NOLEGEIN-Journal of Consumer Behavior & Market Research

Artificial Intelligence in Retail: Redefining Consumer Expectations and Retail Innovation

2026-05-09T07:22:50+00:00

This study offers an updated and comprehensive perspective on the integration of artificial intelligence (AI) within the retail sector and its transformative impact on modern commerce. The rapid expansion of digital technologies, combined with the dominance of global technology leaders such as Amazon, Alibaba, Apple, Google, and Microsoft, has significantly elevated consumer expectations and redefined retail dynamics. AI technologies, including machine learning, predictive analytics, and automation, enable retailers to process vast volumes of data efficiently, resulting in faster, more accurate, and customer-centric decision-making. The widespread adoption of the Internet and smartphones has accelerated digital transformation, fundamentally altering how consumers communicate, explore products, and complete purchases. Modern consumers today are more knowledgeable and empowered than ever, seeking tailored experiences, smooth interactions, and immediate responses.AI-driven tools such as recommendation engines, chatbots, and smart inventory systems allow retailers to meet these expectations while optimizing operational efficiency. Furthermore, AI facilitates enhanced demand forecasting, supply chain management, and dynamic pricing strategies, enabling businesses to remain competitive in a rapidly evolving market. This paper highlights key AI-driven innovations and examines their role in reshaping retail operations and consumer engagement. It concludes that AI is not just a technological innovation but also a critical strategic requirement for retailers seeking to maintain growth and provide enhanced customer value in the digital era.

Consumer Trust in AI-Driven Responsible Marketing

2026-07-01T10:42:28+00:00

Artificial Intelligence (AI) is rapidly becoming a core element of modern marketing practice. Organisations increasingly rely on AI-based tools such as personalised recommendations, automated messaging, and targeted advertising to improve customer engagement and marketing efficiency. These technologies allow brands to communicate with consumers in ways that feel more relevant and customised. At the same time, the widespread use of AI has raised important concerns about data privacy, transparency, and ethical responsibility. Such concerns directly influence the extent to which consumers trust brands that rely on AI systems. Responsible marketing emphasises transparency, fairness, and respect for consumer interests. When AI technologies are implemented responsibly, they have the potential to strengthen trust and foster long-term customer relationships. This study explores consumer trust in AI- driven responsible marketing by examining consumer awareness of AI applications, perceptions of responsible AI practices, and resulting behavioural responses. Drawing on existing Indian research in the areas of AI marketing and ethical practices, the study proposes a conceptual framework linking awareness, responsible AI perception, consumer trust, and behavioural outcomes.

Impact of Pricing Strategies on Hotel Reservation Decision-Making: An Empirical Study in the Rajasthan Tourism Sector

2026-05-14T08:41:51+00:00

Pricing in the hospitality industry is a critical factor in the determination of reservations by customers in the hospitality industry, which is very competitive. Nevertheless, even though the dynamic and promotional pricing models were widely used, there is not much empirical evidence on what dimension of pricing would have the strongest influence on accommodation booking behavior. The main issue is how various pricing perceptions e.g. fairness, corporate pricing, seasonal adjustments, and psychological framing are interconnected to form consumer decision-making. This study aimed to determine how six strategies of pricing (Dynamic Pricing, Seasonal Pricing, Discount Pricing, Corporate Pricing, Psychological Pricing, and Price Fairness) affected the reservation decision that had been undertaken before. A quantitative research design was taken, and primary data were gathered with the help of a structured questionnaire which had five-point Likert scale measurements. ANOVA and Multiple Linear Regression were applied on a sample population of 50 respondents. Part, Eta Squared was used to measure effect size, which was determined to discover the extent and magnitude of the influence of each variable. The findings indicated that any pricing strategy has a significant influence on the decision of reservation (p < 0.05). Of them, the most influential predictors were Price Fairness, Corporate Pricing and Psychological Pricing. The regression model exhibited a high explanatory power (R2= 0.72) meaning that the perception of pricing can explain about 72% of the variation in the booking decisions. The analysis has found that price-related mechanisms driven by fairness and orderliness are more effective than just dynamic changes, provide strategic information to the hotel industry, and competitive positioning.

A Study on Consumer Insights into Music Listening Habits and Preferences: A Study of Emerging Trends

2026-04-16T07:10:15+00:00

The impact of music on youth has been at the heart of how young people develop their identity, express emotion, and interact socially, particularly within the diversity of cultural regions. This research investigates how the musical preferences, listening habits, and engagement behaviours of youth in regional communities are informed by cultural identity and social context. The rapid advancement of digitised music and access to streamed music from all around the world has significantly increased young
people’s exposure to new types of music. In contrast, there are still many ways that regional languages and culturally significant symbols contribute to young people’s individual music preferences. This research is conducted using a quantitative survey strategy to identify key characteristics that shape the development of music preferences amongst youth 15–25 years of age, including music genre selection, frequency of music consumption, cultural influences on music preferences, use of technology to facilitate music preferences, and peer-driven behaviours related to music consumption. The results of this research will further clarify how the social environments of young people, their background culture, and their regional identity impact their music preferences and how young people incorporate music into their daily routines for purposes of emotional regulation, human connection, and expression of self. The results of this research will provide actionable insights to music producers, marketers, and creators who wish to create culturally relevant music albums that are likely to engage young audiences that represent a wide variety of cultures. By better understanding the regional music preferences and cultural expectations of young people, this research has the potential to provide more culturally relevant, authentic, and impactful music content to support a variety of contemporary youth and culture.

Consumer Behaviour Towards Online Food Delivery Services: A Study of Rajkot City

2026-07-01T10:49:38+00:00

The rapid expansion of online food delivery services (OFDS) has significantly transformed consumer purchasing behaviour in India, particularly in urban and semi-urban regions. Driven by increasing smartphone penetration, digital payment systems, and evolving lifestyle patterns, platforms such as Swiggy and Zomato have redefined food consumption practices. This research investigates consumer behavior related to online food delivery services in Rajkot city, emphasizing the factors that shape usage habits, customer satisfaction, and long-term loyalty toward these platforms. The study follows a descriptive and analytical research approach, using primary data gathered through a structured questionnaire administered to 60 participants within the age group of 18 to 60 years.. Convenience sampling was employed, and statistical tools including descriptive statistics and Analysis of Variance (ANOVA) were used to interpret the data. The study evaluates various factors such as convenience, pricing strategies, food quality, delivery efficiency, application usability, and customer reviews. The results indicate that convenience is the primary factor shaping consumer behavior, with pricing strategies and promotional discounts emerging as the next most important influences. Additionally, the ease of app navigation and the quality of the user interface contribute significantly to improving the overall user experience, especially among younger users. While overall satisfaction levels are high, challenges such as inconsistent food quality and occasional delivery delays persist. Furthermore, demographic analysis indicates that income level and age significantly influence consumer preferences, with higher-income users prioritizing service quality over price sensitivity. The study contributes to the existing literature by focusing on post-pandemic consumer behaviour in a tier-2 city context, which remains underexplored. The results provide valuable insights for online food delivery platforms and restaurant partners to enhance service quality, optimize pricing strategies, and improve customer retention mechanisms. The study concludes by offering practical suggestions for stakeholders and highlighting potential directions for future investigations, particularly in exploring the behavioral and psychological aspects that influence digital consumption patterns.

Impact of AI-Powered Chatbots on Customer Satisfaction in E-Commerce: A Secondary Data Analysis

2026-06-26T07:11:55+00:00

Today many online shopping websites use AI chatbot to talk with customers The chatbot answer fast and work all day and night 24/7. Because of this customer support becomes easy and quick. In this study we tried to see how these chatbots change customer satisfaction. For this we used old data which was already available online. In total, 45,782 customer reviews from different e-commerce platforms were used for analysis.To understand customer feelings, we used sentiment tools like BERT and VADER. We also used LDA method to find common topics in reviews. After that, we checked customer satisfaction using simple statistics. The results show that after using chatbots, customer ratings increased by 10.2% and positive sentiment also increased by 29.2%. But chatbot performance was not same in all cases. For simple questions, results were very good (34.7% improvement). But for complex problems, improvement was very less (only 8.3%).We also noticed that when chatbots replied in a more personal way, customers felt more satisfied. Their satisfaction increased by 42% and they were more interested to buy again (28% increase). Even though chatbots are useful for basic help, they still fail when problems are difficult. So, giving option to talk with human support is still very important.