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This paper investigates the stock market reaction to three different events related to the UEFA Champions League – the announcements of draws, odds and match results. The aim of the paper is to test whether these events are informative for stock market operators, i.e. whether they produce abnormal returns.

Applying the event study methodology, the authors investigate the stock market reaction before (at two events: the draw date and on the release of betting odds) and after the matches of 11 listed soccer teams in the period 2003–2019. The authors also conduct OLS regression analyses in order to disentangle the impact of firm specific variables and match characteristics on cumulative abnormal returns.

This paper finds that match outcomes affect the stock market performance of listed teams, while the announcements of draws and odds do not. More specifically, the market does not consider match outcomes involving wins and ties as informative events, while it penalizes losing teams. Moreover, investor reactions to events related to the UCL competition depend more on match characteristics than on company specific variables.

The study enriches the ongoing debate about the impact of soccer team results on stock market performance in several ways: using the widest time span ever adopted in this area; focusing on UCL, which is the most important soccer competition played by private clubs; disentangling for the first time the effects of draws, odds release and sporting outcome on stock returns of listed soccer clubs.

The aim of this paper is to investigate the gap between cost-based and time-based revenue recognition schemes in the accounting of ship-owning corporations, and to propose cost-based revenue recognition (as in general accounting practice) in connection with the performance obligations.

For a comparative analysis of time-based (traditional approach) and cost-based schemes, a sample of dry bulk ships is selected and voyage estimations are performed by certified professional shipbrokers (Fellow of the Institute of Chartered Shipbrokers) (data collection and voyage estimation by practitioner). Performance obligations are also defined by certified shipbrokers (i.e. survey and expert opinion) and certified public accountant based on common shipping business practice and accounting practice in general.

Empirical results indicate the significant gap between two alternative schemes. Cost-based revenue recognition accelerates the revenue recognition (benefit of shipowner), and it enables comparability among other industries since cost-based allocation is the common practice in accounting (matching principle, Generally Accepted Accounting Principles).

It is obviously impossible to observe all kinds of freight market transactions for all different kinds of vessel particulars. The sample size does not undervalue the current study since the central idea of this paper is not the verification of the cost-based recognition in all possible transactions.

The proposed approach debiases the existing recognition practice as well as improving the speed of revenue recognition. In the existing practice, time-based recognition is still based on voyage estimations (time estimation). Voyage estimations conventionally answer two questions: “What is the cost of the voyage?” and “What is the duration of the voyage?” Therefore, the proposed approach does not require any additional work done. Common practice also clarifies the cost-based schedule for revenue recognition.

This paper addresses the unconventional accounting practice and its incomparability problem for the first time. To the best of the authors’ knowledge, this paper is also the first study on accounting economics of the shipping business. This paper proposes a practical solution to the debate raised by Financial Accounting Standards Board 2014-09 regulation on accounting standards by utilizing a staging approach and cost-based revenue allocation.

The static world of flight scheduling where schedules rarely change once published is becoming more responsive with schedule change updates leading up to the departure date due to demand volatility and unpredictable demand patterns. Innovation in cash flow generation will take center stage to operate the business in these uncertain times. Forecasting demand for future flights is a challenge since historical demand patterns are not meaningful which requires a new adaptive robust revenue management approach that monitors key metrics, detects anomalies and quickly takes corrective action when performance targets cannot be achieved.

The novel COVID-19 pandemic decimated the travel industry in 2020 and continues to plague us with no end in sight. With the steep drop in revenues, airlines need to adapt to a new marketing planning process of scheduling, pricing and revenue management that is more nimble to adapt quickly to changing market conditions. This new approach will continue to be relevant in a post-COVID-19 world during and after economic recovery.

A methodology for airline revenue planning: scheduling, airline pricing and revenue management, has been proposed that will also work in a post-COVID-19 era.

The limitation of the proposed model is that it needs to be applied in practice to determine the true benefits of this novel approach to airline revenue planning.

Flight scheduling will rely more on clean sheet scheduling, schedule revisions and close in refleeting to better match demand to supply. The office of the chief financial officer will have a permanent task force to monitor cash flow and come up with innovative solutions to generate cash flow for liquidity. Adaptive robust revenue management workflows will be integrated into traditional revenue management workflows in the future for competitive advantage.

In a post-COVID-19 world it is anticipated that airline business processes will transform to be nimbler and more proactive in making timely decisions at a greater velocity.

The approach to airline revenue planning for scheduling, pricing and revenue management is a new business process that does not exist today at scale in the airline industry.

Far from all, football clubs can provide the same level of exposure effects as global football brands, even on local level, and many of these clubs also operate in a context of commercial immaturity. The purpose of this paper is to show what value a football club can provide for sponsors in a context of commercial immaturity with limited expected exposure effects.

The study is based on a case study approach, taking its point of departure in two sponsor brand management paradigms, the projective and relational paradigm. The case of Malmö FF in the Swedish top tier league and the club’s official partners has been chosen to exemplify the commercially immature context.

The study has shown that the most important value the club can provide for sponsors is to act as a mediator in sponsor–stakeholder relations. Exposure effects are subordinate to the relational effects sponsors achieve through their sponsorship.

The study indicates that the relational construct in the sponsorship literature should to a greater extent include sponsor–stakeholder relations, beyond the sponsor–club dyad, in a context of commercial immaturity.

The results indicate that club management should engage in stakeholder management with a strong focus on stakeholders of sponsors to provide value for these sponsors.

This study explores a new dimension to the relational construct of sponsorship, using the relational paradigm of brand management in a context of commercial immaturity. The mediating effect of the club is a contribution to the discourse on the relational construct.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

The purpose of this paper is to examine the hotel growth model including hotel brand, culture and life cycle phases of the Myrtle Beach, South Carolina, the fastest growing tourism destination in the United States.

Culture reflecting consuming behaviour of low-context innovators and high-context imitators is measured by the price elasticity of demand (PED). Hotel brand reflecting guests’ hotel class is measured by the income elasticity of demand. Autoregressive distributed lag has been conducted on the Smith Travel Research data in 33 years (1989–2022) to determine the relationship among hotel brand, culture and life cycles.

Skilled labour is the key to make hotels grow. Therefore, increase room rates when hotels possess skilled professionals and decrease room rates when hotels have no skilled professionals. During the rejuvenation in Myrtle Beach (1999–2003), hoteliers increased room rates for innovators due to skilled professionals to increase revenue. Otherwise, a decrease in room rates due to lack of skilled professionals would lead to increase revenue.

(1) Although Myrtle Beach is one of the fastest growing tourism destinations in the US, it has a relatively small geographic area relative to the country. (2) Data cover over one tourist life cycle, so the time span is relatively short. Hoteliers can forecast the number of guests in different culture by changing room rates.

To optimize revenue, hoteliers can select skilled labour in professional design hotel brands which could make an increase in demand for leisure transient guests no matter what room rates increase after COVID-19 pandemic.

The study has considered the applied ethical processes regarding revenue management that would maximize both revenue and customer satisfaction when it set up an increase in room rates to compensate for professional hotel room design or it decreases room rates for low-income imitators in exploration and development.

This research highlights that (1) skilled design in the luxury hotel brand is the key for the hotel growth and (2) there is a steady state of the growth model in the destination life cycle.

Focusing on two beer festivals held in Nottingham, England, this study aims to evaluate their indirect impact on the performance of city hotels. This study builds on theoretical insights from the revenue management literature to shed empirical light on the potentially beneficial effects of events on the hotels’ performance. This study investigates the impact of the differential support offered by the destination management organisation (DMO) over two years.

Using online prices posted in advance of the events on an online travel agent, the authors assess hotel performance for each day of the events relative to the same day of the week in a week with no event. A similar comparison is made to assess the impact across two different years. In both cases, an ordinary least squares methodology was used.

Both events appear not to have had a strong impact on hotel prices and occupancy in 2016, i.e. when the DMO’s promotional effort was more proactive. Instead, in 2017, one event registered higher hotel prices and occupancy both relative to the year before and to the “business as usual” week.

The study identifies the existence of an indirect positive economic impact of the events on the hospitality sector.

The investigation adopts a more naturalistic experimental design to collect the data, which allows the authors to control for both the impact on prices and occupancy at the level of the single hotel. The evidence is therefore micro-founded. Moreover, results shed light on the role played by the DMO.

Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.

This paper evaluates the effects of tax incentives on business innovation in Brazil that were established by Law 11,196/05 (the “Fiscal Incentives Law”) to test whether they have had a positive impact on beneficiary firms' innovation input and output and on their performance.

The policy impacts are estimated using microdata on 13,706 firms available in the 2008 and 2011 editions of the Brazilian Innovation Survey (PINTEC) and by applying propensity score matching with difference-in-differences.

The results suggest a positive and statistically significant impact of the policy on research and development (R&D) expenditures (average of approximately US$ 264,000 in 2011), the number of research staff (average of five researchers) and total employment (approximately 5% of the beneficiary firms' mean size). However, no impact was found on the overall spending on innovative activities, the percentage of sales and exports from new products, net revenue or net revenue per employee.

The findings provide empirical support in favor of tax incentives as a policy tool to boost business innovation in the country. However, the absence of significant effects on innovative activities expenditures and on most indicators of innovation output and firms' performance reveals shortcomings of the policy that need to be addressed.

The study complements and advances the findings of previous studies by assessing policy impact on total innovative activities expenditures and on innovation output and firm performance.

The main streams of the supply chain are defined as material, information and financial flow. There have been many studies and practical cases regarding the flow of material and information including information sharing. However, financial flow related studies have not been widely examined relatively, compared with their importance.

The information sharing is recognized as the method that can reduce the Bullwhip effect in supply chain management. The author intends to analyze the impact of financial information sharing on the results of the supply chain.

In the point of supply chain risk management view, the author examined the impact of financial flow among the various factors that can impede the stability of the supply chain.

In this study, the author embodied the simulation regarding the impact of financial information flow on supply chain performance and stability based on the system dynamics methodology and analyzed the performance.

Assuming the supply chain, composed of supplying company, manufacturing company and sales company , the author embodied the simulation model and assumed that working capital and cash information sharing were achieved. The author embodied the model to affect the settlement conditions according to the results of financial information sharing.