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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 case evolves around the Powerball lottery and the rule changes implemented in 2015, which, among other things, changed the chances of winning the jackpot from 1 in 175 million to 1 in 292 million. What is the impact of such rules on lottery revenues? The expected value rule is unable to explain why people play in the first place and fails to give the appropriate weight to the factors that explain the attractiveness of a lottery. This case is ideal to introduce the notion of decision weights as put forward by Kahneman and Tversky's prospect theory. By calculating decision weights, we obtain a reasonable prediction for the willingness to pay for the lottery as a function of different jackpot amounts. Using past data, we can correlate lottery revenues with predicted willingness to pay for a ticket. Quantitative-inclined audiences can then develop a simulation model of how likely it is that the jackpot grows, which, coupled with the prediction of revenues as a function of the jackpot, would give the evolution of the revenues under the new rule. The accompanying spreadsheet provides data for students to work out various scenarios to narrow objectives and maximize revenue from Powerball tickets.

This paper aims to explore how music festival organisers negotiate diversity and inclusion in marketing and promotion practices through symbolic and social boundaries.

Based on semi-structured interviews with 18 festival organisers in Rotterdam and participant observation with six festival photographers I show that symbolic and social boundaries are employed in three areas: (1) boundaries in festival format (i.e. [partially] free or ticketed), (2) boundaries in distribution partners and technologies and (3) boundaries in promotional content.

Symbolic and social boundaries are intentionally used by festival organisers to build and delineate festival audiences. Implications are drawn on current understandings of the accessibility of music festival spaces, arguing that festival research should move beyond within-space dynamics to grasp the negotiation of diversity and inclusion at festivals more fully.

While music festivals are often marketed as celebratory spaces that are “welcoming to everyone”, few studies have investigated diversity and inclusion nor marketing and promotion practices at music festivals. This study shows how festival audiences are shaped through marketing and promotion practices.

This study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the most visible process to the customer, and therefore, its punctual and fluent order management is vital. It is observed that the high degree of manual work in the O2C process causes mistakes, delays and rework in the process. The purpose of this article is therefore to analyze the case company's current state of the O2C process as well as to identify the areas of development in this process by deploying the means of Lean Six Sigma tools such as value stream mapping (VSM).

The study was conducted as a mix of quantitative and qualitative analysis. Based on both the quantitative and qualitative data, a workshop on VSM was organized to analyze the current state of the O2C process of a case company, engaged in the energy and environment sector in Finland.

The results found that excessive manual work was highly connected to inadequate or incorrect data in pricing and invoicing activities, which resulted in canceled invoices. Canceled invoices are visible to the customer and have a negative impact on the customer experience. This study found that by improving the performance of the O2C process activities and improving communication among the internal and external stakeholders, the whole O2C process can perform more effectively and provide better customer value.

The O2C process is the most visible process to the customer and therefore its punctual and fluent order management is vital. To ensure that the O2C process is operating as desired, suitable process performance metrics need to be aligned and followed. The results gathered from the case company's data, questionnaire interviews, and the VSM workshop are all highlighted in this study. The main practical and managerial implications were to understand the real-time O2C process performance, which is necessary to ensure strong performance and enhance continuous improvement of the O2C process that leads to operational excellence and commercial competitiveness of the studied case company.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts.

Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results.

The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally.

Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience.

Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost.

Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams.

This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

Brief loving-kindness meditation (LKM) is introduced here as a valid social marketing intervention. LKM positively influences prosocial cognitions and affects. However, it remains unclear whether brief meditation interventions can influence prosocial behavior. This study aims to provide evidence of the effects of short LKM on prosocial behavior.

This study reports the results of three experiments examining the effects of brief LKM on donations to unknown others. The results are then integrated with the results of seven other studies testing the effects of brief LKM on prosocial behavior using a meta-analysis (n = 683).

LKM increased love more than the control group (focused breathing) in the three experiments; however, its effects on donations were mixed. The meta-analysis shows that LKM has a small-to-medium significant effect compared to active control groups (d = 0.303); moreover, age and type of prosocial measure used moderate the effects.

Results suggest that LKM can nurture prosocial emotions such as love and lead young individuals to donate. However, these emotions may not be sufficient to lead adult meditators to share their resources with unknown others. This study presents the first meta-analysis of brief LKM and provides insights into the use of meditation in social marketing programs.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This study aims to provide an overview of the dimension of stored collections displayed in visible storage and to indicate the main factors which hinder their accessibility.

This study is based on quantitative analysis: a survey was conducted through the offices of International Council of Museums and direct invitations to 2,558 museums located worldwide.

The study estimated 32% on average the share of stored collections displayed in visible storage. The analysis provides a picture of how many stored items are made accessible in visible storage across the continents, according to the collection’s type and size and the museums’ legal status. In addition, several aspects of visible storage are investigated to highlight whether or not it truly enables museums to achieve accessibility of their stored collections and which factors might hinder the accessibility. Amid them, the foremost factors involve the inadequacy of resources, such as the lack of staff (71%) and poor budget (68%). Because of it, museums are prone to setting up offsite storage (37%), often 16 km far from the city centre, thereby questioning the concept of accessibility itself.

One major limitation of this study is that it does not consider people’s standpoints. Therefore, the author recommends that future studies focus on what people opine on visible storage, such as their appreciation of the display format, the behind-the-scenes, their need for interpretation and the degree of satisfaction with their information needs, as well as their perception of the size of stored collections.

These findings suggest that museums could take action in areas whereby the data demonstrated weaknesses in terms of accessibility. For instance, museums could set up a shuttle service or arrange public transportation service to allow people to visit offsite storage. Additionally, financial accessibility might be achieved by not charging some groups (elderly, students, etc.).

The topic of stored collections and their accessibility has crucial social implications because not displaying collections triggers inequality amid social groups of excluded people and a small elite.

This study focuses on visible storage as a possible solution to enhance the accessibility of collections and indicates to what extent visible storage provides this accessibility. On the contrary, previous research did not estimate how much visible storage impacts the accessibility of stored collections.

Human brands in the music industry use self-presentation tactics on social media to manage audience impressions. This practice has led to many posts asking followers to adopt behaviors favoring the human brand. However, its effectiveness in leveraging relevant performance metrics for musicians outside social media, such as popularity in specialized media, music sales and number of contracted concerts, needs further exploration. This study aims to reveal the effect of impression management tactics conveyed on social media on the market performance of musicians’ human brands.

Secondary data research classifies 5,940 social media posts from 11 music artists into self-presentation tactics (self-promotion, exemplification, supplication and ingratiation). It shows their predictions on three market performance metrics in an annual balanced panel study.

Impression management tactics via posts on social media are mostly self-promotion, improving the musicians’ market performance by increasing the number of contracted concerts. Conversely, ingratiation generated the most positive effect on the musician’s popularity but reduced music sales. Besides lowering the musicians’ popularity, exemplification also reduced the number of contracted concerts, while the supplication had no significant effect.

To the best of the authors’ knowledge, the research is the first to use social media postings of musicians’ official human brand profiles based on self-presentation typologies as a complete impression management tool. Furthermore, it is the first to test the effects of these posts on market performance metrics (i.e. outside of social media) in a longitudinal study.