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Online shopping cart abandonment presents a major problem for online fashion apparel retailers today. This exploratory research aims to validate scales that measure antecedents of online shopping cart abandonment (OSCA) and examine how these reasons contribute to OSCA behaviour. The findings indicated that the eight different reasons (financial reasons, organisational tool, time pressure, intangibility, privacy issues, aesthetic design, social influences and entertainment factors) that drive OSCA are distinct and account for unique variance in the model, validating the measures. Also, the findings revealed that financial reasons and using the cart as an organisational tool are the top two reasons why consumers abandon their carts. This study provides researchers with a better theoretical understanding of the reasons why consumers abandon their online shopping carts. It validates the various reasons why consumers abandon their shopping carts and provides valuable managerial insights on how online marketers may enhance the translation of online browsing behaviour into actual purchases.

Shopper marketing describes the planning and execution of all marketing activities that influence a shopper along – and beyond – the path-to-purchase, from shopping trigger to purchase, consumption, repurchase, and recommendation stages. Shopper marketing practices at manufacturers and retailers are growing at a tremendous pace and a rising portion of marketing budgets are now devoted to shopper marketing. The first phase of shopper marketing research and practice, Shopper Marketing 1.0, addressed interesting issues, primarily relating to in-store marketing. In the next phase, Shopper Marketing 2.0, will significantly extend to out-of-store marketing, including online and mobile marketing, resulting in an integrated practice. In this new environment, to formulate and execute effective shopper marketing strategies, managers need to better understand the complete picture of how online, offline, mobile and in-store marketing influence shoppers in the path-to-purchase-and-beyond cycle. In this chapter, we present the evolution of shopper marketing, summarize key learnings, outline important issues, and discuss the opportunities and challenges of Shopper Marketing 2.0.

The last few years have witnessed massive artificial intelligence (AI) and gaming adoption that has navigated the emerging markets. Moreover, according to the WOG summit (world government summit report, by Nielsen) 2020 reports, AI with gaming mechanisms are expected to enrich marketing services in the coming future in the emerging markets. Countries such as India, China and South Korea contribute significantly to this area, and recent forecasting allows the need to increase in emerging markets. Similarly, these countries have a maximum number of youth gamers and AI-driven technology adopters. The adoption of AI-driven technologies and amplification of gamification in marketing services are new phenomena. Moreover, gaming and AI dynamics are relatively new in emerging countries and need greater attention. Thus, this book chapter proposes a dyad model that would explain users' and companies' perspectives to understand the role of AI and gamification for the emerging markets. The chapter will explain how AI-driven gamification helps the users of emerging markets. The chapter will also illustrate how companies in emerging markets use AI for gamification. Therefore, the dyad model would also comprehend the gap, opportunities and challenges in this area and the subsequent strategies to help all the stakeholders.

In recent years, research on children's tourism experiences has gained prominence, as children are becoming an increasingly vital market for the tourism industry. While events are a main sector of the industry and host millions of children every year, there is a lack of research specifically focussed on children's experiences in events. This chapter focusses on children's entertainment events which can provide children with a satisfying, memorable and educational experience. This study has developed a framework to facilitate deeper mixed studies on children's experiences in event tourism. The framework is composed of four pillars based on various social, tourism and event theories and models, including the Cognition–Affect–Behaviour (CAB) theoretical framework, the Orchestra Model of Experience, the Event Experience Scales (EES), the Theory of Child Well-being and the Transtheoretical Model of Behaviour Change (TTM). The framework can be used by future researchers as an analytical evaluation tool to study children's experiences in different types of events and understand the mechanisms of behaviour change in this context.

This chapter identifies research advances in theory and analytics that contribute successfully to the primary need to be filled to achieve scientific legitimacy: configurations that include accurate explanation, description, and prediction – prediction here refers to predicting future outcomes and outcomes of cases in samples separate from the samples of cases used to construct models. The MAJOR PARADOX: can the researcher construct models that achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This chapter presents a way forward for solving the major paradox. The solution here includes philosophical, theoretical, and operational shifts away from variable-based modeling and null hypothesis statistical testing (NHST) to case-based modeling and somewhat precise outcome testing (SPOT). These shifts are now occurring in the scholarly business-to-business literature.

Data has become one of the most significant instruments in e-commerce innovation. Benefits to the entire society can be summarized as following: from the government’s perspective - to assess the impact of e-commerce to the economy; for merchants - to understand consumers’ needs; and for consumers - to be offered with the right product he/she is looking for. The digital revolution in the past five years has shown the need to offer more differentiated services than the physical stores, when consumers are not able to try and touch products. It is for this reason that e-commerce has continuously developed and transformed Research Online, Purchase Offline into a true experience. Considering the future of e-commerce is to enhance economic development and growth, this research will discuss the disruption of Research and Development through big data. The core objective of this research is to propose a predictive model to deeply understand consumer behavior by analyzing new regulations and transaction records.