Predict Consumer Behavior Research Articles

Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm

The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users’ point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of “customization” has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers.

The impact of family business brand trust and crisis management practices on customer purchase intention during Covid-19

PurposeThe aim of the current study is to investigate the effect of consumers' brand trust in family businesses and the impact of crisis management practices on customer purchase intention during the period of Covid-19.Design/methodology/approachThis is a primary quantitative research on a sample of 817 consumers who bought products from 20 food and beverage family companies. Simple random sampling was used to collect the primary data. Data collection was made with the use of questionnaires. The questionnaires were developed based on previous studies and were tested in terms of reliability and validity.FindingsThe research findings indicate, among others, that there is a positive significant relationship between brand trust and purchase intention. As a result, the higher the trust of the consumers in a family business brand name, the higher their intention to make purchases. Additionally, it was found that the higher the performance of family businesses on crisis management practices, the more the consumers intent to make purchases.Research limitations/implicationsNonetheless, there are some limitations in the current research. First, the research sample consists of companies and consumers from a specific location, which can create issues regarding the generalization of the findings. Thus, to ensure improved research implications, a future research should include sample units from different locations and countries in order to reinforce the research findings and enable comparisons and more easily generalized outcomes. Moreover, a future research could assess additional factors that potentially affect purchase intention within a family business environment. Factors such as human resources and the relationship between customers and companies as well as communication and promotional efforts will allow the creation of a more stable and holistic framework and the prediction of consumer behavior. This potential follow-up research will further contribute to the theoretical argumentation of the findings and highlight the connection among purchase intention, trust and crisis managements practices within the family business environment.Practical implicationsRegarding the managerial and practical implications, the research outcomes can lead to specific strategies related to brand trust and crisis management practices. Specifically, family companies should invest on their brand name and their relationship with the consumers by reinforcing any action which can potentially affect the trust of the consumers. This could be realized by providing a safe purchase environment according to health and safety standards and through their response strategy and adaptability to the current pandemic conditions through the use of crisis management practices. Finally, a practical response to social and physical distancing measures would increase brand trust, and the performance on crisis management practices could also lead to sufficient financial outcomes since all these factors positively affect consumers' purchase intention.Originality/valueThe outbreak of the pandemic directly affected the profitability but also the viability of companies, regardless of industry. In particular, the impact that the pandemic had on family businesses, financially, organizationally and operationally, was massive and in many cases, catastrophic. Several studies have focused on family businesses, analyzing their structure, advantages and disadvantages. However, in these studies, there is little focus on the brand's influence on consumer behavior and market intention, especially compared to nonfamily businesses and under unique circumstances such as those of a pandemic.

Deployment of the Microeconomic Consumer Theory in the Artificial Neural Networks Modelling: Case of Organic Food Consumption

Organic food consumption has become a significant trend in consumer behaviour, determined by various motives, among which the price does not play a major role, thus reflecting the Lancaster approach to the microeconomic consumer theory. Additionally, artificial neural networks (ANNs) have proven to have significant potential in providing accurate and efficient models for predicting consumer behaviour. Considering these two trends, this study aims to deploy the Lancaster approach in the emerging area of artificial intelligence. The paper aims to develop the ANN-based predictive model to investigate the relationship between organic food consumption, demographic characteristics, and health awareness attitudes. Survey research has been conducted on a sample of Croatian inhabitants, and ANN models have been used to assess the importance of various determinants for organic food consumption. A Three-layer Multilayer Perceptron Neural Networks (MLPNN) structure has been constructed and validated to select the optimal number of neurons and transfer functions. One layer is used as the first input, while the other two are hidden layers (the first covers the radially symmetrical input, sigmoid function; the second covers the output, softmax function). Three versions of the testing, training, and holdout data structures were used to develop ANNs. The highest accuracy was achieved with a 7-2-1 partition. The best ANN model was determined as the model that was showing the smallest percent of incorrect predictions in the holdout stage, the second-lowest cross-entropy error, the correct classification rate, and the area under the ROC curve. The research results show that the availability of healthy food shops and consumer awareness of these shops strongly impacts organic food consumption. Using the ANN methodology, this analysis confirmed the validity of the Lancaster approach, stating that the characteristics or attributes of goods are defined by the consumer and not by the product itself.

Identification of Perspectives and Challenges of Behavioural Economics for the Next Generation

In the globally competitive business scenario the implication of behavioural economics is necessary to predict the behavioural shifts among the next-generation customers. The present study includes a critical evaluation regarding different perspectives and challenges associated with the implication of BE to assess the behavioural changes to occur among the future generation consumers. A secondary qualitative strategy is adopted in this study along with including an exploratory research design to evaluate the topic in a critical manner. Thematic analysis is performed in this study based on key findings obtained from secondary sources available on the online platform. The key findings show that BE is an effective tool for modern business units such as public goods organizations and tourism units to anticipate the behavioural changes to occur among the next generation consumers in respect of purchasing goods and availing services. The influence of social factors and changes in emotional requirements are found to be major factors to influence customers’ shifts in behaviours in the future period. Challenges can also be faced in respect of the communication gap and thus an appropriate implication of BE would contribute to an effective prediction of consumer behaviour to ensure suitable strategic changes in the future period. Keywords : Behavioural Economics, Cognitive, Cultural Influence, Homo Economicus, Next-Generation Customers, Public Goods.

Інформаційно-аналітичне забезпечення прогнозування споживання результатів модернізації організаційно-технологічних систем

One of the most important challenges that appear when consumers expect the results of modern organizational and technological systems in industrial production within a certain territorial formation, is to forecast the level of the results consumption of high-tech enterprises that are being modernized. Today, the advantage factor of consumer requirements plays a significant role in the field of high-tech products and services. The need to solve this problem is due to the fact that due to various reasons, high-tech enterprises are located very unevenly. There is a problem of rational spatial redistribution. In order to solve this problem classic or modified Reilly models are used, which are gravitational analogues to determine consumer preferences. The influence of the distance factor on the consumer's choice is reflected in using the hypotheses: in the classical model is a proportional square of the distance between the centres, i.e. the organizational and technological system and the consumer; in the modified model is in accordance with the exponential law. The proposed model for predicting consumer behaviour of hightech products, which allows to solve the problem: according the known financial sufficiency of the consumer and a given "geometry" of territorial education it is possible to calculate the optimal location of consumption centres with the required level of service quality; for existing enterprises and its organizational and technological structures to develop recommendations for improving the required quality of services to attract consumers; by known quality and location of consumption centres indirectly determine the financial sufficiency of consumers of this territorial education.

The Prediction of Consumer Behavior from Social Media Activities.

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.

A practical review of electroencephalography’s value to consumer research

Electroencephalography (EEG) offers insights into the neural responses of consumers to marketing stimuli which can be used to evaluate the effectiveness of such stimuli in directing favorable consumer behavior in real-world settings. This practical review provides guidelines for using EEG in consumer and marketing research. It provides recommendations for how EEG may effectively be employed in neuromarketing in the future. EEG requires careful experimental design, and as such, we outline current recommendations for the collection, processing, analysis, and interpretation of EEG evoked potentials (i.e., event related potentials; ERP) and spectral content (i.e., EEG frequency). By providing an introductory overview of EEG measures in marketing and consumer research, this practical review extends previous literature that is primarily focused on other neuroimaging techniques (e.g., functional magnetic resonance imaging) and other disciplines (e.g., economics and organizational behaviour). Furthermore, by reviewing how EEG has been used throughout psychophysiological and neuromarketing research, we provide recommendations on how EEG can be used to measure marketing-related outcomes. These include processes relating to perception, attention, memory, emotion, and cognitive load, demonstrating the unique value of considering the neural responses captured by EEG in understanding and predicting consumer behavior.

Development and demonstration of software application functionality for solving the problem of consumer behavior prediction

Abstract Markov chains have a wide application in predicting the movement of various phenomena. The aim of this paper is to present the development and functionality of a software application for solving the problem of predicting consumer behavior using the Markov chain method. The software application is primarily intended for predicting the use of electronic services and forecasting the number of users. The work of the software application was tested on the example of an organization whose activity is the provision of electronic and financial services. The importance of predicting the use of services and forecasting the number of users is reflected in the assessment of the capacity of servers on which the services of the organization are located. The software application contains user instructions for easier use and interpretation of the obtained results.

Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper

An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users’ needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers’ behaviour by using a decision-making model, which analyses the consumer’s choices and helps the decision-makers to understand their potential clients’ needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making.

Measurement and structural modelling on factors of Islamic Fintech adoption among millennials in Malaysia

Purpose This study aims to examine the results of structural equation modelling in applying unified theory of acceptance and use of technology in adopting Islamic Fintech among millennials in Malaysia via measurement and structural models. Design/methodology/approach A total of 418 valid responses have been obtained from Malaysians who are using Islamic Fintech. Before the data is analysed into measurement and structural modelling preliminary analysis such as common method bias has been conducted. Findings All the requirements for model fit in this study have been achieved. Four exogenous constructs are performance expectancy, effort expectancy, social influence and facilitating condition. The mediating construct is behavioural intention, whereas the endogenous variable is user adoption. All exogenous constructs show significant p-values except for effort expectancy. Practical implications This study offers important implications, specifically for the digital economy that is currently making its way throughout every aspect of human life, namely, social, religious, financial transaction, entertainment and others. The impact of the digital economy can be traced through the emergence of Fintech. The adoption of Islamic Fintech is one of the least discussed areas academically, therefore, this study is considered necessary to explore the prediction of consumer behaviour in Islamic Fintech adoption as a part of the digital economy in Malaysia. Originality/value This study fills the perceived gap in the existing financial technology literature by assessing Islamic financial technology adoption via measurement and structural modelling.

Understanding why consumers in China switch between wild, farmed and synthetic bear bile products.

An important rationale for legally farmed and synthetic wildlife products is that they reduce illegal, wild‐sourced trade by supplying markets with sustainable alternatives. For this to work, more established illegal‐product consumers must switch to legal alternatives than new legal‐product consumers switch to illegal wild products. Despite the widespread debate on the magnitude and direction of switching, studies among actual consumers are lacking. We used an anonymous online survey of 1421 traditional Chinese medicine consumers in China to investigate switching among legal farmed, synthetic, and illegal wild bear bile. We examined the past consumption behavior, applied a discrete choice experiment framed within worsening hypothetical disease scenarios, and used latent class models to investigate groups with shared preferences. Bear bile consumers (86% respondents) were wealthier, more likely to have family who consumed bile, and less knowledgeable about bile treatments than nonconsumers. Consumer preferences were heterogenous, but most consumer preferences switched between bile types as disease worsened. We identified five distinct latent classes within our sample: law‐abiding consumers (34% respondents), who prefer legal products and were unlikely to switch; two all‐natural consumer groups (53%), who dislike synthetics but may switch between farmed and wild; and two nonconsumer groups (12%), who prefer not to buy bile. People with past experience of bile consumption had different preferences than those without. Willingness to switch to wild products was related to believing they were legal, although the likelihood of switching was mediated by preferences for cheaper products sold in legal, familiar places. We found that consumers of wild bile may switch to legal alternatives, given the availability of a range of products, whereas legal‐product consumers may switch to illegal products if the barriers to doing so are small. Understanding preferences that promote or impede switching should be a key consideration when attempting to predict consumer behavior in complex wildlife markets.

Order Online or Call it in? Conceptualization and Measurement of Preferences for Computer versus Human Interaction.

Technology is increasingly integrated into everyday life and consequently, traditional social exchanges between human agents have evolved to include human-computer interaction, providing scientists new directions for understanding and predicting consumer behavior. Despite progress, there are conceptual and empirical limitations in current measures applied to consumer/user preferences. This paper documents the development and validation of, the 10-item preference for computer versus human interaction (PCHI) scale across three distinct samples and incorporating items that (a) include direct comparisons between computers and humans, (b) are independent of specific contexts or technologies, and (c) capture major theoretical domains of social, consumer, and human factors psychology. Results support the hypothesized three-factor structure (efficiency, ease of use, and trust) and demonstrate the utility of this measure to predict everyday consumer decisions beyond extant measures. Additionally, the PCHI offers marketing, user experience, and other practitioners a brief instrument for developing interventions, training protocols, and modeling attitudes.

Determinants of Electric Cars Purchase Intention in Poland: Personal Attitudes v. Economic Arguments

Urban e-mobility, seen as a part of complex and multidimensional European Green Deal plan, is essential for cities. However, it cannot be implemented without a common social commitment accompanied by a shared, strong belief in its advantages. Even if urban authorities and central governments would encourage their citizens to buy or share an electric vehicle (EV), the shift to EV will not be significant without people convinced that the idea of becoming zero-emission is economically viable and rational to them privately. This is especially true and important in countries like Poland—which is classified as an “EV readiness straggler”. The main purpose of this study is to develop a robust forecasting model with the aid of advanced machine learning methods. Based on the survey conducted, we identified factors useful for predicting consumer behaviour in terms of willingness to purchase an EV. The proposed machine-learning tool (specifically, the Random Forest algorithm) will allow automotive companies to more effectively target factors supporting the promulgation of urban individual e-mobility.

Embracing biodegradable bags: effects of ethical self-identity on consumer buying behavior

PurposeThis study aims to predict ethical self-identity’s effect on consumer buying behavior of biodegradable bags. Moreover, a moderated mediation model is tested by analyzing the mediating role of attitude and the moderating role of religiosity to investigate buying behavior. Few studies have been conducted to explore moderated mediation to predict consumer behavior based on belief congruence and planned behavior theories, according to previous literature, indicating a literature gap in consumer behavior studies. Accordingly, the moderating role of religiosity in the relationship between ethical self-identity and purchase intention in the context of biodegradable bags was used to contribute to the consumer behavior literature.Design/methodology/approachThe population of the study included literate consumers who are aware of biodegradable bags and keen to buy them. The data were collected from consumers of twin cities (Rawalpindi/Islamabad) via purposive sampling. The data were collected from 310 consumers five-time lags.FindingsThe findings suggest that ethical self-identity influences the buying behavior of consumers toward biodegradable bags. In this study, the theory of planned behavior provides greater implications to predict consumer buying behavior because the subjective norm, perceived behavioral control and attitude were found to have a significant influence on purchase intention, which further predicts the actual consumer behavior.Research limitations/implicationsThe study’s sample was limited to one geographical area and the sample was limited to only biodegradable bags. Future studies may go for a cross-comparison of industries or multiple environment-friendly products to enhance the generalizability of the research.Practical implicationsThis study provides useful insights to the companies of consumer-based brands to use eco-friendly practices from production to packaging and consumption. Environment-friendly consumers are ready to pay a premium price for environment-friendly products, which encourages decision-makers companies to cater to this niche market. Moreover, companies can focus on factors that influence their decision-making, such as ethical and moral values of responsibility, while planning their advertising strategies. Furthermore, the positioning of the environmental or consumer personal care products should be done by considering the personal disposition factors because they reinforce the actual behavior of employees.Social implicationsFor researchers, this research opens new avenues for predicting consumers’ buying behavior by applying the theory of planned behavior in the plastic bag industry. This study provides useful insights to the companies of consumer-based brands to use eco-friendly practices from production to packaging and consumption.Originality/valueThe proclivity to buy biodegradable bags increased over time, providing opportunities to the companies to offer environmentally friendly products to an increasing number of customers. However, personal deposition factors, such as religiosity and consumer views, are less well understood in terms of predicting actual behavior. Few studies have been conducted to explore moderated mediation to predict consumer behavior based on belief congruence and planned behavior theories, according to previous literature, indicating a literature gap in consumer behavior studies.

Predicting consumer behaviour toward digital K-pop albums: An extended model of the theory of planned behaviour

This study examines intent to purchase digital K-pop albums among K-pop fans in Indonesia. It employs the extended theory of planned behaviour (TPB) by integrating two additional variables: (1) frequency of K-pop consumption and (2) idol worship. The data, collected from 421 Indonesian K-pop fans, were processed using the structural equation model—partial least square modelling technique. The results indicate that, with the exception of attitude toward digital K-pop albums, all TPB constructs positively and significantly affect the purchase intentions of K-pop fans. Meanwhile, the attitude toward the digital K-pop album variable has a significant negative impact on purchase intention. The two additional variables (ie frequency of K-pop consumption and idol worship) influence fans to be more open to Korean culture and music. These findings will help the K-pop industry develop a strategy to better understand fan purchase behaviour in order to increase the popularity of K-pop both at home and abroad, and hence drive sales. This study is the first to empirically employ the extended model of TPB by integrating idol worship and frequency of K-pop consumption to analyse the purchase intentions of K-pop fans.

Social media analytics for nonprofit marketing: #Downsyndrome on Twitter and Instagram

AbstractIntroductionSocial media listening and monitoring of user‐generated content (UGC) in commercial marketing is central to measuring social media users' perceptions of a brand or company. Applications of social media analytics (SMA) have become common practice in marketing and are employed to predict consumer behavior. However, critical reflections on SMA applications to nonprofit marketing are lacking, despite the increased usage of SMA by nonprofit organizations.ObjectiveThe article proposes to apply SMA to analyze UGC and identify how computational methodologies can help bolster strategic communication for nonprofit organizations and drive marketing strategy.MethodologyThe article presents results from a 2‐year (October 2017–January 2020) social media monitoring of the hashtag #DownSyndrome on Twitter and Instagram. SMA tools will be used. Specifically, sentiment analysis and topic modeling are employed to analyze tweets and Instagram posts, while image classification is used to analyze Instagram images.FindingsThe results highlight a strong stereotypical characterization of people with Down syndrome in content that is generated by social media users and identify possibilities and challenges ahead of nonprofit organizations pushing specific agendas.Originality and ContributionThis study is the first to offer a review of SMA, apply them to a nonprofit context and reflect on aspects of representation and stereotypization through them. It ultimately proposes to support nonprofit and voluntary marketing research and practice by integrating UGC research and computational techniques into the broader discussion of ethical content strategy in social media nonprofit marketing.

Uncovering the antecedents of trust in social commerce: an application of the non-linear artificial neural network approach

PurposeThe internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a challenge that must be addressed. Hence, this study aims to examine the antecedents of trust in mobile social commerce by applying linear and non-linear relationships based on partial least squares structural equation modeling and artificial neural network model.Design/methodology/approachThis study applied a non-linear artificial neural network approach to provide a further understanding of the determinants of trust in mobile social commerce based on a non-linear and non-compensatory model. Besides, a questionnaire was distributed to 340 social commerce customers in Malaysia.FindingsThe conceptual framework for investigating trust in mobile social commerce has various advantages and contributions to predicting consumer behavior. The results of the study showed there is a positive and significant relationship between social support, presence and unified theory of acceptance and use of technology2 (UTAUT2). In addition, UTAUT2 has fully mediated the relationship between social support, presence and trust in social commerce. Finally, the results concluded the relationship between UTAUT2 and trust in social commerce would be stronger when the diffusion of innovation and innovation resistance is high and low, respectively.Research limitations/implicationsThe current study provides a novel perspective on how customers can trust social m-commerce to provide real solutions to managers of encouraging e-marketing among consumers.Practical implicationsThis paper shows how businesses can develop trust in social m-commerce in Malaysian markets. The findings of this study probably could be extended to other businesses in Asia or other countries. Because trust in social e-commerce has a dynamic role in consumer behavior and intention to purchase.Originality/valueThis study provided a new perspective on mobile social commerce and paid more attention to an investigation of such emerging commerce. The originality of this study is embodied by investigating an integrated model that included different theories that presented new directions of trust in mobile social commerce through social and behavioral determinants.

TOPSIS-based Regression Algorithms Evaluation

This paper developed a multi-criteria decision-making approach using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to benchmark the regression alternatives. Regression is used in diverse fields to predict consumer behavior, analyze business profitability, assess risk, analyze automobile engine performance, predict biological system behavior, and analyze weather data. Each of these applications has its own set of concerns, resulting in various metrics utilizations or those of similar measures but with diverse preferences. Multi-criteria decision-making analyzes, compares, and ranks a set of alternatives utilizing mathematical and logical processes with a complicated and contradictory set of criteria. The developed approach established the weights, which were the core of the evaluation process, to various values to mimic and address the regression’s utilization in multiple applications with different concerns and using distinct datasets. The alternative judgment identified positive and negative ideal alternatives in the alternative space. The compared regression alternatives were scored and ranked based on their distance from these alternatives. The results showed that different preferences led to varying algorithm rankings, but top-ranked algorithms were distinguished using a specific dataset. Following that, using three datasets, namely Combined Cycle Power Plant, Real Estate, and Concrete, Voting using multiple classifiers (k-means-based classifiers) was the top-ranked in the Combined Cycle Power Plant and Real Estate datasets. In contrast, Decision Stump was the top-ranked in the Concrete dataset.

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost.

Comparison of localized and foreign restaurant brands for consumer behavior prediction

This study used two comparison studies to examine the potential brand perception of localized and foreign restaurants to predict consumer behavior. A total of 680 participants (e.g., 318 foreign restaurant customers and 362 localized restaurant customers) were included to discover and confirm the causal relationships among the quality of food and meals, environmental evaluation, willingness to pay more, and recommendations. Our study of foreign restaurant brands (Study 1) found that food and meals may indirectly affect the willingness to pay more and recommendations through providing a friendly environment. The destination's moderating role is also confirmed. Additionally, our study of localized restaurant customers (Study 2) identified the mediating roles of risk evaluation and willingness to pay more, which link the relationship between food and meals and recommendation behavior. Furthermore, destination trust was found to strengthen the relationship between risk evaluation and willingness to pay more.