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This paper seeks to analyze consumer preferences toward fresh rabbit meat and obstacles and interest in consuming the product.

The paper uses the dual response choice experiment (DRCE) design which allows for analyzing forced and unforced options in choice experiments using the same sample. The heteroscedastic extreme‐value (HEV) model is used due to its relaxation of the restrictive assumption made in the multinomial logit model regarding the identically distributed error term across alternatives. The empirical analysis uses consumer‐level questionnaires to elicit information regarding consumer attitudes toward rabbit meat in Catalonia (Spain).

The results demonstrate a higher preference for rabbit meat from “Catalan” origin followed by higher quality certification information. Convenience and “ready‐to‐eat” products made from rabbit meat may help bolster increased consumption. An effective communication campaign is needed to educate individuals regarding the health characteristics of rabbit compared to other types of meat. Furthermore, results demonstrate that the ordering of attributes is not significantly different from forced and non‐forced choices obtained from the DRCE design. However, significant differences on the magnitude of the preferences for some attributes' levels are found.

From a methodological point‐of‐view, the study follows a similar design to the dual response choice experiments. However, the study is differentiated by asking consumers whether they are willing to buy the product. More emphasis is made on the purchasing context of the task leading consumers to focus more on their budget constraints by considering the price. The study uses the DRCE design as an alternative of the traditional single stage choice experiments. Owing to budgets constraint, the study could be improved by comparing results from both designs. At the empirical level, it would be interesting to extend the study to other geographical parts in Spain.

The study can help policy makers in stimulating demand for rabbit meat. Rabbit meat farmers in Catalonia were forced to abandon their farms due to the lack of demand. Consumption has decreased dramatically. As recommended in the paper, high quality labelling of the meat, improving the carcass format and its presentation and the healthiest characteristic of the meat could be a good way to promote and stimulate demand.

From the empirical point‐of‐view, this study, to the authors' knowledge, is the first application of CE and CV to analyze consumers' preferences towards rabbit meat. Furthermore, it is the first paper that applies the HEV model in agro‐food economics and specifically towards rabbit meat.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

The purpose of this paper is the investigation of a new coordinated switching strategy to improve the transient performance of a fuel cell (FC)- supercapacitor (SC) electric vehicle. The proposed switching strategy protects FCs from large currents drawn during abrupt power variations. Furthermore, it compensates the poor FC transient response and suppresses the transient ripples occurring during power source switching instants.

Coordinated power source switching is achieved using three different transition functions. Vehicle model is fractioned into computational and console subsystems for its simulation using real time (RT) LAB simulator. Blocs containing coordination switching strategy, power sources models and their power electronics interface are placed in the computational subsystem that will be executed, in RT, on one of real time laboratory simulator central processing unit cores.

Coordination switching strategy resulted in reducing transient power ripples by 90% and direct current (DC) bus voltage fluctuations by 50%. Switching through transition functions compensated the difference between FC and SC transient responses responsible for transient power ripples. Among the three proposed transition functions, linear transition function resulted in the best transient performances.

The proposed coordinated switching strategy allows the control of the switching period duration. Furthermore, it enables the choice of adequate transition functions that fit the dynamics of power sources undergoing transition. Also, the proposed switching technique is simple and does not require the knowledge of system parameters or the complex control models.

Online web searches have played crucial roles in influencing consumers’ purchasing decisions. Web search traffic information enables researchers and practitioners to better understand consumers in terms of their preferences and interests, among other things. The purpose of this paper is to use web search traffic information provided by Google Trends to derive relationships among product brands as well as those between product brands and product attributes to propose a method to enhance the visibility of consumer brand positioning.

This study builds upon the interesting observation that consumers’ behavior in performing simultaneous searches, or searches including two or more keywords, can be converted into data indicating relationships among brands as well as those between brands and their attributes. The study focuses on the cases of hybrid cars and tablet PCs, and applies a social network analysis method to identify these relationships. Time series information on web search traffic is used because it can track these two product groups from the early stages to the present. This step is completed to verify the changes in the status of each brand and in their relationships that occurred in consumers’ minds over time.

Results show that consumers’ web search behaviors reveal the brand positioning and brand-attribute associations in their minds. Specifically, using consumers’ simultaneous search data, the authors derived relationships among brands (brand-brand network) from consumers’ behaviors of searching simultaneously for two brands and the relationships between brands and attributes (brand-product attributes network) from consumers’ behavior of searching simultaneously for a specific brand and certain product attributes.

Theoretically, this study verifies that consumers’ web search traffic information can be used to microscopically identify the positions of individual brands and their relationships in the minds of consumers. Regarding practical applications, this study proposes a method that can be used by companies to track how consumers perceive their brands by performing a simple and cost-effective analysis using the free search traffic information provided by Google.

This chapter explains how electric driving has been transforming car mobility in The Netherlands since 1990, highlighting the role of a specific Dutch policy mix as direct factor, and the conditions through which this policy mix came about as indirect factors. The analysis is based on triangulation of findings from three methods: (1) discourse analysis of national newspapers and online blogs to understand the changing meanings of car mobility as well as changing stakeholder competences; (2) interview analysis with Dutch stakeholders to understand policy effects as well as their changing competences; and (3) analysis of relevant documents that provide the numbers of vehicles sold, implemented infrastructures and policy instruments. The study describes market changes in terms of ‘reconfiguring’ (entangled) practices of Dutch motorists, vehicle manufacturers and policy-makers, constituted by the (changing) relations between meanings, materialities, competences and policy incentives. The analysis finds a gradual reconfiguration of car mobility in three stages: The hegemony of Internal Combustion Engine (ICE) mobility (1990–2008), Surge in Plug-in Hybrid Electric Vehicle (HEV) mobility (2009–2015), and Surge in full-electric mobility (2016–2020). The analysis shows that the specific Dutch policy incentives were critical to orchestrating the co-evolution of ICE-based and electric mobility towards low-carbon alternatives, that is, towards more electrification. The policy mix was adapted in three successive steps, in which inconsistencies towards electric mobility (e-mobility) were solved, entailing three distinct reconfiguration pathways in each period. The relatively strong policy incentives for e-mobility in The Netherlands can be explained by the absence of an established car industry as well as particular air quality challenges in cities (triggering local support for the provision of charging infrastructure). The conclusion includes policy recommendations for countries that seek to promote e-mobility, although further research should clarify how contextual differences require specific elements in the policy mix.

Sustainability is increasingly becoming an essential aspect of technological innovations. In addition, the diffusion of sustainable new technology-based products appears to be uneven across the globe. The authors examine the effect of three country-level Hofstede measures of culture and two national-level innovation characteristics on the diffusion of Sustainable New Technology-based Products (SNTP).

Regression and Necessary Conditions Analysis were used to analyze a panel dataset of electric and hybrid vehicles sales from 2008 to 2017 across 89 countries.

Results suggest Long-Term Orientation (LTO) was correlated with SNTP diffusion, Indulgence (IVR) was partially correlated with SNTP diffusion and was also a necessary condition. Surprisingly, Uncertainty Avoidance (UAI) was not correlated with SNTP diffusion. In addition, a national proclivity for developing innovations and a history of utilizing prior generic innovations were both correlated and necessary for SNTP diffusion.

This paper measures the impact of several macro-level variables (culture and other innovation related characteristics of countries) on SNTP diffusion. In addition to regression analyses to measure the average effect size, the authors conduct Necessary Conditions Analysis, which assesses the necessity of a variable for the outcome. These insights may help multinational companies better strategize entry decisions for international markets and aid governments in formulating more effective policies by recognizing and accommodating the influences of national culture and innovation attitudes.

The purpose of this paper is to probe the potentials of computational intelligence (CI) and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s (PHEVs) fuel cost and optimize its fuel consumption rate. Besides, the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.

The hybrid intelligent tool is implemented in two different levels. The hyper-level algorithm is a Pareto-based memetic algorithm, known as the chaos-enhanced Lamarckian immune algorithm (CLIA), with three different objective functions. As a hyper-level supervisor, CLIA tries to design a fast and accurate identifier which, at the same time, can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.

Based on the conducted numerical simulations, a set of interesting points are inferred. First, it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features. It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.

To the best knowledge of the authors, this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.

This paper presents the results of a choice experiment carried out from August to October 2000 on the visitors of the Galleria Borghese Museum, a worldwide known heritage site located in Rome. The main objective of this work is to study the relevancy of choice experiment techniques as a tool aimed at measuring economic values and assessing user preferences concerning the multi‐attribute and multi‐value services as supplied by cultural institutions. A set of alternative incremental changes in service attributes showing improvements in supply are designed and presented to visitors. Alternative conditional logit specifications are used for analysing stated choices over the hypothetical incremental changes in museum attributes. Willingness to pay for incremental variations concerning site attributes is positive and statistically significant for most changes. Conditional logit specifications, which incorporate heterogeneity by adding interaction socio‐economic terms, are generally robust and do not violate the IIA assumption. In addition, in the present case study, non‐IIA models do not outperform conditional logit models. Choice experiments confirm as being a practical and effective tool for non‐market valuation, and they should be used to provide information to decision makers for justifying demand led policies.