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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.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

Advertisers frequently use social media for interactive and customer-oriented relationship marketing (RM) purposes. Moreover, sports clubs and players have been using their social media accounts to post content of their sponsors and other advertising companies. Such posts create visibility and have value for these advertising companies, something which has not been empirically quantified in the existing literature. Hence, this paper's purpose is to identify the factors or attributes that influence the value of such advertisement posts.

A discrete choice approach is used to empirically estimate the utility that sponsorship managers derive from a post advertising their company or product on football clubs' and players' social media.

The results indicate that more followers, better on-field performance and a lower price significantly increase the advertising company's utility. Moreover, the used social media channel has a significant influence too, since Facebook and Instagram are preferred over Twitter, due to the latter's limited degrees of freedom for advertisers.

Considering additional factors such as the image fit between sponsor and sponsee and presence on the Chinese social media market offers an interesting avenue for future research.

The empirical estimates allow commercial managers of clubs and players to derive companies' relative willingness to pay (WTP) for changes in characteristics of advertisements on their social media from the calculated utilities. This information can be used in the pricing decision when social media posts are sold or included in sponsorship packages.

This is the first study applying discrete choice modelling to link social media marketing (SMM) and sports marketing.

This research aims to analyse the housing demand in northern France with respect to socio-demographic variables and the distance between the residence and the workplace.

Econometrics with discrete choice models are used to study the three main dimensional choices of housing demand: tenure, type and location. A contribution is to use a heteroscedastic logit model where the variance of the error term is allowed to differ over alternatives and to capture in particular the heterogeneity of tastes. As a matter of fact, household characteristics are very likely to influence the magnitude of the scale parameter in the choice of housing alternatives and then influencing the results if it is not taken into account. Applications for housing demand are nearly non-existent. This paper fills this gap.

Econometric estimation confirms that residential choices are influenced by age, income and size of the household, as well as by the rent-to-income ratio. An increase in any of these variables decreases the probability of choices of all the alternatives other than the most often chosen alternative (which is for this application house ownership in the suburb). Moreover, the distance to work systematically influences the housing choice for single-parent families and two-earner households. Additionally, preferences are found to significantly differ between local housing markets, specifically between Lille (a large agglomeration and capital city of the North area) and Dunkerque (an industrialised area). The geographical areas are defined based on INSEE employment zones (“zones d’emplois”).

This research has been performed for the north of France and may not hold for other areas even though the methodology can be replicated and the mechanisms at play are quite similar elsewhere.

An important conclusion for sustainable development is the importance to improve city centre amenities relative to those of the suburb or to increase the services associated with high-density dwelling because clearly the most desired alternative remains a house in the suburb. The housing market in the Dunkerque area has some special features characterised by a strong industrial landscape (with port and heavy-duty industrial activities). In this context, amenities provided by the city centre offset the strong attraction of a house in the suburb.

This research shows that households with similar characteristics tend to prefer the same type of real estate property. Therefore, to avoid social segregation, it is important that housing supply respond to different household preferences and needs in the different segments of the housing market. Moreover, the housing supply should take into account the specificities of the geographical areas (both in terms of population who may have a different profile) and in terms of amenities.

This research is one of the very few conducted ones on discrete housing choices in France (with the notable exception of De Palma et al., 2007 for the choice of location). Three simultaneous choices are considered: tenure (including social housing which is almost always ignored), type of building and location. The authors have shown that it is important to take into account the heterogeneity of the preferences in the econometric model with a heteroscedastic logit model.

This article presents the results of a study using discrete choice analysis (DCA) in the dine‐in pizza industry. DCA offers an effective approach for incorporating customer preferences into operating decisions in service businesses. Our results show how customers tradeoff among several determinant attributes (e.g. price, waiting time, quality) when choosing a dine‐in pizza restaurant. The article also offers evidence that managers’ perceptions of customer choice patterns are not the same as customers’ actual choice patterns for the businesses we examined. Finally, we show how our results can be easily incorporated into a decision support system for structuring service operations according to customer preferences.

Discrete choice modeling has been discussed by both academics and practitioners as a means of analytical support for B2C relationship marketing. This paper aims to discuss applying this analytical framework in B2B marketing, with an example of cross‐selling high‐tech services to a large business customer. This example is also used to show how an algorithm of genetic binary choice (GBC) modeling, developed by the author, performs in comparison with major techniques used nowadays, and to analyze the financial impact of these different approaches on profitability of B2B relationship marketing operations.

Predictive models based on the regression analysis, the classification tree and the GBC algorithm are built and analyzed in the context of their performance in optimizing cross‐selling campaigns. An example of business case analysis is used to estimate the financial implications of the different approaches.

B2B relationship marketing, although differing from B2C in many aspects, can also benefit from analytical support with discrete choice modeling. The financial impact of such support is significant, and can be further increased by improving the predictive accuracy of the models. In this context the GBC modeling algorithm proves to be an interesting alternative to the algorithms used nowadays.

The generalizability of the findings, concerning performance characteristics of the algorithms, is limited: which method is best depends, for example, on data distributions and the particular relationships being modeled.

The paper shows how B2B marketing managers can increase the profitability of relationship marketing using discrete choice modeling, and how implementing new algorithms like the GBC model presented here can allow for further improvement.

The paper bridges the gap between research on binary choice modeling and the practice of B2B relationship marketing. It presents a new possibility of analytical support for B2B marketing operations together with financial implications. It also includes a demonstration of an algorithm newly developed by the author.

In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each client's assets under management (i.e. a volume pricing plan).

With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management.

Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information for analysis. Substantively, the results show that clients perceive both types of pricing plans as equally attractive; however, the service provider's revenues would increase by up to 12 per cent if it uses the volume pricing plan.

This research extends the stream of literature on the measurement of pricing plan preferences and offers guidance for service industries, such as telecommunications, cloud computing services, insurances, or transportation. It extends the use of discrete choice experiments to study client preferences for different pricing plans and also integrates a decision aid, i.e. a price calculator, in the experiment to assist clients in comparing alternatives more effectively.

This paper aims to discuss the link between socio-economic characteristics of house buyers and their housing location choices. The major objective of the study is an examination of the role of household socio-economic characteristics. The research addresses the importance of previous residence location and latent housing motives for intra-urban housing mobility.

The research examines housing preferences structure and analyzes housing location choices in the city of Krakow (Poland) using discrete choice model (conditional logit model). The research is based on stated preference data from Krakow.

The results of this study suggest that demand for housing alternatives is negatively linked to the distance from current residence. Other factors stay equal, the further the distance, the less likely a household is willing to choose a location within the metropolitan area. The study indicates that housing motives can help explain housing location decisions.

The paper provides an empirical assessment of housing decisions in Krakow, one of the major metropolitan areas in Poland.

The paper contributes to a better understanding of the nature of housing decision and housing preferences in emerging markets in Central and Eastern Europe. As a result, presented research helps to fill the gap in housing market and urban economics literature.

The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.

The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.

When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).

This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

Operatic events are an important sector of the performing arts industry and are currently facing the challenges of decreasing demand and price-based competition from other sectors of the performing arts industry. It is posited that adding value and ensuring satisfaction may enhance consumer loyalty, and therefore, the likelihood of sales and continued subscriptions may be increased. The purpose of this paper is to examine bundling as a marketing management technique for opera companies and hypothesises that offering attractive “package deals” that bundle various benefits with the seat ticket may increase participation and loyalty.

A discrete choice experiment with opera patrons is used to evaluate the application of bundling to improve increased demand and loyalty.

It is concluded that offering bundles creates a greater likelihood of customer satisfaction and favourable behavioural intentions, which may lead to increased loyalty. Participants expressed strong support for value-added elements. Such package deals included a variety of elements: opportunity for a visit backstage to meet the cast, informative introductions to operas and facilitated parking options. Consumers’ level of overall willingness to purchase a bundle was altered based on the attributes that made up that bundle. The findings of this research confirm that the theory of bundling can be applied to arts marketing and provides support for the development of bundling strategies to enhance opera attendance.

It is recognised that the research needs to be tested in different countries in order to know the extent to which the findings of this study can be generalised. Additionally, future research could use other statistical methods such as regression and structural equation modelling to holistically model behaviour. Finally, as well as testing customer-stated intentions, the model also needs to be tested with actual patronage behaviour following the development and application of bundling strategies. Future research could also consider how bundling and other aspects relating to opera attendance could be used as part of the branding strategy associated with opera attendance; in particular how to develop, increase and maintain loyalty and therefore brand resonance in opera attendees.

The findings have useful implications for event organisers and policymakers and suggest bundling strategies that could be utilised. It is has been found that loyalty can be enhanced by adding value and ensuring satisfaction, and therefore, increase the likelihood of sales and repeat purchase.

Opera represents a significant cultural heritage and is a valuable component of the performing arts, both historically and currently. Opera is a form of art whose survival is threatened by an increasingly diminished audience whose average age is steadily increasing. This decrease in audience attendance has led to radical changes in the management and marketing of opera houses, where theaters have moved increasingly towards a business-oriented model where improved branding and bundling strategies can be utilised.

This makes a theoretical contribution by advancing performing arts research and furthering the notion that bundling can increase the likelihood of opera attendance, satisfaction, ongoing loyalty, and also addresses a managerial need of an arts marketing organisation.