PurposeThe purpose of this paper is to arrive at a brand architecture model for promoting India as a tourism destination brand, carrying with it a diversity of tourism products and states/regions.Design/methodology/approachThe principal methodology adopted is discursive analysis and argument. Relevant examples from other countries have been drawn upon. Brand architecture concepts are used in the analysis.FindingsThe Government of India's tourism ministry has been promoting the country as a monolithic brand with the tagline “Incredible India” over the past seven years. This approach has so far been quite successful. However, to maintain growth momentum, the paper proposes migration towards a cohesive brand architecture model with a hierarchy of well‐connected brands. At the apex would be “India” as the master brand, which would endorse sub‐brands along two principal dimensions – tourism product categories and geographic regions/states. Regional aspirations would thus be accommodated. At the same time, India and its numerous constituents can be promoted in a structured manner with greater clarity and focus.Originality/valueThe paper offers a framework for reorienting India's tourism branding strategy so as to be more cohesive and effective. The model can also be applicable to other large countries with competing geographic regions and varied tourism products.

Purpose The purpose of this paper is to develop and empirically test a process model (comprising of seven dimensions), for identifying online customer engagement patterns leading to recommendation. These seven dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation. Design/methodology/approach The authors used a non-participant form of netnography for analyzing 849 comments from Australian banks Facebook pages. High levels of inter-coder reliability strengthen the study’s empirical validity and ensure minimum researcher bias and maximum reliability and replicability. Findings The authors identified 22 unique pattern of customer engagement, out of which nine patterns resulted in recommendation/advocacy. Engagement pattern communication-interaction-recommendation was the fastest route to recommendation, observed in nine instances (or 2 percent). In comparison, C-I-E-S-CI-B-R was the longest route to recommendation observed in ninety-six instances (or 18 percent). Of the eight patterns that resulted in recommendation, five patterns (or 62.5 percent) showed bonding happening before recommendation. Research limitations/implications The authors limited the data collection to Facebook pages of major banks in Australia. The authors did not assess customer demography and did not share the findings with the banks. Practical implications The findings will guide e-marketers on how to best engage with customers to enhance brand loyalty and continuously be in touch with their clients. Originality/value Most models are conceptual and assume that customers typically journey through all the stages in the model. The work is interesting because the empirical study found that customers travel in multiple different ways through this process. It is significant because it changes the way the authors understand patterns of online customer engagement.

Artificial Intelligence (AI) is a rapidly growing field of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human intelligence, such as perception, learning, reasoning, problemsolving, and decision-making. AI involves the study and development of algorithms and computer programs that can simulate human cognitive abilities and can improve their performance over time through machine learning and deep learning.The goal of AI is to create intelligent machines that can think, reason, and learn like humans, and that can perform complex tasks such as natural language processing, image recognition, and decision-making in real-world scenarios. AI has the potential to revolutionize and manufacturing, by improving efficiency, accuracy, and speed in various Processes There are different types of AI, including rule-based AI, which follows a set of predefined rules to make decisions, and machine learning-based AI, which learns from data to improve its performance. Deep learning, a subset of machine learning, uses neural networks to model complex relationships in data and is responsible for many of the recent advances in AI, including image and speech recognition. Despite the many benefits of AI, there are also concerns about its impact on society, including job displacement, bias in decisionmaking, and the potential misuse of AI for harmful purposes. Therefore, it is important to develop

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