Table 1 provides an overview of how four specific types of technology-enabled interactions are reflected in the seven articles included in this Special Issue of JAMS. Although our narrative connects with key ideas and issues noted in all articles, we provide a more extended discussion of selected articles where our focal four interactions are reflected more directly.
Changing interactions in healthcare
The article by Agarwal, Dugas, Gao, and Kannan highlights the complexity of the healthcare sector by describing how various entities (e.g., employers, pharmacists, insurers, primary care providers, regulators, consumers, etc.) are inextricably linked with one another in the complex healthcare ecosystem. Using this ecosystem as a backdrop, Table 1 shows four technology-enabled interactions among these entities that are noted in the article.
Consumers can interact with firms (e.g., primary care providers or pharmacists) using various technologies, such as desktop portals and specialized apps on mobile devices. These interactions can focus on a range of activities, starting with the specification of individual preferences. By effectively capturing and understanding heterogeneity at the individual patient level, healthcare providers can use technology-enabled interactions to deliver healthcare services with increased precision. Consumer-consumer interactions (among patients) in social media environments can be harnessed in a variety of ways (e.g., understanding emerging epidemiological trends). In the realm of firm-firm interactions, technological facilitation of such interactions can go a long way in managing the substantial complexity that exists in the healthcare ecosystem. Overall, technology-enabled interactions can significantly impact all three elements of value-centered marketing of healthcare: preference, precision, and process.
Changing interactions in AI-enabled environments
Current and future developments related to artificial intelligence (AI), according to Davenport, Guha, Grewal, and Bressgott, will significantly reshape the contours of marketing (see also Thomaz, Salge, Karahanna, and Hulland for a discussion of AI applications pertaining specifically to “conversational agents”). Davenport et al. contend that over time, AI technologies will evolve from their traditional focus on analyzing numerical data to analyzing textual and other contextual data (e.g., online posts, images, and location information). As a result of these enhanced capabilities, AI technologies—whether in a disembodied form (e.g., software) or an embodied form (e.g., robots)—will increasingly shape interactions among consumers and firms in the marketplace. As firms acquire enhanced AI capabilities, they will strive to develop new ways to serve customers (e.g., sophisticated personalization strategies). AI technologies also have many promising applications in the realm of consumer-consumer interactions. For instance, AI technologies can be deployed for large-scale textual and/or image analyses. Such analyses can be useful for monitoring social media environments so that corrective action, if needed, can be taken in a proactive manner (see, e.g., Facebook’s recent anti-bullying initiative that is powered by AI technologies). Finally, Davenport et al.’s analysis suggests that AI technologies are likely to impact numerous firm-firm interactions. AI-powered software may automate many routine firm-firm interactions (e.g., reordering of regularly-used supplies). Also, as the presence of embodied AI systems become more ubiquitous in physical commercial settings (e.g., security robots such as K5 from Knightscope), there will be many more opportunities for firms to share information with one another and take action (if needed) based on this information.
Changing interactions in social media
Appel, Grewal, Hadi, and Stephen discuss the future trajectory of current developments in the area of social media (see also Tong, Luo, and Xu in this Special Issue for a discussion specific to social media applications on mobile devices). As technology-enabled interactions between and among consumers and firms continue to increase, a host of privacy concerns have taken center stage in discussions pertaining to social media. In particular, as consumer-consumer interactions fuel social media platforms, Appel et al.’s narrative devotes substantial attention to how such interactions are likely to keep evolving as a result of advances in new technologies. For instance, in the article’s far-future scenarios, the authors speculate that technological advances (e.g., augmented reality, virtual reality, and haptic feedback) will significantly increase the sensory richness of social media. Finally, in the context of firm-firm interactions, Appel et al. see an important role of social media in spurring online/offline integration. As consumers enter and exit digital (e.g., a firm’s online store) and non-digital environments (e.g., a physical store), information collected on social media platforms can serve as a connecting thread to facilitate what Appel et al. refer to as “complete convergence”—that is, a seamless integration of transaction-related activities delivered by firms across traditional physical and emerging digital environments.
Changing interactions in retailing
To envision the rapidly-evolving in-store technological landscape, Grewal, Noble, Roggeveen, and Nordfält present a useful four-cell typology based on two underlying dimensions: convenience (low/high) and social presence (low/high). As we show in Table 1, in-store technologies can impact all four types of interactions. In the case of consumer-firm interactions, in-store kiosks allow customers to complete many check-out activities themselves. Radical new systems (e.g., Amazon’s Go stores) take this one step further and completely automate the checkout process—consumers simply select products and then walk out of the store when done. In-store technologies are also providing firms an array of enhanced capabilities to interact with consumers in new ways. These capabilities can take a variety of forms that allow firms to be more efficient (e.g., digital price tags) or to interact with customers in a personalized, more engaging manner (e.g., Clas Ohlson’s “click-and-flick” Smart Windows). Although the analysis presented by Grewal et al. is focused primarily on the end-consumer experience, one can also envision scenarios in which information collected by in-store technologies can be shared (as needed) with other upstream business partners in the value chain. For instance, in a grocery store, shopping patterns and interactions gathered by in-store technologies may have considerable strategic and/or tactical value for consumer packaged goods (CPG) firms.
Changing interactions in digital ecosystems
The article by Kopalle, Kumar, and Subramaniam takes a broad look at digital ecosystems with a focus on the concept of “digital customer orientation”. A hallmark of this concept is the emphasis placed on the collection of real-time collection of information during the consumption process and then using this information to optimize value delivery to customers. The real-time collection of information is facilitated by the digital ecosystem in which value delivery occurs. A close examination of Kopalle et al.’s discussion of in-use information—how it is collected and used—reveals the four technology-enabled interactions that operate in digital ecosystems (see Table 1). The generation of in-use information is predicated, at least to some extent, on technology-enabled consumer-firm interactions. For instance, in the case on an Uber ride in progress, some information (e.g., starting time and place of an itinerary) can be collected automatically. However, the collection of other types of information (e.g., a customer’s subjective perception of how a ride is progressing) depends on a customer’s willingness to share such information. In a digital ecosystem, the collection of in-use information allows firms to develop real-time insights about a specific transaction in process and then leverage these insights to enhance the efficiency and effectiveness of the value delivery process. This can be accomplished by modifying the content and structure of firm-consumer interactions—both in the context of a specific transaction and other transactions that may follow. Kopalle et al.’s also discuss a number of new initiatives in which such real-time sharing can facilitate interactions between firms and create innovative opportunities for the proactive management of key processes.
In summary, the discussion presented in this section underscores a key point that we wish to make in this commentary—that the four technology-enabled interactions developed in Yadav and Pavlou (2014) continue to serve as useful conceptual anchors across a broad spectrum of substantive contexts discussed in this Special Issue. In each of these contexts (see Table 1), we find that consumer-firm interactions, firm-consumer interactions, consumer-consumer interactions, and firm-firm interactions are changing rapidly. Understanding the nature of these changes provides a useful conceptual foundation to guide future research efforts.