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The management of consumer privacy has become a critical concern for organizations in the age of artificial intelligence–powered marketing. The impact of data on the market environment has brought both benefits and challenges, with marketers gaining valuable insights but also raising privacy concerns. As artificial intelligence–powered marketing advances, consumer vulnerability increases due to the sensitivity of collected data. This vulnerability leads some consumers to resort to falsifying information, posing a significant threat to the digital economy. Privacy empowerment and customer control play a vital role in addressing these challenges. This chapter explores the influencing factors and ethical considerations surrounding data falsification. It also discusses strategies to mitigate perceived vulnerability through privacy controls and explores the consequences of data breaches and customer vulnerability. The chapter further emphasizes the need for organizations to balance benefits, risks, and customer trust while harnessing the value of customer data. An ethical framework for data privacy marketing audits is proposed to help organizations assess their data practices responsibly and competitively. By integrating personal data protection strategies within an ethical framework, organizations can protect consumer privacy, enhance customer trust, and maintain their competitive edge in the market.

In light of the data economy, data protection law is a key legal element for being able to leverage data-driven innovation and is often regarded as a limitation for businesses and service design. Contrasting this traditional view, this chapter argues why designing with privacy in mind is a win-win situation, not only, but especially in the context of data-based services. On the backdrop of new regulations around the globe setting incentives, we show how research in the domain of usable privacy can be leveraged to embed innovative privacy features for customers into digital services as competitive advantage. Building upon these insights, we argue that a well-designed privacy and/or data protection process should be a key element for customer experience management.

The rapid digital transformation is driving businesses to reexamine their marketing plans and practices. Marketers face new conflicts while they are implementing digital marketing strategies. The main objective of this chapter is to deeply understand these conflicts and to provide appropriate solutions. This chapter addresses six digital conflicts that challenge marketers most: (1) online versus offline channel conflicts, (2) conflicts stemming from digitalization gap, (3) conflicts in influencer marketing context, (4) conflicts in artificial intelligence (AI) context, (5) environmental conflicts and digital marketing, and (6) ethical conflicts and digital marketing. Finally, we discuss conflict management strategies for the most challenging digital marketing conflicts.

Consumption taxes are an integral part of government revenue in countries around the world and are often subject to consumer evasion. The rapid rise of electronic commerce has exacerbated this problem as cross-border selling over the internet has enabled foreign businesses to sell and avoid collection and remittance of tax on their sales.

In this paper, we search for the solution to this problem through the analysis of three tax collection models: vendor, financial institution, and internet service provider (ISP). In addition, we examine administrative tools that enable more effective collection as well as inducements for taxpayers or collection agents to carry out their responsibility.

We conclude that the ISP collection model is not feasible at this time. On the other hand, we find that the vendor model, when supplemented with appropriate administrative tools and inducements, and the financial institution model, both represent viable options for policymakers to consider.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

The study focusses on the legal issues surrounding artificial intelligence (AI), which are being investigated and debated about several European Union initiatives to manage and regulate Information and Communication Technologies. The goal is to discuss the benefits and drawbacks of adopting AI technology and the ramifications for the articulations of law and politics in democratic constitutional countries. Thus, the study aims to identify socio-legal concerns and possible solutions to protect individuals’ interests. The exploratory study is based on statutes, rules, and committee reports. The study has used news pieces, reports issued by organisations and legal websites. The study revealed computer security vulnerabilities, unfairness, bias and discrimination, and legal personhood and intellectual property issues. Issues with privacy and data protection, liability for harm, and lack of accountability will all be discussed. The vulnerability framework is utilised in this chapter to strengthen comprehension of key areas of concern and to motivate risk and impact mitigation solutions to safeguard human welfare. Given the importance of AI’s effects on weak individuals and groups as well as their legal rights, this chapter contributes to the discourse, which is essential. The chapter advances the conversation while appreciating the legal work done in AI and the fact that this sector needs constant review and flexibility. As AI technology advances, new legal challenges, vulnerabilities, and implications for data privacy will inevitably arise, necessitating increased monitoring and research.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Nowadays, most of our activities and personal details are recorded by one entity or another. These data are used for many applications that fundamentally enrich our lives, such as navigation systems, social networks, search engines, and health monitoring. On the darker side of data collection lie usages that can harm us and threaten our sense of privacy. Marketing, as an academic field and corporate practice, has benefited tremendously from this era of data abundance, but has concurrently heightened the risk of associated harms.

In this chapter, we discuss both the great advantages and potential harms ushered in by this era of data collection, as well as ways to mitigate the harms while maintaining the benefits. Specifically, we propose and discuss classes of potential solutions: methods for collecting less data overall, transparency of code and models, federated learning, and identity management tools, among others. Some of these solutions can be implemented now, others require a longer horizon, but all can begin through the advocacy of marketing research. We also discuss possible ways to improve on the benefits of data collection – by developing methods to assist individuals pursue their long-term goals while advocating for privacy in such pursuits.

Mobile marketing, the two- or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, platform, or technology, has made rapid strides in the past several years. Mobile marketing has entered its second phase or Mobile Marketing 2.0. The surpassing of desktop by mobile devices in digital media consumption, diffusion of wearable devices among customers, and an overall integration and interconnectedness of devices characterize this phase. Against this backdrop, we present a synthesis of the most recent literature in mobile marketing. We discuss three key advances in mobile marketing research relating to mobile targeting, personalization, and mobile-led cross-channel effects. We outline emerging industry trends in mobile marketing, including mobile app monetization, augmented reality, data and privacy, wearable devices, driverless vehicles, the Internet of Things, and artificial intelligence. Within each extant and emerging area, we delineate the future research opportunities in mobile marketing. Finally, we discuss the impact of mobile marketing on customer, firm, and societal outcomes.