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Understanding consumer behavior and predicting market trends are critical for enterprises looking to advance their innovation in the rapidly changing world of Cross-border E-commerce (CBEC). To advance the logistical facility occurrence for consumers and optimize inventory organization, e-commerce businesses are focused on using Artificial Intelligence (AI) approach to amplify the accuracy of sales prediction. This study proposes a marketing prediction model that employs Enhanced Tabu Search Optimized Dynamic Gradient Boosting Machines (ETSO-DGBM) to aid CBEC businesses in building enhanced assessments. The CBEC enterprises’ data were collected and used for predicting their marketing approach. To advance the capability of the model to predict, pre-processing procedures, including normalization, are used to certify data stability and Independent Component Analysis (ICA) assists in extracting features. The result illustrates that the ETSO-DGBM marketing prediction model develops forecasting accuracy significantly. When assessing with traditional techniques, the proposed ETSO-DGBM model performs better. The method assists organizations in predicting their marketing strategy to gather the diverse demands of different segments by providing perceptive information about customer behavior. It uses AI approach to extend adaptable marketing programs for CBEC companies, facilitating informed decision-making and promoting expansion and profitability in international markets. It underscores the significance of AI in marketing strategies to handle cross-border customer behavior.

This study examines the differences in credit card behaviors between rural and urban households in China. Using data from a national survey of Chinese households, results show distinct differences between rural and urban respondents in credit card adoption and usage even after controlling for relevant demographic, financial, attitude, and expectation variables. Rural households are less likely to possess a credit card, possibly due to supply side limitations such as lack of financial institutions and low rate of credit card acceptance in rural areas. No urban–rural difference is found in credit card payment behavior. Implications for policymakers and credit card issuers are discussed.

Buy-price auction has been successfully used as a new channel of online sales. This paper studies an online sequential buy-price auction problem, where a seller has an inventory of identical products and needs to clear them through a sequence of online buy-price auctions such that the total profit is maximized by optimizing the buy price in each auction. We propose a methodology by dynamic programming approach to solve this optimization problem. Since the consumers’ behavior affects the seller’s revenue, the consumers’ strategy used in this auction is first investigated. Then, two different dynamic programming models are developed to optimize the seller’s decision-making: one is the clairvoyant model corresponding to a situation where the seller has complete information about consumer valuations, and the other is the Bayesian learning model where the seller makes optimal decisions by continuously recording and utilizing auction data during the sales process. Numerical experiments are employed to demonstrate the impacts of several key factors on the optimal solutions, including the size of inventory, the number of potential consumers, and the rate at which the seller discounts early incomes. It is shown that when the consumers’ valuations are uniformly distributed, the Bayesian learning model is of great efficiency if the demand is adequate.

A consumer behavior model is considered in the context of a network of interacting individuals in an energy market. We propose and analyze a simple dynamical model of an ensemble of coupled active elements mimicking consumers' behavior, where "word-of-mouth" interactions between individuals are important. A single element is modeled using the automatic control system framework. Assuming local (nearest neighbor) coupling we study the evolution of chains and lattices of the model consumers when varying the coupling strength and initial conditions. The results are interpreted as the dynamics of the decision-making process by the energy-market consumers. We demonstrate that a pitchfork bifurcation to the homogeneous solution leads to bistability of stationary regimes, while the autonomous system is always monostable. In the presence of inhomogeneities, this results in the formation of clusters of sharply positive and negative opinions. We also find that, depending on the coupling strength, the perturbations caused by inhomogeneities can be exponentially localized in space or delocalized. In the latter case, the coarse-graining of opinion clusters occurs.

This paper aims to model the impact of retail consumers’ behavior on a new banking dual market featuring both conventional and Islamic banking products. To build the model, we conduct an empirical qualitative and quantitative survey on Moroccan market consumers in order to appraise their preferences with regard to banking products’ attributes. Then, we use conjoint analysis method to determine the consumers’ decision function. We run market simulations on a Multi-Agents Simulation platform and analyze the results. Our findings indicate that in new dual markets, and under a range of assumptions, it is predicted that Islamic banks will face excess liquidity while conventional banks will be exposed to liquidity shortage.

Despite extensive studies on consumer behavior and decision making, the social influence of consumers on each other has not been widely investigated. To incorporate such interactions, in this study, we propose and apply an agent-based simulation model where consumers are defined as agents. The purchase behavior of each agent is characterized as a function based on the concept of the black-box model for consumer behavior. In particular, we investigate the effect of consumers’ social network and its interaction with the marketing mix parameters (4Ps). A case study of household appliances in a local market is used to demonstrate how the dynamics of preferences between domestic and foreign brands occurs. The simulation model is used to examine the effect of eight scenarios related to these interactions. The obtained results are compared and the most important factors are determined as product features and price.

This study explores the influence of anchoring price (ANE) on consumer behavior using a quantitative research method. An online survey was piloted, yielding 246 useable responses from 4G and 5G users in China. SmartPLS 3.00 was used to analyze the data, and the assumptions were assessed using the partial least squares-based structural equation modeling (PLS-SEM) technique. The empirical findings reveal that the anchoring price (ANE) component partially mediates the connections between purchase intention (CPI) and behavior (CPB) of 5G technology items. This is the first study to investigate the impact of anchoring price (ANE) on intention–behavior relationships. Manufacturers and marketers of 5G technology goods that want to influence positive changes in consumer behavior should look into the study’s findings and implications.

In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

The purpose of this study is to examine how the changing Nigerian marketing environment affects consumer behavior with emphasis on the role of government and the criticisms that follow. A total of 323 questionnaires were distributed to the respondents cutting across consumers of various categories of products in the Nigerian manufacturing sector including employees and government officials of business regulatory agencies in the 36 States of the six geo-political zones of the Nigerian Federation including Abuja, the Federal Capital Territory. The study adopted simple random sampling technique to select the sample of the study. The study was analyzed using descriptive statistics, Chi-square and multiple linear regression analysis and qualitative descriptive method to help in achieving the research objectives. The study revealed that changes brought about by the environment are caused by several macro-environmental factors such as economic, technological, socio-cultural, legal/political and international factors which have significant effect on consumers’ buying characteristics (social, cultural, personal and psychological) which subsequently affect consumers’ satisfaction and purchase decision. Economic and legal/political variables were found to exhibit the most significant impact while the technological variable exhibits the least impact.

This study aims to investigate elements associated with consumer behavior towards the demand for halal logistics, namely concern about halal (COHA), knowledge of halal (KNHA), awareness of halal logistics (AWHL), and perceptions of halal logistics (PEHL). This research applied a quantitative methodology that gathered primary data from Muslim consumers residing in Brunei Darussalam. Data were collected using an online self-administrated survey questionnaire, and regression and correlation analyses were tested to investigate the proposed hypotheses. The result demonstrates that COHA, KNHA, AWHL, and PEHL constructs significantly influence demand for halal logistics (DEHL). All four elements show a medium to strong positive correlation with the dependent variable, inferring that the independent variables and the DEHL construct are closely related or associated — suggesting that an increase in the independent variables will increase DEHL and vice versa. This research does not fully represent the general consumers in Brunei markets as this research solely focuses on Muslims. Future research on non-Muslim consumers might be an option to give a bigger picture of the need for halal logistics in Brunei Darussalam. This study is beneficial to relevant stakeholders such as halal businesses, logistics service providers (LSP), policymakers, and consumers to reconsider the extent of halal logistics in not only safeguarding halal integrity across the logistics chain but also its significance in shaping continuous and viable demands in the pursuit of competitive advantage. This paper is among the first attempts to investigate Muslim consumer demand for halal logistics in Brunei, considering that past research focused heavily on Malaysian and Indonesian markets. This paper could contribute to future theoretical and empirical studies and suggest practical initiatives towards relevant stakeholders.

Taiwanese indigenous peoples (e.g. Amis) applied the juice of Shoulang yam (i.e. Dioscorea matsudae Hayata) to dyeing on fishing nets and reducing the erosion by sea. In this study, the cold dyeing method of the natural pigment is extracted directly by hammering the leaf and flower. This study conducted a survey on consumer behaviors of innovation products in the hammered leaf and flower prints and preferences and willingness to purchase the cultural merchandises of Taiwanese indigenous people. A total of 1000 questionnaires were dispatched, with a response rate of 87.3 % (n = 873). Results of analysis showed that 48% of consumers are very interested in cultural merchandises of indigenous people. Consumers are most interested in merchandises of hammered the leaf and flower products for T-shirts (28%), followed by notebooks (22%), handbags (18%) and backpacks (14%), respectively. 43% of consumers want to DIY (Do It Yourself) their unique products of hammered the leaf and flower prints. Further, The analysis of 4P (Product, Price, Place, Promotion) and SWOT provided the information to enhance and promote cultural and creative industries of indigenous peoples.

Based on 4Cs and filtered by industry experts, 16 consumer decision indicators of casual women's wear online retail channel were obtained. The indicator influence on 244 consumers was collected by questionnaires and indicators were classified to five factors (Experience, Safety, Consumption, Display, Aftermarket) by the exploratory factor analysis (EFA), which revised by the confirmatory factor analysis (CFA). The research hypotheses were developed by using structural equation modeling (SEM), and proved that the performance of Display and Safety would have positive effect on the consumer judgments on Experience and Consumption while Experience also has positive effect on Aftermarket.