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Advances in digital technologies and the growing number of touch points have had a significant impact on the shopping behaviour of omnichannel customers. Several research papers have explored different facets of omnichannel, but only a few have thoroughly explored the literature on showrooming and webrooming simultaneously. This paper aims to identify the key groups of antecedents influencing customer buying behaviour in omnichannel, under the influence of digital technologies, with a particular focus on showrooming and webrooming.

Our study conducted a systematic literature review to identify the factors influencing customers’ buying behaviour in omnichannel, which have been the subject of academic discussion over the last decade. We finalized 149 articles for the thematic analysis and identified three groups of antecedents: channel-related, product-related and consumer-related with their subgroups.

Under channel-related antecedents, cost and perceived benefits, search convenience, need for interaction and situational circumstances have been identified as major attributes. The expressiveness of the product, product demonstration and search and experienced products have been identified under product-related antecedents, followed by price consciousness, past experiences, perceived risks and shopping motivations as leading attributes under consumer-related antecedents. The study revealed the multifaceted influence of digital technologies on omnichannel buying behaviour. Digital technologies are shaping the antecedents related to channels, products and consumers. Digital technologies simultaneously mediate between antecedents and the selection of a specific path within an omnichannel environment. Showrooming and webrooming should no longer be seen as general concepts. The rise of digital technologies has led to the development of new consumer journey patterns and the blurring of distinctions between showrooming and webrooming. A conceptual framework has been proposed to understand consumers' omnichannel behaviour, having considered the identified antecedents and the role of digital technologies.

This study advances the academic understanding of consumer behaviour in omnichannel under the influence of digital technologies and provides important implications for omnichannel management. With the advancement of digital technologies such as augmented reality and virtual reality, retailers should implement channel integration strategies to bridge the gap between online and offline channels, providing a memorable shopping experience for omnichannel customers.

This study is unique because it identifies and analyses the antecedents of consumer behaviour in omnichannel settings under the influence of digital technologies. It also uncovers new potential combinations of showrooming and webrooming patterns. The proposed framework can help retailers in their future planning of omnichannel strategies.

This study aims to identify the determinants of customers' green purchasing behaviors. First, the study examines the relationship between green self-identity (GSI) and green peer influence (GPI) on green purchase behavior (GPB). Second, it examines the relationships between both GSI and GPI, and purchasing behavior mediated by green consumption values (functional value (FV) and social value (SV)). Third, it investigates the moderating effect of customer disidentification (CDI) on the relationships between GSI and both green consumption values. Finally, it investigates the indirect relationships between GSI and purchasing behavior moderated by CDI.

A quantitative study is conducted using a survey of 204 Egyptian buyers of organic food products. AMOS and Hayes's PROCESS macro are used to test the hypotheses under investigation.

The customer's peer influence and GSI are found to have a positive impact on green purchasing behavior. Additionally, the mediating impact of values and the moderating impact of CDI are also confirmed.

This study helps organic food companies in identifying the determinants of customers' green purchasing behavior. The results of the study will guide the efforts of green marketing professionals in promoting green products in the Egyptian market.

Since the notion of green consumption is still in its infancy, there is a need for further exploration on the green consumption concept to better understand customers' predictors of that type of consumption; accordingly, the current research was conducted.

This paper aims to review the literature on “E-fulfillment” with respect to marketing and operations issues in the current dynamic and complex e-tailing environment and thereby generate significant insights.

This paper is based on a systematic literature review on e-fulfillment focusing on marketing and operations issues therein. This systematic literature review consists of a critical review on e-fulfillment under planning (review question initialisation), searching (literature search), screening (literature evaluation), extraction and synthesis and reporting phases to conceptualise e-fulfillment. A total of 122 research articles have been reviewed to explore e-fulfillment and to develop key constructs and propositions.

This review provides the following three outcomes. First, the varied-fulfillment definitions have been critically reviewed, leading to synthesis, and thereby, an e-fulfillment definition is provided. Further, the variations for e-fulfillment across product types, which have been identified as a key variable for e-fulfillment, have been explored. Second, authors find five e-fulfillment components at the marketing and operations interface: website quality, customisation strategy, distribution strategy, last mile delivery and return management. Continuing with the e-fulfillment interface with marketing, the linkages between e-fulfillment and select post-purchase consumer behaviours measures across different product types have been reviewed. The paper thus with a focus on synthesising e-fulfillment literature from a process perspective emphasises the consumer behaviour metric for measuring e-fulfillment performance.

This study would help academicians, researchers, e-tailers and practitioners to understand e-fulfillment from a process perspective. For the researcher, it presents areas for future research by giving possible research directions in this emerging area. This study also brings out the impact of e-fulfillment according to product type on the post-purchase consumer behaviour measures, which will help e-tailers to link e-fulfillment to consumer behaviour metrics.

The paper classifies the fragmented literature to develop constructs and propositions for e-fulfillment. This is the first kind of study on e-fulfillment process and its impact on select post-purchase consumer behaviour measures across product types.

Prior research does not provide a clear picture of how managers can effectively manage customer in-role and extra-role behaviours in a retail setting. This study aims to test the differential impact of the two main customer relationship predictor paths – identity-based and satisfaction-based paths – on customer in-role and extra-role behaviours.

A random sample of 500 customers from the flagship store of an up-market, international department store chain participated in a written survey. Purchase spending data for each customer was obtained from the retailer’s loyalty card database.

The two studied predictor paths possess a differential impact on customer extra-role behaviours. Civic virtue and co-creation behaviours are exclusively driven by the identity-based path, whereas sportsmanship is driven solely by the satisfaction-based path. Moreover, the identity-based path impacts purchase behaviour only when symbolic purchase motivation is high. Overall satisfaction has no impact on purchase behaviour.

In some retailing contexts, extra-role behaviours such as co-creation or civic virtue might simply be irrelevant (e.g. discount chains).

Managers, who have the intention to stimulate customers to give constructive feedback on products or services, or to involve them in co-creation activities, are well advised to also invest in identity-based path activities.

This study is the first to empirically test the effects of customer identification and overall customer satisfaction on the various dimensions of customer in-role and extra-role behaviours. Customer extra-role behaviours should not be conceptualised as one global construct but should comprise distinct dimensions of discretionary behaviours that have different antecedents.

The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.

In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.

First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.

This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.

The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.

The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.

The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

In the retailing sector, consumers typically patronize multiple outlets, which leaves outlets striving to earn a greater portion of consumer expenditures. The purpose of this paper is to improve theoretical and empirical knowledge about the impact of retailing loyalty programmes on customer purchasing behaviour.

The effects of two loyalty programmes on customer behaviour are studied through marketwide panel data on supermarket purchases.

The impact of loyalty programme membership on customer purchase behaviour is significant.

All behavioural indicators show that members and non‐members of loyalty programmes demonstrate significantly different purchase behaviours, irrespective of other factors. The purchase intensity of cardholders, in terms of total and average shopping baskets, share of purchases, purchase frequency and inter‐purchase time, is significantly higher than that of non‐members throughout the entire three‐year period and the trading areas. The findings require confirmation in other retailing sectors before they may be considered fully generalisable.

Retailers may apply the findings in their attempts to segment their target market, which enables them to allocate their marketing expenditures more effectively.

The study contributes to more “generalisable” knowledge by investigating marketwide scanner panel data about competitive purchasing, loyalty programmes and store locations.

Risk perception is an essential factor affecting how individuals evaluate risk, make decisions and behave. The impact of risk perception on customer purchase behavior has been widely studied; however, the association has been debated. Therefore, the purpose of this paper is to examine the relationship between risk perception and customer purchase behavior and to examine factors that could moderate it.

This study conducted a meta-analysis of this relationship and examined factors that could moderate it. Six databases were comprehensively searched. Two reviewers independently selected the studies for inclusion, extracted data and assessed quality. Pearson's r was used as the effect estimate. A total of 33 studies were included in the meta-analysis.

The results revealed a negative relationship between risk perception and customer purchase behavior. The geographical region, purchase channel and country development level affected the relationship. The correlation between perceived risk and purchase behavior in European consumers was the highest, followed by the correlation in American consumers; the weakest correlation was found in Asian consumers. For consumers in developed countries, perceived risk had a stronger negative influence on customer purchase behavior than that for consumers in developing countries. The perceived risk of online purchase channels had a stronger negative impact on customer purchase behavior than that of offline purchase channels.

Risk perception is a useful context in which to explain barriers to customer purchase behavior. In addition, reducing consumers’ risk perception and perfecting the market transaction process with respect to buying behavior should be further studied.

The findings of this review indicate a direct negative relationship between risk perception and customer purchase behavior. To the best of the authors’ knowledge, this review is the first to meta-analytically summarize the impact of risk perception on customer purchase behavior in social sciences research, and it also illuminates new perspectives for future studies.

The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict their next purchases from an online air travel corporation.

An operations review of the customer online shopping process of an online travel agency (OTA) is conducted. A customer online shopping behaviour analysis tool is developed. The tool integrates competitors’ pricing data mining, customer segmentation and predictive analysis. The impacts of competitors’ price changes on customer purchasing decisions regarding the OTA’s products are evaluated. The integrated model for mining pricing data, identifying potential customers and predicting their next purchases helps the OTA recommend tailored product packages to its individual customers with reference to their travel patterns.

In the customer segmentation analysis, 110,840 customers are identified and segmented based on their purchasing behaviour. The relationship between the purchasing behaviour in an OTA and the price changes of different OTAs are analysed. There is a significant relationship between the flight duration time and the purchase lead time. The next travel destinations of segmented high-value customers are predicted with reference to their travel patterns and the significance of the relationships between destination pairs.

The developed model contributes to pricing evaluation, customer segmentation and package customization for online customers.

This study provides novel method and insights into customer behaviour towards OTAs through an integrated model of customer segmentation, customer behaviour and prediction analysis.

Predicting customers’ purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers’ individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them.

The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests.

A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior.

The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies.

This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers’ individual purchase behaviors.