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The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.

The purpose of this paper is to show how the bulk of the mobile telecom traffic is starting to migrate towards internet and IP based services, and the choice of alternative paths is widening as new technologies from different backgrounds are emerging. This paper aims to compare some possible approaches to find out suggestions for the most feasible technology selections.

The case study focuses on big Western European operators being in the position to choose the 3G and beyond technology track – whether continuing to the UMTS path or taking a more radical shift towards a full IP based WiMAX technology. The work includes full quantitative modeling of both cost and revenue sides, separating the network provisioning business from the service provisioning for more detailed economical analysis of the technology choices. For the revenue estimation, specific user benefit modeling has been performed against the technology related parameters. Special emphasis in this paper is on the risk and sensitivity analyses, which reveal more of the case dynamics than bare economic key figures.

The UMTS/HSPA path was found to be the most beneficial and robust continuum for the European incumbent mobile operator. Basing the new data services on WiMAX technology was identified to include more risks, although for an operator not having a license for UMTS network it provides a possibility to serve the high end advanced users profitably. The challenge lies especially in the narrower terminal and user base. In the worst development scenarios, especially the network provider of WiMAX is vulnerable, as making big investments on the network.

Not all possible business cases were analyzed, so that no conclusion can be made, based on this study, on operators without wide GSM background, or WiMAX approaches of a limited scope.

The study gives support for the operators in deciding on strategic deployment plans.

The study provides unique information through comprehensive data gathering, analysis and forecasts, with feasible accuracy level for this kind of case study, combining both technical and business aspects of the still widely uncharted and fast developing mobile data arena.

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.

Using the lens of upper echelons theory, this study examines the degree to which National Collegiate Athletic Association athletic department performance outcomes are associated with the personal characteristics and experiences of the athletic director leading the organization.

The authors match organizational performance data with athletic director and institutional characteristics to form a robust data set spanning 16 years from the 2003–04 to 2018–19 seasons. The sample contains 811 observations representing 136 unique athletic directors. Fixed effects panel regressions are used to analyze organizational performance and quantile regression is used to analyze organizational revenues.

The authors fail to uncover statistically significant evidence that athletic director personal characteristics, functional experience and technical experience are associated with organizational performance. Rather, the empirical modeling indicates organizational performance is primarily driven by differentiation in the ability to acquire human capital (i.e. playing talent). The results also indicate that on average, women are more likely to lead lower revenue organizations, however, prior industry-specific technical experience offsets this relationship.

In opposition to upper echelons research in numerous settings, the modeling indicates the personal characteristics and experiences of the organization's lead executive are not an economically relevant determinant of organizational performance. This may indicate college athletics is a boundary condition in the applicability of upper echelons theory.

This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses “forecasted demand” arrivals generated from the real data.

The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic parameters like reservations, cancellations, duration of stay, no shows, seasonality, trend, etc. and simulates forward in time the actual process of reservations to obtain the forecast. On the other hand, the proposed optimization module extends existing optimization techniques for hotel revenue management to address group reservations, while including integrality constraints and using “forecasted demand” arrivals generated from the data. The optimization model is based on large‐scale integer programming model to optimize decision rules for accepting reservations.

A case study based on three different sets of reservation records of simulated hotel data was conducted to test the operation of the system on real data. Results showed that the model is able to generate effective recommendations to maximize revenue.

The main value of this paper is that it presents an integrated framework for hotel room revenue maximization. The novelty introduced in this approach is that it is based on an advanced room demand forecast model that simulated the reservation process from its first principles and produces demand scenarios that are used by an optimization model to generate proper recommendations.

This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate (1)-(2) into research stream recommendations for the open literature with an empirical focus.

The authors use Nickerson’s taxonomy development method from the field of information systems to build the taxonomy.

New forecasting areas include developing a metric for the degree of strategic fit of a hotel’s pricing strategy and using it in conjunction with quantifications of online reviews for predictions. New price optimization avenues include determining whether a lack of congruence between customer perceptions of fairness and trust and pricing history has a detrimental effect on overall hotel performance and determining which combinations of flexible products, decision-maker risk aversion, nonparametric forecasting and reference effect optimization features work best in which situations.

This is the first study to combine vendor activities outside the technical realms of forecasting and price optimization with an emphasis on the choice modeling technical framework. This study points to several promising studies using qualitative methods, action research and design science.

The objectives of this study are to investigate the relationship between hotel room demand and room rates, and to find a viable solution for the optimal room rate that maximizes the total profit.

There are various studies in the literature on how room rates affect profitability, and how the optimal room rate that maximizes the total revenue can be determined. Most of these studies assume an algebraic relationship between room rates and room demand, and obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts the alternative approach of using a model with a demand function that has been shown to be a superior causal forecasting model in some markets, and develops a new method to optimize the total profit.

The traditional method of applying calculus to the profit function based on a causal forecasting model leads to unrealistic solutions. This gives rise to the paradox that, on the one hand, there is a superior causal forecasting model based on room rates, but on the other hand, the traditional method does not yield a realistic solution for room rate optimization. This study analyzes the underlying causes of this paradox and proposes a method to resolve it.

The findings can be used by hotels to fine‐tune the room rates determined by conventional methods to arrive at a realistic and definitive value for the optimal room rate.

This study highlights the problems that arise with the traditional method of applying calculus to revenue and profit optimization and proposes a new method to resolve it.

The purpose of this paper is to utilize a cost and revenue driver model for commercial banking to examine the differential effects of the drivers within and between banking functions, and to examine the role of information technology (IT) in moderating the relationship between costs and cost drivers and revenue and revenue drivers.

The model is estimated on a cross‐sectional sample of 121 banks from the functional cost and profit analysis data set collected by the Federal Reserve Banks. Multivariate regression analysis with interaction terms is utilized to examine the differential impact of IT in two contrasting banking functions.

The results document the role of transactional IT on the cost driver relationships in the labor cost models in both the demand deposit and commercial loan functions. The role of strategic IT in the revenue driver models is documented for the demand deposit function but not for the commercial loan function.

Only two banking functions are selected. Expanding the model and testing it on other banking functions may be useful.

By disaggregating the IT variable and incorporating IT in a cost and revenue driver model managers can utilize the model to examine the impact of IT in banking.

A model that disaggregates the IT variable by allocating support costs to functions and delineates links between IT variables and cost and revenue drivers in banking.

The purpose of this paper is to contribute to land redistribution policy which is presently not only one of the most defining political and development issues, but also perhaps the most difficult in South Africa.

The paper develops a programming model for farm‐level land‐use changes and output supply response to estimate the effects of agricultural land tax in South Africa. The modeling approach is based on baseline statistics from a descriptive analysis.

The results indicate that changes in land use and output supply response are marginal. The highest effects are observed on irrigated areas of sunflower seed (0.23 percent) and wheat (0.17 percent). This results to declines in supply of 0.07 percent for sunflower seeds; 0.06 percent for wheat; 0.04 percent for soya beans; 0.03 percent for each of white maize and sorghum; and 0.02 percent for yellow maize production. Therefore, levying a land tax may discourage investment on irrigation facilities and consequently irrigated farming.

The programming modeling approach captures the significant differences in the farm characteristics and the overarching profit‐maximizing behaviour of farmers, which an econometric approach may not easily capture.

This paper aims to investigate the moderating impacts of seasonality on the effectiveness of new product commercialization strategies in short-lifecycle markets. The authors contextualize their theory in the vast and culturally significant entertainment industry sector and contrast the effects between independent films and big budget movies.

This study uses an econometric modeling approach.

This study finds that unlike new films by well-resourced studios, which must launch in a high season for best performance, independents can generate more revenue in low seasons under certain conditions. The study shows how seasonality moderates the effectiveness of new films’ commercialization strategies and how new product outcomes are different for small independent products than for big-budget productions with regards to distribution duration, advertising expenditure and product characteristics.

This research extends the literature on launch timing, which examines various strategic tradeoffs. In contrast with the few extant studies whose concern is sensitizing to the effects of seasonality (Siqueiraet al., 2016), this research treats seasonality as an exploitable opportunity that can be strategically factored into business planning for small producers. Accordingly, this is the first study to theoretically and empirically investigate the moderating relationship between seasonality, marketing decisions, product characteristics and performance.

To achieve useful specificity, the study constructs its discussion around the highly seasonal entertainment industry sector. The study shows that seasonality moderates the effectiveness of new films’ commercialization decisions and that the strategic outcomes are different for small independent products than for major studio productions in particular.

In contrast with extant research whose concern is sensitizing to the effects of seasonality, our research treats seasonality as an exploitable opportunity that can be strategically factored into business planning. Accordingly, ours is the first study to theoretically and empirically investigate the moderating relationship between seasonality, marketing decisions, product characteristics and performance.