International Journal of Research in Marketing最新文献

While self-service technologies (SSTs) enable customers to produce services such as food ordering, hotel check-in, and retail store checkout on their own, they involve the use of devices that are either firm-owned (e.g., the retailer provides a handheld device for self-checkout) or customer-owned (e.g., a customer uses a personal smartphone for self-checkout). With the increasing relevance of customer-owned devices, the role of firm-owned devices is an open question. Therefore, this study examines the role of devices in SSTs. In a series of six empirical studies and drawing on data privacy theory, we explore consumer responses to firm-owned (vs. customer-owned) devices. The findings reveal that consumers prefer firm-owned devices in SSTs and that their general need for data privacy guides these preferences. The findings also show that the interaction with firm-owned (vs. customer-owned) devices is associated with increasing perceptions of data privacy because consumers feel less vulnerable when interacting with firm-owned devices. However, this effect changes depending on the service firm’s practices of customer data usage (data sensitivity and transparency). These findings add to knowledge about consumer response to SSTs and devices, and thereby unfold how devices are interwoven with consumer data privacy. Practitioners learn how consumers respond to device ownership in SSTs and when firm-owned (vs. customer-owned) devices induce favorable customer responses.

New technologies create novel tensions, such as between providing consumers with greater control over their personal data and enhancing the technological sophistication of firms’ offerings (e.g., through automation). Resolving such control-related tensions requires specific, comprehensive conceptualizations and measures of consumers’ perceived data control. Drawing from conceptual notions of control, the authors develop a novel, multistage account of control across consumer–firm data exchanges in various traditional (active and passive) and new, hybrid data disclosure settings. By distinguishing, defining, and operationalizing control activities pertaining to the collection, submission, access, and use of personal data, this article proposes a means to capture consumers’ subjective control perceptions. Study 1, focused on quantitative measure validation, establishes that consumers can distinguish the four data control activities; Study 2, a qualitative account using depth interviews, reveals that consumers identify the four control activities unprompted and across diverse situations. Collectively, these findings present both theoretical and practical implications. This article concludes with a robust set of research directions for an expanded understanding of data control activities in technology-mediated environments.

How can academic scholars come up with great ideas, such that their research becomes even more important, relevant, and interesting? Based on ideation practices of sophisticated companies, this paper triggers academic researchers to self-reflect on: (1) the source used for ideation, (2) the scope applied to ideation, (3) the sharing of ideas during ideation, and (4) the selection of ideas. The paper also offers concrete improvements that researchers can implement in their ideation practices on ideation processes, tools, and methods along three ideation phases: domain exploration, domain immersion, and research project design. It reviews recent advances in AI and how researchers can leverage AI in their research ideation. The paper aims to stimulate more research on (academic) research ideation (i.e., “more research on research”) and advances a research agenda.

Even as online advertising continues to grow, a central question remains: Who to target? Yet, advertisers know little about how to select from the hundreds of audience segments for targeting (and combinations thereof) for a profitable online advertising campaign. Utilizing insights from a field experiment on Facebook (Study 1), we develop a model that helps advertisers solve the cold-start problem of selecting audience segments for targeting. Our model enables advertisers to calculate the break-even performance of an audience segment to make a targeted ad campaign at least as profitable as an untargeted one. Advertisers can use this novel model to decide whether to test specific audience segments in their campaigns (e.g., in randomized controlled trials). We apply our model to data from the Spotify ad platform to study the profitability of different audience segments (Study 2). Approximately half of those audience segments require the click-through rate to double compared to an untargeted campaign, which is unrealistically high for most ad campaigns. Our model also shows that narrow segments require a lift that is likely not attainable, specifically when the data quality of these segments is poor. We confirm this theoretical finding in an empirical study (Study 3): A decrease in data quality due to Apple’s introduction of the App Tracking Transparency (ATT) framework more negatively affects the click-through rate of narrow (versus broad) audience segments.

Advances in technology change the way consumers search and shop for products. Emerging is the trend of home-shopping devices such as Amazon’s Alexa and Google Home, which allow consumers to search or order products. We investigate how consumer brand and technology preferences may interact with the functionalities of technology-enabled shopping (TES) devices to determine the channel structure and market competition.

In specific, we break the functionalities of the TES devices into two: (1) the shopping support functionality (SSF), and (2) the ordering convenience functionality (OCF). Via a series of experiments, we document that stronger brand preferences are negatively correlated with the willingness to use a TES device that offers SSF. However, there is no association with brand preferences and desire to use a TES device when it offers OCF.

We build an analytical model integrating the findings from these experiments, and then derive the equilibrium channel and pricing strategies for two competing retailers. Our findings show that the functionality of TES devices results in vastly different distribution and pricing strategies in retail markets. In particular, consumers’ heterogeneous valuation of the SSF results in a monopolistic adoption of TES devices by the retailers in equilibrium, and generates Pareto improvements for both. In contrast, when the TES devices offer OCF, in equilibrium, retailers adopt TES channels competitively, resulting in a prisoners' dilemma outcome. In the extensions, studying a third-party technology developer's decision to invest in OCF and SSF technologies, we show that the contrast between the channel strategies under the OCF and the SSF also impact the incentives to develop TES. We show that in some cases, in an effort to mitigate downstream retail competition, the provider may prefer not to offer the best possible OCF technology to consumers. These findings shed light on the future adoption and the functionalities of shopping technologies offered by retailers.

As artificial intelligence (AI) applications proliferate, their creators seemingly anticipate that users will make similar trade-offs between costs and benefits across various commercial and public applications, due to the technological similarity of the provided solutions. With a multimethod investigation, this study reveals instead that users develop idiosyncratic evaluations of benefits and costs depending on the context of AI implementation. In particular, the tensions that drive AI adoption depend on perceived personal costs and choice autonomy relative to the perceived (personal vs. societal) benefits. The tension between being served rather than exploited is lowest for public AI directed at infrastructure (cf. commercial AI), due to lower perceived costs. Surveillance AI evaluations are driven by fears beyond mere privacy breaches, which overcome the societal and safety benefits. Privacy-breaching applications are more acceptable when public entities implement them (cf. commercial). The authors provide guidelines for public policy and AI practitioners, based on how consumers trade off solutions that differ in their benefits, costs, data transparency, and privacy enhancements.

The pandemic has led to a significant increase in the use of video conferencing platforms such as Zoom, Google Meet and Microsoft Teams. Yet little is known about the impact of video conferencing on subsequent consumer decisions. Across six studies, we examine the effects of video conferencing in both consumption (e.g., sales) and nonconsumption (e.g., school and work) contexts and find that video conferencing can trigger greater interest in products that can enhance the self - physically, intellectually, and/or mentally - due to heightened social appearance anxiety. This effect is attenuated when technology allows users to reduce social appearance anxiety (e.g., the use of ring lights or the ability to turn off web cameras) but accentuated when social anxiety is increased (e.g., the use gallery/speaker views) and is more pronounced among consumers who are low in self-esteem.

In this research, we examine the role of reviewer badges in the spread of opinion in the context of online reviews. Specifically, we investigate how ratings from badged and non-badged reviewers influence the opinions of subsequent reviewers, how this influence varies depending on whether the subsequent reviewer is badged, and the dynamics of review time and recent product popularity. Using a multimethod approach and archival data covering a two-year period of 458,305 reviews for products in both hedonic and utilitarian categories on Amazon (with data from TripAdvisor as a robustness check), we uncover several unique insights. First, badged reviewers tend to give lower ratings than non-badged reviewers. Second, prior ratings from badged reviewers have a smaller positive influence on subsequent reviewers than ratings from non-badged reviewers. Third, when we distinguish between badged and non-badged subsequent reviewers, we find that ratings from prior non-badged reviewers have a negative effect on ratings awarded by subsequent badged reviewers. These three effects are consistent with badged reviewers’ drive to differentiate their reviews. Finally, we find that badged reviewers tend toward stronger differentiation behavior when the reviewed product has recently become available online and when it has a high proportion of recent reviews from non-badged reviewers. The findings have important implications for marketing managers seeking to influence the review-generation process for their products and for platform managers working to incentivize the quantity and quality of reviews on their websites by awarding badges or other forms of recognition.