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Purpose The purpose of this paper is to examine online purchase behaviours amongst young consumers in Australia and the USA. It also aims to develop and test a theoretical framework of young consumers’ online purchase behaviour. Design/methodology/approach Data were collected through online surveys targeting young online shoppers in Australia and the USA. A multi-group structural equation modelling was used to test the proposed structural model and hypotheses. Findings The model shows a good fit with the data. Young consumers in Australia and the USA have positive attitude towards online shopping that significantly affects their online purchase intentions. Social motive negatively impacts online purchase intentions in the Australian sample. Escapism and value motives positively affect Australian and American young shoppers’ online purchase intentions. Young consumers in Australia and the USA are very familiar with the online shopping process. The familiarity strongly triggers their information search behaviour that leads to online purchase intentions. Practical implications The results of this paper assist the marketers and policy makers to target and appeal to this young segment, based on their unique motivations, values and characteristics. Originality/value Using the generational cohort theory, this paper contributes to the extant literature by providing insights on the Australian and American young generation’s unique values and characteristics that influence their online purchase behaviours. This research also contributes insights for the marketers and policy makers to improve their marketing efforts and services and appeal to this young segment, based on their unique values and characteristics.

As a key marketing tool, online sales promotion has been widely used by online retailers to increase sales of products and brands. Most previous researches on online sales promotion have ignored the effect of consumers' psychological factors and the heterogeneity of product and consumers. The purpose of this study is to examine the role of psychological distance and involvement on consumers' buying behavior in large online promotion activities. The research model was examined using empirical analysis of data obtained from consumer surveys after the Double 11 promotion. Our results indicate that temporal distance has positive impact on purchase decision of high involvement products, while having negative impact on purchase decision of low involvement products. Social distance has negative impact on consumers' purchase decision. Temporal distance is positively associated with consumers' purchase-decision involvement, and then purchase-decision involvement positively impacts consumers' total consumption. Social distance has no impact on consumers' purchase decision involvement. These findings not only advance the understanding of the role of psychological distance and involvement in online sales promotion but also offer implications regarding strategies that online retailers can employ to publish their promotions at different times and encourage consumers more to share promotional information among their friends.

Consumer behavior on the Internet is influenced by factors that can affect consumers' perceptions and attention to products. Understanding these processes at the neurobiological level can help to understand consumers' implicit responses to marketing stimuli. The objective of this study is to use electroencephalography (EEG) to investigate the differential effects of selected online purchase decision factors that are becoming increasingly important in online shopping. Using event-related potentials (ERPs) and simultaneous eye-tracking measurements, we identified differences in the perception of utilitarian and hedonic products when the products are exposed together with visual elements of the factors review, discount, and quantity discount. The ERP analysis focused on the P200 and late positive potential components (LPP). By allowing free-viewing of stimuli during measurement, early automatic and later more complex attentional affective responses could be observed. The results suggest that the review and discount factors are processed faster than the product itself. However, the eye-tracking data indicate that the brain processes the factor without looking at it directly, i.e., from a peripheral view. The study also demonstrates the possibilities of using new objective methods based on neurobiology and how they can be applied, especially in areas where the use of neuroscience is still rare, yet so much needed to objectify consumers' knowledge of their need satisfaction behavior.

In the current competition process of e-commerce platforms, the technical and algorithmic wars that can quickly grasp user needs and accurately recommend target commodities are the core tools of platform competition. At the same time, the existing online purchase behavior prediction models lack consideration of time series features. This paper combines the Recurrent Neural Network, which is more suitable for the commodity recommendation scenario of the e-commerce platform, with Naive Bayes, which is simple in logic and efficient in operation and constructs the online purchase behavior prediction model RNN-NB, which can consider the features of time series. The RNN-NB model is trained and tested using 3 million time series data with purchase behavior provided by the Ali Tianchi big data platform. The prediction effect of the RNN-NB model and Naive Bayes model is evaluated and compared respectively under the same experimental conditions. The results show that the overall prediction effect of the RNN-NB model is better and more stable. In addition, through the analysis of user time series features, it can be found that the possibility of user purchase is negatively correlated with the length of time series, and merchants should pay more attention to those users with shorter time series in commodity recommendation and targeted offers. The contributions of this paper are as follows: (1) By constructing an online purchasing behavior model RNN-NB, which integrates the N vs 1 structure Recurrent Neural Network and naive Bayesian model, the validity limitations of some single-architecture recommendation algorithms are solved. (2) Based on the existing naive Bayesian model, the prediction accuracy of online purchasing behavior is further improved. (3) The analysis based on the features of the time series provides new ideas for the research of later scholars and new guidance for the marketing of platform merchants.

PurposeThe purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method.Design/methodology/approachThis study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.FindingsCompared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity.Originality/valueFuture study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

Due to the complexity of the e-commerce system, a hybrid model for online-purchasing behavior forecasting is developed to predict whether or not a customer makes a purchase during the next visit to the online store based on the previous behaviors, i.e., online-purchasing behavior. The proposed model makes contributions to literature from two perspectives: (1) a classification model is proposed based on the “hybrid modeling” concept, in which an effective artificial intelligence (AI) technique of support vector machine (SVM) is employed for classification forecasting and further extended by introducing the promising AI optimization tool of firefly algorithm (FA), to solve the crucial but tough task of parameters selection, i.e., the FA-based SVM model; (2) an appropriate predictor set is carefully designed especially considering online shopping cart use which was otherwise neglected in existing models, apart from other common online behaviors, e.g., clickstream behavior, previous purchase behavior and customer heterogeneity. To verify the superiority of the proposed model, an online furniture store is focused on as study sample, and the empirical results statistically support that the proposed FA-based SVM model considering online shopping cart use significantly beat all benchmarking models (with other popular classification methods and/or different predictor sets) in terms of prediction accuracy.

The paper aims at exploring the antecedents of customers’ online purchase intention and behaviour, and at uncovering sources of heterogeneity. A sample of customers was surveyed to measure perceived risk and benefits, trust, online purchase intention and behaviour. The study confirmed the causal chain of perceived risks-trust-perceived benefits-online purchase intention-actual purchase. A Finite Mixture Partial Least Squares (FIMIX-PLS) was performed to uncover sources of heterogeneity. It found that the level of security of the payment methods is relevant to understand the relationship between purchase intention and behaviour, while the level of previous experience with the online medium clarifies the relationship between perceived risk and trust. The study contributes to understanding the antecedents of online purchase intention and their relationship with actual purchase behaviour. Additionally, it offers evidence of heterogeneity in the proposed causal relations, particularly, concerning the level of trust in the payment methods and the level of Internet experience.

PurposeThis study examines consumer online purchase behaviour in the Nigerian fashion industry.Design/methodology/approachA cross-sectional study was conducted with a total useable sample size of 241 respondents contacted through on-site visitation. Descriptive and inferential statistics were used to test the influence of customer value on online purchase behaviour in the fashion industry.FindingsConsumer values are categorised into terminal (happiness, love and satisfaction) and instrumental (time-saving, price-saving discount, service convenience and merchandise assortment) values. The findings show that both values have significant influence on online consumer purchase behaviour, while fashion consciousness moderates the relationship between consumer values and online purchase behaviour.Practical implicationsOnline fashion retailers should focus on increasing the terminal and instrumental values of their products and making available goods that meet the needs of different generational cohorts in society.Originality/valueStudies have examined various factors, for example, consumer values that are determinants of consumer online purchase in the fashion industry; however, there has been limited focus on the nature of fashion and online purchasing in emerging markets, particularly those in Sub-Saharan Africa.

Theoretical framework: The study measures influence of the independent variables such as product price, quality, brand image, data privacy concerns, and cultural dimension (UAI) over the past couple of years of the pandemic. The proposed conceptual framework has one dependent variable (online customer purchasing behaviour) and one mediator (online customer purchasing intention). Covid 19 Pandemic is hypothesized to moderate the relationship between the mediator and the dependent variable.   Design/Methodology/Approach: The authors used inferential statistics to accomplish the purpose of the research. Via quantitative analysis using the SPSS, the factors that influence online shopping have been revealed by gathering and analysing data obtained over web-based surveys. The authors approached 500 respondents from the 12th of April 2021 to the 5th of June 2022 and got close to a 73% response rate, 364 responses.   Findings: The results exhibit that all mentioned factors except brand image and data privacy concerns have a momentous and significant relationship with consumers’ online shopping behaviour which further gets stronger due to the moderating effect of Covid-19.   Research practical and social implications: The study contributes to the existing literature and theories in terms of monitoring online customer purchase behaviour.  The findings will also help companies to develop and enhance their current CRM strategy and innovations to manage online shopping behaviour and achieve their commercial targets.   Originality/Value: The value of this study lies in its contribution to an understanding of online purchase behaviour, particularly in the context of the Covid-19 pandemic. By identifying influential factors and exploring their impact, the findings provide actionable insights for companies to adapt to changing consumer behaviours.