Search results

Pricing studies have largely focused on sellers' pricing strategies and price determinants. To expand earlier work on sellers' pricing decisions, this study considers time as a major factor driving sellers' ticket prices in the secondary market. Specifically, because most secondary market transactions occur in the last moments before a game, this study considers how resellers adjust ticket prices within a few days prior to a game day including an actual game day.

To examine the impact of time on secondary market ticket prices for Major League Baseball (MLB), ticket prices were collected from StubHub (one of the largest secondary ticket markets) four times per game: from 3 days to 1 day prior to a game day and on the actual game day. Additionally, 10 control variables were obtained from previous research on price determinants (N = 19,155). A multiple regression model was created based on the extant literature regarding secondary market ticket prices.

Results indicate the number of days before a game negatively influenced ticket prices: resellers decreased ticket prices consistently during the last few days prior to a game's first inning. Specifically, secondary market ticket prices decreased relatively dramatically on an actual game day. Time had no significant effects on ticket prices 2 days prior to a game day. In addition to the role of time, league affiliation and the number of all-star players were identified as key price determinants in the secondary market. Moreover, changes in weather forecasts and the home team starting pitcher's ERA played significant roles in price changes.

Despite containing a relatively high number of data observations compared with prior pricing studies, this study's findings were limited to certain teams. Additionally, as only MLB secondary market ticket pricing was considered, different outcomes and implications may apply in other major sport ticket markets (e.g. NBA, NFL, NHL and MLS) featuring distinct league structures, policies and demand.

This study offers practical guidance for sellers' pricing decisions. Most secondary ticket market sellers lowered their ticket prices relatively dramatically on an actual game day. Reducing ticket prices prior to a game day can lead to greater chances to avoid unsold tickets that compromise revenue management. This study's results also afford professional sport organizations and secondary ticket market consumers a clearer understanding of the factors resellers consider when setting ticket prices.

Although previous studies have uncovered essential elements influencing ticket prices and consumer demand in the secondary ticket market, little work has examined how time affects sellers' pricing decisions within a few days prior to a game day. Little is known about the elements that significantly influence sellers’ decisions to adjust (i.e. increase or decrease) ticket prices in the secondary market as well. This topic deserves ongoing attention, as new outcomes can supplement previous studies' findings due to changing market environments.

Digitalization and marketing technologies have made it possible to overcome some barriers to pricing – a multidisciplinary field between marketing, finance and IT – and have set the stage for a paradigm shift in the pricing profession. Value creation, the pricing process, and price communication have been transformed by innovative business models and advanced algorithmic and human–machine solutions. This chapter synthesizes the literature to date and provides a comprehensive framework for an all-encompassing 360° pricing approach that broadens the understanding of pricing in the context of digital business across all steps of the price management process. Starting from product attributes and motivational beliefs in consumers' value assessment and adoption of (technological or digital) products or services, new business models and pricing models emerge in the digital economy, human–machine solutions for price implementation and repricing are increasingly applied, and price search and communication take place through a variety of digital communication channels. Each stage of this framework discusses concrete examples, highlighting the freemium strategy, the subscription model, price tracking and repricing tools, and digital price information channels such as e-commerce, marketplace, or price comparison platforms. The implications for price management in a digital, technology-driven landscape are discussed from the executive level to the analyst level.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

This study aims to examine how the partnership between StubHub and MLB affected consumers' perceptions of StubHub. The case of StubHub and MLB was selected based on their partnership history and the reputation of StubHub.

A Qualtrics survey panel was used to collect the survey data. Structural equation modeling was used to analyze the relationships between sponsor congruence, brand equity and purchase intention.

Sponsor congruence plays a significant role in consumers' perceived quality of StubHub. Additionally, brand equity significantly influenced purchase intention. More specifically, brand loyalty was the strongest indicator of intent to purchase tickets from StubHub. Brand loyalty and perceived quality indirectly affected the relationship between sponsor congruence and consumers' purchase intentions of StubHub.

Sponsor congruence between secondary ticket markets and sport leagues can provide a competitive advantage, helping create revenue generation and leverage for partnerships. Perceived quality can help facilitate this relationship and increase revenue generation.

Scholars note there are limited studies analyzing ticket price determinants. Using the common seat approach, the authors sought to advance this line of research by analyzing determinants of National Basketball Association (NBA) ticket prices in the secondary ticket market. The authors’ research seeks to ask two questions. The first is how ticket prices in the secondary market are associated with common determinants of consumer demand. The second question is what impact the COVID-19 pandemic has on ticket prices in the secondary market.

Ticket prices of NBA regular season games in the 2021–2022 season were collected a week before the game day from Ticketmaster.com. A regression model was estimated with a group of independent variables: income, population, consumer preference, quality of viewing, quality of contest and pandemic (the number of COVID-19 cases).

Results indicate income, population, consumer preferences (e.g. team quality and star players) and quality of viewing (e.g. arena age and weekend) impact prices. Further, the number of COVID-19 cases did reduce the ticket price.

The present study illuminates the theoretical significance of analyzing ticket prices as a proxy of demand in professional sport, while providing practical implications regarding the potential opportunity to increase revenue.

Previous literature shows that market sentiment and the steepness of index option's implied volatility slope have a negative relation. This paper investigates the relation between firm-specific sentiment and individual option's implied volatility slope both theoretically and empirically.

The authors develop a simple model with option traders' sentiment heterogeneity to show that sentiment and the steepness of individual option's implied volatility slope have a positive relation.

When firm-specific sentiment is higher (more bullish), individual option's implied volatility slope becomes steeper. The positive relation is stronger when option traders' beliefs on risk are more dispersed. Empirical results support the theoretical model predictions.

Although both firm-specific sentiment and individual options implied volatility slope predict future stock returns, there is no research exploring the relation between them. In particular, none of previous studies associates implied volatility slope's stock return predictability to investor behavior such as sentiment. The authors’ findings provide a behavior-based explanation on why steep implied volatility slope negatively predicts cross-sectional stock returns.

With refineries contributing 68% of CO₂ emissions from stationary combustion sources alone, smart technologies and the circular economy (CE) model for resource loop optimization can be a solution for carbon neutrality, especially within petroleum. Thus, this study aims to explore energy conservation by green technology improvement as a CE strategy for resource loop optimization and digital incorporation to maximize reprocessing lead ability rate and carbon-neutral benefits.

A game theory approach with Stackelberg equilibrium is considered under government cap-and-trade regulation to stimulate green technology improvement. The refinery acts as a Stackelberg leader and invests in green technology and the retailer as the Stackelberg follower, collects end-of-life lubricants against refund price and offers a two-part-tariff contract to the manufacturer having a significant role in smart technologies.

First, green technology improvement is directly influenced by the reprocessing capability and refund price and digital technologies are significant to consider. Second, a two-part-tariff contract coordinates the supply chain for limited reprocessing capability by the retailer. Lastly, the government can effectively manipulate the development of green technology by changing the permit price depending on the intentions.

The primary limitation is this study has focused on the petroleum sector and data was referenced from the oil refineries of a single country.

Overall, this study provides empirical guidance for policymakers on how to leverage energy-efficient smart technologies for lubricant reprocessing, enabling resource optimization as part of a CE strategy in the petroleum industry and advancing sustainable development goals.

The suggested model responds to the contemporary literature related to CO₂ emissions and CE initiatives across the petroleum sector with the extended role of smart technologies and government cap-and-trade regulations.

Status demotion in hierarchical loyalty programs (HLPs) has received considerable academic attention. However, existing research is relatively silent on whether HLP status demotion fosters service relationship fading by influencing demoted customers’ psychological disengagement and the likelihood of patronage reduction. Drawing on the relationship fading literature and the stimulus–organism–response framework, this study aims to examine these effects. It further investigates the moderating role of psychological ownership on the links of status demotion with psychological disengagement and the likelihood of patronage reduction.

Two studies (Studies 1 and 2) were conducted in the context of airline HLPs. Study 1 was a structured survey conducted among 213 demoted airline HLP customers in Australia, and Study 2 was an experiment conducted among 178 executive MBA students in Bangladesh. The PROCESS macro was used to test the moderated mediation model.

The results of both studies show that HLP status demotion significantly influences customers’ psychological disengagement and the likelihood of patronage reduction. The findings also reveal that psychological disengagement mediates the relationship between status demotion and the likelihood of patronage reduction. Further, customers with high (low) psychological ownership feel high (less) psychological disengagement and show high (less) likelihood of patronage reduction due to their HLP status demotion.

This study extends the existing literature on relationship marketing and HLPs by offering a better understanding of how and under what conditions status demotion elicits customers’ psychological disengagement and the likelihood of patronage reduction.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.