AbstractBy using scenarios based on moral dilemmas, Gill (2020) found that when consumers are riding in an autonomous vehicle (AV), they are more willing to harm a pedestrian than when they, themselves, are driving a regular car. By taking a first-person perspective, in contrast to most prior research that has taken a third-person perspective, the problem is framed in a personal way that allows identification of a mechanism of responsibility attribution. In this commentary, a generalized framework is developed in which we can locate the work of Gill (2020), as well as prior research that uses moral dilemmas, to understand how consumers believe that AVs should respond when faced with competing life-and-death alternatives. The framework shows the distinct positions that research to date has adopted, points out gaps in research, and suggests a family of four research agendas that can be pursued going forward, driven in large part by the perspective taken to the moral dilemma. Research employing these different perspectives, including the unresearched problem of taking the perspective of the object, holds promise for using moral dilemmas for enabling our understanding of consumer experience and consumer–object relationships with AVs.

Abstract We examine consumers’ interactions with smart objects using a novel mixed-method approach, guided by assemblage theory, to discover the emergence of automation practices. We use a unique text data set from the web service IFTTT, (“If This Then That”), representing hundreds of thousands of applets that represent “if–then” connections between pairs of Internet services. Consumers use these applets to automate events in their daily lives. We quantitatively identify and qualitatively interpret automation assemblages that emerge bottom-up as different consumers create similar applets within unique social contexts. Our data discovery approach combines word embeddings, density-based clustering, and nonlinear dimensionality reduction with an inductive approach to the thematic analysis. We uncover 127 nested automation assemblages that correspond to automation practices. Practices are interpreted in terms of four higher-order categories: social expression, social connectedness, extended mind, and relational AI. To investigate the future trajectories of automation practices, we use the concept of the possibility space, a fundamental theoretical idea from assemblage theory. Using our empirical approach, we translate this theoretical possibility space of automation assemblages into a data visualization to predict how existing practices can grow and new practices can emerge. Our new approach makes conceptual, methodological, and empirical contributions with implications for consumer research and marketing strategy.

AbstractThe consumer Internet of Things (IoT) has the potential to revolutionize consumer experience. Because consumers can actively interact with smart objects, the traditional, human-centric conceptualization of consumer experience as consumers’ internal subjective responses to branded objects may not be sufficient to conceptualize consumer experience in the IoT. Smart objects possess their own unique capacities and their own kinds of experiences in interaction with the consumer and each other. A conceptual framework based on assemblage theory and object-oriented ontology details how consumer experience and object experience emerge in the IoT. This conceptualization is anchored in the context of consumer-object assemblages, and defines consumer experience by its emergent properties, capacities, and agentic and communal roles expressed in interaction. Four specific consumer experience assemblages emerge: enabling experiences, comprising agentic self-extension and communal self-expansion, and constraining experiences, comprising agentic self-restriction and communal self-reduction. A parallel conceptualization of the construct of object experience argues that it can be accessed by consumers through object-oriented anthropomorphism, a nonhuman-centric approach to evaluating the expressive roles objects play in interaction. Directions for future research are derived, and consumer researchers are invited to join a dialogue about the important themes underlying our framework.

The authors address the role of marketing in hypermedia computer-mediated environments (CMEs). Their approach considers hypermedia CMEs to be large-scale (i.e., national or global) networked environments, of which the World Wide Web on the Internet is the first and current global implementation. They introduce marketers to this revolutionary new medium, propose a structural model of consumer navigation behavior in a CME that incorporates the notion of flow, and examine a series of research issues and marketing implications that follow from the model.

Intuition and previous research suggest that creating a compelling online environment for Web consumers will have numerous positive consequences for commercial Web providers. Online executives note that creating a compelling online experience for cyber customers is critical to creating competitive advantage on the Internet. Yet, very little is known about the factors that make using the Web a compelling experience for its users, and of the key consumer behavior outcomes of this compelling experience. Recently, the flow construct has been proposed as important for understanding consumer behavior on the World Wide Web, and as a way of defining the nature of compelling online experience. Although widely studied over the past 20 years, quantitative modeling efforts of the flow construct have been neither systematic nor comprehensive. In large parts, these efforts have been hampered by considerable confusion regarding the exact conceptual definition of flow. Lacking precise definition, it has been difficult to measure flow empirically, let alone apply the concept in practice. Following the conceptual model of flow proposed by Hoffman and Novak (1996), we conceptualize flow on the Web as a cognitive state experienced during navigation that is determined by (1) high levels of skill and control; (2) high levels of challenge and arousal; and (3) focused attention; and (4) is enhanced by interactivity and telepresence. Consumers who achieve flow on the Web are so acutely involved in the act of online navigation that thoughts and perceptions not relevant to navigation are screened out, and the consumer focuses entirely on the interaction. Concentration on the navigation experience is so intense that there is little attention left to consider anything else, and consequently, other events occurring in the consumer's surrounding physical environment lose significance. Self-consciousness disappears, the consumer's sense of time becomes distorted, and the state of mind arising as a result of achieving flow on the Web is extremely gratifying. In a quantitative modeling framework, we develop a structural model based on our previous conceptual model of flow that embodies the components of what makes for a compelling online experience. We use data collected from a largesample, Web-based consumer survey to measure these constructs, and we fit a series of structural equation models that test related prior theory. The conceptual model is largely supported, and the improved fit offered by the revised model provides additional insights into the direct and indirect influences of flow, as well as into the relationship of flow to key consumer behavior and Web usage variables. Our formulation provides marketing scientists with operational definitions of key model constructs and establishes reliability and validity in a comprehensive measurement framework. A key insight from the paper is that the degree to which the online experience is compelling can be defined, measured, and related well to important marketing variables. Our model constructs relate in significant ways to key consumer behavior variables, including online shopping and Web use applications such as the extent to which consumers search for product information and participate in chat rooms. As such, our model may be useful both theoretically and in practice as marketers strive to decipher the secrets of commercial success in interactive online environments.

We develop and test an optimization model for maximizing response rates for online marketing research survey panels. The model consists of (1) a decision tree predictive model that classifies panelists into “states” and forecasts the response rate for panelists in each state and (2) a linear program that specifies how many panelists should be solicited from each state to maximize response rate. The model is forward looking in that it optimizes over a finite horizon during which S studies are to be fielded. It takes into account the desired number of responses for each study, the likely migration pattern of panelists between states as they are invited and respond or do not respond, as well as demographic requirements. The model is implemented using a rolling horizon whereby the optimal solution for S successive studies is derived and implemented for the first study. Then, as results are observed, an optimal solution is derived for the next S studies, and the solution is implemented for the first of these studies, etc. The procedure is field tested and shown to increase response rates significantly compared to the heuristic currently being used by panel management. Further analysis suggests that the improvement was due to the predictive model and that a “greedy algorithm” would have done equally well in the field test. However, further Monte Carlo simulations suggest circumstances under which the model would outperform the greedy algorithm.