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The authors' research examines how negative electronic word-of-mouth (e-WOM) alters focal customers' post-recovery justice perceptions and attitudes to determine their future behavior with the service provider. Specifically, this paper develops and tests a conceptual model to investigate how negative e-WOM alters focal customers' perceptual and attitudinal outcomes after the service recovery experience. It also examines the post-recovery effect of negative e-WOM on focal customers’ willingness to patronize the service after their recovery experience.

To test the hypotheses, two pretests and two experimental studies with created scenarios in the retail context were conducted.

The authors' findings reveal that services are judged during and well beyond failure and recovery occurrences. To maintain a loyal customer base, service managers should develop processes that address service complaints both within and beyond the service consumption stage. The authors also find that despite a favorable recovery, focal customers gravitate toward the failure experience and develop unfavorable attitudes toward the service provider, leading to likely defections.

The authors' research demonstrates the persuasive power of negative e-WOM at the post-service recovery stage, making a unique contribution to the service recovery literature. This research also contributes to the persuasive effect of negative e-WOM, demonstrating message context as a boundary condition of negative e-WOM effects. In general, the authors' work highlights the importance of understanding the psychological processes involved in eliciting the persuasive influence of negative e-WOM in the post-service recovery stage that may lead to the defection of “so-called” successfully recovered customers.

This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

Researchers explore the impact of an intelligent assistant in virtual teams by applying the theoretical lens of a transactive memory system (TMS) to understand the relationships between trust in a specific technology, knowledge sharing and knowledge application.

An online survey was administered to a Qualtrics-curated panel of individual, US-based virtual team members utilizing an intelligent assistant with team collaboration software. Partial least squares structural equation modeling (PLS-SEM) was utilized to examine the hypothesized relationships of interest.

Results suggest that knowledge application is strongly influenced by trust in a specific technology and knowledge sharing. Additionally, a transactive memory system positively increases trust in the intelligent assistant, and similarly, trust in the intelligent assistant has a significant positive relationship with knowledge sharing.

The research model contributes to our understanding of the impact of an intelligent assistant in virtual teams. Although the transactive memory system construct has been explored in various contexts and models, few have explored the impact of an intelligent assistant and trust in a specific technology.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

The purpose of this study was to examine consumer data acquired by branded prescription drug websites and the ethics of privacy related to the interconnected web of personal information accessed, packaged and resold by tracker technologies.

The research used the DMI Tracker Tool to collect data on the top 17 branded prescription drug websites, with a specific interest in the tracker technologies embedded in those websites. That data was analyzed using Gephi, an open-source data visualization tool, to map the network of trackers embedded in those branded prescription drug websites.

Findings visualize the interconnections between tracker technologies and prescription drug websites that undergird a system of personal data acquisition and programmatic advertising vehicles that serve the interests of prescription drug marketers and Big Tech. Based on the theory of platform ethics, the study demonstrated the presence of a technostructural ecosystem dominated by Big Tech, a system that goes unseen by consumers and serves the interests of advertisers and resellers of consumer data.

The 17 websites used in this study were limited to the top-selling prescription drugs or those with the highest ad expenditures. As such this study is not based on a random sampling of branded prescription drug websites. The popularity of these prescription drugs or the expanse of advertising associated with the drugs makes them appropriate to study the presence of tracking devices that collect data from consumers and serve advertising to them. It is also noted that websites are dynamic spaces, and some trackers within their infrastructures are apt to change over time.

Branded prescription drug information has over the past three decades become part of consumers’ routine search for information regarding what ails them. As drug promotion moved from print to TV and the Web, searching for drug information has become a part of everyday life. The implications of embedded trackers on branded prescription drug websites are the subject of this research.

This study has significant social implications as consumers who are searching for information regarding prescription medications may not want drug companies tracking them in a way that many perceive to be an invasion of privacy. Yet, as the Web is dominated by Big Tech, web developers have little choice but to remain a part of this technostructural ecosystem.

This study sheds light on branded prescription drug websites, exploring the imbalance between the websites under study, Big Tech and consumers who lack awareness of the system that operates backstage.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

Advances in technology and the popularity of social media influencers have sparked a rise of “virtual influencers (VIs).” This study examines consumer attitudes toward VIs in social networking services and explores factors that enhance or dampen these attitudes. In three experiments, it was explored how contextual factors (background and companion) interact with human (HIs) and VIs, influencing attitudes through trustworthiness.

Three experiments were conducted using an online panel (N = 894). All experiments were based on the comparison between HIs and VI with mediating role of trustworthiness. In Studies 2 and 3, moderated mediation analyses were conducted to test hypotheses.

The results revealed that human-like VI was less trusted than animi-like VI or HIs. However, the perceived trustworthiness toward VI was enhanced when the VI was in a virtual (vs. real) environment (Study 2) or accompanied by a virtual (vs. human) companion (Study 3), leading to positive attitudes.

This research not only enriches relevant literature (avatars and SMIs) but also provides actionable insight for practitioners who design and utilize VIs. This paper proposes novel and handy tactics to enhance consumers' trustworthiness in and attitudes toward VIs.

This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting.

Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0.

The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement.

To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots.

This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.