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Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

Suppliers and marketers have started planning toward postpandemic scenarios where logistics and retail will happen in a new way with the help of technological advances. This shift means new challenges for manufacturers, suppliers and retailers, and there is a need for strategic sourcing decisions for a robust supply chain system, logistics and on-time delivery system, as consumers have shown a positive change in online buying behavior. Furthermore, with digital transformation, customers are expected to not return to traditional buying. Hence, it becomes essential to identify the factors acting as enablers of online purchase behavior for sustainable digital business. This study aims to analyze the positive shifts in online purchasing by consumers, identify and model the enablers of positive transformations in online purchasing by consumers.

The interpretative structural modeling (ISM) technique is used to draw the interrelationships among the variables and their impact on online buying. A context-oriented relationship among the factors has been set up through the expert opinion technique. A total of 40 specialists have been approached for this. ISM with Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis was used to prioritize these drivers, identify the most critical factors and establish a driver-dependence relationship among these drivers.

Several significant categories of enablers like health, trust, convenience, work from home, referral buying, panic purchase and overstocking possess a strong influence on the shift to online due to the pandemic. The results will help the policymakers, suppliers, retailers, managers and practitioners with insights to plan, prepare for challenges and make decisions toward preparation and shifting to the emergent digital world. In addition, the study provides academicians scope for further research in the related area.

Consumer behavior significantly impacts retail and supply chain business, as it is an interface with the customer and links between a manufacturer and a customer. This study provides an insight into the shift in purchase behavior which can help suppliers in this transition phase to be better prepared for tomorrow to achieve sustainable competitive advantage.

This study assists practitioners and researchers in understanding the interrelationships among the factors using ISM-MICMAC analysis in a realistic way rather than daydreaming with overambitious goals.

Word‐of‐mouth (WOM) has been found to significantly influence consumers' decision making. Much attention has been paid to the effect of WOM characteristics such as the number of postings, the dispersion of online conversations, the reputation of the reviewers, and the review quality on product sales. Little research, however, has examined the interaction process of online reviews. The purpose of this paper is to investigate the consumers' product attitude formation process in online WOM. Three research questions will be addressed in this paper, i.e. the effect of prior responses on the following repliers' product attitude, the negativity effect and the role of main messages in shaping consumers' product attitude formation process.

The product attitude formation process of online WOM is investigated using the data of product reviews (main messages) and their corresponding responses. The paper collected 26 product reviews from various web sites and kept the first 40‐50 responses for each review, which resulted in 26 main messages and 1,173 observations (i.e. responses) in total. A hierarchical Bayesian ordinal choice model is then specified and estimated with the Markov Chain Monte Carlo method to address the research questions and to capture the main message heterogeneity.

The paper finds that the impact of prior responses (e.g. the proportion of positive and negative responses) on the product attitudes of the following responses differs significantly across products. This heterogeneity can be well explained by the characteristics of the main messages at the second‐level specification. Thereby, factors that influence consumers' product attitudes in the interaction process of online WOM include prior responses and main message characteristics. Another interesting finding is that positive responses have larger impacts on product attitudes than negative ones.

This research contributes to both academic research and the firms' online WOM management. Theoretically, this research is the first attempt to examine the formation process of attitudes toward new products in online communications. This research contributes by modeling how the dynamic process of online WOM influences new product attitudes. Furthermore, inconsistent with the “negativity effect” proposed by researchers (e.g. Skowronski and Carlston), the paper finds that positive responses matter more than negative ones in online communications. In addition, the way the paper configures the data for online communications is innovative and provides a perspective to quantitatively model the communication process. Managerially, this research provides implications for firms to intervene in the online communication process and influence consumer attitude of new products.

In search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.

This is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.

The findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.

Drawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

This chapter provides guidelines and strategies for higher education faculty and faculty developers who wish to implement inquiry-based teaching models online. The chapter focuses on two specific inquiry-based (IB) instructional models: guided and open inquiry as these two models are considered more relevant to higher education students. The chapter will present validated processes for implementing IB teaching models and consider how these processes can be authentically replicated in online learning environments. The chapter will also examine issues and challenges involved in implementing IB teaching models online. Grounded in the challenges that faculty face in translating their instructional practice in online environments, the chapter suggests strategies and interactive tools to scaffold and model IB learning in online environments.

This study sets out to employ a cross‐cultural perspective to explore alternative explanations in the development of the online repurchase intentions model.

The proposed constructs of alternative models deal with key online consumer behavior variables such as customized information, web use applications, online service and perceived interactivity which lead to the formation of purchase intentions. These variables form the theoretical foundation for four alternative models. To test these four alternative models 448 online consumers were surveyed in 2007. Structural equation modeling is used to test these models.

The results demonstrate that the power of customized information lies in its ability to influence consumer satisfaction and perceived interactivity that are proximate to repurchase intentions. The results also show that the link of satisfaction→repurchase intentions provides a meaningful empirical representation of the hierarchical sequence in which the cycle of customer repurchasing process (satisfaction→attitude→repurchase intentions) is related.

One may examine applications of the findings in the travel industry in practice and reflect upon potential pitfalls when applying the findings of this piece of the present research. Although previous studies reveal the significance between the two constructs, further research can investigate the issue to be generalizable to the many types of e‐B2C services. Second, particularly in terms of the ability to generalize the model, the study did not provide fairly consistent results for different service categories. Studies on other service classes, such as online bookstore and online banking services, may reveal findings that extend the authors' approach.

Similarity of purchasing attitudes between Korean and UK consumers may allow e‐tailors to design a systematic strategy for generating favorable attitudes toward their web sites, even though standardized online treatment of consumers is not the golden path of internet marketing. Further, the proposed research model (PRM) provides a means of identifying the underlying dispositions associated with the mediating variable.

This is the first study to examine online repurchasing models using alternative explanations. By structuring such alternative models, McKenzie argues that researchers may be better able to judge how the evidence relates to each alternative view. The present approach can be viewed as a significant early step on the path toward a comprehensive understanding of online consumer behaviors in new information communication technology.

The purpose of this study was to examine purchasing intentions in online sports ticketing websites. Based on previous research related to business-to-consumer (B2C) e-commerce, this study developed a conceptual model to test the effect of perceived risk, trust and the Technology Acceptance Model (TAM) on purchase intentions in online secondary ticket websites. College students (N = 251) from the northeastern region of the United States were chosen as the sample. Structural Equation Modeling (SEM) was used to investigate the proposed relationships among four major components (i.e. perceived risk, trust, TAM and behavioural intention). The results showed that there were positive effects of key TAM constructs (i.e. perceived usefulness and ease of use) and trust on purchasing intention, but perceived risk was not a significant indicator of purchase intention.

Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors.

This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website.

Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits.

This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.