals and therefore managing expectations as well.
By spreading their network across borders, platform businesses may be subject to a great deal of administrative or political concerns and complexity impacting reputational outcomes. Amazon and its reputational assessment in countries such as France, a country which has for the past centuries built a system of state capitalism mixing government intervention and entrepreneurial spirit, varies from how it is viewed elsewhere. In the US, Amazon has frequently been in the top ten of the most admired companies ranking (published by Fortune magazine), an accomplishment whose benefits impact members of its platform ecosystem. However, its reputational standing in France may depart from such stellar accomplishment based on the qualitative evidence which we gathered from French Newspapers (Dromard & Macke, 2021; Piquard, 2020). We note that such contrast may be explained by many variables; however, the company has been the target of many criticisms and public protest since it symbolizes (to a certain extent) American imperialism (Dromard & Macke, 2021; Piquard, 2020). Furthermore, the expectations of consumers for quick delivery may not be met as easily when the labor rights and standards, often associated with unions, conflict with meeting such logistical expectations.
An additional concern is the complexity of signal management due to cross-national variations relating to the development of economic institutions that could impact, for example, consumer reviews and product/service descriptions. Indeed, countries largely differ as it relates to the quality and reliability of information available in the product market. In some institutional settings such as emerging countries, underdeveloped economic institutions may hinder the quality/availability of information available to constituents to assess firms, which is likely to have an impact on our notion of Platform Paradox since such issues may exacerbate the problem of informational asymmetries and makes much more difficult to know what actors are responsible for successes and failures in the shared reputational value chain. Furthermore, reputation is often considered a social construct that may be influenced by the level of expectations within certain levels of market development, as well as, the ability to compare one company’s offerings with that of another, which may also be determined by the level of economic competitiveness.
The geographic dimension refers to not only physical characteristics of a country such as its size, but also man-made geographic attributes including the country’s transportation and communications infrastructure (Ghemawat, 2001). Undoubtedly, many of these characteristics are likely to have some influence on a platform ecosystem from an operational standpoint. A prominent example relates to a country’s physical size and the availability/quality of transportation infrastructures. Indeed, in large countries such as Russia or Brazil, the presence of many time zones and infrastructure issues may complicate the logistics of platforms’ delivery of products and services. In such cases, reputational damage inherent to logistical and transportation issues may affect multiple members of a platform ecosystem due to the inability of consumers and other stakeholders to correctly distinguish qualitative efficiencies of the product from productive efficiencies in ordering and delivery. Further complicating the issues with geographic distance and logistics is the inability to precisely assess the time for delivery if there are issues and properly set expectations for consumers, hurting the value expectation efficiency.
Conclusion and Practical Recommendations
What are the implications of this paper for practice? As a first attempt to explore the shared reputation of platform businesses, we hope that this paper could help entrepreneurs and practitioners factor in reputational issues and the notion of platform paradox when evaluating the prospects of a platform business, especially by considering their global nature and challenges that arise due to cross-national differences. The consideration of shared reputational outcomes may help practitioners obtain a more realistic picture of the long-term potential of platform businesses. As highlighted by Zhu & Iansiti (2019), a great deal of investment euphoria exists regarding the extraordinary scale and strength of platform ecosystems and networks partly due to the highly publicized success of digital giants such as Uber or Alibaba. However, it is important to remember that while the network effect of platform businesses allows them to scale up at a low cost, their long term success depends largely on the health of the ecosystem in which they operate. Lack of efficiency of a member, or a few members, of such ecosystems may cause reputational damage to all members due to informational asymmetries.
There are several other key takeaways from this article. First, the extent to which the supply chain is globalized and the scope of network constituents producing information signals are the key factors in the reduction or creation of informational asymmetries. The more globalized (and managers need to assess this on various dimensions such as those included in the CAGE framework) the network of constituents that create signals, the more likely it is that diverse signals will be generated. Also, the larger scope of network constituents (the number of people and businesses involved in the value creation process for a platform business), the less likely a platform will be able to control or manage signals effectively. If platform businesses wish to reduce informational asymmetries, the global structure of the ecosystem is a key consideration.
Second, platform businesses derive a great deal of value from their reputation as being honest brokers of information. Therefore, related to the first point, if platforms do not manage signals and information well, and develop a bad reputation for not meeting expectations, what real value do they provide? Platforms that have grown to develop and internalize global supply chains and logistics, including physical and digital warehousing facilities, may provide substitute value through pricing and convenience. But, while they face high Global Signal Generation and Management Concerns, they may not be at as much risk as firms yet to internalize or grow to scale economies and still not deriving a dominant market position.
Third, platform business models rely heavily on the production of value from third-parties such as consumers who produce reviews. Stated another way, platforms are not responsible for generating a lot of the signals for the quality of products and services, or qualitative efficiency signals. The key area of control for managers is in information and signal management which leads to productive efficiency signals by enhancing the user experience and meeting consumers’ expectations, or expectation efficiency signals. Managers of platform ecosystem information must focus on providing the most accurate information possible to help generate value for their firms.
Fourth, related to this latter point of managing and providing information, what information is shared and with whom may be controlled by the platform, inviting all sorts of ethical concerns related to the deliberate creation of information asymmetries. Indeed, managers may be faced with tough decisions on whether to withhold information to gain access to certain global markets, or to promote certain products or services over others to generate higher revenues, distorting algorithm searches for information and comparisons artificially. Managers must weigh such ethical concerns alongside the potential for reputational damage across global markets, variations in ethical standards have long been a concern of global businesses.
Fifth, managers should develop global signal management tactics and policies based on data collected from the environmental scanning of the countries in which platform ecosystems are present. This could help understand the ability of the platform to meet the performance expectations of all stakeholders. Managers could use tools such as the CAGE framework to explore how various dimensions of distance or differences can affect signal generation and management. Ultimately, allocation of resources to appraise and manage risks should be weighed against the costs as is always the case when considering the reduction of informational asymmetries.
About the Authors
Keith J. Kelley is an Associate Professor of International Business at the School of Management, University of Michigan-Flint. His recent publications have focused on global and shared reputational value management, artificial intelligence and political risk management, IT and signal management strategies, and global supply chain strategies during uncertainty. His work is published in Multinational Business Review, the Journal of Business Research, Thunderbird International Business Review, and Corporate Reputation Review, among other outlets.
Yannick Thams is an Associate Professor of Strategy at Florida Atlantic University. At the intersection of strategy and international business, her research examines the interplay between institutions and firms’ global strategy, board composition, and CEO succession. Her latest work has been published in outlets such as the Global Strategy Journal, the Journal of Business Research, Asia Pacific Journal of Management, and Multinational Business Review. She currently serves on the editorial review board of the Journal of International Business Studies.