Reading Between the Stars: Understanding the Effects of Online Customer Reviews on Product Demand

2021 ◽  
Author(s):  
Hallie S. Cho ◽  
Manuel E. Sosa ◽  
Sameer Hasija
Keyword(s):  
Choice Model ◽  
Online Reviews ◽  
Causal Link ◽  
Problem Definition ◽  
Exogenous Shock ◽  
Customer Reviews ◽  
Product Demand ◽  
On Demand ◽  
The Impact ◽  
Positive Effect

Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. We are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality and how they are shared via customer reviews are of extreme relevance to the firm, but we still do not understand how product demand is affected by the quantitative and qualitative aspects of customer reviews. Our paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002–2013 data for the U.S. automobile market, we investigate empirically the impact of star ratings and review sentiment on product demand. Thus, we estimate an aggregated multinomial choice model after performing a machine learning–based sentiment analysis on the entire corpus of customer reviews included in our sample. We take advantage of a quasi-exogenous shock to establish a causal link between online reviews and product demand. Results: We find robust empirical evidence that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews increase the positive effect of ratings when the effect of ratings is decidedly positive while they also compensate for the tendency of consumers to discount extremely high star ratings. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews to better understand how consumers perceive the quality of its offerings.

The impact of hurricane strikes on cruise ship and airplane tourist arrivals in the Caribbean

2021 ◽  
pp. 135481662110374
Author(s):  
Pablo Carballo Chanfón ◽  
Preeya Mohan ◽  
Eric Strobl ◽  
Thomas Tveit
Keyword(s):  
Autoregressive Model ◽  
Cruise Ship ◽  
Vector Autoregressive Model ◽  
Dynamic Effects ◽  
Exogenous Shock ◽  
Vector Autoregressive ◽  
Percentage Points ◽  
The Caribbean ◽  
The Impact ◽  
Positive Effect

We investigate the impact of hurricanes on airplane and cruise ship arrivals in the Caribbean. To this end, we construct a monthly panel of airline and cruise ship arrivals and hurricane destruction and employ a panel vector autoregressive model with an exogenous shock (VARX) to quantify the dynamic effects of tourist arrivals after a hurricane for 18 Caribbean countries over the period 2000–2013. The results suggest an immediate decline in the month of a strike and up to one month after on cruise ship (2.33 and 1.21 percentage points) and airplane (0.57 and 0.27 percentage points) arrivals. Moreover, a strong recovery in airplane arrivals in months 3–6 following a hurricane was sufficient to induce a net positive effect of around 2 percentage points of total tourist arrivals into the region.

Cross-Category Retailing Management: Substitution and Complementarity

2021 ◽  
Author(s):  
Chenxu Ke ◽  
Ruxian Wang
Keyword(s):  
Choice Model ◽  
Problem Definition ◽  
Substitution Effects ◽  
Sequential Choice ◽  
Secondary Products ◽  
Estimation Algorithms ◽  
Managerial Implications ◽  
Complementarity Effects ◽  
Sequential Choice Model ◽  
The Impact

Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.

scholarly journals We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

2020 ◽  
Author(s):  
Guiyun Feng ◽  
Guangwen Kong ◽  
Zizhuo Wang
Keyword(s):  
Waiting Time ◽  
Heuristic Method ◽  
Problem Definition ◽  
Lower Efficiency ◽  
Average Waiting Time ◽  
On Demand ◽  
Trade Offs ◽  
Matching Mechanism ◽  
Service Paradigm ◽  
The Impact

Problem definition: Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smartphones to submit trip requests and match them to drivers based on their locations and drivers’ availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system—in particular, whether it will help reduce passengers’ average waiting time compared with traditional street-hailing systems. Academic/practical relevance: The on-demand ride-hailing problem has gained much academic interest recently. The results we find in the ride-hailing system have a significant deviation from classic queueing theory where en route time does not play a role. Methodology: In this paper, we shed light on this question by building a stylized model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. Results: We discover the inefficiency in the on-demand ride-hailing system when the en route time is long, which may result in nonmonotonicity of passengers’ average waiting time as the passenger arrival rate increases. After identifying key trade-offs between different mechanisms, we find that the on-demand matching mechanism could result in lower efficiency than the traditional street-hailing mechanism when the system utilization level is medium and the road length is long. Managerial implications: To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand ride-hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also examine the impact of passenger abandonments, idle time strategies of taxis, and traffic congestion on the performance of the ride-hailing systems. The results of this research would be instrumental for understanding the trade-offs of the new service paradigm and thus enable policy makers to make more informed decisions when enacting regulations for this emerging service paradigm.

scholarly journals The Impact of Social Media on Restaurant Corporations’ Financial Performance

2020 ◽  
Vol 12 (4) ◽  
pp. 1646 ◽  
Author(s):  
Sergio M. Fernández-Miguélez ◽  
Miguel Díaz-Puche ◽  
Juan A. Campos-Soria ◽  
Federico Galán-Valdivieso
Keyword(s):  
Social Media ◽  
Financial Performance ◽  
Total Population ◽  
Essential Element ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Nonparametric Techniques ◽  
The Impact ◽  
Positive Effect ◽  
Corporate Level

Social media, in the form of online reviews (ORs), has become an essential element for consumers in the restaurant industry, providing reliable and unbiased information based on the dining experiences of other consumers. Social media is not only a crucial phenomenon for the strategy of restaurants, but also for their corporations. However, previous literature has focused on the analysis at the establishment level, rather than at the corporate level, especially when referring to financial performance. The present study tries to verify if social media also affects corporate financial performance. For this, the impact of ORs on advanced measures of financial performance was examined at the corporate level on a sample of 800 restaurants selected from the total population of active restaurants in Europe in 2018. The investigation applied both regression analysis and nonparametric techniques. They demonstrate a positive effect of ORs on financial performance, and a heterogeneous relationship between both variables across the European countries. Restaurants are becoming aware of the implications of this phenomenon since it could provide strategies for sustainable economic development.

scholarly journals Emergency-Dependent Supply Decisions with Risk Perception and Price Control

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Shaofu Du ◽  
Huifang Jiao ◽  
Rongji Huang ◽  
Jiaang Zhu
Keyword(s):  
Risk Perception ◽  
Loss Aversion ◽  
Price Control ◽  
Price Cap ◽  
On Demand ◽  
Negative Effect ◽  
Increasing Demand ◽  
The Impact ◽  
Positive Effect ◽  
Share Information

This paper studies the impact of emergencies on the supplier’s decision-making behaviors including production and information sharing in consideration of consumer risk perception, consumer loss aversion phenomenon, and government price control. The intensity of emergencies is sequential and emergencies can be divided into two types (positive or negative) according to their effect on demand. When emergencies have negative effect on demand, the supplier’s sales will reduce and he would share information to the market. When emergencies have positive effect on demand, we find that when the price is under price cap the supplier will not share information to the market; when the price reaches price cap, the supplier will share a certain amount of information to the market. We were surprised to find that increasing demand is not always good for the supplier when there exist government price control and lost sales penalty, and information helps the supplier to effectively manipulate demand.

Investigate the Influence of Online Ratings and Reviews in Purchase Behavior Using Customer Choice Sets

2021 ◽  
Author(s):  
Kangcheng Lin ◽  
Harrison M. Kim
Keyword(s):  
Large Scale ◽  
Predictive Ability ◽  
Online Reviews ◽  
Purchase Behavior ◽  
Customer Choice ◽  
Product Demand ◽  
Average Rating ◽  
Online Ratings ◽  
The Impact ◽  
Choice Set

Abstract The exponentially growing online reviews have become a great wealth of information into which many researchers have started tapping. Using online reviews as a source of customer feedback, product designers are able to better understand customers’ preferences and improve product design accordingly. However, while predicting future product demand as a function of product attributes and customer heterogeneity has proved to be effective, not many literatures have studied the impact of non-product-related features, such as number of reviews and average ratings, on product demand using a large-scale dataset. As such, this paper proposes a data-driven methodology to investigate the influence of online ratings and reviews in purchase behavior by using discrete choice analysis. In the absence of information about the true customer choice set, we generate an estimated customer choice set based on a probability sampling using customer clustering and product clustering. In order to examine the effect of number of reviews and average rating, we have computed, for all the laptops in the choice set of each customer, the number of reviews and thus average rating at the date of this particular customer’s review. Using laptops for our case study, our experiment has shown that the number of reviews and average ratings are statistically significant, and the inclusion of these features will greatly improve the predictive ability of the model.

Logistics Performance, Ratings, and Its Impact on Customer Purchasing Behavior and Sales in E-Commerce Platforms

2022 ◽  
Author(s):  
Vinayak Deshpande ◽  
Pradeep K. Pendem
Keyword(s):  
Service Quality ◽  
Choice Model ◽  
Problem Definition ◽  
Third Party ◽  
Delivery Time ◽  
Delivery Performance ◽  
Logistics Service ◽  
Time Performance ◽  
Delivery Times ◽  
The Impact

Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales because of improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study’s insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales.

Analyzing the Impact of e-WOM Text on Overall Hotel Performances

2020 ◽  
pp. 240-264 ◽  
Author(s):  
Aakash Aakash ◽  
Anu G. Aggarwal ◽  
Sanchita Aggarwal
Keyword(s):  
Online Reviews ◽  
Negative Influence ◽  
Travel Agency ◽  
Linguistic Features ◽  
Customer Reviews ◽  
Digital Era ◽  
Guest Satisfaction ◽  
Hotel Sector ◽  
The Impact ◽  
Data Analysis Result

A flourishing of the importance of customer reviews has been observed in this digital era. This is especially true in hotel sector, which allows guests to express their satisfaction towards the service in the form of open-structured online reviews and overall ratings over travel agency websites. Using reviews data of 2001 hotels from Tripadvisor.com, the chapter analyzes the overall hotel performances through linguistic features of e-WOM such as its length, readability, sentiment, and volume. The chapter develops a regression model for evaluating guest satisfaction by using overall ratings as its measure, validated through hotel review data. Data analysis result shows that review volume, sentiment index, and readability have significant positive affect over guest satisfaction whereas length shows the negative influence. This chapter discusses beneficial implications for researchers and practitioners working in this field.

scholarly journals Impact of shopping emotion towards impulse buying in e-commerce platform

2020 ◽  
Vol 13 (1) ◽  
pp. 47
Author(s):  
Fajar Destari ◽  
Ketut Indraningrat ◽  
Maulita Nanda Nilam Putri
Keyword(s):  
Structural Equation ◽  
Total Population ◽  
Online Reviews ◽  
Equation Model ◽  
Impulse Buying ◽  
Significant Positive Effect ◽  
Website Quality ◽  
The Impact ◽  
Buying In ◽  
Positive Effect

<p>This empirical research aims to examine the influence of discount programs, website quality, and online reviews directly and indirectly on the impact of shopping emotion towards impulse buying on the e-commerce website. Data were purposively obtained from a total population of 130 respondents and analyzed using a Structural Equation Model (SEM). The results showed a significant positive effect on discount Programs and Website Quality on impulse buying with shopping emotions as a mediating variable. Besides, the result also showed an insignificant impact on online reviews.</p>

Analyst Coverage and Expected Crash Risk: Evidence from Exogenous Changes in Analyst Coverage

2018 ◽  
Vol 94 (4) ◽  
pp. 345-364 ◽  
Author(s):  
Jeong-Bon Kim ◽  
Louise Yi Lu ◽  
Yangxin Yu
Keyword(s):  
Implied Volatility ◽  
Causal Effect ◽  
Crash Risk ◽  
Exogenous Shock ◽  
Analyst Coverage ◽  
Information Intermediaries ◽  
Ex Ante ◽  
General Experience ◽  
The Impact ◽  
Positive Effect

ABSTRACT Using brokerage mergers and closures as two sources of exogenous shock to analyst coverage, this study explores the causal effect of analyst coverage on ex ante expected crash risk as captured by the options implied volatility smirk. We find a significant increase in a firm's ex ante expected crash risk subsequent to an exogenous drop in analyst coverage; this positive effect is stronger for firms initially receiving less coverage. Further, we find analysts' ability matters to investors' assessment of future crash risk. Specifically, we find the impact is more pronounced for the coverage terminations of analysts with more firm-specific or general experience, with greater access to resources, or whose prior forecasts are more accurate than those of their peers. Overall, our results suggest that investors in the options market do recognize analysts as important information intermediaries and monitors and, thus, that analyst coverage influences the underlying stock's expected crash risk. JEL Classifications: G12; M41.

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