Platform rewards for online reviews, now deployed by major e-commerce platforms including Amazon, Walmart, Taobao, and JD, may generate outcomes that run counter to their intended objectives. Using comprehensive review- and merchant-level data from a crowdsourced review platform, we find that platform rewards generate systematically different effects across merchants: while all merchants experience changes in review patterns, those with limited prior visibility are disproportionately harmed. Specifically, platform rewards decrease overall review quantity while increasing review positivity. Crucially, platform rewards disproportionately reduce review quantity for less popular merchants compared to established ones, a differential effect that increases review inequality by 16.85% (measured by the Gini coefficient) and translates into heterogeneous sales effects across merchants. While platform rewards decrease sales overall, less popular merchants experience significantly larger sales declines than their established counterparts. Mechanism analyses indicate that platform rewards crowd out intrinsic motivation among less experienced reviewers and in reviews for less popular merchants, leading to shorter, less informative, and more imitative reviews, although experienced reviewers respond differently. Our findings reveal that platform rewards, contrary to their democratizing intent, actually exacerbate disparities between established and less popular merchants within the platform ecosystem. These results highlight the complex challenges of designing effective review reward programs and their unintended consequences for platform fairness and merchant diversity.

The global agri-food system is simultaneously a major contributor to, and severely affected by, climate change. Agroecological farming systems can contribute to creating resilient agri-food systems. Based on a multiyear qualitative case study, ...Supporting transitions to sustainable, resilient agri-food systems is important to ensure stable food supply in the face of growing climate extremes. Agroecology, or diversified farming systems based on ecological principles, can contribute to such ...

This research examines the interdependencies in users' sequential app adoptions within and across diverse app categories. We employ a Zero-inflated Negative Binomial (ZINB) model to analyze a unique, granular, and individual-level mobile app adoption dataset, revealing three main findings. First, users' app adoption decisions are highly history -dependent and category-specific in a nonlinear fashion. Early adoption can enhance subse-quent downloads within the same category for app categories with high needs evolvement and horizontal differentiation (e.g., Game and Education apps). However, it may crowd out subsequent downloads in other categories with low needs evolvement and horizontal dif-ferentiation (e.g., Communication and Social media apps). Second, these effects are further moderated by users' individual characteristics such as app usage tenure and phone price. Third, there exist nontrivial app adoption spillovers across app categories. For example, users' adoptions of apps with relatively high hedonic values (e.g., Game and Music apps) can suppress their subsequent need for apps with relatively high utilitarian values (e.g., Education and Online banking apps), and vice versa. Together, these results offer novel managerial implications for app developers and platforms to promote apps in different cat-egories based on users' adoption histories.(c) 2023 Elsevier B.V. All rights reserved.

Cohn et al. (2019) conducted a wallet drop experiment in 40 countries to measure "civic honesty around the globe," which has received worldwide attention but also sparked controversies over using the email response rate as the sole metric of civic honesty. Relying on the lone measurement may overlook cultural differences in behaviors that demonstrate civic honesty. To investigate this issue, we conducted an extended replication study in China, utilizing email response and wallet recovery to assess civic honesty. We found a significantly higher level of civic honesty in China, as measured by the wallet recovery rate, than reported in the original study, while email response rates remained similar. To resolve the divergent results, we introduce a cultural dimension, individualism versus collectivism, to study civic honesty across diverse cultures. We hypothesize that cultural differences in individualism and collectivism could influence how individuals prioritize actions when handling a lost wallet, such as contacting the wallet owner or safeguarding the wallet. In reanalyzing Cohn et al.'s data, we found that email response rates were inversely related to collectivism indices at the country level. However, our replication study in China demonstrated that the likelihood of wallet recovery was positively correlated with collectivism indicators at the provincial level. Consequently, relying solely on email response rates to gauge civic honesty in cross-country comparisons may neglect the vital individualism versus collectivism dimension. Our study not only helps reconcile the controversy surrounding Cohn et al.'s influential field experiment but also furnishes a fresh cultural perspective to evaluate civic honesty.

This research is a first empirical investigation into the effect of mobile social app usage on consumers’ decisions to visit offline stores and how such an effect varies across consumers and carries over time. The analysis combines data on mobile app usage at the individual consumer level with a fine-grained geolocation data set. The authors find a positive effect of social app usage on consumers’ visits to brick-and-mortar stores. This effect is amplified by the consumer's mobility level and the offline store density in the consumer's neighborhood. The positive impact of social app usage carries over for up to nine days, indicating a short-term effect. Additional analyses indicate that such an effect is likely due to consumers’ social discovery of product- and store-related information via word of mouth on strong-tie social apps (i.e., instant messaging apps). These results point to new opportunities for offline retailers seeking to acquire customers via the mobile channel.

Redemption hurdles, such as finite expiration terms and redemption thresholds, are common for customer reward programs. In “An Analysis of ‘Buy X, Get One Free’ Reward Programs,” Yan Liu, Yacheng Sun, and Dan Zhang study the economic rationale behind redemption hurdles and how they should be optimally set. They show analytically that redemption hurdles can be used as a price-discriminating vehicle that increases firm profitability. Redemption hurdles can facilitate the firm’s price discrimination on consumers whose valuations may vary over time. Redemption threshold alone cannot ensure profitability, unless it is coupled with a finite expiration term or a positive transaction utility from the rewarded free product. Optimal design of redemption hurdles is not straightforward, and the interdependence between the two types of redemption hurdles and the price is nontrivial. Optimally set redemption hurdles may not only increase firm profitability but also, increase the welfare of consumers who purchase frequently.

当前,大数据方法被广泛应用在各种营销问题上,如实时洞察和响应客户需求、精准广告投放、预测产品和服务的需求量、明确品牌定位、优化广告投放策略等.万物互联时代的营销创新被赋予了新的内涵.大数据营销学术研究主要集中在如何利用大数据技术和方法改进传统营销决策方面,较少涉及大数据营销理论的创新,尚未深入探...>>详细当前,大数据方法被广泛应用在各种营销问题上,如实时洞察和响应客户需求、精准广告投放、预测产品和服务的需求量、明确品牌定位、优化广告投放策略等.万物互联时代的营销创新被赋予了新的内涵.大数据营销学术研究主要集中在如何利用大数据技术和方法改进传统营销决策方面,较少涉及大数据营销理论的创新,尚未深入探讨传统营销理论在万物互联时代的适用性.因此,大数据营销的相关研究还需要进一步聚焦理论创新,深入研究万物互联背景下由数据驱动的企业竞争战略、品牌管理、客户管理和消费者对数据驱动的营销活动的反应等理论问题,要结合万物互联时代的新变化,深度挖掘数据价值,为学术领域和实践领域提供与时俱进的指导.

We examine how operational or technological transformation impacts consumer value, as well as the effectiveness of a firm’s pricing strategies. We develop a model of multidimensional screening featuring forward-looking consumers who make short-run consumption and long-run purchase decisions. Using a detailed panel of consumer data from a rental-by-mail firm, we estimate consumer utility for current consumption, obtaining heterogeneous preferences for bunching and smoothing consumption. Using counterfactual analysis, we evaluate the impact of improving service time. We find that the firm with improved service time might create more value for all consumers, but its profits and even revenues could diminish because value extraction becomes more difficult. We find a novel mechanism that causes this effect, which is driven by increased consumer heterogeneity in the valuation for each product and reduced differentiation across products. This result persists even when the firm can reoptimize its price levels based on the service time. We find that a change in the pricing strategy might be required for the firm to obtain higher revenue with improved service time. This paper was accepted by Matthew Shum, marketing.

A little-understood phenomenon of customer reward programs is the prevalent use of finite reward expiration terms. We develop a theoretical framework to investigate the economic rationale behind this phenomenon and the trade-off between short and long expiration terms. In our model, a monopolistic firm sets the expiration term, along with the price and reward size, and interacts with consumers over an infinite horizon. Consumers are heterogeneous in shopping probabilities and product valuations and forward-looking in making purchase decisions. We find that a customer reward program with a finite expiration term can increase firm profits when (i) the valuation difference within the consumer population is intermediate and (ii) the shopping probabilities and valuations are negatively correlated among consumers. Several model extensions confirm the robustness of these results. Finally, we conduct an empirical investigation on the reward program practice of the top 100 U.S. retailers, which provides directional support for several key theoretical predictions. This paper was accepted by Gad Allon, operations management.

自2009年出现"全渠道零售"的概念以来,相关讨论一直持续不断.回顾国内外的相关研究成果可以发现,全渠道零售有三个主要进展:(1)全渠道的含义,是指零售商通过与其他利益相关者进行有效的渠道协同创新,在目标顾客购买过程的每个环节提供尽可能多的渠道类型,以满足顾客对于渠道的个性化偏好,最终实现顾客价值及零售企业目标;(2)全渠道零售的成因,是技术创新改变了消费者需求,出现了消费者的全渠道购买行为,进而导致全渠道零售的企业行为;(3)全渠道零售的策略,是指零售业通过有效的市场细分,选择适当的目标顾客,进而选择适当的营销定位,以及相匹配的全渠道产品设计、全渠道定价、全渠道销售和全渠道传播,最终实现顾客价值和公司目标.最后提出了全渠道零售商店的形成和运行机制、全渠道零售与智能零售的融合机制、全球视角下的全渠道零售运营机制等三大未来研究方向.

The creation and sharing of user-generated content such as product reviews has become increasingly “social,” particularly in online communities where members are connected. While some online communities have used monetary rewards to motivate product review contributions, empirical evidence regarding the effectiveness of such rewards remains limited. We examine the possible moderating effect of social connectedness (measured as the number of friends) on publicly offered monetary rewards using field data from an online review community. This community saw an (unexpected) overall decrease in total contributions after introducing monetary rewards for posting reviews. Further examination across members finds a strong moderating effect of social connectedness. Specifically, contributions from less-connected members increased by 1,400%, while contributions from more-connected members declined by 90%. To corroborate this effect, we rule out multiple alternative explanations and conduct robustness checks. Our findings suggest that token-sized monetary rewards, when offered publicly, can undermine contribution rates among the most connected community members. Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1022

Bucket-based price discrimination is a unique price format that involves monthly subscription fees and instantaneous quotas (the number of rental products that can be checked out). We propose an empirical model in which consumers make dynamic purchase decisions under consumption uncertainty, accounting for the constraints imposed by the instantaneous quota. Applying the model to an online DVD rental data set, we find that (1) consumers incur a large disutility (∼$8) from stockout (i.e., unmet consumption needs); (2) such a disutility drives consumers' overpurchase of the service quota as a way to avoid potential stockout situations; and (3) the dynamics of overpurchase are driven by the interplay between trends in consumption needs and the magnitude of consumers' plan-switching costs. We run counterfactual exercises to better understand how the instantaneous quota and stockout risk affect consumers' consumption rates, purchase decisions, and firm profitability. We find that the instantaneous quota induces a greater stockout compared with a monthly quota. We further demonstrate that the company should recognize the drivers of the dynamics in overpurchase to balance short- and long-term profitability—for example, by offering targeted discounts to customers with excess overpurchase.

We estimate the joint impact of the frequency reward and customer tier components of a loyalty program on customer behavior and resultant sales. We provide an integrated analysis of a loyalty program incorporating customers' purchase and cash-in decisions, points pressure and rewarded behavior effects, heterogeneity, and forward-looking behavior. We focus on four key research questions: (1) How important is it to combine both components in one model? (2) Does points pressure exist in the context of a two-component loyalty program? (3) How is the market segmented in its response to the combined program? (4) Do the programs complement each other in terms of the incremental sales they produce?Our most basic message is that the frequency reward and customer tier components of loyalty programs should be modeled jointly rather than in separate models. We find strong evidence for points pressure for both the customer tier and frequency reward components using both model-based and model-free evidence. We find a two-segment solution revealing a “service-oriented” segment that highly values cash-ins for room upgrades and staying in “luxury” hotels, and a “price-oriented” segment that is more price sensitive and highly values the frequency reward aspects of the loyalty program. Furthermore, we find that both components generate incremental sales. Also, there was slight synergy between the programs but not a huge amount. Overall, each component contributes to increased revenues and does not interfere with the other.

Bucket pricing entails a prepaid price and a maximum consumption limit, which requires consumers to make advance purchase decisions before their consumption needs are fully revealed. We propose a dynamic model that involves how consumers form expectations of future consumption needs, learn to reduce uncertainty through experience, and make optimal advance purchase decisions. Applying the model to an online DVD rental history data, we examine how consumption uncertainty may drive advance purchase decisions. Consumers tend to overpurchase to cover the unexpected volatility of future consumption needs, avoid stockouts and switching costs. Over time, consumers learn to reduce uncertainty but only those with lower switching costs adjust their overpurchases to adapt to their reduced uncertainty. Bucket pricing could lead to greater profits if higher-level plans were more attractive, which would induce consumers to overpurchase more. By explicitly modeling advance purchase decision making and empirically investigating consumer decision processes, this article helps managers better understand the effects of price/quota combinations on consumer choice.