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Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

The purpose of this paper is to report on the innovative employment of students as technology mentors as part of a Blended Learning Program (BLP) that supported a group of owner-managers of small businesses to adopt appropriate information and communication technologies (ICT) to enhance their work practices.

This discussion uses various evaluations undertaken throughout the project to examine why the technology mentor role is vital in supporting small businesses to develop digital literacies. The participants’ self-reporting of their ICT skills as well as their progress in using ICT was also assessed by technology mentors in the course of the program and reported in mentor reflections. Academic staff also evaluated the performances of technology mentors in relation to each business.

Participants in the BLP pilot program singled out the technology mentors as being essential in promoting their uptake of ICT and in their ability to use specific technologies at work.

Findings are based on a pilot program involving six learners and two technology mentors. While this is a statistically insignificant number of evaluations, both the findings and the model of the BLP remain of interest. This model has the capacity to address a long-standing and global challenge to support small businesses in the use of ICT. A scaled up version of the program is required to validate the findings.

In the final evaluation, participants all self-assessed as having increased ICT knowledge and skills. They provided specific examples of how they now use ICT. The BLP could be taken up by local and state governments who periodically attempt to support small businesses in the uptake of technologies. The BLP could also be applied to vocational education students in business, information technologies or information systems. As well as supporting small businesses to adopt ICT, this model also provides an important alternative to resource-intensive work-placement programs that are designed to develop students’ employability skills through work-integrated learning.

Less effective attempts to support small businesses often involve face-to-face training for unrealistic periods of time that foreground technology rather than real world, useful and relevant outcomes (IBSA, 2013). This BLP successfully supported owner operators of small businesses to identify, apply and evaluate a range of software applications (“apps”) and online programs to enhance their work practices.

The purpose of this paper is to develop an optimal bidding strategy for a generation company (GenCo) in the network constrained electricity markets and to analyze the impact of network constraints and opponents bidding behavior on it.

A bi‐level programming (BLP) technique is formulated in which upper level problem represents an individual GenCo payoff maximization and the lower level represents the independent system operator's market clearing problem for minimizing customers' payments. The objective function of BLP problem used for bidding strategy by economic withholding is highly nonlinear, and there are complementarity terms to represent the market clearing. Fuzzy adaptive particle swarm optimization (FAPSO), which is a modern heuristic approach, is applied to obtain the global solution of the proposed BLP problem for single hourly and multi‐hourly market clearings. Opponents' bidding behavior is modeled with probabilistic estimation.

It is very difficult to obtain the global solution of this BLP problem using the deterministic approaches, even for a single hourly market clearing. However, the effectiveness of this new heuristic approach (FAPSO) has been established with four simulation cases on IEEE 30‐bus test system considering multi‐block bidding and multi‐hourly market clearings. The joint effect of network congestion and strategic bidding by opponents offer additional opportunities of increase in payoff of a GenCo.

FAPSO having dynamically adjusted particle swarm optimization inertia weight uses fuzzy evaluation to effectively follow the frequently changing conditions in the successive trading sessions of a real electricity market. This approach is applied to find the optimal bidding strategy of a GenCo competing with five GenCos in IEEE 30‐bus test system.

This paper is possibly the first attempt to evaluate an optimal bidding strategy for a GenCo through economic withholding in a network constrained electricity market using FAPSO.

One of the most important problems concerning the toll roads is the setting of an appropriate cost for traveling through private arcs of a transportation network. The purpose of this paper is to consider this problem by stating it as a bilevel programming (BLP) model. At the upper level, one has a public regulator or a private company that manages the toll roads seeking to increase its profits. At the lower level, several companies-users try to satisfy the existing demand for transportation of goods and/or passengers, and simultaneously, to select the routes so as to minimize their travel costs. In other words, what is sought is kind of a balance of costs that bring the highest profit to the regulating company (the upper level) and are still attractive enough to the users (the lower level).

With the aim of providing a solution to the BLP problem in question, a direct algorithm based on sensitivity analysis (SA) is proposed. In order to make it easier to move (if necessary) from a local maximum of the upper level objective function to another, the well-known “filled function (FF)” method is used.

The paper proposes and tests two versions of the heuristic algorithm to solve the toll optimization problem (TOP) based upon SA for linear programming (LP) problems. The algorithm makes use of an SA procedure for the LP problem at the lower level, as well as of the “filled” function technicalities in order to reach the global optimum when “jammed” at some local optimum. Numerical experiments with a series of small and medium dimension test problems show the proposed algorithm’s robustness and decent convergence characteristics.

Numerical experiments with a series of small- and medium dimension test problems show the proposed algorithm’s robustness and reasonable convergence characteristics. In particular, while ceding in efficiency to other algorithms when solving small problems, the proposed method wins in the case of medium (higher dimensional) test models. Because of that, one can expect a serious real-life impact on the TOP when the proposed methods and/or their improved versions are developed further to be applicable in practice in the near future.

The proposed algorithms are original and perform well when solving small and medium test numerical problems. The proposed heuristics aim at filling in a gap in a series of numerical approaches to the solution of TOP problem listed in the Introduction. To the authors knowledge, no systematic attempts to apply the SA tools to the toll assigned problem have been recently made. Moreover, the combination of these powerful tools with the “FFs” techniques brings forward some new global optimization ideas. Exactly these features build up the knowledge this specific paper offers in relation to previous relevant works.

Authorization and access control have been a topic of research for several decades. However, existing definitions are inconsistent and even contradicting each other. Furthermore, there are numerous access control models and even more have recently evolved to conform with the challenging requirements of resource protection. That makes it hard to classify the models and decide for an appropriate one satisfying security needs. Therefore, this study aims to guide through the plenty of access control models in the current state of the art besides this opaque accumulation of terms meaning and how they are related.

This study follows the systematic literature review approach to investigate current research regarding access control models and illustrate the findings of the conducted review. To provide a detailed understanding of the topic, this study identified the need for an additional study on the terms related to the domain of authorization and access control.

The authors’ research results in this paper are the distinction between authorization and access control with respect to definition, strategies, and models in addition to the classification schema. This study provides a comprehensive overview of existing models and an analysis according to the proposed five classes of access control models.

Based on the authors’ definitions of authorization and access control along with their related terms, i.e. authorization strategy, model and policy as well as access control model and mechanism, this study gives an overview of authorization strategies and propose a classification of access control models providing examples for each category. In contrast to other comparative studies, this study discusses more access control models, including the conventional state-of-the-art models and novel ones. This study also summarizes each of the literature works after selecting the relevant ones focusing on the database system domain or providing a survey, a classification or evaluation criteria of access control models. Additionally, the introduced categories of models are analyzed with respect to various criteria that are partly selected from the standard access control system evaluation metrics by the National Institute of Standards and Technology.

This study aims to verify how the product-relevant market for wines should be defined. To do so, the authors apply an empirical methodology to determine the levels of substitution among wine-categories, identifying each relevant market in Argentina.

The authors perform an econometric analysis applying the nested logit methodology that will enable us to estimate cross elasticities in wine segments in the Argentine market. The database contains 1,367 brands and a maximum of 395 firms offering products of different segments. If cross elasticities between wine segments are positive and significantly different to zero, the products belong to the same relevant market. In the methodological section, the authors discuss the pros and cons of this approach and its alternatives, while in the empirical analysis, they perform several robustness controls.

The proposed method and results provide an alternative to exogenously defining where each product category begins and ends. The results show that the relevant market for wines should be segmented by categories as the substitution between each one is very low.

In this empirical work, the study analyzes whether each segment constitutes a relevant, independent market. In Argentina, the practice of competition policy does not recognize substitution between different categories of wine; thus, each category constitutes a relevant market by itself, while according to the international practice, the relevant market includes all wine categories. The results suggest exploring the existence of different relevant markets of wine.

Under the label “wine,” different types or qualities can act as substitutes among them in different possible relevant markets. A more precise definition of relevant markets permits informed decisions facing proposed mergers or anticompetitive practices.

This study provides a mechanism to determine the levels of substitution among wine categories (i.e. to find the boundaries of each relevant market). Wine is a differentiated product and, as such, offers different qualities (categories) for consumers. The consideration of those differences in winery mergers has consequences on social welfare.

According to the international practice in competition policy, the relevant market includes all wine categories. This study provides an alternative to defining exogenously where each category of product begins and ends and does not assume a priori the direction or intensity of substitution among products.

The purpose of this paper is to propose a methodology that determines vehicle-level specifications for new-car program by balancing market environments and engineering feasibility in the early stages of the vehicle development processes using statistical analysis of historical data.

The proposed methodology effectively captures the interplay among key factors in preliminary vehicle planning: engineering feasibility constraints, market demands, and economic conditions. Engineering design constraints, derived by statistical analysis of historical data, define the strategic feasible space. Within the defined design space, the methodology determines a set of specifications that maximize the customer utility which is built as a function of preferences on each attribute of a vehicle.

The present paper develops an “extrapolation” approach using historical vehicle data, rather than attempt to model a complex system with limited information. In doing so, the proposed approach avoids the difficulties of understanding an entire complex system by determining only the moderate level of specifications. Moreover, its quantification of revealed customer preferences as expressed in sales data resolves the confusions in vehicle planning arising from the translation of customer requirements to engineering specification.

The proposed methodology can provide feasible prediction values with a new, historical-data-based statistical approach that effectively surmounts the difficulty of mechanically understanding complex systems. Moreover, through quantification of the target market's customer requirements as well as effects of market-environmental changes, the methodology enables designers to plan complex products for new concept in objective and reasonable manner.