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This paper compares the socio-demographic features, wine consumption preferences and habits of individuals of Generations X and Y. The influence of age, as a life cycle effect, on individuals' consumption preferences from the same generation is analysed. In addition, the generational effect and period effect on consumption by each generation are studied to ascertain changes in preferences due to specific values of their birth cohort or period.

Mixed ordered logit models are estimated to identify which traits influence the probability of consuming a certain quantity of wine for each generation. The variability of the effects of different variables on the consumption decision is analysed in depth, as a reflection of the unobserved heterogeneity present in individuals' decisions. For this purpose, data from two exhaustive surveys carried out in Tenerife (Canary Islands) over a time interval of 13 years are used.

Individuals' age and period are relevant factors in the consumption decision. There are some similarities in the behaviour of both generations, although the variability of the effects of some individual traits is greater for Generation X.

This paper contributes to a better understanding of the profile of wine consumers from different generations through a joint analysis of age effects, period and cohort, which have rarely been addressed jointly in the literature. In addition, it provides an exhaustive analysis of the heterogeneity in consumption preferences that highlights the variability of the effects found.

The globalization of wine markets together with a prolonged decline in wine consumption, especially in traditional wine-producing countries such as Spain, make it more relevant than ever to study population segments such as female wine consumers. Such segments could contribute to increasing consumption. The purpose of this paper is to investigate the distinctive features of wine consumption according to gender, identifying sociodemographic profiles of female wine consumers and non-consumers in Tenerife (Canary Islands). The Canarian archipelago is one of the Spanish regions with a long tradition in wine production.

This study uses data from a survey carried out on a representative sample of 1,028 Tenerife residents. Independent tests and correspondence analyses were performed. Moreover, an ordered mixed logit model was estimated.

Gender is a determining factor when analysing the frequency of wine consumption. The profiles of female consumers and female non-consumers differ substantially. Considering unobserved heterogeneity in the estimated model also allows us to verify the variability of the effects on the probability of women with the same sociodemographic characteristics having the same consumption pattern.

Non-sociodemographic variables could be considered in this paper.

Marketing strategies should not consider women as a homogenous group.

This study examines in depth the sociodemographic factors that influence the frequency of wine consumption according to gender. It is relevant given the scarcity of studies that analyse these factors. Likewise, unobserved heterogeneity in consumer decisions is taken into account, an aspect that has not been sufficiently considered in wine consumption literature so far.

Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed.

This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results.

The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration.

This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

The Spanish wine sector has gained ever greater relevance not only in economic terms but also from a social and environmental perspective. One Spanish region with a deeply rooted history of growing and producing wine is the Canary Islands, and specifically the island of Tenerife. The wine sector there has undergone notable evolution since the end of the past century. The main aim of these changes has been to improve the quality of Canarian wines, although it is also crucial to design marketing strategies that contribute to maintaining and increasing market share. In this context, it is essential to improve our knowledge of wine consumption among the population, especially given the scarcity of studies that analyze consumption preferences. Thus, the aim of this study is to identify wine consumption profiles.

This study uses data from a survey carried out on a representative sample of 1,028 Tenerife residents. Discrete choice models are estimated to identify profiles of wine consumers.

The main results show that the probability of consuming wine is the greatest among adult men compared to a lower consumption by younger individuals and women. Furthermore, it was shown that the probability of occasional consumption increases with higher educational levels.

This study examines in depth the individual factors that influence decisions on wine consumption (frequency and amount) in Tenerife (Canary Islands), which has a wine-growing landscape that produces several unique grape varieties. The study quantifies the probability of wine consumption depending on resident individuals’ sociodemographic characteristics. Thus, it contributes to a greater understanding of demand and consumption patterns in the island´s wine sector and allows comparisons to be made with studies for other geographical areas. It is, therefore, a valuable framework for designing targeted marketing strategies to increase consumption and attract demand from potential consumers with specific characteristics.

Crime against women is an entrenched issue in India in general and its states in particular. The existing literature unveils so many socioeconomic factors responsible behind such a social curse. Economic disparities in terms of income have also been identified as one of the crucial factors in determining the crime rates in some countries of the world. The present study seeks to examine whether income inequality has any sort of associations with the crime against women in the states of India. The study has observed rising trends of crime rates and per capita incomes across the states in India for the period 2000–2019 and crime rates in the states are positively and significantly correlated with rising inequality in income. There, thus, needs the policies related to reduction of crime against women and reduction of income inequality. The study thus suggests the interventions of the legislative system, governments' tax policies toward the rich persons, public awareness programs, etc. to reduce violence against women.

The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them.

A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management.

City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization.

A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.

Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with “U”- and “L”-shaped structures, which responds to the needs of the Fourth Industrial Revolution.

In this paper, the authors propose an online robot programming methodology that eliminates unnecessary steps traditionally performed in robotic welding, so that the operator only performs three steps to complete the welding task. First, choose the piece to weld. Then, enter the welding parameters. Finally, it sends the automatically generated program to the robot.

The system finally managed to perform the fillet welding task with the proposed method in a more efficient preparation time than the compared methods. For this, a reduced number of components was used compared to other systems: a structured light 3 D camera, two computers and a concentrator, in addition to the six-axis industrial robotic arm. The operating complexity of the system has been reduced as much as possible.

To the best of the authors’ knowledge, there is no scientific or commercial evidence of an online robot programming system capable of performing a fillet welding process, simplifying the process so that it is completely transparent for the operator and framed in the Industry 4.0 paradigm. Its commercial potential lies mainly in its simple and low-cost implementation in a flexible system capable of adapting to any industrial fillet welding job and to any support that can accommodate it.

In this study, a robotic robust system is achieved, aligned to Industry 4.0, with a friendly, intuitive and simple interface for an operator who does not need to have knowledge of industrial robotics, allowing him to perform a fillet welding saving time and increasing productivity.

To date, the validity of the empirical tests that employ the mean‐variance approach for testing the risk‐return relationship in the research stream named Bowman’s paradox is inherently unverifiable, and the results cannot be generalized. However, this problem can be solved by developing an econometric model with two fundamental characteristics: first, the use of a time‐series model for each firm, avoiding the traditional cross‐sectional analysis; and, second, the estimation of a model with a single variable (firm’s rate of return), whose expectation and variance are mathematically related according to behavioral theories, forming a heteroskedastic model similar to GARCH (generalized autoregressive conditional heteroskedasticity). The application of this methodology for Bowman’s paradox is new, and its main advantage is that it solves the previous criticism of the lack of identification. With this model, we achieve results that agree with behavioral theories and show that these theories can also be carried out with market measures.

This article aims to offer empirical evidence pertaining to the relationship among entrepreneurship, market orientation and business performance within the context of disadvantaged socio‐economic regions of the European Union.

Two groups of hypotheses investigate the relationship between entrepreneurship and market orientation and the joint effect of these dimensions on performance. All questions are approached using survey data from 183 firms located in the Castilla y Leon region, Spain.

A strong relationship exists between entrepreneurship and market orientation. Although these orientations may be implemented separately, firms emphasise entrepreneurship when they are market‐oriented. Therefore, the strong relationship and complementarities between entrepreneurship and market orientation reduce the effort involved in the joint adoption of both orientations. Both orientations also demonstrate a strong relationship with performance, such that each contributes specifically.

The article shows that, despite little evidence of synergic effects of the joint adoption of both orientations, the specific aspects that differentiate entrepreneurship and market orientation both contribute to improving performance, and therefore, firms should foster a market‐oriented, entrepreneurial organisational culture.

Improving and assuring the quality of higher education has become a key element of policy agendas worldwide. To this end, a complete accountability system has been developed through various evaluation procedures. Specifically, this study analyzes the perceptions of university teaching staff on the impact of performance appraisal systems on their professional activity, health and personal lives.

The study adopted a nonexperimental descriptive and causal-comparative design using a questionnaire that was completed by a sample of 2,183 Spanish teachers. The data obtained were analyzed using descriptive statistics and comparisons of differences.

The results show that, according to teachers, the evaluation criteria undermine the quality of their work by encouraging them to neglect teaching, increase scientific production and engage in unethical research practices. Their views also emphasize the social and health-related consequences of an increasingly competitive work climate, including increased stress levels. Finally, significant differences are observed regarding gender, professional category and academic discipline, with women, junior faculty and social sciences teachers expressing particularly strong views.

The originality of this study lies in the application of a method that contributes to the international debate through a national perspective (Spain) that has so far received little attention.