International Journal of Research in Marketing最新文献

Peer-to-peer marketplaces and the sharing economy are reshaping many markets. Airbnb is attractive to many customers because of its non-standardized, diverse selections and superior value over hotels, but Airbnb properties also come with more uncertainty than hotels. Images of the property can help resolve uncertainty. This study focuses on the background image (the image displayed in search results) and examines how the background image’s content features (living room, bedroom, and interior design) and aesthetic features (clarity, brightness, and contrast) affect the booking rate during a 16-night end-of-year holiday period. The authors develop a model that includes the amount of visual information, property characteristics, and host characteristics, and correct for endogeneity by using a pair of markets (New York City and San Francisco) to calculate propensity scores and construct instrumental variables. The results show that having a background image that features the living room and shows more interior design elements increases the booking rate, while featuring a bedroom decreases it. The effects of the content features are larger than the effects of the aesthetic features, and the effects are economically significant: for example, the effect of featuring the living room in the background image translates into a 35% increase in the booking rate, which amounts to $728 more revenue during the holiday period.

This research examines product uniqueness in its most pure form—products that are literally one of a kind. It does so in mass customization (MC) settings where consumers configure their own products. Due to extremely large solution spaces that are typically available in MC, a specific product configured by a consumer is often one of a kind in that it has never been produced before. How might firms leverage such strict product uniqueness to enhance consumer value? A natural way of doing so is to simply inform consumers that the product they have configured is one of a kind—through a feedback message that is either present-oriented (e.g., “this specific product has never been produced before”) or future-oriented (e.g., “this product will remain one of a kind in perpetuity”). Evidence from five experiments, including a large-scale field experiment, shows that one-of-a-kind feedback can cause a substantial increase in consumers’ product valuation. This value-enhancing effect of one-of-a-kind feedback specifically occurs in product domains that are primarily matters of subjective taste. By contrast, such feedback tends to reduce consumers’ product valuation in domains that are primarily matters of objective quality. These findings advance our understanding of the concept of product uniqueness, and of how firms can harness the one-of-a-kindness of mass-customized products to unlock consumer value.

Online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. Consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. In this paper we show how new achievements in natural language processing can provide an important assist for different kinds of review-related writing tasks. Working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). We conduct a kind of “Turing Test” to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. Rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. We next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. For this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. For each of these applications, we adapt the Transformer neural net architecture. The work herein is broadly applicable in marketing, particularly in the context of online reviews. We close with suggestions for additional applications of our model and approach as well as other directions for future research.

Distributed Ledger Technologies (DLTs), like Blockchain, could help improve brand safety, consumer privacy and transparency in digital advertising. However, paid advertisements transfer attention, money, and data between three parties: advertiser, consumer, and publisher. Therefore advertising-focused DLTs face more complex design considerations than currency-focused DLTs. We describe four key DLT characteristics: structure, participation/governance, transparency, and terms of exchange. We survey current advertising-focused DLTs and find they each serve only two of the three contracting parties in advertising transactions. We make design recommendations for future advertising-focused DLTs, including a goal of serving both consumers and publishers in addition to advertisers. We also recommend governance, transparency and terms-of-trade considerations. Advertising-focused DLTs have significant promise but also significant obstacles, including multi-sided “chicken-and-egg” problems in standards adoption.

Non-fungible tokens (NFTs) have registered tremendous growth in the past year, and their importance is expected to increase with the expected development of Web3. From a brand perspective, NFTs can be seen as representations of the brand components, such as the product, the logo, or the image. I argue that NFTs have immense potential to become standalone brand assets. I illustrate how this can be achieved by relating the brand's NFT strategies to the marketing funnel stages. Brands can turn into an NFT their physical products such as shoes, shirts, or art to attract brand awareness, generate cross-selling opportunities, and spark stronger perceived ownership of certain brand elements. Importantly, NFTs can allow brands to form a highly engaging brand community that can support the brand, blend online and offline product ownership, and potentially create a bond between the brand and consumers. These exciting possibilities generate a rich research agenda I present in this paper.

Awarding users digital badges for their contributions such as generating valuable content and participating in community activities has become an increasingly popular practice on user-generated content (UGC) platforms to incentivize user engagement. Beyond their immediate effects, digital badges can have enduring effects on UGC consumption and generation by affecting users’ underlying engagement states. To account for both effects, the authors develop a bivariate negative binomial hidden Markov model of content consumption and generation and estimate the model using a panel data set obtained from a large UGC platform. They find that users increase their content generation but decrease their content consumption immediately after being awarded a digital badge. Furthermore, being awarded a digital badge has a positive enduring effect on both content consumption and content generation behaviors of a user. This positive enduring effect mainly applies to users in relatively engaged states but not to users in the least engaged state. Finally, the authors demonstrate the value of the proposed model with respect to assessing consumer responses to the design of digital badges.

Infomediaries, such as movie critics, often evaluate product quality using discrete grades, such as thumbs-up and thumbs-down. Their reports are used by potential consumers to decide whether to purchase the product. Our aim is to examine how the grading can be improved by answering research questions such as: What is the quality cutoff for each grade? How many grades should the grading scale use? How should grading be done for other goals than informativeness, such as maximizing reviewer’s traffic? How should grading be done to encourage producers to improve quality? Using an analytical model, we show that optimal grade cutoffs depend on the distributions of product quality and consumer taste, and the resulting scale may have unequal intervals. As few as five grades can be near-optimal for consumer welfare and grades should be made more difficult to achieve when producers can invest in quality improvement. These results should help improve the design of scales for reporting quality.

Marketing has enjoyed unparalleled transformation in terms of its effectiveness and ability to delivery measurable return on investment due to the digital revolution. The spread of blockchain technology has led to suggestions that it may have significant marketing applications. Though blockchain is indeed an amazing technology with significant applications, this paper argues that its usefulness in many traditional marketing applications may be limited. The reasons why throw important light on the interactions between blockchain technologies, data and privacy. Blockchain, by its nature, may lead to substantial privacy harms when used indiscriminately for marketing purposes.¹

Review platforms often use transient status reward systems where the granted status is not permanent, and reviewers can experience gains and losses in status. To evaluate the effectiveness of such systems, review platforms need to consider a trade-off between the potential positive effects of a status gain and the potential negative effects of a status loss on the quantity and characteristics of reviewers’ contributions. This article examines this trade-off. The results of an empirical study that uses matched difference-in-differences analysis of data from Yelp show that: (i) the positive effect of a status gain on the number of reviews is about three times greater than the negative effect of a status loss; (ii) these effects are more pronounced for reviewers with less experience; and (iii) status changes also change the valence and elaborateness of reviews.

This article provides a critical review of pricing issues related to cryptocurrency and NFTs from a marketing perspective and tries to answer the question of whence comes the value for cryptocurrencies and NFTs. It shows that the value for a cryptocurrency like Bitcoin hinges on the collective belief in the marketplace in its long-term survival as a store of value and a unit of exchange. As such, the belief can give rise to a plethora of pricing equilibria for Bitcoin, and it can also be swayed by many demand factors. Consequently, Bitcoin prices will beset with uncertainties and speculations for the foreseeable future and can swing widely and wildly at times. However, the plethora of pricing equilibria also suggests that it is unlikely that Bitcoin may suddenly disappear after its long, successful run so far. In contrast, NFT prices find their value bases not so much in the collective belief in NFTs as a viable product category, although there are many reasons for optimism in that regard, but in the willingness to pay by individuals that depends on many familiar and well-studied factors. This article proposes many pricing-related research questions.