Internet of Things (IOT) involves devices that can interact with one another by connecting to the internet and sending information, ultimately improving human lives. This research is one of the first in literature that seeks to understand the variables that influence consumer adoption of IOT products offered as services against a monthly premium. The influence of value dimensions on IOT on its adoption and consumer willingness to pay a premium was studied. A sample consisting of 1623 responses collected through an online survey was analysed using partial least squares structural equation modelling. The moderating role of age groups, gender and income levels was also analysed. Out of sample prediction and overfitting of the model were used to assess predictive validity both for the full data set and across groups using PLS Predict. Perceived playfulness, personal innovativeness and convenience value were the most significant variables affecting attitude and intention to use IOT services. Attitude mediated most relationships in the model. Moderation analysis using demographic variables of gender, age groups and income levels revealed significant differences in the strengths of the relationships in the model. Predictive validity assessment with PLS showed revealed high predictive power of the model for adoption outside the selected sample. The results can help marketers segment and design business strategies to expedite the diffusion and adoption of the technology in the market. The need for a shift in the B2C IOT market from product‐centric to a product‐as‐a‐service business to maximize value to consumers is thus proposed.

Today, digital tools offer multiple avenues for consumers to learn about themselves. Self‐discovery or knowing “who am I” is fundamental to our everyday experience. However, there is a paucity of research that investigates the “how and why” of self‐discovery, made possible by technological advancements. Adopting the theoretical tenets of extended self, possible selves, storied selves, and the twin metaphors of self and identity, we follow a multimethod qualitative approach to explore consumer self‐discovery in the context of AR‐based makeup and grooming apps and filters. We establish a framework for AR‐facilitated self‐discovery by analyzing the data obtained via Netnography and 22 in‐depth interviews. The findings suggest that digital tools enable the discovery of previously unknown facets of the consumers' self‐concept. Theoretically, this study demystifies the process of technology‐enabled self‐discovery, which is related to better life decisions and consumer well‐being. Brands may apply these insights to inculcate the discovery components into the AR design, which can facilitate the adoption of new products. Finally, this study highlights the possible challenges to be avoided to ensure consumer well‐being while using AR‐enabled digital tools.

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Rising concern for health and environmental issues associated with the rigorous use of chemical fertilizers has necessitated an alternate form of agriculture in Kerala, and that's how organic production came into existence. The organic agricultural production technique is considered to be quite conflicting with conventional production technique where the use of human-made fertilizers like pesticides, weedicides and insecticides are entirely banned. Organic products are perceived to be minimally processed to maintain the integrity of the food without artificial ingredients and preservatives. Therefore right from mid-eighties, consumers, particularly from the developed countries, started switching over towards organically produced food products as they more nutritious, healthy, and nature-friendly. Considering the organic market in Kerala, the state is still at its infant stage with a market share of less than 1%. Hence it is critical to investigate Kerala consumers low consumption level towards environmentally friendly products. The current research intents to explore the factors influencing Kerala consumers’ organic purchase behaviour based on data collected from 200 respondents (100 regular and 100 irregular organic users) using a structured questionnaire. Analysis techniques comprising of correlation and multiple linear regression has been applied for data evaluation. The research findings support the formulated hypothesis and aim at providing necessary guidelines for various stakeholders who are involved in the organic industry.