Big Brother is Listening to You: Digital Eavesdropping in the Advertising Industry

In the Digital Age, information is more accessible than ever. Unfortunately, that accessibility has come at the expense of privacy. Now, more and more personal information is in the hands of corporations and governments, for uses not known to the average consumer. Although these entities have long been able to keep tabs on individuals, with the advent of virtual assistants and “always-listening” technologies, the ease by which a third party may extract information from a consumer has only increased. The stark reality is that lawmakers have left the American public behind. While other countries have enacted consumer privacy protections, the United States has no satisfactory legal framework in place to curb data collection by greedy businesses or to regulate how those companies may use and protect consumer data. This Article contemplates one use of that data: digital advertising. Inspired by stories of suspiciously well-targeted advertisements appearing on social media websites, this Article additionally questions whether companies have been honest about their collection of audio data. To address the potential harms consumers may suffer as a result of this deficient privacy protection, this Article proposes a framework wherein companies must acquire users\u27 consent and the government must ensure that businesses do not use consumer information for harmful purposes

Conversational commerce: entering the next stage of AI-powered digital assistants

Digital assistant is a recent advancement benefited through data-driven innovation. Though digital assistants have become an integral member of user conversations, but there is no theory that relates user perception towards this AI powered technology. The purpose of the research is to investigate the role of technology attitude and AI attributes in enhancing purchase intention through digital assistants. A conceptual model is proposed after identifying three major AI factors namely, perceived anthropomorphism, perceived intelligence, and perceived animacy. To test the model, the study employed structural equation modeling using 440 sample. The results indicated that perceived anthropomorphism plays the most significant role in building a positive attitude and purchase intention through digital assistants. Though the study is built using technology-related variables, the hypotheses are proposed based on various psychology-related theories such as uncanny valley theory, the theory of mind, developmental psychology, and cognitive psychology theory. The study’s theoretical contributions are discussed within the scope of these theories. Besides the theoretical contribution, the study also offers illuminating practical implications for developers and marketers’ benefit

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots.

Abstract Chatbots are technological tools equipped with artificial intelligence that allow companies to interact with their consumers. Through their computers or mobile devices, consumers can use this technology to search for information, make purchases or request after-sales services. This study aims to identify the role of attitude toward chatbots and privacy concern in the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. After reviewing the literature, the study proposes a moderated mediation model. Through a survey, the study shows that attitude toward mobile advertising does not have a direct effect on the behavioral intent to use chatbot, but is rather mediated by one’s attitude toward chatbots. In fact, the interactivity is unidirectional in the case of mobile advertising (from the company to the consumer), but bidirectional in the case of chatbots (in which consumers have an active role in communication). In line with these assumptions, the data analysis shows that internet privacy concerns only negatively moderate the relationship between attitude toward chatbots and behavioral intent to use this technology. These results can be useful for companies and researchers in terms of developing and testing new digital marketing strategies. The paper concludes with a discussion of the results’ theoretical and managerial implications

Siri, Alexa, and Other Digital Assistants: A Study of Customer Satisfaction With Artificial Intelligence Applications

Siri, Alexa, and other digital assistants are rapidly becoming embraced by consumers and the adoption is projected to grow from 390 million to 1.8 billion for the period of 2015 to 2021. Digital assistants are offering benefits to consumers while also proving to be a disruptive technology for businesses. Coupling digital assistants with other artificial intelligence technologies offers the potential to transform companies by creating more efficient business processes, automating complex tasks, and improving the customer service experience. Businesses have begun integrating this technology into their operations with the expectation of achieving significant productivity gains. Customer satisfaction has been discussed extensively throughout marketing literature. Yet, there is little empirical evidence of customer satisfaction with digital assistants. This study used PLS-SEM to analyze 244 survey responses obtained from a cross-section of consumers. Using the Expectations Confirmation Theory as its foundation, the study identified that expectations and confirmation of expectations substantially explained customer satisfaction with digital assistants. For practice, the study provides guidance which allows firms to prioritize marketing and managerial activities. Firms should focus priorities on assisting digital assistant users to become aware of new skill capabilities while also providing relevant examples of how these skills can be used to meet user needs. In addition, priorities should be focused on assisting users with understanding how the average person can use digital assistants to perform more than just mundane tasks with relative ease. These priorities were identified as areas of high importance for customer satisfaction and require performance improvements

Trust and Privacy Permissions for an Ambient World

Ambient intelligence (AmI) and ubiquitous computing allow us to consider a future where computation is embedded into our daily social lives. This vision raises its own important questions and augments the need to understand how people will trust such systems and at the same time achieve and maintain privacy. As a result, we have recently conducted a wide reaching study of people’s attitudes to potential AmI scenarios with a view to eliciting their privacy concerns. This chapter describes recent research related to privacy and trust with regard to ambient technology. The method used in the study is described and findings discussed

Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM

Consumer Adoption of Artificial Intelligence: A Review of Theories and Antecedents

Recently, people are increasingly adopting technologies powered by artificial intelligence (AI) in their everyday lives. Several researchers have investigated this phenomenon using several theoretical perspectives to explain the motivations behind such behaviour. Our paper reviews this body of knowledge to highlight the technologies, theories, and antecedents of AI adoption investigated this far in academic research. By analysing publications found in Harzing's Journal Quality List, this paper identifies 52 publications on user adoption of AI, 198 antecedents, and 36 theoretical perspectives used to explain user adoption of AI. The most widely used theoretical perspectives in this area of research are the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT). Meanwhile, perceived usefulness, perceived ease of use, and trust are the most studied antecedents. Finally, we discuss the implications of these findings for future research on AI adoption by consumers