Predict Consumer Behavior Research Articles

Artificial Intelligence Effect on Marketing Mix

AI and big data to fight against the COVID-19 pandemic and review state-of-the-art solutions using these technologies. Thus, this study aimed to examine the effects of marketing mix dimensions (product, price, promotion, and place) to predict consumer behaviour, emotions and trend using AI for health care. This study will use mix method. First the qualitative research method which involved semi-structured interviews with different marketing professional belonging from different firms of Malaysia. A sample size of 10 is selected and the researcher interviewed 10 marketing professionals A quantitative approach whereby 300 questionnaires are distributed to different marketing professional from various firms in Malaysia. The analysis method used the Partial Least Square smart PLS descriptive methods. As Malaysian consumers exhibit serious concern towards to predict consumer behavior drives consumer resistance to healthcare, it is recommended that the managers focus their strategies on marketing strategies significance on to predict consumer behavior. The study discusses the implications to various stakeholders, its limitation, and recommendations for future studies.

Transforming the automotive retail: Drivers for customers' omnichannel BOPS (Buy Online & Pick up in Store) behavior

This study aims to identify the drivers in BOPS (Buy Online & Pick up in Store) usage intention of omnichannel consumers in the automotive retail sector. The study proposes a conceptual framework, extended UTAUT-TTF, synthesizing various theoretical perspectives and revealing the mechanism behind consumer behavior. The research surveyed 436 potential customers in South Korea, incorporating a factorial survey method. The findings suggest that Performance expectancy, Effort expectancy, and Facilitating conditions have a mediation effect through Task-technology fit affecting Usage intention. The results also support the influences of decisive factors in predicting the consumers' preferences to use BOPS. Social influence, however, did not present a significant effect on Usage intention. Personal innovativeness, selected for the extension of UTAUT, was the strongest predictor for the customer's BOPS usage intention. The research contributes to future technology acceptance literature, proposing that UTAUT-TTF can serve an essential role in predicting consumer behavior.

Utility Maximization of Bangladeshi Consumers within Their Budget: A Mathematical Procedure

A consumer is considered as a person or a group of people who uses purchased goods, products, or services only for personal use, and not for manufacturing or resale. Consumers usually purchase valuable and useful commodities or goods by spending all or partial of their income. The property of a commodity that enables it to satisfy human wants is called utility. Producers must be conscious to increase the utility among the consumers. This study has considered the maximization of utility problem of consumers of Bangladesh subject to two constraints; namely, budget constraint and coupon constraint. Consequently, in the study two Lagrange multipliers are used and interpreted these with mathematical analysis. Prediction of consumer behavior will help both producers and consumers to take decision of their future economic productions and consumptions, respectively. This article is ornamented with sufficient theorems and economic analyses. So that all the readers find interest when go through the economic analysis of utility maximization.

Methodology for assessing the competitive positions of a product in consumers perception

The article is devoted to the study and analysis of practical views on the nature of the concept of positioning, highlighting the main features, determining the importance of the positioning process in the formation of the overall marketing strategy of the company. In today's market the winners are those who sets the strategic goals and competes with the use of theoretically justified from a scientific point of view of methods and techniques. The processes that ensure the creation of strong positions in the market are reflected in the methodological recommendations developed by the author for the positioning of the product. The effectiveness of the positioning strategy depends on identifying the correct and accurate target audience, analyzing competitors' organizations and predicting consumer behavior. All this allows to determine the strengths and weaknesses of the enterprise, as well as to find out the public opinion about this enterprise.

State-trait interactions in regulatory focus determine impulse buying behavior.

Little research has focused on motivational state-trait interactions to explain impulse buying. Although the trait chronic regulatory focus has been linked to impulse buying, no evidence yet exists for an effect of situational regulatory focus and no research has examined whether the fit of chronic and situational regulatory focus can influence impulse buying with actual consumptive consequences rather than purchase intentions. Two laboratory experiments (total N = 250) manipulated situational regulatory focus before providing opportunities for impulse buying. In addition, cognitive constraint was manipulated as a potential boundary condition for regulatory focus effects. Situational promotion focus increased impulse buying relative to situational prevention focus in participants with strong chronic promotion, consistent with regulatory fit theory and independently of cognitive constraint. Surprisingly, situational promotion focus also increased impulse buying in participants with strong chronic prevention, but only under low cognitive constraint. These results may be explained by diverging mediating cognitive processes for promotion vs. prevention focus' effect on impulse buying. Future research must focus more on combining relevant states and traits in predicting consumer behavior. Marketing implications are discussed.

MINDFUL CONSUMPTION BEHAVIOR ON SECOND-HAND FASHION PRODUCTS: INTERVARIABLE INFLUENCE ANALYSIS OF STIMULUS-ORGANISM-RESPONSE (S-O-R) MODEL

Manuscript type: Empirical Research   Research Aims: This study was conducted to analyze and develop the intervariable influence within stages in S-O-R Model in second-hand fashion products context Design/methodology/approach: To fulfill the research objective, an online self-administered survey was conducted to respondents who had second-hand thrifting purchase experience. 130 eligible responses were gathered and further analyzed using Structural Equation Modeling (two-way approach)  Research Findings: It is observed that eWOM and Consumer Engagement have positive significant effects on Mindful Consumption Behavior (MCB), while no significant influence is found in relationship Attitude toward MCB. No mediating effect is observed in relationship eWOM toward MCB through mediating variable Attitude and Consumer Engagement. Theoretical Contribution/Originality: This research contributes to provide insight on factors that influence mindful consumption behavior in Indonesia   Practitioner/Policy Implication: Based on the result of this research, related stakeholder can utilize eWOM and encourage consumers to form mindful consumption behavior (i.e: through social connection that would increase consumers' enthusiasm and attention towards MCB )   Research limitation/Implications: The study is limited as it only analyzed some variables to predict consumer behavior in fashion thrifting. Based on the research, eWOM and Consumer Engagement are proven to influence MCB, and there is no effect observed of Attitude on MCB. Therefore, this research contributes to consumer behavior that proves eWOM and Engagement are strong predictors to predict consumer behavior in the purchase of second-hand fashion.

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

Impact of perceived brand authenticity on consumer behavior: an evidence from generation Y in Asian perspective

PurposeIn the modern era, brand authenticity is one of the most powerful positioning strategies for sustainable business growth. This study investigated the impacts of perceived brand authenticity dimensions (i.e., quality commitment, heritage, sincerity) on brand love to predict Generation Y's behavior from the Asian context.Design/methodology/approachThis is new empirical research that tested the proposed hypotheses through PLS-SEM, as PLS is the most robust technique for predicting consumer behavior. Importantly, consumers (of Generation Y) from five Asian countries contributed to this study, and data collected from 427 Asian millennials on global brands.FindingsThe results analysis revealed that perceived brand authenticity dimensions significantly impacted brand love, which positively affected Asian millennials' behavioral outcomes (i.e., continuous purchase intention and price premium).Research limitations/implicationsThis study investigated dimensions of perceived brand authenticity to predict Asian millennials' behavioral outcomes in a broader perspective. Future researchers may investigate a specific culture with a larger sample size to predict millennials behavior.Practical implicationsThis study has several implications that guide the global managers of several service and manufacturing industries to develop various positioning and relationship strategies for global brands to target Asian markets effectively.Originality/valueUsing attribution theory, this is the first novel research study that empirically discussed the dimensions of perceived brand authenticity, brand love, and Asian millennials' behavior toward global brands.

Transformation of Capitalism in the 21st Century: “Surveillance Capitalism” Concept by Shoshana Zuboff

Среди работ, попадающих под зонтичный термин «цифровой капитализм», видное место занимает исследование, обобщенно представленное в книге Шошаны Зубофф «Эпоха надзорного капитализма» (2019) и выступающее продолжениемее же концепции «распределенного капитализма». «Надзорный капитализм» – это новыйэкономический порядок, использующий в качестве капитала человеческий опыт как сырье и характеризующийся распространением коммерческого мониторинга – тотального наблюдения за поведением граждан. В статье исследуется теория Зубофф о «трех модернах» и ключевых императивах «надзорного капитализма»: извлечении данных и прогнозировании поведения потребителя. В завершение приводятся аргументы, указывающие на слабые места этой концепции.

What links neuroscience to agricultural economics? A review of neuroscientific methods literature in agricultural economic research and marketing

Research into consumer behavior is confronted with a multitude of challenges and special features. These become apparent in economic decision-making situations in which actual behavior deviates massively from rational explanatory models, such as the model of homo economicus, or when discrepancies are observed between statements made by individuals and their actions. As a common intersection between the economic and the neurosciences, neuroeconomics investigates human decision-making behavior from a neuroscientific perspective. The focus is particularly on explaining these antagonisms of human behavior and deriving motives. With the help of this potentially expandable knowledge, it is possible to subtly influence individual purchasing decisions at the neural level and to predict consumer behavior at the market level. In agricultural economics, for example in the field of food marketing, neuroeconomics could contribute to more reflective purchasing decisions and thus counteract global health challenges such as obesity. To date, no research has been conducted into the extent to which neuroeconomics has already been applied in agricultural scientific research. The objectives of the article are to provide an aggregated basic knowledge in the field of neuroeconomics, taking into account the applied methods as well as a literary overview of previous research in the context of agricultural economics. The article addresses all those interested in getting an overview of what neuroeconomics entails and how it is already being applied in agricultural research without any prior neuroscience background.

Consumers' Green Purchase Behavior in West Java, Indonesia: Applying The Theory of Planned Behavior

According to a survey conducted by WWF Indonesia and Nielsen, 63 percent of Indonesians are willing to pay a premium price for green products. However, there are still several elements that prevent people from using green products. In 2018, sales green products only reached 22.30% compared to sales of conventional product categories of 47.3%. This study uses a theoretical framework of planned behavior (TPB) to understand consumer behavior towards green purchase behavior in Indonesia, particularly in West Java. Previous studies used TPB to predict consumer behavior, but TPB was used to predict green purchase behavior in this study to help industries and companies in determining effective marketing strategies to shape green purchase behavior as part of solution for environmental problems in West Java, Indonesia. By conducting an online survey of 257 respondents in West Java, it was found that the attitude of green purchase behavior has a positive effect on green purchase intention. Furthermore, the attitude of green purchase behavior is positively influenced by behavioral beliefs and control beliefs but not influenced by normative beliefs.

Theory of planned behavior to predict consumer behavior in using products irrigated with purified wastewater in Iran consumer

Water shortages in some countries such as Iran have led farmers to use purified wastewater to irrigate their farms. The analysis of consumer behavior using these products is considered as one of the main issues in this regard. Among the behavioral predictor models, the theory of planned behavior is a model that can be a reliable model due to the focus on some variables such as consumer attitudes, subjective norms, as well as their perceived behavioral control. Another advantage of this model is related to the flexibility of the model in adding other variables and the possibility of increasing the power of predicting behavior. The present study aimed to examine the production of some agricultural products with purified wastewater in Iran and predict consumer behavior. The analysis of the consumer behavior in using products irrigated with purified wastewater can help plan more carefully for the cultivation of such crops. This study used the extended theory of planned behavior by adding risk perception to predict people behavior towards consuming products irrigated with purified wastewater. Data were collected from 400 villagers consuming products irrigated with purified wastewater through a questionnaire and analyzed using PLS and SPSS software. The results showed that the original theory of planned behavior explained 42.7% of the variance in consumer behavior towards the consumption of this product. The main variables of the original theory included attitudes, perceived behavioral control, and subjective norms, which positively affected the consumer intentions. The risk perception was found to be the strongest predictor of consumer behavior, and their relationship was negative and significant. The addition of risk perception to the extended theory of planned behavior increased the ability of the models to explain consumer behavior by 15.4%. Policymakers and relevant organizations can promote behavior towards the consumption of this product by emphasizing risk perception and providing the consumers with the required types of knowledge and information.

The role of organismic integration theory in marketing science: A systematic review and research agenda

Researchers have repeatedly applied the same models/theories to predict consumer behaviour, whereas theories that have been extensively used to promote motivation are disregarded in marketing science. For instance, organismic integration theory (OIT) is a prominent theory of human motivation that provides a framework for categorizing customers’ regulatory styles into different forms of motivational regulations, which could be used in marketing as customers can be motivated to engage in behaviour for a diverse array of reasons. Therefore, we aim to enrich the field of marketing by reviewing extant knowledge on OIT, noting tensions and inconsistencies, and identifying important gaps with reference to how the constructs underpinned by OIT have been applied and what research themes, contexts, and methodologies have been carried out. With this aim in mind, we reviewed three decades of research between 1991 and 2020, outlined underdeveloped and emerging marketing issues, and set comprehensive research agendas (presented as testable propositions).

Investigation of Influential Factors of Predicting Individuals' Use and Non-use of Fitness and Diet Apps on Smartphones: Application of the Machine Learning Algorithm (XGBoost).

Objectives: In this study, we aimed to find the influential factors in determining individuals' use and non-use of fitness and diet apps on smartphones. To this end, we focused on diverse groups of predictors that would significantly affect people's use and non-use of these apps. Methods: Overall, we considered 105 factors as potential predictors and included them in further analyses using a machine learning algorithm, XGBoost. The main reason for selecting this particular algorithm was that it had been known as one of the most accurate and popular algorithms for predicting consumer behaviors. Results: We found the accuracy score of those factors for predicting people's use and non-use of fitness and diet apps was approximately 71.3%. In particular, the most influential predictors were mainly related to social influence, media use, overeating, social support, health management, and attitudes toward exercise. Conclusion: These findings contribute to helping scholars and practitioners to develop more practical strategies of the implementation of fitness and diet apps.

Which Data Fairly Differentiate? American Views on the Use of Personal Data in Two Market Settings

Corporations increasingly use personal data to offer individuals different products and prices. I present first-of-its-kind evidence about how U.S. consumers assess the fairness of companies using personal information in this way. Drawing on a nationally representative survey that asks respondents to rate how fair or unfair it is for car insurers and lenders to use various sorts of information—from credit scores to web browser history to residential moves—I find that everyday Americans make strong moral distinctions among types of data, even when they are told data predict consumer behavior (insurance claims and loan defaults, respectively). Open-ended responses show that people adjudicate fairness by drawing on shared understandings of whether data are logically related to the predicted outcome and whether the categories companies use conflate morally distinct individuals. These findings demonstrate how dynamics long studied by economic sociologists manifest in legitimating a new and important mode of market allocation.

IRRATIONALITY OF CONSUMERS CHOOSING PLANE TICKETS

Economic models can be used to describe consumer behavior or to predict expected behavior of the consumers. In order for the application of the selected economic model to describe realistically the issue under study, it is necessary that the individual components of the economic model are able to reliably identify and possibly even quantify main characteristics of the studied problematique. In the last few years, one of the basic theses based on which several economic models are built upon has been "questioned" within the scientific community, namely the one that portraits human as always rationally behaving individual. The goal of this article is to verify whether, in the framework of predicting consumer behavior, one can still use a person as a so-called homo oeconomicus (economic man who always behaves rationally) or whether this theory has been already overcome, and human behavior should be rather described as irrational. To validate the theory of irrationality in the context of consumer behavior, an experiment in the field of air transport will be used, monitoring consumer decision making while choosing the plane tickets.

Challenges in Decoding Consumer Behavior with Data Science

Decoding the ever-evolving consumer behavior is one of the biggest challenges faced by marketers around the world. The future of consumer behavior research is put into question by the advances in data science. Today, when consumers are all the time exposed to new technologies, trends such as facial recognition, artificial intelligence, and voice technology did not advance as rapidly as predicted, marketing intelligence gained a significant share of the spotlight. This paper gives an overview of possible ways to anticipate consumer data intelligence development from the perspectives of a robust data set and deep artificial intelligence expertise for better understanding, modeling, and predicting consumer behavior. Showing that marketing cannot happen in isolation in the era of digital overexposure, it requires a deeper understanding of consumer behavior. Data scientists, analysts, and marketers around the world have to work together to increase consumer loyalty, grow revenue, and improve the predictiveness of their models and effectiveness of their marketing spend. Efficiently integrating consumer behavior data into marketing strategies can help companies improve their approach towards attracting and winning the diverse and dynamic consumer segments and retaining them. This synthesis of current research will be helpful to both researchers and practitioners that work on the use of data science to understand and predict consumer behavior, as well as those making long-range planning marketing decisions.

A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic.

The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.

An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.

Customer Value Types Predicting Consumer Behavior at Dutch Grocery Retailers.

The purpose of this paper is to profile three grocery retail formats (non-discounter, soft discounter, and hard discounter) in the Netherlands using Holbrook’s value types. These value types are linked to three consumer behavior outcomes, i.e., Satisfaction, Repurchase intention, and Word-of-Mouth. The impact of the retail formats is evaluated on the importance and performance of the value types, using a questionnaire for each of the retail formats. The relationship between the value types and outcomes is tested with partial least squares structural equation modeling (PLS-SEM). Aesthetics, Altruistic value, and Efficiency are positively linked to Satisfaction. In addition, Efficiency is the key driver for Repurchase intention and has a positive impact on Word-of-Mouth. A positive Word-of-Mouth is predicted by Social value and Service excellence. The three examined retail formats show a difference in Holbrook’s value types. Overall, the results of the non- and soft discounters differ from the hard discounters. Remarkably, and contrary to previous studies, the soft discounter’s scores are the highest meaning that consumers are most critical for this retail format. It seems that consumers expect the best of both worlds at soft discounters: low prices, interesting bargains, easy access, but also appealing store design, and excellent service.