Demand Behavior Research Articles

What matters for sustainability and climate change actions in developing countries: A stimulus–organism–behavior– consequence (SOBC) perspective

Sustainable behaviors demand wielding communication strategies in social and political spheres for public understanding of scientific issues like climate change and the severe consequences of deteriorating environmental quality. Reliable information can improve public understanding of science and enhance public support for climate change actions at social and political levels. This study strives to examine the socio-political and psychological factors that affect climate change actions based on the stimulus-organism-behavior-consequences paradigm. This study further categorizes sustainable behavior into mitigation and adaptation strategies to capture behavioral consequences with the moderating effects of information literacy and a regulative environment. Empirical findings demonstrate interesting associations between socio-political and psychological factors. Significant effects were observed for environmental quality and belief in climate change in explaining pro-environmental behavior. This study reveals that people are more prone to mitigation than adaptation strategies to ensure sustainability and recommends better communication strategies to empower those already engaged in mitigating climate change and encourage those still struggling to adapt to climate change actions.

Conditions for increasing the competitiveness of agro-food enterprises

Increasing the competitiveness of agro-food sector enterprises from a strategic perspective is inextricably linked to ensuring sustainable development and gaining long-term competitive advantages based on sustainable values and priorities. This implies the need to consider sustainable and environmentally neutral production methods in the management policy of business entities, the result of which should be new competitive products, original in terms of their quality parameters. Competitiveness implies enterprises’ high efficiency and ability to adapt to constantly changing environmental conditions. The level of competitiveness is formed not only at the level of individual producers but also at the level of the agro-food value chain in general and, thanks to the effect of scale and synergy, creates additional inclusive opportunities for improving the competitive positions of producers in the market. World experience has shown that solving these problems is possible by forming a competent, competitive strategy, which must meet the following criteria: the presence of clearly formulated relevant goals; the presence of a forecast of the development of the situation and scenarios for achieving the goal; the presence of a system of management and control of the main business processes. At the same time, the new policy of ensuring competitiveness should be aimed at strengthening the ties between agro-food chains and the market, consumers, and society, considering the interests of all parties. The priorities for achieving the goals of increasing the competitiveness of business entities along with commercial values should be increasing the added value of the agro-industrial production sphere, ensuring stable incomes of all participants in the agro-food chain, improving the use of resources and gradual transition to circular business models; formation of responsible consumer behavior and maximum satisfaction of consumer demand. The high variability of the external environment, the difficulties of identifying numerous and closely interacting factors, risks, and dangers, and the entropy of connections and relationships pose urgent tasks for businesses to form competitiveness as a system of strategies, measures, and technologies for maintaining market share and financial stability. Keywords: competitiveness, environment, agricultural enterprise, agro-industrial sphere, factors, drivers.

Abnormal Activity Recognition in Private Places Using Deep Learning

Using computer and machine vision technology, the process of analysing human motion is known as "human activity recognition," or HAR. Anomaly detection in security systems is one of the situations in which human activity recognition is useful. As the demand for security growing, surveillance cameras have been widely installed as the foundation for video analysis. Identifying anomalous behaviour demands strenuous human effort, which is one of the main obstacles in surveillance video analysis. It is necessary to establish video recording in order to automatically catch anomalous activities. Using deep learning methods, our intelligent video surveillance system can identify an anomaly in a video. Real-time detection of the actions is also possible, and these video frames will be afterwards preserved as photographs in the system for the user to examine.The suggested Abnormal Activity Recognition system was created with the goal of identifying and detecting irregularities through a live feed in the banking sector, more specifically in an ATM setting.The initial phase of the study focuses on the application of image deep learning techniques to recognise various items and spot unusual behaviour using ATM monitoring systems.

Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach

The prediction of air traffic demand (passengers and cargo) in a regional/national air transport system is essential. Knowing the behavior of future demand helps, on the one hand, the design and execution of air transport public policies, which, for example, help to focus, guide and prioritize investment (public and private) for the expansion / modernization of airport infrastructures (or development of new airports), act on tariff policies, implement changes in regulatory policy, etc.; on the other hand, it helps airport managers to plan the airport. Therefore, in this paper, a short-term forecast (5 years) of the demand for air cargo transport was carried out, applied to a specific case study (Colombia), taking into account the most severe pandemic period (the year 2020). To perform the forecast, an approach based on Machine Learning/Deep Learning (ML/DL) method comprising a hybrid of convolutional and recurrent memory neural networks (that allow space-temporal non-linear analysis, such as multi-variable spaces and temporal multi-steps), is presented. The analysis developed here establishes the optimal length of the prediction period; on the other hand, the proposed methodology allows the identification of the most relevant socioeconomic features in the prediction of air cargo demand (domestic and international), i.e., interpreting the ML/DL results obtained through the variational analysis of different combinations of features. The results show that international air cargo demand is strongly dependent on Gross Domestic Product (GDP) and PCG (Per Capita GDP), while domestic air cargo demand is significantly dependent on PCG. Finally, the results show, for the case study country, very rapid recovery of air cargo demand at pre-pandemic rates (behavior already found in other recent studies and research).

Reducing electricity peak loads through ‘pause hours’ - a community-based behavioural demand response approach

Households can provide demand-side flexibility by changing their consumption behaviour and shifting energy-intensive activities – such as vacuuming, cooking or charging electric cars – to off-peak hours. Such behaviour-based demand response management could balance out consumption peaks without the need for costly smart home devices or automation technologies. However, existing research has struggled to motivate consumers to adapt their behaviour or maintain behaviour change over extended periods of time. This field study explored a scalable and cost-effective behavioural demand response tool and investigated its short- and long-term impacts on average and peak hour electricity consumption under realistic conditions: A smartphone app provided users with social comparison feedback on their electricity consumption and invited them to participate in “pause hours” by avoiding energy-intensive activities during peak hours. To appeal even to hard-to-reach energy users and elicit longer-term engagement, the app also contained a neighbourhood newsfeed and was framed as a local social network. In a 15-month trial with 550 student apartments in Sweden, more than half of the residents voluntarily installed the app, many app users stayed engaged over an extended period of time and pause hour participants achieved substantial peak-load consumption reductions of 46% on average. These results indicate that smartphone apps can achieve peak load consumption reduction and long-term engagement, although they may be particularly attractive to already energy efficient households. Avenues for future research are discussed.

Word-of-Mouth on Action: Analysis of Optimal Shipment Policy When Customers are Resentful

Word-of-Mouth (WoM) communication via online reviews plays a vital role in customers’ purchasing decisions. As such, retailers must consider the impact of WoM to manage customer perceptions and future demand. This paper considers an online shopping system with premium and regular customers. Building on the behavioral and operations management literature, we model customer preferences based on the perceived service quality indicated by WoM and integrate this into the retailer’s operational problem to determine a shipment policy regarding the timing of consolidated shipments and the treatment of regular demand. First, we study the e-tailer’s problem when they have no knowledge of WoM and only react to the changes in demand. We analyze the long-term behavior of customer demand and show that potential market size and customer sensitivity are the key parameters determining this behavior. Then, we build a model to integrate the knowledge of WoM into operational decision-making and partially characterize the optimal solution. We show that (i) underpromise-and-overdeliver can be a hurtful strategy since it creates a false sense of fast delivery for the regular service, (ii) relaxations in operational constraints may hurt profitability due to the associated difficulties of managing perceptions, and (iii) seeking a stationary policy can lead to suboptimal solutions; therefore, cyclic policies should also be considered when appropriate.

Spatiotemporal synchronous coupling algorithm for urban rail transit timetables design under dynamic passenger demand

This paper focuses on the problem of designing the schedule of urban rail transit systems under dynamic passenger demand considering limited train capacity and congestion. From the point of view of operators, the goal of timetable optimization consists of using as few as possible service trains to transport the arriving passengers to their destinations securely and quickly. On the other hand, passengers want to spend the minimum time travelling to their destinations, including the time they wait on platforms until they board the first and successive trains in case of transferring between lines. Due to the variability of passenger flows and the difficulty in exactly solving full-day timetabling problems in short computation times, it is difficult in practice to use the approaches proposed in the literature. Moreover, when the capacity of trains is considered and oversaturation conditions emerge, the problem becomes even more complex, converting into a challenge the design of schedules from an operational point of view. By considering fixed train dwell times at stations and train running times between stations, we first propose a non-linear programming formulation to model the problem of determining the most convenient interdeparture times of services at the first station of a line, aiming at minimizing the waiting time of passengers. Since the dynamic behaviour of passenger demand and the maximum sectional passenger flow are approximately synchronous, an innovative method, the spatiotemporal synchronization coupling algorithm, based on working with the time-varying maximum sectional passenger flow instead of the demand patterns is proposed to quickly solve in an approximate way the demand-driven timetabling problem. The proposed approach is applied to a real case of Chengdu Metro Line 1 in China. The results show the effectiveness of the algorithm, both in terms of computational effort and in terms of reducing the waiting time currently experienced by passengers.

Brain metabolite profiles provide insight into mechanisms for behavior sexual dimorphisms in zebrafish (Danio rerio)

The zebrafish (Danio rerio) has historically been a useful model for research in genetics, ecology, biology, toxicology, and neurobehavior. Zebrafish have been demonstrated to have brain sexual dimorphism. However, the sexual dimorphism of zebrafish behavior demands our attention, particularly. To evaluate the behavior and brain sexual dimorphisms in zebrafish, this study assessed sex differences in adult D. rerio in four behavioral domains, including aggression, fear, anxiety, and shoaling, and further compared with metabolites in the brain tissue of females and males. Our findings showed that aggression, fear, anxiety and shoaling behaviors were significantly sexually dimorphic. Interestingly, we also show through a novel data analysis method, that the female zebrafish exhibited significantly increased shoaling behavior when shoaled with male zebrafish groups and, for the first time, we offer evidence that male shoals are beneficial in dramatically alleviating anxiety in zebrafish. In addition, there were significant changes in metabolites in zebrafish brain tissue between the sexes. Furthermore, zebrafish behavioral sexual dimorphism may be associated with brain sexual dimorphism, with significant differences in brain metabolites. Therefore, to prevent the influence or even bias of behavioral sex differences on results, it is suggested that behavioral studies or behavioral-based other relevant investigations consider sexual dimorphism of behavior and brain.

Multi-objective optimization of IoT-based green building energy system using binary metaheuristic algorithms

Green building energy optimization systems (GBEOS) are of great interest to academics and industry professionals. One of the main challenges of GBEOS is optimizing energy consumption while maintaining people's comfort level. This paper proposes a metaheuristic-based GBEOS for optimizing energy consumption within a smart green building operating under a smart grid paradigm while considering the local environment and energy demand behaviors. In this regard, two complementary control procedures are developed: the supply-part optimizer (SPO) and the demand-part optimizer (DPO). By combining SPO and DPO, six binary multi-objective optimization algorithms, including Xtreme gradient boosting (XGBoost), multiverse optimizer (MVO), moth flame optimization (MFO), shuffled frog leaping algorithm (SFLA), moth flame optimization, imperialist competitive algorithm (ICA), and chimp optimization algorithm (ChOA) are developed to simultaneously minimizes costs and maximizes client comfort satisfaction (CS). First of all, considering the importance of convergence speed, seven unimodal, six multimodal, and twenty complex benchmark optimization functions are used to evaluate the proposed approach's convergence behavior. Afterward, an efficient weighting mechanism is used to handle the cost-comfort trade-off, allowing the user to discover a reasonable trade-off between monetary expense and comfort level. Then, in order to determine the influence of the pricing scheme's nature on the potential savings achieved through EMS, three individual models named real-time pricing (RTP), time-of-utilize pricing (TUP), and fixed-pricing plan (FPP) are proposed. The results show that ChOA presents the best result in terms of convergence, with 19 out of 33 test functions; also, XGBoost has the second-best performance. In addition, results show that suggested metaheuristic algorithms significantly increase PV energy penetration for self-consumption and lower power costs while keeping an optimal balance between CS and economical costs. Moreover, using dynamic pricing systems, i.e., RTP or TUP, results in great user price savings than FPP. To sum up, ChOA is found to be the best algorithm in this challenge, followed by XGBoost in second place.

Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors

Increasing penetration of electric vehicles brings a set of challenges for the electricity system related to its energy, power and balance adequacy. Research related to this topic often requires estimates of charging demand in various forms to feed various models and simulations. This paper proposes a methodology to simulate charging demand for different driver types in a local energy system in the form of time series of charging sessions. The driver types are extracted from historical charging session data via data mining techniques and then characterized using a kernel density estimation process. The results show that the methodology is able to capture the stochastic nature of the drivers’ charging behavior in time, frequency and energy demand for different types of drivers, while respecting aggregated charging demand. This is essential when studying the energy balance of a local energy system and allows for calculating future demand scenarios by compiling driver population based on number of drivers per driver type. The methodology is then tested on a simulator to assess the benefits of smart charging.

Moral Disjunction and Role Coadunation in Business and the Professions

We consider the problem of moral disjunction in professional and business activities from a virtue-ethical perspective. Moral disjunction arises when the behavioral demands of a role conflict with personal morality; it is an important problem because most people in modern societies occupy several complex roles that can cause this clash to occur. We argue that moral disjunction, and the psychological mechanisms that people use to cope with it, are problematic because they make it hard to pursue virtue and to live with integrity. We present role coadunation as a process with epistemic and behavioral aspects that people can use to resolve moral disjunction with integrity. When role coadunation is successful, it enables people to live virtuous lives of appropriate narrative disunity and to honor their identity-conferring commitments. We show how role coadunation can be facilitated by interpretive communities and discuss the emergence and ideal features of those communities.

Impact of Kindergarten Transition Practices in Promoting Positive Behavioral School Readiness Skills

Successfully adjusting to the behavioral demands of kindergarten is a pivotal, yet challenging, developmental milestone for students, making it imperative that schools have a comprehensive menu of universal transition practices and targeted transition interventions available. This systematic review was conducted to synthesize and evaluate the existing research on the outcomes associated with universal transition practices and targeted transition interventions aimed at improving social-emotional behavioral skills important to the transition to kindergarten. 17 studies were identified that met the inclusion criteria. Results from this review highlight the utility of targeting self-regulation skills in students transitioning to kindergarten through multi-component interventions that incorporate caregiver involvement. Limitations, directions for future research, and implications for practice are discussed.

Application of the Generalized Normal Distribution Optimization Algorithm to the Optimal Selection of Conductors in Three-Phase Asymmetric Distribution Networks

This article addresses the problem of the optimal selection of conductors in asymmetric three-phase distribution networks from a combinatorial optimization perspective, where the problem is represented by a mixed-integer nonlinear programming (MINLP) model that is solved using a master-slave (MS) optimization strategy. In the master stage, an optimization model known as the generalized normal distribution optimization (GNDO) algorithm is proposed with an improvement stage based on the vortex search algorithm (VSA). Both algorithms work with discrete-continuous coding that allows us to represent the locations and gauges of the different conductors in the electrical distribution system. For the slave stage, the backward/forward sweep (BFS) algorithm is adopted. The numerical results obtained in the IEEE 8- and 27-bus systems demonstrate the applicability, efficiency, and robustness of this optimization methodology, which, in comparison with current methodologies such as the Newton metaheuristic algorithm, shows significant improvements in the values of the objective function regarding the balanced demand scenario for the 8- and 27-bus test systems (i.e., 10.30% and 1.40% respectively). On the other hand, for the unbalanced demand scenario, a reduction of 1.43% was obtained in the 27-bus system, whereas no improvement was obtained in the 8-bus grid. An additional simulation scenario associated with the three-phase version of the IEEE33-bus grid under unbalanced operating conditions is analyzed considering three possible load profiles. The first load profile corresponds to the yearly operation under the peak load conduction, the second case is associated with a daily demand profile, and the third operation case discretizes the demand profile in three periods with lengths of 1000 h, 6760 h, and 1000 h with demands of 100%, 60% and 30% of the peak load case. Numerical results show the strong influence of the expected demand behavior on the plan’s total costs, with variations upper than USD/year 260,000.00 between different cases of analysis. All implementations were developed in the MATLAB® programming environment.

Application of the Gradient-Based Metaheuristic Optimizerto Solve the Optimal Conductor Selection Problemin Three-Phase Asymmetric Distribution Networks

This study addresses the problem of selecting the conductor sizes for medium-voltage distribution networks with radial configurations. The optimization model that represents this problem is part of the mixed-integer non-linear programming (MINLP) models, in which a power flow must be solved for each possible combination of conductor sizes. The main objective of this optimization problem is to find the best set of conductor sizes that minimize an economic objective function composed of the total costs of conducting materials added with the expected annual costs of the energy losses by proposing a new hybrid optimization methodology from the family of combinatorial optimization methods. To solve the MINLP model, a master–slave optimization method based on the modified version of the gradient-based metaheuristic optimizer (MGbMO) combined with the successive approximation power flow method for unbalanced distribution networks is presented. The MGbMO defines the set of conductor sizes assignable for each distribution line using an integer codification. The slave stage (three-phase power flow) quantifies the total power losses and their expected annual operating costs. Numerical results in the IEEE 8-, 27-, and 85-bus grids demonstrate the effectiveness of the proposed master–slave optimizer when compared with multiple combinatorial optimization methods (vortex search algorithm, the Newton-metaheuristic optimizer, the traditional and Chu and Beasley genetic algorithms, and the tabu search approaches). Two scenarios regarding the demand behavior were analyzed for the IEEE 8- and 27-bus grids: a peak load operation was considered, and, for the IEEE 85-bus grid, the daily demand behavior, including the presence of renewable generators, was considered. The 85-bus grid allowed showing that the most realistic operative scenario for selecting conductors is the case where a demand curve is implemented since reductions over 40% in the annual investment and operating costs were found when compared to the peak load operating condition. All numerical validations were performed in MATLAB software.

The impact of emotional intelligence, increasing job demands behaviour and subjective well-being on teacher performance: teacher-gender differences

PurposeThe purpose of this paper is to explore the critical roles of emotional intelligence, increasing job demands behaviour and subjective well-being in teachers' performance throughout their gender.Design/methodology/approachIn this study, the authors used multi-group structural equation modelling and mediation analysis of a sample of 602 primary school teachers in Iran.FindingsThis study found that emotional intelligence significantly affected teachers' performance. Teachers' emotional intelligence and increasing job demands behaviour were significant predictors of teacher performance in both genders. Furthermore, increasing job demand behaviour had a stronger mediating effect than subjective well-being on the relationship between emotional intelligence and teachers' performance in both genders.Originality/valueThis model is an attempt to examine possible gender differences on the relationships between teachers' emotional intelligence and their job performance by mediating roles of subjective well-being and increasing job demands behaviours in a specific societal and educational context.

Sources of price elasticity of demand variability among Spanish resort hotels: a managerial insight

设计/方法/途径该论文评估了位于西班牙不同目的地的七家四星级度假酒店在旺季期间的在线需求函数。比较不同的价格弹性值, 分析影响价格弹性的不同因素。研究目的本文的目的是使用需求行为估计来找出西班牙不同目的地度假酒店价格变化的来源。研究发现主要调查结果表明：（1）大多数旺季期间的需求弹性, 但诸如度假村的中心位置、最近的翻新、额外设施/服务的可用性以及针对夫妻和/的酒店等因素或半食宿使需求更加缺乏弹性; (2) 特内里费酒店的价格弹性需求最大; (3) 在离入住日期最近的预订期间, 需求通常更具弹性; (4) 更多的本地竞争者压低了酒店价格。研究原创性/价值本论文强调了酒店经营者应关注更有利可图的需求细分市场的管理意义。研究发现对于开发旨在改进预测的收益管理软件特别有价值。

A new approach to the uniqueness of equilibrium with CRRA preferences

This paper investigates the uniqueness of equilibrium in the economy where agents have preferences with possibly distinct levels of relative risk aversion. Equilibrium prices exist in a price range determined by micro and macroeconomic information. Macroeconomic information is inferred from two representative-agent economies, which provide lower and upper bounds on aggregate demand, respectively. Analysis of the first and second-order price effects shows that individual demand has a unique inflection point at which the income effect maximally dominates the substitution effect. In contrast, aggregate demand may have multiple inflection points, which creates multiple equilibria. Sufficient conditions for the uniqueness of equilibrium are characterized by the local behavior of individual demand at the inflection point. They require that aggregate demand must have no more than two inflection points in the equilibrium price range. The result of this paper sheds light on the Sonnenschein-Mantel-Debreu theory that has negative implications for the uniqueness of equilibrium.

Public information needs during the COVID-19 outbreak: a qualitative study in mainland China

PurposeThe impact of COVID-19 has led to a surge in the public’s reliance on the Internet for pandemic information, and the policy of home isolation has exacerbated this. This study aimed to investigate public information needs and ways of accessing and disseminating information during COVID-19 in mainland China.Design/methodology/approachThis study used a qualitative research approach to conduct semi-structured interviews with 15 participants from 9 cities in mainland China about information needs and access behaviors during the COVID-19 outbreak. All interview recordings were converted into text and proofread, then coded and summarised in correspondence with the research questions using the grounded theory.FindingsThis study summarized the dynamics of public information needs during the 2.5-year pandemic and identified the difficulties in accessing certain information.Originality/valueAlthough information needs of public health emergencies have been a hot topic during COVID-19, scant studies focus on information needs in specific countries in Asia, especially in mainland China, the first country with a major outbreak and stringent lockdown mandates. Therefore, the current study is well enriched by focusing on information demand behavior in the context of COVID-19. Possible measures for improvement were also given to existing and potential problems, taking into account the participants’ views.

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.

A Review of the Role of the School Spatial Environment in Promoting the Visual Health of Minors.

Rising childhood myopia rate has detrimental health consequences that pose a considerable challenge to health systems. The school spatial environment, which is where students are for the longest period of time, has a high health value for myopia systematic intervention. While research has demonstrated associations between physical daylight environments, medical gene and visual health, the literature currently lacks a synthesis of evidence that will act as a spatially-organized resource for school designers. This study is based on literature from the period 2000-2022 and has been taken from the Web of Science, scopus, Medline and CNKI core collection database. Collaboration, literature co-citation and quantitative and qualitative analysis, in addition to keyword co-occurrence are adopted to conduct a visual health research review. The results indicate that intensive near work activity (as a risk factor) and longer time spent outdoors (as a protective factor), are involved in visual health factors. Two main research themes are obtained and relate to: (1) The environment of visual work behavior (especially the near work learning environment) and adaptable multimedia learning environment; and (2) the environment of outdoor exposure behavior. Furthermore, with the variation of educational demands, models and concepts, there are different demands for near work behavior, and this study makes an important contribution by pointing to two future research directions, including the accurate and controllable environment of near work behavior, which operate in accordance with various educational mode requirements and the active design of the environment of outdoor exposure behavior. In referring to differences between regions and countries, as well as the development of the educational environment, it provides insight into how these demands can be controlled.