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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.

This study aims to examine the impact of AI service robots on restaurant customers' engagement and acceptance and the moderating role of robot anthropomorphism on the relationship between AI robot service quality and customer engagement.

Using a three-wave time-lagged design, 416 customers of service robots-enabled restaurants participated in the study. Mplus was used to examine the hypotheses.

The results confirmed that customers' perception regarding automation, personalization, efficiency and precision of robot service quality determine customer engagement, which further influences customer acceptance of AI service robots. Additionally, robot anthropomorphism moderates the relationships between AI robot service quality in terms of automation, personalization, efficiency and precision and customer engagement. This study confirms that AI service robots-customer engagement contributes to better acceptance of AI service robots.

The proposed framework can be used as a diagnostic tool to enhance customer acceptance of AI service robots in restaurant settings. This research provides guidelines to restaurant owners to employ AI service robots in front-line services that provide better quality, ultimately enhancing customer engagement and acceptance.

This study fills the gap in the literature by investigating the influence of AI robot service quality on customer engagement and customer acceptance with the moderating effect of robot anthropomorphism in an emerging market context.

The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

Switching behavior is predominantly seen in the consumer buying behavior of the mobile industry. This research aims to identify the factors influencing consumers to switch from their present mobile service provider. The consumer of the mobile industry operates in a dynamic and ever-changing environment that is difficult to predict, so this paper aims to focus on these issues.

The selection of factors was made with the help of qualitative study and quantitative research methods for further findings; with the help of a structured questionnaire, a total of 514 valuable responses were collected to get the results. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the data.

The finding shows that technology and edge-on-competition (TEC) and pricing have a negative influence on customer switching behavior. The switching cost (SC) is the most significant factor and has a positive impact, while service encounter failure (SEF) also positively impacts switching behavior.

The findings provide important implications for consumers switching brands if they are finding alternative offers that are cost-effective and SEF from service providers

The study of one of the largest mobile markets is learning lessons for other markets around the world. This study will be helpful for mobile service provider companies in their branding and marketing strategies. This study will also be helpful to practitioners, educators and researchers in understanding the consumer behavior of mobile users.

The learning of the largest mobile market will be a great learning lesson for other mobile markets around the world. Consumer behavior will help marketers follow ethical practices and make their strategy so a consumer does not switch brands and remain satisfied with the existing brand.

The study provides unique learning for practitioners, educators and researchers to understand the consumer behavior of mobile users. This will help marketers create factors that stop consumers from switching brands and develop strategies to retain customers.