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Using panel data on gasoline and grocery transactions in Korea, the purpose of this paper is to empirically explore the effect of a retail chain store’s establishment of on-site fuel sales. The empirical analyses present strong empirical evidence that the sale of fuel had statistically and economically significant effect on retail store traffic and revenue in the short run. However, the effect did not remain significant in the longer run. To explain the dramatic decrease in the effect of the fuel sale, the authors consider the enhanced competition in the local gasoline retail industry and examine cross-sectional price variations at the station level. The results suggest that the increased competition led to the reduction in the price dispersion across stations and thereby to an increase in consumer welfare.

Using a linear specification that has traditionally been used to model retail chain data, the authors developed a series of difference-in-differences models. This technique is ideal for estimating the effect of a treatment in the presence of possible selection bias and has been widely employed in many social-science studies on policy intervention.

In a certain environment, introducing fuel sales did not increase retail chain store traffic or revenue in the long run, despite having statistically and economically significant effects in the short run. The results document empirical evidence of myopic management in a common marketing practice, which often leads to a negative impact on the firm value in the long run.

The span of data and sample size were limited to meet the company’s data protection policy.

Considering that many of developed countries are characterized by a gasoline retail environment similar to that which is investigated in this paper, the authors believe that the implications of the results are particularly valid for practitioners and policy makers.

The findings document empirical evidence of myopic management in a common marketing practice, which often leads to a negative impact on the firm value in the long run. Marketing researchers should make efforts in establishing metrics to help identify myopic management decision.

This paper addresses an interesting and practical issue related to the effects of the introduction of gasoline sales by a supercenter store on its store traffic.

Motivated by the news media and a lack of comprehensive research on the USA, the purpose of this paper is to examine the relationship between changes in road fatalities and gasoline prices, per capita disposable personal income, alcohol consumption per adult, blood alcohol concentration (BAC) limits and gender.

This study employs both static and dynamic panel data models, making use of annual data over the 2000–2013 period collected from the 50 states of the USA and the consistent system GMM estimators of the parameters, to estimate the impact of these variables on fatalities per 100,000 persons and per 100,000 vehicles.

The results highlight the importance of gasoline prices in determining the level of road fatalities, underscoring that a 10 percent decrease in gasoline prices leads to a 248 increase in the total number of road fatalities, but with many more injuries. Increases in the female-to-total driver ratio have a greater significant positive impact on road fatalities where a 10 percent increase in this ratio increases road fatalities by 1,008 deaths. Increases in registered vehicles per capita also increase the number of fatalities. Other variables such as alcohol consumption per adult and BAC limits are not as important. Policy implications are also provided.

The results of this study highlight the importance of gasoline prices in determining the number of road fatalities. This factor can be an effective policy measure by which policymakers can offset increases in fatalities due to further drastic declines in future gasoline prices. But the effects of the gasoline prices in determining the number of road fatalities are not as strong as the media would lead us to believe. The media ignores the impact of other factors on fatalities, which results in an overestimation of the impact of gasoline prices.

This study uses the panel data of 50 US states and the dynamic panel data model. In addition to gasoline price effects on the road fatalities, this study also considers other factors such as gender, gasoline taxes, per capita disposable personal income, per capita alcohol consumption, BAC limits and number of registered vehicles.

The Bureau of Economics in the Federal Trade Commission has a three-part role in the Agency and the strength of its functions changed over time depending on the preferences and ideology of the FTC’s leaders, developments in the field of economics, and the tenor of the times. The over-riding current role is to provide well considered, unbiased economic advice regarding antitrust and consumer protection law enforcement cases to the legal staff and the Commission. The second role, which long ago was primary, is to provide reports on investigations of various industries to the public and public officials. This role was more recently called research or “policy R&D”. A third role is to advocate for competition and markets both domestically and internationally. As a practical matter, the provision of economic advice to the FTC and to the legal staff has required that the economists wear “two hats,” helping the legal staff investigate cases and provide evidence to support law enforcement cases while also providing advice to the legal bureaus and to the Commission on which cases to pursue (thus providing “a second set of eyes” to evaluate cases). There is sometimes a tension in those functions because building a case is not the same as evaluating a case. Economists and the Bureau of Economics have provided such services to the FTC for over 100 years proving that a sub-organization can survive while playing roles that sometimes conflict. Such a life is not, however, always easy or fun.

The purpose of this paper is to understand consumer preferences concerning electric vehicles (EV) in Portugal, based on comparisons with other vehicles with different powertrains.

The analysis incorporated two survey-based approaches: choice-based conjoint analysis (CBC) and multicriteria decision analysis (MCDA) based. The survey interviewed 252 respondents. The criteria characterizing each vehicle are purchase price, range, fuel consumption and CO₂ emissions. Another criterion was added to verify the potential of EV privileges to influence consumer preferences. A sensitivity analysis on the influence of purchase price and fuel price in the global utility of the vehicles was performed.

The results showed that monetary criteria are those influencing vehicle purchase decisions the most, whereas the existence of privileges for EV owners has little relevance. EV are chosen by the consumer only if their price decreases or if gasoline and diesel prices increase sharply. The position of PHEV in the rankings makes the promotion of this type of vehicle an interesting path to exploit as potential intermediates to the diffusion of EV.

The results underline the need of improving technical barriers of EV that are responsible for consumers’ relevant concerns and that a price subsidy could eventually be effective to increase EV sales at its current market price.

This study compares a wide range of vehicles (conventional, hybrid and electric), addresses the Portuguese market and proposes an MCDA-based approach to obtain preference information, which is compared with a CBC approach.

In class action antitrust litigation, the standards for acceptable economic analysis at class certification have continued to evolve. The most recent event in this evolution is the United States Supreme Court’s decision in Comcast Corp. v. Behrend, 133 S. Ct. 1435 (2013). The evolution of pre-Comcast law on this topic is presented, the Comcast decision is thoroughly assessed, as are the standards for developing reliable economic analysis. This article explains how economic evidence of both antitrust liability and damages ought to be developed in light of the teachings of Comcast, and how liability evidence can be used by economists to support a finding of common impact for certification purposes. In addition, the article addresses how statistical techniques such as averaging, price-dispersion analysis, and multiple regressions have and should be employed to establish common proof of damages.

Social media facilitates consumer exchanges on product opinions and provides comprehensive knowledge of online products. The interaction between consumers and e-retailers evolves into a collective set of dynamics within a complex system. Agent-based modeling is well suited to stimulate such complex systems. The purpose of this paper is to integrate agent-based model and technique for order performance by similarity to ideal solution (TOPSIS) to simulate decision behaviors of e-retailers in competitive online markets.

An agent-based network model using the TOPSIS driven by actual price data is developed. The authors ran an experimental model to simulate interactions between online consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data analysis and evaluate the sensibility of parameters.

Simulation results showed that different profits could be obtained for various brands under different social network structures. E-retailers could achieve more profits through cross-selling than single-selling; however, the highest profits can be achieved when some adopt cross-selling, whereas others use single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit.

This study provides new insights into the evolutionary dynamics of online markets. This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS and how to use an integrated simulation model and interpret its results.

In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets.

Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score.

The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability.

The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices.

This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.

As businesses become more sophisticated and welcome new technologies, artificial intelligence (AI)-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy-makers to consider.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.