Purpose Despite the proliferation of service chatbots in the tourism industry, the question on its continuance intentions among customers has largely remain unanswered. Building on an integrated framework using the task–technology fit theory (TTF) and the expectation–confirmation model (ECM), the present study aims to settle this debate by investigating the factors triggering customers to continue to use chatbots in a travel planning context. Design/methodology/approach The research followed a quantitative approach in which a survey of 322 chatbot users was undertaken. The model was empirically validated using the structural equation modelling approach using AMOS. Findings The results reveal that users’ expectations are confirmed when they believe that the technological characteristics of chatbots satisfy their task-related characteristics. Simply, the results reveal a significant and direct effect of TTF on customers’ confirmation and perceived usefulness towards chatbots. Moreover, perceived usefulness and confirmation were found to positively impact customers’ satisfaction towards chatbots, in which the former exerts a relatively stronger impact. Not surprisingly, customers’ satisfaction with the artificial intelligence(AI)-based chatbots emerged as a predominant predictor of their continuance use. Practical implications The findings have various practical ramifications for developers who must train chatbot algorithms on massive data to increase their accuracy and to answer more exhaustive inquiries, thereby generating a task–technology fit. It is recommended that service providers give consumers hassle-free service and precise answers to their inquiries to guarantee their satisfaction. Originality/value The present work attempted to empirically construct and evaluate the combination of the TTF model and the ECM, which is unique in the AI-based chatbots available in a tourism context. This research presents an alternate method for understanding the continuance intentions concerning AI-based service chatbots.

Consumer psychology has always been the centre of concern for the marketers from the old time and understanding the underlying aspects leads to effective decision making. The present study elicits the concept of post purchase cognitive dissonance in the consumers and embraces its implications in studying the consumer behaviour. A survey was conducted and well framed questionnaire was constructed covering various dimensions of variables studied. Some of the underlying dimensions of cognitive dissonance have been rigorously discussed and statistically tested in this study. Specifically, the impact of product involvement, time taken to make a purchase decision and level of information search on the cognitive dissonance have been analysed that provides really significant benefits to the marketers.

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India has the highest proportion of diabetes patients, and it is estimated that there will be 134 Million diabetics in India by 2045 as per IDF. Also, the disease burden is increasing to the young population between ages 25-40 as more of them are diagnosed positive according to JAMA recently. Moreover, there are only 4.8 Doctors per 10,000 population, and in villages, the ratio is the lowest possible in this country, according to the Indian Journal of Public Health. Therefore, screening & predicting Diabetes at an early stage remains a priority for clinicians. It reduces the risk of major complications and improves patients' quality of life with diabetes, and builds resilience and well-being amongst other citizens. With the advancement of Computer Science & Artificial Intelligence, it is now possible to predict diabetes and other such diseases through applying deep learning algorithms in high-quality data sets. This helps in a more accurate and faster diagnosis of Pre-diabetes, Diabetes & diabetes-related progressive eye diseases. In this study, a systematic review of the Pubmed repository for current practices to diagnose Diabetes based on AI intervention in the Indian context is carried out. Also, a critical analysis was done on various pioneered companies currently offering AI-based Diabetes diagnostic services in India. The study represents different concepts of AI tools used to predict the diseases currently available in India. Although most of the studies were carried out on Diabetic Retinopathy screening, future opportunities can be in several other areas such as Clinical Decision Support, Predictive Population Risk Stratification and Patient Self-Management Tools.

PurposeThe purpose of this paper is to examine the existence and profile consumer segments based on dissonance in Indian apparel fashion retail market.Design/methodology/approachThis study is based on cognitive dissonance theory (CDT) and analyses data using cluster and discriminant analysis on a sample (n = 354) from India.FindingsThe findings revealed three dissonance segments among consumers based on the intensity of dissonance experienced. This study also validated the clusters and profiled each segment. In doing so, the three clusters exhibited unique differences with respect to purchase and socio-demographic characteristics. Moreover, high dissonance segments were found to inversely impact customer’s satisfaction, loyalty and overall perceived value and positively impact tendency to switch.Practical implicationsUnderstanding the existence of cognitive dissonance (CD) patterns among consumers is critical for fashion apparel retailers. This paper offers unique insights into the specialties of each dissonance segment that assists the marketers to frame appropriate strategies to target them.Originality/valueThis paper advances knowledge on consumer behavior by highlighting the significance of CD.