Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Drivers of Young Consumers’ Willingness to Reduce Food Waste and Buy Intelligent Packaging

Objectives: As food waste harms the sustainable environment and the economy, some innovations have been made to reduce this issue, such as intelligent packaging. However, the factors leading to consumer behavior to reduce food waste and buy intelligent packaging, particularly in developing countries are still untapped.Methodology: This study aims to examine the relationship between green perceived value, intention to reduce food waste, and willingness to purchase intelligent packaging. Data from 230 Indonesian young consumers were analyzed using PLS-SEM.Finding: The results showed that different elements of green perceived value had significant effects on the intention to reduce food waste and willingness to purchase intelligent packaging. Unlike the predicted relationship, the intention to reduce food waste was not significant enough to lead consumers to purchase intelligent packaging.Conclusion: The presented study gives insights to businesspeople on which green perceived values are important for reducing food waste and encouraging consumers to buy intelligent packaging

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

An Intensive Spectrum for Intention Mining Analysis

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

What Do Carbon Labels Signal? The Role of Biospheric Values on Perceptions of “Green” Food Consumers

Costly signaling theory suggests that individuals might be more likely to consume sustainable food products if doing so signals an underlying prosocial value to others. However, it is unclear whether prosocial signals are equally interpreted by others. We study whether consumers of carbon-labeled (vs. non carbon-labeled) products are perceived more positively and if observers’ biospheric values and product prices influence such perceptions. An experimental study (N = 229) assessed participants’ perceptions of consumers of carbon and non-carbon labeled food products described as being either cheaper or more-expensive-than-average. Results indicated that consumers of carbon-labeled products were perceived more positively and that such perceptions were accentuated when observers strongly endorsed biospheric values. Further, positive perceptions of consumers occurred regardless of a product’s price, although effects were strongest amongst observers with high biospheric value endorsement when products were cheap and carbon-labeled. Implications for carbon labeling initiatives and food marketing more generally are discussed

Non-price determinants on intention to purchase of organic foods in State of Kedah, Malaysia

Organic food is becoming popular among todays‘ millennial consumers as consumer awareness towards healthy lifestyle had increased. Scholars and practitioners had put much consideration in understanding what drive consumers‘ attitude and behavior towards organic food mainly to strengthen their strategies and tactics to dominate the market. As past literatures consistently highlighted that organic food enjoyed slightly higher price, this study attempts to examine the influence of non-price determinants on intention to purchase organic food. The study among 117 respondents in state of Kedah, Malaysia revealed that environmental concern has a significant relationship with intention to purchase organic food. Another two determinants namely, product knowledge and attitude towards organic food found insignificant in influencing intention to purchase organic food. As a result, practitioners are urged to aggressively promote the benefits of organic food among public mainly to enhance their attitude towards organic food. Future study should focus in a larger sample as well as consider other non-price determinants on intention of purchase organic foo

Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

The impact of social virtual presence agents and content-based product recommendation system on on-line customer purchase intention

The appearance of the digital market came as turning point factor, obligating companies to maintain the relationship with consumers by improving and keeping a high technological innovativeness on-line overall experience. The lack of studies on antropomorphization of virtual voice assistances chatbot and the possibilities, yet to be found, on customized product recommendation system variation integration, brought the author to this study. The aim of this research is to investigate the effects of using two different chatbot social virtual presences interactions: with a fully pre-recorded computed personification agent versus with a pre-recorded human social virtual agent; and also understand how having a customized content-based product recommendation system can influence the consumers purchase intention at on-line shopping framework. An on-line platform was developed, recreating a possible virtual store interaction, and the core data was treated using a PLS-SEM model. The results indicate that Human Social Virtual Presence Agent, while assisting the shoppers, have a larger model positive effect on Intellectual stimulus and Hedonic Benefits than a computed personification Agent. This might be explained by the fact that computed imagery and sound Agent was perceived with some amount of emotional creepiness by the participants. Also, recommendation system presence is impacting customers purchase intention on a positive way when compared with not using recommendation system. Thus, this study shows how relevant social interactions are for the customers, especially when done by a human, and how recommendation system has an impact on customers purchase intention.Com o aparecimento do mercado "on-line", as empresas que quiseram manter uma relação de qualidade com os seus clientes, tiveram de investir no desenvolvimento de uma experiência de utilizador de qualidade e manter um olhar atento na inovação. A falta de estudos relativos à antropomorfização em "chatbots" virtuais e as possibilidades, ainda por descobrir, do sistema de recomendação de produtos à medida de cada utilizador, trouxeram o autor ao tema deste estudo. O seu objetivo é investigar os efeitos de dois tipos de presença social em "chatbots": uma presença virtual computada versus uma presença virtual humana; e como o sistema de recomendação de produtos à medida de cada utilizador influencia a intenção de compra dos consumidores nas lojas "on-line". Para tal, foi desenvolvida uma plataforma "on-line", recriando uma possível interação em loja virtual. Os dados foram tratados utilizando o modelo PLS-SEM. Os resultados indicam que a presença social virtual feita por um agente humano melhora substancialmente o estímulo intelectual feito pela marca e os seus benefícios hedónicos, quando comparado com um agente virtual computado. Tal resultado pode ser explicado pelo facto dos participantes que interagiram com um agente computado sentirem um maior valor de "creepiness". Considerando que a utilização do sistema de recomendação de produtos tem forte impacto na intenção de comprar do consumidor, este estudo mostra-se relevante ao salientar a importância da presença social nas lojas "on-line", especialmente quando o agente é humano