Technology-enabled interactions in digital environments:a conceptual foundation for current and future research

The expansive scope of articles featured in this Special Issue on “The Future of Technology in Marketing” is impressive. The range of topics examined illustrates, in a very compelling manner, how technology is reshaping wide swaths of markets in digital environments—and the rapidly evolving role of marketing in these markets. These changes raise a number of new challenging questions, but also represent a significant opportunity for scholars in marketing and information sciences. In this commentary we share our perspective on topics discussed in this Special Issue and also share ideas for advancing current and future research in this area.

Our research interest in digital technologies came into sharp focus many years ago at a 2001 conference held in Boca Raton, Florida, that was jointly sponsored by the Marketing Science Institute and the Journal of the Academy of Marketing Science. The theme of the conference focused on the then-emerging topic of Internet’s impact on marketing. Our serendipitous meeting at this conference spurred a long-term research collaboration that eventually led to the development of Yadav and Pavlou (2014) in the Journal of Marketing. In this paper, our overarching goal was to advance a framework that would serve two important purposes: (1) facilitate an integrative organization of research in marketing; and (2) provide guideposts for envisioning future research trajectories (both in marketing and IS).

At the heart of the proposed framework in Yadav and Pavlou (2014) is technology—construed broadly as comprised of internet technologies, devices, and infrastructure related to computer-mediated environments. Although we made technology a central feature of our framework, we noted that it was not technology per se that served as the conceptual underpinnings of changes occurring in the digital marketplace. What was really driving changes in the marketplace was not just technology, but how technology-enabled interactions between the key marketplace entities—consumers and firms—were being transformed by technology. We conceptualized interactions to include, not only communication between marketplace entities, but also exchange-related activities. Specifically, as proposed in that paper, four distinct interactions drive transformation in digital marketplaces: (1) consumer-firm interactions; (2) firm-consumer interactions; (3) consumer-consumer interactions; and (4) firm-firm interactions. These four interactions serve as anchors of the conceptual analysis that we presented in our paper. As discussed in the next section, we found these interactions to be embedded deeply in the overall narrative of the Special Issue articles.

Technology-enabled interactions reflected in the special issue articles

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.

Table 1 Technology–enabled interactions: Thematic coverage in the special issue

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.

Looking ahead: Towards a new generation of technology-technology interactions

Moving beyond technology-enabled interactions between consumers and firms, what type of new interactions are likely to emerge? What will be the nature of these interactions, and how will they impact consumers and firms? A few articles in this Special Issue have hinted at the emergence of technology-technology interactions (e.g., Kopalle et al.’s discussion of potentially automated information sharing in production ecosystems and Appel et al.’s discussion of how ordinary household devices may communicate with other connected devices). However, the primary focus of most articles is not on such interactions. As discussed earlier (see overview in Table 1), the narratives mostly involve interactions between consumers and firms. To facilitate future research, we would like to conclude our commentary by sharing some thoughts on the emergence of direct technology-technology interactions (versus technology merely mediating interactions among consumers and firms, as discussed in Yadav and Pavlou 2014). Looking ahead, we believe that although consumers and firms will continue to rely on technology-enabled interactions, we will increasingly see a shift toward more direct technology-technology interactions.

Recent developments related to the Internet of Things (IoT) provide initial clues about the nature, scope, and likely impact of technology-technology interactions in the marketplace (Pavlou 2018). Technological advancements in sensors and networking technologies have led to a significant growth in IoT devices. Direct interactions among IoT devices are based on AI technologies fuelled by machine learning, computer vision, natural language processing, and advanced analytics. These advances have led to the development of autonomous IoT systems, such as smart homes, smart cities, interconnected cars, and the smart energy grid.

The potential of IoT stems from autonomous AI systems, powered by advanced analytics of data collected from inter-connected IoT devices. Indeed, the potential of IoT is based on the ability to collect, aggregate, and analyze large-scale data created by IoT devices communicating with each other. By leveraging such data, IoT applications can communicate and perform effectively using the power of AI, without human intervention. Indeed, commercial IoT platforms (e.g., Amazon AWS, IBM Watson, and Microsoft Azure) are evolving fast, and these systems are constantly expanding their scope and reach by adding new AI technologies. For example, a prominent IoT platform is IBM Watson, which offers “cognitive computing” as a form of AI-based automated human interface tool for language, speech, vision, and decision-making. Overall, IoT powered by AI provides a novel approach to direct technology-technology interactions with minimal human-centric design or human input.

The direct technology-technology interactions, fueled by powerful AI technologies, are distinct from the four interactions between consumers and firms that serve as conceptual anchors in Yadav and Pavlou (2014). However, technology-technology interactions are complementary with this broader set of four interactions. The rationale for this complementarity is that technology-technology interactions are shaped, at least in terms of initial design, by consumers and firms who seek to automate (human) interactions with AI tools. Accordingly, the technology-technology interactions can be viewed as the next-generation automation—to varying degrees—of all types of existing dyadic interactions.

In the not-so-distant future, depending on progress in the evolution of AI technologies, direct technology-technology interactions may substitute for (or even replace) interactions that now occur among consumers and firms. The next immediate step in this evolutionary path is likely to be the augmentation of human intelligence (as reflected in the respective interactions among consumers and firms) with AI technologies. This approach has been termed “augmented intelligence” as it involves the augmentation of human intelligence by AI to take advantage of the best of both worlds (Jain et al. 2018; see also Pavlou 2018). In our context, augmented intelligence can be viewed as a means to enrich existing consumer and firm interactions with technology mediating or even replacing consumer and firm interactions. We believe that augmented intelligence can be a valuable complement to both traditional consumer and firm interactions and also direct technology-technology interactions.

In terms of opportunities for future research that arise from expanding the direct technology-technology interactions to complement and augment existing firm-consumer interactions, some key research questions involve: In which task contexts will direct technology-technology interactions likely replace human-driven interactions (among firms and consumers)? In which task contexts is this type of replacement likely to remain partial? Why? In addition to such task characteristics, what is the role of individual characteristics in explaining such contingencies? Over the long term, as direct technology-technology interactions replace traditional human interactions, what is the impact on relationships between human communicating entities?

As AI and IoT may increasingly facilitate many technology-technology interactions without human intervention, legitimate concerns could arise as to whether technology-technology interactions can be trusted to fully circumvent human interactions (and intent). Consumers and firms face multiple potential risks from technology dominating or even taking over their interactions, such as mechanistic decision-making and loss of human control. Therefore, firms and consumers need to consider carefully when AI and IoT applications can be deployed most effectively to automate existing interactions.

Many consumers and firms may embrace an expanded set of automated technology-technology interactions, but they must make such decisions based on the expected quality of interaction, cost, and risk stemming from autonomous interactions that operate without human oversight. Even in highly automated environments, human-oriented interactions (e.g., a salespeople interacting with consumers during a store visit) could play a valuable role. For instance, beacon technology and eye-tracking devices can optimize the placement of merchandise and facilitate the automatic replenishment of products in a retail store, but salespeople could leverage personalized information from IoT devices to sell products and serve customers in a distinctive personal manner. A key challenge for technology designers is to configure technology-technology interactions and interfaces to make the resulting interactions more efficient and effective to enable appropriate human control. Consumers and firms should strive to effectively integrate the cognitive and emotional abilities of humans with AI’s computational power to design interactions that enable human-computer symbiosis.

In the future, the reach of technology-technology interactions in digital environments may expand significantly. However, both in the interim period and in the future, there will always be contexts where technology, consumers, and firms work jointly in accomplishing various tasks. Moving forward, all marketplace entities will have to adapt to the societal, legal, economic, policy, and ethical implications of increasingly automated firm-consumer interactions driven by technology. In this period of transition, characterized by range of emerging phenomena (see Yadav 2018), designers of technology-technology interactions should maintain an appropriate level of human control and oversight and afford consumers and firms the opportunity to get acquainted with delegating control to technology in their interactions.