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Horizontal web portals fill an important place in the operation of the internet and this paper seeks to look at how these portals may become profitable. A portal is simply a gateway, and a web portal can thus be seen as a gateway to content and services on the internet, or on a corporate intranet. This paper aims to compare several different views of what constitutes a portal, and to offer a specific definition. The main focus of the paper is on general horizontal (or public) portals and the relationship between their business‐revenue models and the content they provide. This paper aims to briefly review the relevant literature before describing a revenue model consisting of players, strategies and content. This is then examined and synthesised to match various revenue models and content.

After proposing the model, the paper tests how the matches proposed between revenue and content in the model compare with two currently operating popular horizontal portals.

An examination of two popular portals (one world‐wide and one based in Australia) has provided typical examples of how the advertising/revenue can be employed by horizontal portals and shown that they appear to match fairly closely with the proposed revenue/content model. After having examined the content of these two well‐known portals, it appears at this early stage that the revenue‐content model may show some promise.

While the paper cannot claim complete generalisability of a model based on a comparison of only two horizontal portals, the results are promising and should be useful for horizontal portal managers looking for how to balance the revenue of their portal with the content that they generate and the services they offer.

When fully tested, the model will provide horizontal portal operators assistance with the process of determining suitable content for their portals to match their chosen revenue strategies.

It is believed that this model is one of the few revenue/content matching models developed for horizontal portals.

This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry.

This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis.

This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU.

In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

In 2014 WMS Gaming, a manufacturer and seller of slot machines to casinos, was considering a redesign of its existing revenue model. As technology evolved and customer demand for gaming solutions intensified, new and innovative revenue models were being adopted in other technology markets. Most notably, the subscription revenue model, in which customers paid a monthly subscription fee rather than a large upfront fee, was becoming widely adopted in the software industry. Product manager Dayna Stone had the task of evaluating several revenue models and recommending one that most suited WMS's business needs and at the same time took customer needs and wishes into consideration. Complicating this decision were several factors that would have to be kept in mind. Americans' love of gaming had led to a mushrooming of casinos, which meant increased competition for casino dollars. Yet the financial crisis of 2008 and its aftermath had weakened demand for casinos. In addition, casinos, depending on the type of customers they attracted, differed in their appetite for innovation and maintenance of their slot machines. Students will step into the shoes of Dayna Stone as she undertakes the task of weighing these factors and selecting the right revenue model.

The purpose of this study is to analyse the impact of two different financial reporting models (revenue-expense vs asset-liability) on several earnings attributes.

The analysis compares the earnings attributes of non-financial private firms using the Italian generally accepted accounting principles (Italian GAAP, based on a revenue-expense model) with those of the Italian non-financial private firms voluntarily adopting the international financial reporting standards (IFRS, based on the asset-liability model). To address major methodological concerns, the research design is based on a single-country analysis and on three different samples as follows: firms voluntarily adopting IFRS; a matched sample of Italian GAAP firms; Italian GAAP firms belonging to the Elite programme, and therefore, comparable to the IFRS adopters in terms of incentives towards financial reporting transparency.

The results show that firms reporting under a revenue-expense model are characterized by a stronger revenue-expense matching degree, along with higher earnings’ persistence, earnings’ predictability and conditional conservatism than firms adopting an asset-liability model. In addition, contrary to the expectations, Italian GAAP firms do not present smoother earnings and do not report greater abnormal accruals than IFRS adopters do. Overall, the findings suggest that the switch from a revenue-expense model to an asset-liability model negatively affects several earnings attributes of non-financial private companies, shedding new light on the drawbacks associated with the adoption of the IFRS accounting model.

This study addresses a theme characterized by sparse research efforts, adding new insights to the debate on the decline in the quality of earnings and on the drawbacks associated with the adoption of the IFRS accounting model.

While management-accounting research continues to focus on cost drivers, research has recently begun to examine revenue drivers. We review the research on revenue drivers with reference to five revenue-driver models in the accounting literature. The revenue drivers identified by quantitative empirical research are located in a revenue-driver model based on their levels of analysis (customer, product, organization, industry) and other characteristics of a revenue driver–revenue relation. Implications of this model for research are discussed.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

In the hybrid electricity market consisting of renewable and conventional energy, the generation output of renewable power is uncertain because of its intermittency, and the power market demand is also fluctuant. Meanwhile, there is fierce competition among power producers in the power supply market and retailers in the demand market after deregulation, which increases the difficulty of renewable energy power grid-connection. To promote grid-connection of renewable energy power in the hybrid electricity market, the authors construct different contract decision-making models in the “many-to-many” hybrid power supply chain to explore the pricing strategy of renewable energy power grid-connecting.

Considering the dual-uncertainty of renewable energy power output and electricity market demand, the authors construct different decision-making models of wholesale price contract and revenue-sharing contract to compare and optimize grid-connecting pricing, respectively, to maximize the profits of different participants in the hybrid power supply chain. Besides, the authors set different parameters in the models to explore the influence of competition intensity, government subsidies, etc. on power pricing. Then, a numerical simulation is carried out, they verify the existence of the equilibrium solutions satisfying the supply chain coordination, compare the differences of pricing contracts and further analyze the variation characteristics of optimal contract parameters and their interaction relations.

Revenue-sharing contract can increase the quantity of green power grid-connection and realize benefits Pareto improvement of all parties in hybrid power supply chain. The competition intensity both of power supply and demand market will have an impact on the sharing ratio, and the increase of competition intensity results in a reduction of power supply chain coordination pressure. The power contract price, spot price and selling price have all been reduced with the increase of the sharing ratio, and the price of renewable power is more sensitive to the ratio change. The sharing ratio shows a downward trend with the increase of government green power subsidies.

On the basis of expanding the definition of hybrid power market and the theory of newsvendor model, considering the dual-uncertainty of green power generation output and electricity market demand, this paper builds and compares different contract decision-making models to study the grid-connection pricing strategy of renewable energy power. And as an extension of supply chain structure types and management, the authors build a “many-to-many” power supply chain structure model and analyze the impact of competition intensity among power enterprises and the government subsidy on the power grid-connecting pricing.

This article presents a study of revenue forecasting in a Florida municipal government. Seven techniques, including the budget officers' judgmental approach, time series models, a deterministic model, and an optimized model, are employed with franchise and utility receipts in the Town of Davie. The authors found that simple time series models outperformed deterministic models and the judgmentally derived forecasts of local officials. Consistent with prior research, findings here suggest that the time series models are not only accurate, but also easy to implement and readily comprehensible by local officials.

The purpose of this study is to propose a comprehensive strategic model to manage profitability. Strategic management accounting concepts and tools are adopted to explore and manage the main profitability drivers (cost, assets, and revenue).

A deductive approach is used to identify the variables of the profitability model. Phase one of this study rely on reviewing prior literature in the field in order to identify the key profitability drivers that uses in managing profitability (costs, assets and revenue).Phase two of the research focuses on testing the perceptions of the managers of Egyptian “Information and communications technology” sector, the relative merits of such a model.

The most important finding in the current study, which has not been investigated in previous studies, is that the proposed comprehensive profitability model which contains cost, the assets and revenue techniques was a better predictor of profitability than the alternative models, which contain a combination of two variables.

As the first study of its kind, this model contributes to the theoretical literature in the field. It is also a practical contribution in managing profitability of the Egyptian “Information and communications technology” sector.

This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy.

Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue.

All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues.

Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments.

It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.