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This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

The past several years have seen dramatic increases in oil prices, which have adversely impacted airlines, with the average price of jet fuel increasing from $1.34 per gallon between 1995 and 2005 to $2.81 per gallon between 2006 and 2009. As a partial response to these increases in costs, many airlines have introduced fees for services that were previously provided to their customers free of charge. One such charge is a fee on checked baggage, which most airlines introduced in 2008. These charges have been successful in increasing airline revenues, so successful that many airlines have increased their fees multiple times over the past two years. Baggage fees have also enabled airlines to avoid dramatic increases in their airfares, which may result in significantly fewer customers, as these additional fees generate revenues, but since they are not collected when passengers book their tickets, the cost of air travel on these airlines appears lower than it actually is. The most notable exception to this pattern of charging baggage fees is Southwest Airlines, which has launched a “Bags Fly Free” advertising campaign in an attempt to differentiate their product from that of fee charging airlines. In this chapter, we use a spatial autoregressive model to analyze what impact the increase in fuel costs, and the introduction of baggage fees have had on ticket prices. Our results suggest that increases in jet fuel prices are passed along to travelers in the form of higher ticket prices but that baggage fees actually reduce ticket prices, as airlines may substitute baggage fee revenue for ticket revenue to become more competitive on their airfare. We also find that Southwest Airlines has increased their ticket prices on routes in which they compete with fee charging firms, leveraging their “Bags Fly Free” product differentiation to increase their revenues.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

Currently, there is a conflict in developing countries between the requirements for the self-development of forestry and the insufficient investment in the forestry sector, and the forest ticket system is an innovative forestry management method to solve this contradiction. In the research on the forest ticket system, the study of its price formation mechanism is relatively important. The key issues of the forest ticket system are how to form the forest ticket price and whether the forest ticket pricing methods are reasonable. Solving these problems is the purpose of this study.

This study will use three methods, namely the forest ecosystem service value evaluation index method, the ecosystem service value based on per unit area evaluation method and the contingent valuation method, to study the forest ticket price formation mechanism, filling the gap in the current research on forest ticket pricing methods. It will analyze how these three pricing methods specifically price the forest ticket and evaluate whether these pricing methods are reasonable. This study will then summarize and comprehensively study the forest ticket price formation mechanism and provide policy recommendations for decision-making departments.

The contingent valuation method and the forest ecosystem service value evaluation index method should be mainly used and given priority in the forest ticket pricing process. When the forest ticket is mainly issued for local residents' willingness to compensate for the forestry ecological value, the contingent valuation method should be mainly considered; when the forest ticket is mainly issued for compensating for the ecological value of local used forest land, the forest ecosystem service value evaluation index method should be mainly considered. The ecosystem service value based on per unit area evaluation method does not need to be the focus.

Compared with existing research studies, which focus more on the forest ticket system itself and the definition of forest ticket, this study mainly focuses on the forest ticket price formation mechanism, emphasizing how to form the forest ticket price and whether the forest ticket pricing methods are reasonable, which has a certain degree of innovation and research value and can partially fill the gap in related fields. At the same time, this study has certain help for the enrichment of the forest ticket system and the extension of related research studies.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

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.

The purpose of this exploratory study was to examine the workplace experiences of current and former underrepresented ticket sales employees.

The study utilized survey methodology to answer the research questions. Purposive sampling was used to identify potential respondents: individuals who were either currently employed as sport ticket sales employees or had been previously. The researchers examined online media guides from North American major league sports teams and identified ticket sales representatives who were compiled into a database and then cross-referenced through social media platform LinkedIn. Individuals were located via LinkedIn profiles and sent an anonymous, electronic survey which included a combination of Likert scale and open-ended questions regarding respondent experiences working in ticket sales departments.

Survey responses from 511 ticket salespeople revealed negative experiences related to management and career advancement opportunities, as well as significant differences in negative experiences for underrepresented populations regarding mentorship and culture.

Sport sales managers desire to expand employee diversity (Wells et al., 2019) and improve overall retention rates, but if a clear understanding of organizational vision toward diversification is not established within sports teams, managers are less likely to embrace diversity initiatives. Thus, it is imperative to understand the lived experiences of underrepresented ticket salespeople in order to improve workplace culture and effectiveness.

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.