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Reviews, Reputation, and Revenue: The Case of Yelp.Com
Abstract
Do online consumer reviews affect restaurant demand? I investigate this question using a novel dataset combining reviews from the website Yelp.com and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp’s rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5% to 9% increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant’s average rating is affected by the number of reviews and whether the reviewers are certified as “elite” by Yelp, but is unaffected by the size of the reviewers’ Yelp friends network.
... The "TripAdvisor effect" refers to the significant influence that user-generated online ratings have on consumer decision-making and business performance within the travel and hospitality sectors, leading organisations to actively construct and manage their online reputation. A landmark study found in US data that a one-star increase in a restaurant's Yelp rating led to a 5-9% increase in revenue (Luca, 2016). In hospitality, significant correlations are found between ratings and hotel metrics such as occupancy rates and revenue (Tuominen, 2011). ...
... Wellresourced organisations, public or private, can deploy resources to optimise their online reputation, boosting their ratings. Ratings possess performative power, as businesses strategically and actively manage and respond to them, often employing reputation management tactics (Luca, 2016). ...
Digital place has been variously characterised as a set of online augmentations and representations that influence how physical places are imagined, cognised, and interacted with. In an evolving media ecosystem, star ratings enjoy a pervasive presence on platforms. Ratings provide information and alter people’s decision-making not only when choosing consumer products but also places. Focusing on numeric ratings, through a scoping review, this article (1) traces the origins and rapid proliferation of place ratings, categorising them into primary, secondary, volunteered, transactional, expert, and user-generated types, and examines how they are applied geographically, from countries to points of interest. Then, it reports on (2) a global survey of rating platforms, accounting for their variety and similarities across sectors and countries. (3) It engages with significant issues are entangled with place ratings: the challenges in their interpretation and aggregation; their potential cognitive, spatial, and demographic biases; their impact on power dynamics between businesses and consumers; and their contribution to the socio-spatial inequalities that exist between digital and physical realms. These insights converge into a geographical research agenda to study place ratings’ deep and ubiquitous effects.
... Yelp allows customers to rate businesses they have used on a scale of 1 to 5 stars for the benefit of other potential customers. It has been found through previous research that a 1-star increase in Yelp scores translates to a 5-9% increase in restaurant revenue [7]. This correlation was driven by independently owned restaurants, with chains playing a less prominent role. ...
... A consensus exists that restaurant success is most accurately predicted by its attributes, including but not limited to cuisine quality, supplementary services offered, ambiance, price INTENSIF, Vol.9 No.1 February 2025 ISSN: 2580-409X (Print) / 2549-6824 (Online) DOI: https://doi.org/10.29407/intensif.v9i1.23476 range, noise level, and parking [3], [7]. This is even more true than the influence of location. ...
Background: The purpose of this research is to measure of customer satisfaction for newly established independent restaurants and, consequently, good predictors of independent restaurant success. Urban communities face several challenges, including how to best use scarce resources like real estate and support small enterprises. Smart businesses are essential to the development of smart cities because they use data analytics to inform their strategic planning and design choices, and the target of this topic is restaurant. Objective: Restaurants control a sizable portion of the city market's small business sector. As part of the Yelp Data Challenge, Yelp just made available an open dataset that includes important details, ratings, and Yelp scores for every restaurant in different cities. Methods: Our methodology utilizes a vector of crucial factors to accurately forecast a business’s prospective success and exclusively evaluate eateries located inside the city limits of Las Vegas. The dependent variables will consist of the mean Yelp ratings for each restaurant and constructed our model by following the subsequent stages. Conclusion: The findings of this research is corroborated by the discovery that the statistically significant properties of restaurants, shown by a low p-value, varied across various restaurant categories, the unique modeling technique to forecast future restaurants' Yelp rankings based on their design choices. This will assist owners of restaurants in making better design choices, which will result in more prosperous small enterprises in urban settings.
... Chevalier and Mayzlin (2006) [14]] conducted one of the seminal studies, finding a positive correlation between online book reviews and sales. This was followed by Luca (2011) [15], who demonstrated that higher Yelp ratings led to increased restaurant revenue, highlighting the significant influence of online reviews on consumer behavior. These foundational studies established UGC as a crucial factor in shaping consumer perceptions and business outcomes. ...
... Similarly, Luca (2011) [15] researched the impact of Yelp reviews on restaurant demand, providing a foundation for understanding the significance of online reviews. By incorporating sentiment analysis, our study provides a more comprehensive understanding of consumer preferences and the factors that drive business popularity. ...
This research paper represents a persuasive data-driven strategy for identifying regions with the highest business popularity. This study investigates the complex dynamics of business success by analyzing comprehensive review counts, star ratings, sentiment scores, and overall popularity scores across multiple states. Utilizing robust OLS regression techniques, the analysis reveals the significance of factors such as many reviews, exceptional star ratings, and positive sentiment in determining the popularity of a business. In addition, using an inclusive spatial autocorrelation analysis with Moran’s I coefficient, the study reveals an intriguing reality: geographical proximity plays a limited role, with no significant spatial clustering or dispersion observed between states. The findings illuminate the diverse landscape of business metrics across states, highlighting the diversity of business activity and customer engagement. This research encourages further investigation into the factors influencing business popularity, empowering businesses to chart their unique path to success in a data-driven world.
... These experiments aim to determine the causal effect of social influence on rating behavior, as well as the mechanisms driving socio-digital influence. Although these experiments are first-of-a-kind, they are motivated and informed by multiple overlapping streams of literature and build on substantial prior work from multiple fields such as: herding behavior from theoretical [11,8,26] and empirical viewpoints [54,65,36,14,2]; social influence in networks [6,37,46,3,49]; collective intelligence [64,12,11,29]; and online rating systems [16,65,42,15,49,44,19,20,30,38,67,18]. Interestingly, most of the previous work is geared towards marketing science because of the close relationship between business and consumer opinion. ...
... The dynamics of online reviews, ratings and votes have received a lot of recent attention in the computing and marketing literature because the dynamics of online reviews for books, restaurants, hotels, etc. have become a vital business interest [44,19,20,30,38,67,18]. Recent work in text mining is able to automatically determine the positivity and negativity of user-opinion [42,41,43] even among different aspects of a certain product (e.g., large can be a good thing when talking about portion size, but bad when talking about camera size) [40]. ...
At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.
... Nowadays, online product rating systems are often used in numerous web or mobile applications, e.g., Amazon, eBay, TripAdvisor, Google App Store, etc. Online product rating systems aim to reveal the ground-truth quality of products via user contributed ratings or reviews. Product ratings not only improve users' purchasing experience [1][2][3], but can also improve revenues of sellers [4][5][6]. Formally, each user provides ratings to a subset of products, and their ratings are known to all users. For each product, the historical collective opinion (i.e., aggregation of historical ratings) and shortlisted representative product reviews are usually displayed to assist users assess the product quality. ...
... Consider the majority rule, i.e., satisfies (5). If the number of ratings satisfies ...
Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up. We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms. To show the utility of our framework, we design a maximum likelihood algorithm to infer model parameters from ratings, and conduct experiments on rating datasets from Amazon and TripAdvisor. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor by 41% and 62% respectively.
... SETTP offers three Key Advantages over previous TST methods: First, Figure 1 demonstrates SETTP has strong few-shot learning capabilities and outperforms the strong baseline by 16.24% in extreme low-resource scenarios. Second, the prompt pool is a modular component that allows for the easy addition, reuse, and removal of soft prompts, sharing knowledge across different tasks. ...
... Datasets We selected high-resource and low-resource datasets as follows: YELP [16] includes parallel sentences of positive and negative reviews. GYAFC [24] provides parallel sentences of formal and informal expressions within the domains of Entertainment&Music and Family&Relationships, respectively. ...
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24\%.
... Online reviews also play a crucial role in determining the business's success. Luca (2016) found that a 1-point increase in customer reviews led to a 5% to 9% increase in revenues. Yang et al. (2018), Nieto-Garcia et al. (2019), and Sayfuddin and Chen (2021) discovered that the financial performance of hotels is influenced by the number and degree of reviews. ...
This research compares TripAdvisor data with managers' opinions from the eWOM perspective. In this study, TripAdvisor data of accommodation businesses operating in Kastamonu were determined and compared with the data obtained from the managers. Content analysis and interview techniques, which are among qualitative research methods, were used in the research. The reviews of the top 10 accommodation businesses with the most comments on TripAdvisor in Kastamonu were analyzed. Additionally, face-to-face interviews were conducted with the managers of accommodation businesses. Nine out of ten accommodation businesses agreed to the interview. Comments and interview statements were divided into themes, grouped, and coded. The MAXQDA analysis program was used to visualize the encoded data. The findings of the research revealed that negative comments are most likely on “service”, “noise”, and “food” themes, while positive comments are on “service”, “staff”, and “food” themes. Managers stated that TripAdvisor plays an important role, especially in sales/marketing, and that they regularly follow and respond to TripAdvisor's comments. The findings clearly show that accommodation businesses should take online reviews into account. In this context, accommodation businesses should include social media platforms in their strategic management processes.
... Data from Dianping displays detailed reviews and rating information for each store, which can reflect consumers' perceptions and evaluations of their consumption. In recent studies, star ratings and online reviews have been used to measure the quality of consumer amenities (Luca, 2016;Raval et al., 2024). Local businesses with higher average ratings and more reviews are more likely to attract customers and offer a better consumer experience, although it is possible for sellers to manipulate the ratings and reviews. ...
Subway is an effective public transportation infrastructure that attracts many urban consumer amenities in developing countries. This paper uses points of interest (POI) data from Dianping.com in 2020 in Shanghai to measure the quantity, quality, and diversity of consumer amenities by six indices: numbers, types, comments, ratings, star ratings, and takeout rate. We find that subway stations have a positive spatial correlation with vitality of consumer amenities within a 2-km radius. In addition, subway stations attract more newly added consumer amenities with higher quality within a 2-km radius, and the results remain robust by using the propensity score matching method. There exists heterogeneity in the ridership of subway stations. Subway stations with higher ridership have a greater effect on the consumer amenities and newly added consumer amenities. In terms of mechanism, based on the perspective of agglomeration economy, this paper uses Baidu Street View big data to verify that pedestrian flow is the key mechanism. This study accurately evaluates the economic and social benefits of subway stations and provides fundamental policy implications for the spatial layout of subways and consumer amenities of large cities in developing countries.
... Notably, Aster CMI Hospital exhibited the most pronounced deterioration in patient satisfaction metrics. The emergence of digital reputation management challenges (Gurău et al., 2022;Luca, 2016) further complicates the healthcare quality landscape. This study contributes to the literature on patient satisfaction dynamics in rapidly evolving healthcare markets and offers evidence-based recommendations for systemic reform. ...
This longitudinal study examines negative patient reviews across eight major hospitals in Bangalore over a six-year period (2018-2024), employing a mixed-methods analytical framework to identify temporal patterns in healthcare service deficiencies. Through rigorous statistical analysis and qualitative assessment, we observe a statistically significant increase in complaint severity, particularly in surgical outcomes and staff-patient interactions. Our findings reveal substantial heterogeneity in institutional performance trajectories, with Apollo Hospitals and Venkat Center demonstrating persistently elevated severity indices. Notably, Aster CMI Hospital exhibited the most pronounced deterioration in patient satisfaction metrics. The emergence of digital reputation management challenges (Gurău et al., 2022; Luca, 2016) further complicates the healthcare quality landscape. This study contributes to the literature on patient satisfaction dynamics in rapidly evolving healthcare markets and offers evidence-based recommendations for systemic reform. These findings have significant implications for healthcare governance, quality assurance frameworks, and regulatory policy in emerging economies.
... This unique aspect of customer service in local businesses calls for a differentiated customer service strategy compared to large firms. Online reviews and social media play a crucial role for local businesses as customers often view reviews as a reflection of their reputation (Luca 2016). Negative online feedback can significantly influence customer decisions regarding whether to visit a business and can further impact its performance (Pee 2016). ...
... Besides the social media and online reviews that have been the significant factors in the role of social proof on consumer behavior are also increased. Luca (2016) found that positive online reviews and ratings are able to change consumers' purchase decisions to great extent, and people will be affected by the opinions and experiences of others in the decision-making process. Complementing these findings, Duan et al. (2008) revealed that consumer decisions are influenced not only by the valence (positive or negative) of online reviews but also by their volume and distribution, suggesting that the perception of consensus among others plays a crucial role in shaping consumer choices. ...
... Luca et al. [40] conducted a study using a regression discontinuity framework to examine the causal impact of Yelp ratings on consumer demand. Their analysis focused on the rounding thresholds used by Yelp. ...
While existing research primarily focuses on optimizing established businesses, it overlooks a critical group: aspiring entrepreneurs seeking to establish ventures in their own locales. Instead of relocating, businesses often pivot based on region-specific attractiveness. Rather than relocating businesses to more attractive areas, it is better to determine the intrinsic value of each geographical location. Such a methodology will explore additional factors impacting business success and have the potential to significantly enhance urban planning, policy-making, and resource allocation strategies, thereby fostering a more conducive environment for economic growth and development. However, selecting a high-demand business idea for the current location involves navigating various physical, economic, social, and environmental factors, underscoring the complexity of entrepreneurship in today's landscape.
In the past decades, the rapid increase of smartphones and enhanced location-based applications has united individuals on platforms like Yelp, Trip Advisor, Foursquare, and Zomato, facilitating the sharing of experiences across different locations. These platforms, known as location-based social networking (LBSN) platforms, are crucial for business owners seeking to understand customer interests through reviews and visitation patterns. Similar to finding the proper location and time for businesses, we can enhance business category selection mechanisms for a given location using data from LBSN platforms.
By analyzing the Yelp Dataset, we aim to establish a methodology that accurately assesses the suitability of different business categories for specific locations. To achieve this, we first identify key factors influencing business success and filter them based on their availability in the Yelp dataset. Our methodology prioritizes the Size Index aspect of the given area. Finally, we developed a recommendation model that predicts the order of suitable business categories, ranking them from highest to lowest suitability, with one model notably achieving an accuracy of 77.97% while testing the current success of the existing businesses.
Keywords: Location-based Social Networking (LBSN), Business category selection, Yelp Dataset, Entrepreneurship, Urban planning
... Besides the social media and online reviews that have been the significant factors in the role of social proof on consumer behavior are also increased. Luca (2016) found that positive online reviews and ratings are able to change consumers' purchase decisions to great extent, and people will be affected by the opinions and experiences of others in the decision-making process. Complementing these findings, Duan et al. (2008) revealed that consumer decisions are influenced not only by the valence (positive or negative) of online reviews but also by their volume and distribution, suggesting that the perception of consensus among others plays a crucial role in shaping consumer choices. ...
Consumer purchase decisions are shaped by various psychological biases and heuristics, deviating from rational choice models. This conceptual paper proposes an integrative framework examining five core behavioral variables influencing consumer choices: social proof (influence of others' purchases), scarcity (motivational pull of limited availability), price relativity (assessing value based on reference prices), the curse of knowledge (experts' failure to understand novice perspectives), and habit (power of routines and automaticity). Drawing from behavioral economics and psychology literature, the model elucidates how these factors interact to produce suboptimal judgments and decisions. Theoretical implications challenge rational choice assumptions, providing a holistic view of cognitive biases in decision-making. Practical implications offer insights for marketers to leverage persuasive campaigns and for policymakers and consumer advocates to promote informed choices and consumer welfare.
... This dataset provides insights into users' physical visitation behaviors, making it ideal for evaluating spatial and temporal user-item interactions. • Yelp [40]: A service-oriented dataset with 485,151 ratings from 13,820 users on 38,048 businesses. It captures user preferences across a wide variety of services, enabling the evaluation of recommendation models in contexts where user reviews and service quality are key. ...
Recommendation systems have advanced significantly in recent years, achieving greater accuracy and relevance. However, traditional approaches often suffer from a mismatch between the losses used during training and the metrics used for evaluation. Models are typically trained to minimize a loss function, while their effectiveness during testing is assessed using different ranking metrics, leading to suboptimal recommendation quality. To address this limitation, reinforcement learning (RL) has emerged as a promising solution. Although RL has been applied in recommendation systems, the integration of graph neural networks (GNNs) within this framework remains underexplored. In this study, we bridge this gap by integrating GNNs and RL to enhance ranking accuracy and recommendation quality. We propose two key innovations: (1) leveraging learnable graphs to embed user-item interactions, with RL optimizing user rewards to improve ranking quality, and (2) modifying GNN architectures with skip connections to enhance recommendation accuracy while reducing training time and improving convergence. Our comprehensive analysis on multiple real-world datasets demonstrates the impact of different GNN architectures and their modifications on the effectiveness of recommendation systems. Our findings demonstrate the potential of combining GNNs and RL to overcome the limitations of traditional recommendation models and achieve state-of-the-art performance, with XSimGCL-skip achieving an average improvement of approximately 2.5% over baseline methods.
... We also apply our method to the "review and business data in 2021" from the yelp dataset (Luca, 2011). For this dataset, we construct a business graph by distance as restaurants with close distance might influence each other (Fan et al., 2023). ...
Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have been developed for this purpose. Among them, the score-matching method has demonstrated superior performance across various evaluation metrics, particularly for the commonly encountered Additive Nonlinear Causal Models. However, current score-matching-based algorithms are primarily designed to analyze independent and identically distributed (i.i.d.) data. More importantly, they suffer from high computational complexity due to the pruning step required for handling dense Directed Acyclic Graphs (DAGs). To enhance the scalability of score matching, we have developed a new parent-finding subroutine for leaf nodes in DAGs, significantly accelerating the most time-consuming part of the process: the pruning step. This improvement results in an efficiency-lifted score matching algorithm, termed Parent Identification-based Causal structure learning for both i.i.d. and temporal data on networKs, or PICK. The new score-matching algorithm extends the scope of existing algorithms and can handle static and temporal data on networks with weak network interference. Our proposed algorithm can efficiently cope with increasingly complex datasets that exhibit spatial and temporal dependencies, commonly encountered in academia and industry. The proposed algorithm can accelerate score-matching-based methods while maintaining high accuracy in real-world applications.
... This also points toward a difference in willingness to pay between frequently and infrequently reviewed apartments. Similar results have been found in earlier studies on other marketplaces (Resnick et al. 2006;Ye, Law, and Gu 2009;Luca 2011). ...
We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction of options does not have any reviews available. Such options are frequently neglected as viable choices, and in turn are unlikely to be evaluated, creating a vicious cycle. Platforms can engage users to deviate from their preferred choice by offering monetary incentives for choosing a different option instead. To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm - Coordinated Online Learning (CoOL) - for learning with structural information modeled as convex constraints. We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our findings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform.
... This attack style happens in many real applications, e.g., online sellers on Amazon may fake a few customer rating profiles to demote their competitors' high-quality shoes; writers may hire several readers to give high scores to promote their low-quality books. In fact, it has been shown that systems are seriously affected by small amounts of unorganized malicious attacks, e.g., the first maliciously bad rating can decrease the sales of one seller by 13% (Luca, 2016). ...
Recommender system has attracted much attention during the past decade. Many attack detection algorithms have been developed for better recommendations, mostly focusing on shilling attacks, where an attack organizer produces a large number of user profiles by the same strategy to promote or demote an item. This work considers a different attack style: unorganized malicious attacks, where attackers individually utilize a small number of user profiles to attack different items without any organizer. This attack style occurs in many real applications, yet relevant study remains open. We first formulate the unorganized malicious attacks detection as a matrix completion problem, and propose the Unorganized Malicious Attacks detection (UMA) approach, a proximal alternating splitting augmented Lagrangian method. We verify, both theoretically and empirically, the effectiveness of our proposed approach.
... Such a model is a good descriptor for online reputation and polling systems such as Yelp R and TripAdvisor R , where individuals' recommendations are based on their private observations and recommendations of their friends [45,Chapter 5]. The analysis of such systems is important not only because they play a significant role in generating revenues for the businesses that are being ranked [46], but also for the purposes of designing fair rankings and accurate recommendation systems. ...
We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the actions of their neighboring agents at each time. Successive applications of Bayes rule to the entire history of past observations lead to forebodingly complex inferences: due to lack of knowledge about the global network structure, and unavailability of private observations, as well as third party interactions preceding every decision. Such difficulties make Bayesian updating of beliefs an implausible mechanism for social learning. To address these complexities, we consider a Bayesian without Recall model of inference. On the one hand, this model provides a tractable framework for analyzing the behavior of rational agents in social networks. On the other hand, this model also provides a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for the structure of the action space and utility functions for such agents and investigate the properties of learning, convergence, and consensus in special cases.
... As the amount and value of online texts have grown substantially, researchers' interest in sentiment analysis has increased. Shoppers regularly read posted reviews before choosing a product, hotel, or restaurant, and better reviews help generate higher profits [26]. ...
In the rapidly evolving digital healthcare market, ensuring both the activation of the market and the fulfillment of the product’s social role is essential. This study addresses the service quality of smart running applications by utilizing big data text mining techniques to bridge the gap between user experience and service quality in digital health applications. The research analyzed 264,330 app reviews through sentiment analysis and network analysis, focusing on key service dimensions such as system efficiency, functional fulfillment, system availability, and data privacy. The findings revealed that, while users highly value the functional benefits provided by these applications, there are significant concerns regarding system stability and data privacy. These insights underscore the importance of addressing technical and security issues to enhance user satisfaction and continuous application usage. This study demonstrates the potential of text mining methods in quantifying user experience, offering a robust framework for developing user-centered digital health services. The conclusions emphasize the need for continuous improvement in smart running applications to meet market demands and social expectations, contributing to the broader discourse on the integration of e-commerce and digital health.
... Praktik ini memiliki potensi besar untuk mengurangi kemurnian pasar digital dan mempengaruhi keputusan pembelian pelanggan. Sebagai contoh, penelitian yang dilakukan oleh Luca (2016) menunjukkan bahwa ulasan online memiliki dampak yang signifikan terhadap bagaimana pelanggan bertindak saat membeli barang-barang tertentu, dan ulasan palsu dapat secara langsung berdampak pada keputusan pelanggan untuk melakukan pembelian. ...
Perkembangan teknologi dan digitalisasi telah memberikan dampak signifikan pada sektor marketplace diIndonesia, menjadikannya pilihan utama konsumen termasuk mahasiswa dalam memenuhi kebutuhan. Namun, meningkatnya transaksi di marketplace turut memunculkan fenomena fake review, yakni ulasan palsuyang dibuat untuk memengaruhi persepsi konsumen terhadap produk. Penelitian ini bertujuan untukmenganalisis pengaruh fake review terhadap persepsi risiko dan minat beli mahasiswa Sistem Informasi ITS terhadap produk yang dijual di marketplace. Metode penelitian yang digunakan adalah survei, dengankuesioner sebagai instrumen pengumpulan data. Data dianalisis menggunakan uji validitas dan reliabilitas(Cronbach Alpha) serta uji hipotesis melalui Smart PLS pada tingkat signifikansi 0,05. Hasil menunjukkanbahwa fake review memiliki pengaruh signifikan terhadap persepsi risiko mahasiswa, dengan p-value 0,000.Semakin banyak fake review yang ditemukan, semakin tinggi pula persepsi risiko mahasiswa terhadap produk.Namun, fake review tidak memiliki pengaruh signifikan terhadap minat beli, sebagaimana ditunjukkan olehp-value sebesar 0,873. Selain itu, persepsi risiko juga tidak berpengaruh secara signifikan terhadap minat beli,dengan p-value sebesar 0,220. Mayoritas pernyataan dalam kuesioner terbukti valid, dengan r hitung lebihbesar dari r tabel (0,2759), meskipun beberapa item tidak reliabel dan dikeluarkan dari analisis. Penelitian inimenyoroti pentingnya validasi ulasan di marketplace untuk mengurangi persepsi risiko konsumen akibat fakereview. Dengan mekanisme yang lebih baik, marketplace di Indonesia diharapkan dapat meningkatkankepercayaan konsumen khususnya mahasiswa terhadap produk yang dijual di platform tersebut. Penelitian inimemberikan kontribusi dalam memahami hubungan antara fake review, persepsi risiko, dan minat belimahasiswa di era digital.
Keywords: Fake review, marketplace, minat beli, mahasiswa, persepsi risiko.
... Local retailers and small-scale hospitality businesses are increasingly dependent on sites like Yelp. Evidence from the US restaurant sector shows that their profitability appears connected to reviews on platforms, with favourable Yelp reviews enabling independent restaurants to develop a profile, even re-gaining market share against chains (Luca, 2016). ...
... The impact of online reviews on business revenue, particularly using Yelp.com as a case study, demonstrates that positive reviews can significantly boost sales, while fake reviews can undermine trust and lead to financial losses. Effective monitoring and management of online reviews are essential (Luca, 2016). ...
The prevalence of fake reviews and ratings in online marketing has become a significant issue, undermining consumer trust and damaging business reputations. This study aims to identify the extent and impact of fake reviews and explore innovative strategies for traders to combat this problem and maintain authenticity. A mixed-method approach was adopted, including a survey of 100 respondents to gauge public awareness and perception of fake reviews. The findings highlight the critical need for advanced detection methods, increased transparency, and consumer education to foster trust in online platforms. Statistical analyses, including ANOVA and Chi-Square tests, were used to analyze the data. The survey revealed that 75% of respondents encounter fake reviews frequently, with 40% being highly aware of the issue. The impact of fake reviews on purchasing decisions is significant, affecting 80% of the respondents. To address this issue, traders can implement advanced AI algorithms, encourage genuine reviews through incentives, increase transparency through verification processes, and collaborate with review platforms to establish stricter monitoring systems. Additionally, educating consumers about identifying fake reviews and promoting ethical online behavior are crucial steps towards mitigating this issue. This study concludes that by adopting these innovative strategies, traders can protect their reputations, foster consumer trust, and ensure the authenticity of online reviews and ratings. Future research should focus on developing more sophisticated detection technologies and exploring the long-term effects of fake reviews on consumer behavior and market dynamics.
... Customer satisfaction is essentially a postpurchase appraisal of the entire customer experience (Mano -Oliver, 1993). Internet reviews are crucial for the restaurant business because an extra star could result in a 5-9% revenue boost for the establishment (Luca, 2011). ...
Michelin-starred restaurants are renowned for their use of premium ingredients, and creatively flavored dishes that are consistently prepared to a high degree. Due to the benefits of sharing previous customers’ open experiences and opinions,online reviewshave grown in importance among future customers as online activities related to the purchase of restaurant services have increased. Online reviews serve as a route for knowledge and help clients perceive risk in a more manageable way. This study contributes to the decision-making processes –an important competitiveness factor of Michelin restaurants–by providing insights into contemporary European customers, who use digital platforms to share their real perceptions of these restaurants’performance across Europe.
... Behavioural evidence: Rating systems are widely used in the gig economy and have been shown to have a large effect on behaviour. For example, Luca (2016) found that a one-star increase in ...
Food delivery workers (FDWs) for app-based food delivery platforms (FDPs) face a multitude of work health and safety (WHS) risks. While the number of incidents involving FDWs that have been reported to SafeWork NSW is low, there has been an exponential increase over the past three years, from one incident reported in 2017 to 19 in the first half of 2020 alone. The majority of these incidents are road and traffic related, but previous research has highlighted additional harms that FDWs risk on the job, including physical assault, intimidation, and verbal abuse.
FDWs are independent contractors and are thus primarily responsible for their own WHS. However, the FDPs that contract FDWs are also crucial stakeholders within the WHS landscape. FDPs and FDWs share a duty of care toward anyone involved in or impacted by their work, yet ambiguity remains over who is responsible for which aspects of WHS risk mitigation and management. While contractors in other industries, such as construction, must also manage their own WHS through measures such as procuring their own safety equipment and training, contractors on a construction site, unlike FDWs, work within an environment with a greater degree of control (e.g. rules about personal protective equipment (PPE) upon entering the site) and direct oversight (e.g. from other on-site staff or contractors). These environmental characteristics, in combination with demographic factors and limited WHS knowledge and skills (Convery, Morse, Fung, Wodak, Powell, Quinn, Taylor, Searle, & Vårhammar, 2020), means that FDWs may lack the capacity to fully and effectively manage WHS risks on their own.
While the risk profile and concerns of FDWs are emerging, limited research has explored the WHS perspectives and priorities of FDPs. Understanding these perspectives is critical in developing interventions that improve WHS for FDWs, as well as those impacted by their work, such as customers, restaurants, and members of the public.
The Behavioural Insights Team, in collaboration with Macquarie University and the Centre for Work Health and Safety, is undertaking a four-phase project that aims to improve the WHS of FDWs in the gig economy. The objective of Phase 1 was to describe the characteristics of FDWs and their WHS knowledge, concerns, and behaviours. The findings of Phase 1 are detailed in the report Work health and safety of food delivery workers in the gig economy (Convery et al., 2020). Phase 2 aimed to describe the characteristics of FDPs, their knowledge, concerns, and behaviours in relation to the WHS of those engaged, or caused to be engaged, by their operations. In Phase 3, the findings of Phases 1 and 2 will inform the development of proactive risk prevention activities to improve FDWs’ WHS, which will be co-designed with relevant stakeholders. In Phase 4, the outcomes of these prevention activities will be evaluated in the field. This report details the results of Phase 2.
Amaç –Araştırmanın amacı, Tekirdağ'daki restoranlarda misafir deneyimlerini inceleyerek, bu deneyimleri oluşturan temel unsurları belirlemek ve restoran işletmelerinin müşteri memnuniyetini artırmalarına yönelik öneriler sunmaktır. Çalışma, hizmet kalitesi, fiziksel ortam, fiyat-performans dengesi ve duygusal ifadeler gibi müşteri memnuniyetine etki eden unsurları ele almayı hedeflemektedir.Yöntem –Araştırmada, Tripadvisor platformunda yer alan Tekirdağ’daki en yüksek puana sahip 10 restoranın toplam 1461 müşteri yorumu analiz edilmiştir. Veriler, Strauss ve Corbin'in (1990) nitel veri analizi yöntemi çerçevesinde açık, eksen ve seçici kodlama aşamaları kullanılarak sistematik bir şekilde incelenmiştir. Bu süreçte müşteri deneyimlerini etkileyen ana temalar ortaya çıkarılmıştır.Bulgular –Veri analizi sonucunda, misafir deneyimlerinin beş ana temada toplandığı tespit edilmiştir: deneyim, hizmet kalitesi ve çeşitlilik, fiziksel ortam ve konum, fiyat ve memnuniyet, duygusal ifadeler. Bulgular, güler yüzlü personel ve lezzetli yemeklerin olumlu deneyimler oluşturduğunu, ancak fiyat ve tatlı çeşitliliği gibi bazı unsurların geliştirilmesi gerektiğini göstermiştir. Fiziksel ortamda temizliğin ve dekorasyonun müşteri memnuniyetine katkıda bulunduğu, gürültü ve park yeri sorunlarının iseolumsuz etkiler yarattığı belirlenmiştir.Tartışma –Araştırmanın bulguları, restoranların müşteri memnuniyetini artırmak için hizmet kalitesi, fiziksel ortam ve fiyatlandırma stratejilerini iyileştirmesi gerektiğini ortaya koymaktadır. Özellikle duygusal bağ kurmanın, müşterilerin restoran deneyimlerini unutulmaz hale getirdiği vurgulanmıştır. Bu sonuçlar, yerel restoran işletmelerinin müşteri odaklı stratejiler geliştirmesi ve hizmet süreçlerini optimize etmesi için rehber niteliğindedir. Araştırma, Tekirdağ restoranlarında müşteri deneyimlerine odaklanarak literatüre özgün bir katkı sağlamaktadır.
In a world where five stars have become the standard for evaluating many transactions and consumers turn to the crowd for guidance when making a wide variety of choices, organizations cannot dismiss online reviews as inconsequential. And whereas we know a lot about how organizations respond to reviews online, there has been a lack of systematic evidence showing how organizations behave in response to online feedback once their screens are turned off. This paper leverages a novel combination of insights from a lab-in-the-field experiment, an archival study, and two rounds of qualitative interviews in the French restaurant industry to examine online and offline responses to reviewer feedback. We identify characteristics of the review, the restaurant, and the respondent that influenced when restaurants in our sample were more likely to align their actions online and offline and when they were more likely to decouple them—that is, posting an online response promising to take corrective action while having no intention to change how the restaurant operates “in real life.” We conclude by speculating on potential mechanisms behind our respondents’ reactions and discussing our contribution to the literature on producer reactivity and the symbolic management of change.
Funding: This work was supported by Università Bocconi, Labex Ecodec, and Fondation HEC.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2020.14091 .
Consulting online reviews before shopping on e-commerce platforms has become an essential activity for consumers on the Internet due to its usefulness. Recognizing the importance of online reviews, many individuals and organizations have engaged in fake reviews that misrepresent products and services. As a result, consumers are gradually losing trust in online reviews or have to invest more time analyzing these reviews. Based on collected articles and reports, this study systematized and clarified the concepts, current status, and impacts of fake online reviews on e-commerce activities. The study also proposes several recommendations for online users on how to recognize and detect fake reviews to protect themselves during their shopping experiences on e-commerce platforms.
Trust is essential for fostering cooperation, especially in global peer-to-peer platform markets where transactions between strangers involve significant risks and uncertainties. The global scope of these platforms introduces cultural differences, further intensifying these challenges. It is well established that social distance shapes trust, with decision-makers typically favoring those who share similarities, leading to trust disparities that advantage some participants while disadvantaging others. But existing theories offer conflicting perspectives on whether quality signals can bridge or exacerbate the gap between advantaged (i.e., socially proximate to focal decision-makers) and disadvantaged (i.e., socially distant from focal decision-makers) participants. Drawing on sociological theories of trust production that highlight how various social systems act as different sources of trust, we offer a new perspective to this puzzle by comparing two types of quality signals: reputation, which is derived from prior exchanges and provided by prior exchange partners, and institutional accreditation, which is linked to organizational institutions. Analyzing a proprietary dataset from a global peer-to-peer lodging platform, we find that prospective guests who are more culturally distant from hosts are in a disadvantaged position: their lodging requests are less likely to be approved by hosts. Furthermore, the positive effect of guest reputation (i.e., ratings) is weaker for culturally distant guests, and thus widens the gap in host acceptance of culturally proximate versus culturally distant guests. By contrast, the positive effect of institutional accreditation (i.e., platform verification) is stronger for culturally distant guests, indicating that it helps narrow the gap. These findings reveal unexplored contingencies to theories of evaluations bias and discrimination, contributing to the broader literature on trust, culture, and inequality in global online markets, and underscoring the challenges of building trust in uncertain environments.
Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These studies have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is validated.
Online platforms that implement reputation mechanisms typically prevent the transfer of ratings to other platforms, leading to lock‐in effects and high switching costs for users. Platforms are able to capitalize on this arrangement, for example, by charging their users higher fees. In this paper, we theoretically and experimentally investigate the effects of platform pricing on workers' switching behavior in online labor markets and analyze whether a policy regime with reputation portability could mitigate lock‐in effects and reduce the likelihood of worker capitalization by the platform. We examine switching motives in depth, differentiating between monetary motives and fairness preferences. We provide theoretical evidence for the existence of switching costs if reputation mechanisms are platform‐specific. Our model predicts that reputation portability lowers switching costs, eliminating the possibility for platforms to capitalize on lock‐in effects. We test our predictions using an online lab‐in‐the‐field experiment. The results are in line with our theoretical model and show that platforms can capitalize on lock‐in effects more effectively in a policy regime without reputation portability. We also find that reputation portability has a positive impact on worker mobility and the wages of highly rated workers. The data further show that the switching of workers is primarily driven by monetary motives, but perceiving the platform fee as unfair also plays a significant role for workers.
A positive place reputation is the important competitive advantage, which allows it to attract resources and to facilitate mutually beneficial partnership with stakeholder groups. The main task is not only to identify the mechanism of the place reputation formation, but to analyze the factors influencing this process and their real-time dynamics. Now the information obtained by monitoring social networks, electronic media, and other looks more reliable and unbiased. Desk research has recently come to the fore, as it allows a fairly rapid analysis of large amounts of information. Another trend is a shift of focus from quantitative indicators' study to the identification of qualitative ones. The article reveals methods of conducting desk research using various digital platforms. On the example of St. Petersburg, the authors analyzed possibilities of monitoring the place reputational capital, allowing to assess the reputation characteristic expressed quantitatively, and a qualitative characteristic in the form of an attitude reflection towards the region expressed in publications' tone.
Cross-border e-commerce refers to transaction entities belonging to different customs, realizing order placement and electronic payment settlement through e-commerce platforms, and completing transactions through cross-border logistics to circulate goods, a type of international commercial activity.
Local review platforms like Yelp and Google Maps use systems combining automated and human judgment to delineate the limits of acceptable speech, allowing some reviews to remain public and removing or obscuring others. This article examines the phenomenon of “review bombing,” in which controversial businesses receive an influx of reviews, using spatiotemporal analysis of review activity to analyze their shifting catchment areas, measuring what sociologist Richard Ocejo calls the “extraterritoriality” of their “taste communities”. Specifically, this article examines businesses in the United States that are caught up in political controversies using the locations of their consumer-reviewers on Yelp. The author compiles a test dataset of affected businesses encompassing national and local politics, including the 2016 and 2020 U.S. elections, the #BlackLivesMatter and #MeToo movements, and the COVID-19 pandemic, and selects two for in-depth case studies and spatial analysis: Washington, D.C.-based pizzeria Comet Ping Pong (subject of the #Pizzagate conspiracy theory) and St. Louis-based Pi Pizzeria (caught up in debates about policing and the Black Lives Matter movement). In Comet Ping Pong's case, review bombing resulted in a wider spatial distribution of primarily negative reviewers, while Pi has a much more local pattern, with a fairly even split of supporters and detractors, showing how different political controversies resonate across different scales. The article contrasts Yelp's interventionist approach to content moderation to the relatively laissez-faire attitude of competitors like Google, and considers the consequences of this form of "algorithmic censorship" for small businesses, communities, and online speech.
Research Summary
This article develops a theoretical framework to explicate how third parties, who are not transactionally involved in a given exchange relationship, can promote or impede the creation and capture of value by influencing market actor beliefs and behaviors. I investigate these issues empirically through an abductive mixed‐method case study of the Michelin Guide's entry into New York City. An examination of two decades of the openings and closings of New York City's elite restaurants indicates that receiving a Michelin star corresponded to an increased likelihood of restaurant exit. Michelin stars appear to have fostered disruptions at recipients' upstream and downstream interfaces, which inhibited their ability to capture value. This ultimately underscores how value network reactivity to third‐party evaluations may lead to unintended consequences for firms.
Managerial Summary
This article explains how third‐party evaluators' reviews, ratings, and rankings can promote or impede the creation and capture of value. This occurs because third‐party evaluations engender reactions by those being evaluated, as well as reactions by other market actors such as competitors and exchange partners. I study these issues within the context of the Michelin Guide's entry into New York City, and my findings indicate that restaurants that received a Michelin star were more likely to close in subsequent years. Evidence suggests that intensified bargaining problems with landlords, suppliers, and employees, along with heightened consumer expectations, created new challenges for these Michelin‐starred restaurants, which ultimately made it more difficult for them to stay in business.
Quality certification is a common tool to enhance trust in marketplaces. Should the certification be based on consumer reports, such as ratings, or administrative data on seller behavior, such as the number of seller-initiated cancellations? In theory, incorporating consumer reports makes the quality certificate more relevant for consumer experience but may discourage seller effort, because consumer reports can be driven by factors not entirely within sellers’ control. Alternatively, using administrative data makes the certification more controllable by sellers, but these data track only a subset of seller behavior and may not be fully aligned with consumer experience. To answer the above question, we study a major redesign of eBay’s quality certification that removed most consumer reports from its criteria and added administrative data. This change motivates seller effort in dimensions highlighted by the new criteria, as well as allowing sellers to more precisely target their effort at the threshold. Buyers place a higher value on the quality certificate and are more likely to purchase again on the platform in markets where administrative data are more correlated with consumer reports. Lastly, the proportion of certified sellers becomes more homogenized across markets, and sales seem to become more concentrated towards large sellers.
Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market performance of entertainment services and the role of critics. The authors do so here. They show empirically that critical reviews correlate with late and cumulative box office receipts but do not have a significant correlation with early box office receipts. Although still far from any definitive conclusion, this finding suggests that critics, at least from an aggregate-level perspective, appear to act more as leading indicators than as opinion leaders.
This paper takes a first step toward understanding the working of the online market for reviews. Most online review firms rely on unpaid volunteers to write reviews. Can a for-profit online review firm attract productive volunteer reviewers, limit the number of ranting or raving reviewers, and marginalize fake reviewers? This paper sheds light on this issue by studying reviewer productivity and restaurant ratings at Yelp, where reviewers are encouraged to establish a social image, and two competing websites, where reviewers are completely anonymous. Using a dataset of nearly half a million reviewer accounts, we find that the number (proportion) of prolific reviewers on Yelp is an order of magnitude larger than that on either competing site, more productive reviewers on all three websites are less likely to give an extreme rating, and restaurant ratings on Yelp tend to be much less extreme than those on either competing site.
The Nutrition Labeling and Education Act (NLEA) requires most food products to include a nutrition label. Prior to the NLEA, labeling was voluntary. This study uses nutrition label information and supermarket scanner data pre- and post-NLEA to examine the impact of moving from a voluntary to a mandatory labeling regime on consumer product choice. The voluntary unraveling of information is shown to be an important market mechanism. Prior to the NLEA, all low-fat salad dressings had a nutrition label, while the majority of the higher fat dressings did not. However, there remained large variation in fat content among dressings that did not voluntarily label. Those with the highest fat levels experienced a significant decline in sales after they were required to disclose. The results indicate that even in markets with credible, low-cost mechanisms to disclose, mandatory labeling can have an impact on consumer behavior and health. Copyright 2000 by the University of Chicago.
How does the release of health plan performance ratings influence employee health plan choice? A natural experiment at General Motors (GM) Corporation provides valuable evidence on this question. During the 1997 open enrollment period, GM disseminated a health plan report card for the first time. By comparing 1996 and 1997 enrollment patterns, our analysis estimates the impact of the report card information while accounting for fixed, unobserved plan traits. Results indicate that employees are less likely to enroll in plans requiring relatively high out-of-pocket contributions. Results with respect to report card ratings suggest that individuals avoid health plans with many below average ratings.
An inherent problem in measuring the influence of expert reviews on the demand for experience goods is that a correlation between good reviews and high demand may be spurious, induced by an underlying correlation with unobservable quality signals. Using the timing of the reviews by two popular movie critics, Siskel and Ebert, relative to opening weekend box office revenue, we apply a difference-in-differences approach to circumvent the problem of spurious correlation. After purging the spurious correlation, the measured influence effect is smaller though still detectable. Positive reviews have a particularly large influence on the demand for dramas and narrowly-released movies. Copyright Blackwell Publishing Ltd. 2005.
Does limited attention among investors affect stock returns? We compare the response to earnings announcements on Friday, when investor inattention is more likely, to the response on other weekdays. If inattention influences stock prices, we should observe less immediate response and more drift for Friday announcements. Indeed, Friday announcements have a 15% lower immediate response and a 70% higher delayed response. A portfolio investing in differential Friday drift earns substantial abnormal returns. In addition, trading volume is 8% lower around Friday announcements. These findings support explanations of post-earnings announcement drift based on underreaction to information caused by limited attention. Copyright (c) 2009 the American Finance Association.
A central assumption in public …nance is that individuals optimize fully with respect to the incentives created by tax policies. In this paper, we test this assumption using two empirical strategies. First, we conducted an experiment at a large grocery store where we posted tax-inclusive prices for certain products (showing both the pre- tax and post-tax price) over a three week period. Using scanner data, we …nd that posting the tax-inclusive prices reduced demand by 6-8% among the treated products relative to control products and nearby control stores. Second, using data on aggregate alcohol consumption by state from 1970-2003, we …nd that state-level increases in excise taxes (which are included in posted prices) reduce alcohol consumption signi…cantly more than increases in sales taxes (which are added at the register and hence less salient). These results indicate that tax salience aects behavioral responses, contrary to canonical models. We propose a simple alternative model of boundedly rational agents to explain why salience matters. In the model, small (second-order) costs of cognition can lead agents to ignore fairly large taxes, even though these taxes may have large (…rst-order) eects on tax revenue and social welfare. We show that small costs of cognition may explain several stylized facts, such as greater awareness of average tax rates instead of marginal rates and larger responses on the extensive margin. Using this framework, we derive Harberger-type formulas for the e¢ ciency cost and incidence of taxes when salience matters.
Online reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. They are emerging as a promising alternative to more established assurance mechanisms, such as branding and formal contracting, in a variety of settings ranging from online marketplaces to Internet search engines. At the same time, they are transforming a concept that had traditionally fallen within the realm of the social sciences into an engineering design problem. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to designing, evaluating and using them. It provides an overview of relevant work in game theory and economics on the topic or reputation. It further discusses how this body of work is being extended and combined with insights from computer science, information systems, management science and psychology in order to take into consideration the special properties of online mechanisms such as their unprecedented scalability, the ability to precisely design the type of feedback information they solicit and distribute, and challenges associated with the volatility of identities and the absence of many familiar contextual cues in online environments.
Since George A. Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods mar-kets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction webpage. This defines a precise contract — to deliver the car shown for the closing price — which helps protect the buyer from adverse se-lection. I test this theory using data from eBay Motors, finding that online disclosures are important price determinants; and that disclosure costs impact both the level of disclosure and prices.
Reputations that are transmitted from person to person can deter moral hazard and discourage entry by bad types in markets where players repeat transactions but rarely with the same player. On the Internet, information about past transactions may be both limited and potentially unreliable, but it can be distributed far more systematically than the informal gossip among friends that characterizes conventional marketplaces. One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features of this system, which facilitates many millions of sales each month. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance. However, the net feedback scores that eBay displays encourages Pollyanna assessments of reputations, and is far from the best predictor available. Fourth, although sellers with better reputations were more likely to sell their items, they enjoyed no boost in price, at least for the two sets of items that we examined. Fifth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.
How do rankings affect demand? This paper investigates the impact of college rankings, and the visibility of those rankings, on students’ application decisions. Using natural experiments from U.S. News and World Report College Rankings, we present two main findings. First, we identify a causal impact of rankings on application decisions. When explicit rankings of colleges are published in U.S. News, a one-rank improvement leads to a 1-percentage-point increase in the number of applications to that college. Second, we show that the response to the information represented in rankings depends on the way in which that information is presented. Rankings have no effect on application decisions when colleges are listed alphabetically, even when readers are provided data on college quality and the methodology used to calculate rankings. This finding provides evidence that the salience of information is a central determinant of a firm’s demand function, even for purchases as large as college attendance.
In regression discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell [1960. Regression-discontinuity analysis: an alternative to the ex-post Facto experiment. Journal of Educational Psychology 51, 309–317] With the exception of a few unpublished theoretical papers, these methods did not attract much attention in the economics literature until recently. Starting in the late 1990s, there has been a large number of studies in economics applying and extending RD methods. In this paper we review some of the practical and theoretical issues in implementation of RD methods.
Although policymakers have increasingly turned to provider report cards as a tool to improve health care quality, existing studies provide mixed evidence on whether they influence consumer choices. We examine the effects of providing consumers with quality information in the context of fertility clinics providing Assisted Reproductive Therapies (ART). We report three main findings. First, clinics with higher birth rates had larger market shares after the adoption of report cards relative to before. Second, clinics with a disproportionate share of young, relatively easy-to-treat patients had lower market shares after adoption versus before. This suggests that consumers take into account information on patient mix when evaluating clinic outcomes. Third, report cards had larger effects on consumers and clinics from states with ART insurance coverage mandates. We conclude that consumers respond to quality report cards when choosing among providers of ART.
Economics Since Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods markets. The remarkable growth in online auctions of used goods, where buyers generally purchase sight unseen, therefore poses a puzzle. I argue that part of the solution is that sellers voluntarily disclose their private information to buyers through photos, text and graphics on the auction webpage. In so doing they define a precise contract between buyer and seller — to deliver the car shown — and this helps protect the buyer from adverse selection. Extending previous theoretical work by Jovanovic (1982), I model the impact of contractible disclosure and changes in disclosure costs on performance and adverse selection on online auction platforms. To test this theory, I examine data from eBay Motors. I find first that sellers selectively disclose information; second that this reduces information asymmetry; and finally that disclosure costs impact both the level of disclosure and the prices obtained by sellers, and consequently incentives for seller participation.
Using two strategies, we show that consumers underreact to taxes that are not salient. First, using a field experiment in a grocery store, we find that posting tax-inclusive price tags reduces demand by 8 percent. Second, increases in taxes included in posted prices reduce alcohol consumption more than increases in taxes applied at the register. We develop a theoretical framework for applied welfare analysis that accommodates salience effects and other optimization failures. The simple formulas we derive imply that the economic incidence of a tax depends on its statutory incidence, and that even policies that induce no change in behavior can create efficiency losses. (JEL C93, D12, H25, H71)
This paper examines whether the salience of a tax system affects equilibrium tax rates. I analyze how tolls change after toll
facilities adopt electronic toll collection (ETC); drivers are substantially less aware of tolls paid electronically. I estimate
that, in steady state, tolls are 20 to 40 percent higher than they would have been without ETC. Consistent with a salience-based
explanation for this toll increase, I find that under ETC, driving becomes less elastic with respect to the toll and toll
setting becomes less sensitive to the electoral calendar. Alternative explanations appear unlikely to be able to explain the
findings.
The evidence that productivity varies greatly across teachers has given rise to the idea that student achievement data should be included in performance evaluation, despite limited empirical evidence on subjective evaluation or the use of objective performance measures in U.S. public schools. In this paper, we examine the results of a randomized pilot program in which school principals were provided with estimates of the performance of individual teachers in raising their students’ test scores in math and English. Our analysis establishes several facts consistent with a simple Bayesian learning model of employee evaluation in the presence of imperfect information. First, objective teacher performance estimates based on student data and principals’ prior beliefs are positively correlated, and the strength of this relationship rises with the precision of the objective estimates and the precision of subjective priors. Second, principals who are provided with objective performance data incorporate this information into their posterior beliefs, and do so to a greater extent when the data are more precise and when their priors are less precise. Moreover, after the provision of performance data, the probability of job separation rises for teachers with low performance estimates, and, in line with this change in attrition patterns, student achievement exhibits small improvements the following year. These results suggest that objective performance data provides useful information to principals in constructing employee evaluations and using these evaluations to improve productivity.
This paper evaluates a pilot program run by a company called OPOWER, previously known as Positive Energy, to mail home energy reports to residential utility consumers. The reports compare a household’s energy use to that of its neighbors and provide energy conservation tips. Using data from randomized natural field experiment at 80,000 treatment and control households in Minnesota, I estimate that the monthly program reduces energy consumption by 1.9 to 2.0 percent relative to baseline. In a treatment arm receiving reports each quarter, the effects decay in the months between letters and again increase upon receipt of the next letter. This suggests either that the energy conservation information is not useful across seasons or, perhaps more interestingly, that consumers’ motivation or attention is malleable and non-durable. I show that “profiling,” or using a statistical decision rule to target the program at households whose observable characteristics suggest larger treatment effects, could substantially improve cost effectiveness in future programs. The effects of this program provide additional evidence that non-price “nudges” can substantially affect consumer behavior.
Rankings and report cards have become a popular way of providing information in a variety of domains. I estimate the response to rankings in the hospital market and find that hospitals that improve their rank are able to attract significantly more patients. The average hospital in my sample experiences a 5% change in non-emergency, Medicare patient volume from year to year due to rank changes. These findings have implications regarding the competitiveness of hospital markets and the effect that the dissemination of quality information in hospital markets can have on individual choice.
eBay’s feedback mechanism is considered crucial to establishing and maintaining trust on the world’s largest trading platform. The effects of a user’s reputation on the probability of sale and on prices are at the center of a large number of studies. More recent theoretical work considers aspects of the mechanism itself. Yet, there is confusion amongst users about its exact institutional details, which also changed substantially in the last few months. An understanding of these details, and how the mechanism is perceived by users, is crucial for any assessment of the system. We provide a thorough description of the institutional setup of eBay’s feedback mechanism, including recent changes to it. Most importantly, buyers now have the possibility to leave additional, anonymous ratings on sellers on four different criteria. We discuss the implications of these changes and provide first descriptive evidence on their impact on rating behavior.
This study examines the effect of an increase in product quality information to consumers on firms' choices of product quality. In 1998 Los Angeles County introduced hygiene quality grade cards to be displayed in restaurant windows. We show that the grade cards cause (i) restaurant health inspection scores to increase, (ii) consumer demand to become sensitive to changes in restaurants' hygiene quality, and (iii) the number of foodborne illness hospitalizations to decrease. We also provide evidence that this improvement in health outcomes is not fully explained by consumers substituting from poor hygiene restaurants to good hygiene restaurants. These results imply that the grade cards cause restaurants to make hygiene quality improvements. © 2001 the President and Fellows of Harvard College and the Massachusetts Institute of Technology
This paper studies the way that word-of-mouth communication aggregates the information of individual agents. The authors find that the structure of the communication process determines whether all agents end up making identical choices, with less communication making this conformity more likely. Despite the players' naive decision rules and the stochastic decision environment, word-of-mouth communication may lead all players to adopt the action that is on average superior. These socially efficient outcomes tend to occur when each agent samples only a few others. Copyright 1995, the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
How can consumers be assured that firms will endeavor to provide good quality when quality is unobservable prior to purchase? We test the hypothesis that reputational incentives are effective at causing restaurants to maintain good hygiene quality. We find that chain affiliation provides reputational incentives and franchised units tend to free-ride on chain reputation. We also show that regional variation in the degree of repeat customers affects the strength of reputational incentives for good hygiene at both chain and nonchain restaurants. Despite these incentives, a policy intervention in the form of posted hygiene grade cards causes significant improvements in restaurant hygiene. (JEL I18, I19, L14, L83).
The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times.
August 28, 1997
A firm's reputation is considered an important asset. I develop a model in which a firm's only asset is its name -- which is associated with its reputation -- and study the economic forces which cause names to be valuable, tradeable assets. A simple adverse selection model together with an assumption on the non-observability of shifts of ownership guarantees that in equilibrium the market for names is active. This result is robust to both finite and infinite horizons, in contrast to standard results in the reputation literature. I also show that situations in which only good types buy names with a good reputation cannot be sustained in equilibrium.
Online feedback mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities, such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology in order to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for OR/MS resear
How do investors respond to predictable shifts in profitability? We consider how demographic shifts affect profits and returns across industries. Cohort size fluctuations produce forecastable demand changes for age-sensitive sectors, such as toys, bicycles, beer, life insurance, and nursing homes. These demand changes are predictable once a specific cohort is born. We use lagged consumption and demographic data to forecast future consumption demand growth induced by changes in age structure. We find that demand forecasts predict profitability by industry. Moreover, forecast demand changes five to ten years in the future predict annual industry stock returns. One additional percentage point of annualized demand growth due to demographics predicts a 5 to 10 percentage point increase in annual abnormal industry stock returns. However, forecasted demand changes over shorter horizons do not predict stock returns. A trading strategy exploiting demographic information earns an annualized risk-adjusted return of approximately 6 percent. We present a model of inattention to information about the distant future that is consistent with the findings. We also discuss alternative explanations, including omitted risk-based factors. (JEL E21, G12, G32, J11, L11, L25)
Standard sufficient conditions for identification in the regression discontinuity design are continuity of the conditional expectation of counterfactual outcomes in the running variable. These continuity assumptions may not be plausible if agents are able to manipulate the running variable. This paper develops a test of manipulation related to continuity of the running variable density function. The methodology is applied to popular elections to the House of Representatives, where sorting is neither expected nor found, and to roll-call voting in the House, where sorting is both expected and found.
The author develops a model in which a firm's only asset is its name, which summarizes its reputation, and studies the forces that cause names to be valuable, tradable assets. An adverse selection model in which shifts of ownership are not observable guarantees an active market for names with either finite or infinite horizons. No equilibrium exists in which only good types buy good names. The reputational dynamics that emerge from the model are more realistic than those in standard game-theoretic reputation models and suggest that adverse selection plays a crucial role in understanding firm reputation.
Recent developments in computer networks have driven the cost of distributing information virtually to zero, creating extraordinary opportunities for sharing product evaluations. We present pricing and subsidy mechanisms that operate through a computerized market and induce the efficient provision of evaluations. The mechanisms overcome three major challenges: first, evaluations, which are public goods, are likely to be underprovided; second, an inefficient ordering of evaluators may arise; third, the optimal quantity of evaluations depends on what is learned from the initial evaluations. Keywords: evaluations, information sharing, product quality, computer network, market (JEL D70, D83, H41, L15) 2 Subjective evaluations by others are a valuable tool for consumers who are choosing which products to buy or how to spend their time. For example, we read magazines devoted to product evaluation before purchasing cars and appliances. We ask our friends and read reviews by professional cr...