6월 5주차

As businesses expand globally, effective website localization becomes crucial, especially in countries where consumers have diverse cultural backgrounds. Traditional website localization strategies focus on tailoring its content and design to the dominant culture of a country. Our research investigates the complexities of localizing websites for countries with multicultural consumers who identify with more than one culture. We found that successful website localization involves priming multicultural consumers with cultural cues (in the forms of language and image) to activate specific cultural identities, for example, individualism or collectivism. The activated culture of multicultural consumers can interact with various website design and content elements to affect multicultural consumers’ trust in the website and crowdfunding support. Our findings offer actionable insights for global businesses. By strategically implementing these insights in website localization, global companies can better navigate diverse consumer landscapes and foster stronger connections with multicultural consumers.

For managers, this research demonstrates that the long-term success of digital platform ecosystems requires identity-coherent orchestration. For example, Apple iOS’s identity domain around a seamless, premium user experience on the iOS ecosystem legitimizes Apple’s “walled garden” and vertical integration approach. Conversely, Google Android’s identity domain of data ubiquity drives Google’s horizontal scaling and proprietary control points. Platform sponsors are cautioned that deviating from their identity domain or appearing as incoherent to their partners and users—as seen in the failures of BlackBerry and Windows Phone—creates strategic dissonance that confuses participants and erodes competitive distinctiveness. For policymakers, this research challenges “one-size-fits-all” regulations, such as the European Union’s Digital Markets Act. Ex ante mandates may disproportionately undermine vertically integrated, privacy-centric models, like iOS, while leaving data-driven ecosystems, like Android, relatively unscathed. Effective antitrust policy must move beyond size thresholds (i.e., gatekeeper status classifications) to consider how the enforcement of ex ante and ex post regulatory approaches can account for the dynamic evolution of digital platform ecosystems as informed by their unique identity domain.

Digital technologies are enabling online retail platforms to connect consumers not only to sellers’ online offerings but also to sellers’ physical stores through options such as local pickup and store-based delivery, which we refer to as local selling. This study examines why a platform would adopt such a strategy even when it gives up fulfillment-related revenue. We show that local selling can still increase platform profits by raising sales commission revenue. By allowing some consumers to pick up orders locally, the platform reveals geographic differences among consumers that traditional online fulfillment masks. This can soften price competition between sellers, increase prices, and benefit both the platform and sellers. Consumers, however, do not necessarily benefit, even when they are offered more fulfillment options. The findings suggest that local selling is not merely a service innovation that improves convenience. It can also be a strategic tool that helps platforms manage seller competition and extract surplus. For practice, the results indicate that platforms should tailor local-selling strategies to product and market conditions rather than apply them uniformly. For policy, the study highlights that expanding consumer choice does not necessarily improve consumer welfare, and that platform innovations should also be evaluated based on their effects on competition, pricing, and surplus allocation.

We examine human decision-making in the presence of Artificial Intelligence (AI) advice in the context of Fintech. Through four studies (two randomized controlled experiments, a field study, and a qualitative interview study), we show that human acceptance of AI advice exhibits asymmetric algorithm aversion that depends on the advice the AI provides. In our Fintech setting, investments are commonly assessed using criteria based on both explicit knowledge and tacit knowledge. Human experts believed that AI had the ability to effectively use explicit knowledge but were less confident in its ability to effectively use tacit knowledge (a common perception regarding AI). As a result, AI advice significantly influenced experts’ evaluations when it recommended against investing, as they believed that this advice could have been correctly generated using explicit knowledge criteria alone. However, AI advice had no significant effect on experts’ evaluations when it recommended investing, as they believed that this advice could not have been correctly generated without using both types of knowledge and that AI could not effectively use tacit knowledge. Instead, AI advice to invest served as a trigger for them to examine the investment with a focus on tacit knowledge criteria, which often meant that the human expert’s evaluation did not match AI’s advice. We did not find this same asymmetric pattern for accepting human advice in this context, indicating that the results are not due to loss aversion. Recognizing asymmetric algorithm aversion—that human experts are more likely to accept AI’s advice when it produces one recommendation and less likely when it produces a different recommendation —is important because an inconsistent attitude towards accepting AI advice may lead to systematic biases in human-AI collaboration that may defeat the very purpose of using AI.