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The paper aims to examine the consumption behavior of Chinese residents during economic transition. It explores whether Western theories of consumption are applicable to the Chinese situation, and then tests the hypothesis based on Chinese traditional culture and dynamic nature of system change.

The paper opted for an empirical‐based approach. A regression model was used to analyze annual consumption data from 1986 to 2008 in China.

The paper provides empirical insights and suggests that under the influence of Chinese traditional culture and dynamic change of the Chinese economic system, Chinese urban residents exhibit a special consumption pattern of an intermittent and cyclical nature.

The paper concludes that in order to make the consumption stimulation policy in China more effective, it is necessary to establish a series of measures such as establishing a sound social welfare system as well as narrowing the gap between the rich and the poor, which will substantially increase the buying power of the less‐privileged groups and thus will increase the overall spending in the society. Although the econometric model used in this paper is adequate, a different approach like time series econometrics may give us additional insights. Researchers are encouraged to test the hypothesis further by employing other methodologies. Second, due to the lack of its own theories in the emerging market, this study remains exploratory.

This paper fulfils an identified need to study the special consumption behavior of Chinese urban residents during the economic transition.

This study aims to investigate how brand love and brand loyalty for three brands evolved during critical moments of the 2020 pandemic, and how they performed in the long run up to 2022.

An online longitudinal study, including a survey and Twitter data for three brands: Corona Extra, with a direct semantic association with the word coronavirus; Virus Vodka, with an indirect association; Modelo Especial, with no association with the virus name but from the same company as Corona Extra.

Despite external data indicating a harmful association between Corona Extra and coronavirus, this study's findings revealed that the brand maintained its brand love in the long run and increased brand loyalty during the critical moments of the pandemic. This study's data suggest that brand love and brand loyalty may be the underlying reasons for the increase in Corona Extra's brand equity during the pandemic.

The COVID-19 pandemic created a highly stressful situation for consumers and brands. Some brands' names had unfortunate semantic similarities with the virus terminology, which became an additional stressor during that time. This study harnessed the opportunity to investigate brand love and brand loyalty during the pandemic at four points in 2020 and one in 2022. The authors also examined relevant Twitter data during 2020.

The purpose of this paper is to describe fresh fruit consumers in Spain according to their food-related lifestyle (FRL).

A random stratified sample of 500 people, representative of the persons responsible for household food purchasing and resident in Spain, was interviewed in 2017 using a revised and adapted version of the FRL instrument (Grunert et al., 1993). Questions about fruit purchasing criteria, consumption habits and demographics were also included. Factor and cluster analysis (Ward method) yielded four segments.

The segments identified are: “Total indifference (TI)”, small segment with disinterest in extra-domestic and social consumption, nutrition and innovation; “Little time to cook, concerned about nutrition and extra-domestic consumption (LICNE)”, the largest consumers of fresh fruit who show interest in nutrition and health, but no interest in the price of products, convenience foods or liking cooking; “Cooks and preference for natural products (COOKNAT)”, the largest segment, with a medium-high consumption of fresh fruits, who are related to cooking at home, natural products and a concern for the price-quality ratio; and “Unconcerned (UNC)” presents the lowest fresh fruit consumption and shows the lowest interest in natural products and some indifference to the other criteria.

The information obtained in this study gives interesting new insights for the marketing strategies of the fresh fruit suppliers to Spain and the Food and Public Health Administrations.

As one of the omnichannel sales models, “buy online and pick up in store” (BOPS) not only is used in the commercial field but also has gradually attracted many scholars’ interests. However, although there are numerous research ideas, most of the current work is still limited to theoretical and empirical research, and few scholars study BOPS through models. This paper aims to discuss the best market conditions and opportunities for the implementation of BOPS against the backdrop of omnichannel by means of mathematical models and data simulations and discuss the optimal price–service strategies under different sales models.

First, from the perspective of different consumer shopping types, this paper separately divides consumers into different groups in traditional “dual channel” and BOPS models. Then, the authors analyze the impact of company market size, consumer service sensitivity and the scale of BOPS on companies’ strategies and the profit of the supply chain. Subsequently, they conduct an empirical analysis through specific values. Finally, the authors further expand the model on the basis of the original research, and discuss the retailer’s fairness concerns and unit compensation strategy to ensure that the research content is more rigorous.

It is observed that whether companies adopt BOPS depends on consumers’ service sensitivity degree and the scale of BOPS consumers and online retailers: when the sensitivity and the proportion of online consumers are high or the number of BOPS consumers is large, it is more advantageous for companies to implement BOPS. Moreover, companies should not only consider the market scale and production cost but also have a precise orientation of consumers’ experience sensitivity and willingness to engage in extra consumption when making price and service strategies. At the same time, the compensation strategy of companies and the peer-regarding fairness concern behavior of offline retailers will affect the optimal price and service strategy in the BOPS model.

These results provide managerial insights for companies preparing to implement BOPS and promote the development of relevant theories in the channel field.

At present, most of the research on BOPS is based on empirical reviews. However, this paper analyzes the applicability and feasibility of implementing BOPS by using specific models, and it will provide some reference for companies preparing to implement BOPS. In addition, this paper also discusses the unit compensation strategy and peer-regarding fairness concern behavior in the BOPS model, which have not been studied by relevant scholars.

Nowadays, an extra consumption of electric energy in the Colombian houses is generated due to electric or electronic elements plugged into the electric network. This fact produces a cost overrun in the user’s electricity bills. To reduce this extra cost, and also with a plus of reducing greenhouse gas emission, a monitoring system for the consumption of electric energy in a household will be designed and implemented to make electricity users realize how much money and energy is being wasted due to the unnecessary electric elements plugged into the network. This paper aims to show a monitoring system that allows the client to supervise the consumption of some appliances inside his/her home, remotely. It is also considered the HMI to be able to log in, choose the intervals of data and generate reports and graphics. The monitoring system is based on the integration of several technologies that are already used and implemented in houses and buildings, such as: measuring and treatment of data electronically using microcontrollers, Wi-Fi technology and dynamic graphic interface (website).

The methodology consists of several tasks, starting from documentation of the variables, instrumentation and methods for getting to the solution; the first part of the methodology focuses on selecting the electric and/or electronic elements to be monitored, so the instrumentation is able to monitor. Then, the power stage was implemented in this stage to measure signals from the sensors while sensing the electric nodes are adjusted, so does the transmission and reception. In the third stage, the design information system was implemented; this is where the received data from the sensors are stored and managed for further organization and visualization. Activities included the following: Analysis of the model of use cases: Identification of actors and actions that are involved in the system. Server selection: Study of the different server to manage the database. Design of the database: The variables, tables, fields, profiles are determined for managing the information. Connection between sensors and database: Correct data transmission and managing to the database from the sensors. Finally, the system is validated in a rural house for a month.

The monitoring system satisfies the main objective of making a tracing of the behavior of some appliances inside a house, showing graphically the instant current generated while connected, the cumulated energy consumed and the cost in Colombian pesos of the energy consumed so far, in real time.

The monitoring system requires the correct functioning of the sensors connected to each household appliance in the home.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money. The innovative impact of the project will be based on the use of hardware and information systems in the measurement of electrical consumption.

This research has a direct impact on the economic aspects of the low-income population by allowing them to manage their energy consumption through the proposed system.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money.

Due to technological improvement and development of the vehicle-to-home (V2H) concept, electric vehicle (EV) can be considered as an active component of net-zero energy buildings (NZEBs). However, to achieve more dependable results, proper energy analysis is needed to take into consideration the stochastic behavior of renewable energy, energy consumption in the building and vehicle use pattern. This study aims to stochastically model a building integrating photovoltaic panels as a microgeneration technology and EVs to meet NZEB requirements.

First, a multiobjective nondominated sorting genetic algorithm (NSGA-II) was developed to optimize the building energy performance considering panels installed on the façade. Next, a dynamic solution is implemented in MATLAB to stochastically model electricity generation using solar panels as well as building and EV energy consumption. Besides, the Monte Carlo simulation method is used for quantifying the uncertainty of NZEB performance. To investigate the impact of weather on both energy consumption and generation, the model is tested in five different climatic zones in Iran.

The results show that the stochastic simulation provides building designers with a variety of convenient options to select the best design based on level of confidence and desired budget. Furthermore, economic evaluation signifies that investing in all studied cities is profitable.

Considering the uncertainty in building energy demand and PV power generation as well as EV mobility and the charging–discharging power profile for evaluating building energy performance is the main contribution of this study.

As climate change impacts residential life, people typically use heating or cooling appliances to deal with varying outside temperatures, bringing extra electricity demand and living costs. Water is more cost-effective than electricity and could provide the same body utility, which may be an alternative choice to smooth electricity consumption fluctuation and provide living cost incentives. Therefore, this study aims to identify the substitute effect of water on the relationship between climate change and residential electricity consumption.

This study identifies the substitute effect of water and potential heterogeneity using panel data from 295 cities in China over the period 2004–2019. The quantile regression and the partially linear functional coefficient model in this study could reduce the risks of model misspecification and enable detailed identification of the substitution mechanism, which is in line with reality and precisely determines the heterogeneity at different consumption levels.

The results indicate that residential water consumption can weaken the impact of cooling demand on residential electricity consumption, especially in low-income regions. Moreover, residents exhibited adaptive asymmetric behaviors. As the electricity consumption level increased, the substitute effects gradually get strong. The substitute effects gradually strengthened when residential water consumption per capita exceeds 16.44 tons as the meeting of the basic life guarantee.

This study identifies the substitution role of water and heterogeneous behaviors in the residential sector in China. These findings augment the existing literature and could aid policymakers, investors and residents regarding climate issues, risk management and budget management.

Urban metropolitan consumers react to the different qualitative categorizations of the product thus creating homogeneous market segments. The aim of this paper is to identify specific market segments which allow for the definition of homogeneous olive oil consumer targets.

This study was based on the stated preferences of consumers and emphasizes the role that different quality scales of olive oil have in the eye of the consumer. The data, collected through a questionnaire, were analysed by means of inferential and multivariate statistics techniques, that is, the study specifically entailed a factorial and cluster analysis.

This paper explores olive oil market segments broken down by the different quality levels of existing products, thus trying to identify main consumer preferences. Our outcomes suggest the existence of three main quality classes of olive oil consumer: basic, popular and premium.

Even though we gathered data and information from a broad sample, the study does not fully reflect the average Italian population since we based our study on a convenience sample of northern Italian consumers. A more extended sample is needed to test our hypothesis in other regional areas.

The outcomes derived from this study provide useful insights both for marketers and olive oil producers by allowing more efficient strategic decisions in terms of product segmentation.

This study, aimed at matching olive oil market segments and consumer preferences, shows the existence of three well-defined quality classes of olive oil consumer: basic, popular and premium. In addition, this study ascertains for the first time how the attitude towards local products is positively influenced by family origin as a result of an inter-generational attitude.

Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Eating out is part of the pattern of food production and consumption by individuals in their household context. Changes in the importance and pattern of eating out indicate changes in its economic function. These changes reflect variations in household organization stemming from long‐term increases in income per head reinforced by significant changes in technology. Consumers will switch towards simpler eating habits and meals bought out will reflect commercially provided time‐intensity or skill‐requirement reduction. With changing technology, consumers will constantly upgrade the kinds of meals that they buy outside. Patterns of experimentation and cellularization of household members in eating out are influenced by economic factors. Re‐examines the concepts of social and convenience eating in the light of economic variables, particularly changes in work and leisure activities that affect food consumption.