Customer Behavior Research Articles

Customer journey mapping framework for SMES: Enhancing customer satisfaction and business growth

Customer journey mapping (CJM) is an essential tool for small and medium-sized enterprises (SMEs) to understand and optimize their interactions with customers, thereby driving satisfaction and business growth. This review outlines a customer journey mapping framework designed specifically for SMEs, aiming to enhance customer experiences through a structured approach to identifying key touchpoints, pain points, and opportunities for improvement across the customer lifecycle. The proposed framework involves systematically mapping out every stage of the customer journey, from initial awareness to post-purchase interactions, highlighting moments that significantly impact customer satisfaction. By leveraging customer data, feedback, and analytics, SMEs can gain valuable insights into customer behavior, motivations, and preferences. This framework emphasizes the importance of personalization, using data to tailor the customer experience and foster deeper connections. Additionally, the framework integrates feedback loops, allowing SMEs to continuously gather and analyze real-time customer data. This iterative process helps businesses to proactively identify emerging trends, address customer concerns, and adapt to evolving market demands. By aligning customer needs with business objectives, SMEs can create more targeted marketing campaigns, improve product or service offerings, and enhance customer retention strategies. The implementation of this customer journey mapping framework can lead to several key benefits for SMEs, including improved customer satisfaction, increased brand loyalty, and greater operational efficiency. It also enables SMEs to compete more effectively in their markets by delivering personalized and seamless customer experiences. The framework supports scalability, allowing SMEs to refine their customer engagement strategies as their businesses grow. In conclusion, the customer journey mapping framework serves as a critical tool for SMEs looking to enhance customer satisfaction and drive sustainable business growth. By focusing on data-driven insights and continuous improvement, SMEs can deliver more value to customers, strengthen brand loyalty, and achieve long-term success.

Personalized customer journeys for underserved communities: Tailoring solutions to address unique needs

In today's diverse marketplace, creating personalized customer journeys for underserved communities is essential for fostering inclusion and promoting equitable access to services. This review explores the significance of tailoring solutions to address the unique needs of these communities, emphasizing the role of personalized experiences in enhancing customer satisfaction and loyalty. Underserved communities often face systemic barriers, including limited access to resources, information, and services. Traditional customer journey models may overlook these specific challenges, leading to ineffective engagement and unmet needs. By adopting a personalized approach, businesses can better understand the distinctive characteristics, preferences, and pain points of these communities. This understanding allows for the development of targeted strategies that resonate with individuals' cultural, social, and economic contexts. The framework for personalized customer journeys involves several key components. First, comprehensive data collection is crucial for identifying the unique demographics and psychographics of underserved communities. Utilizing qualitative and quantitative research methods, businesses can gather insights on customer behaviors, preferences, and challenges. Next, leveraging advanced analytics and artificial intelligence can enhance the personalization of interactions, ensuring that offerings align with the specific needs and aspirations of these communities. Additionally, fostering collaboration with community organizations and leaders is vital. Engaging with local stakeholders helps businesses build trust and credibility, facilitating the co-creation of solutions that genuinely address the community's needs. This collaborative approach can lead to the development of tailored products and services, targeted marketing strategies, and supportive customer service frameworks. Ultimately, personalized customer journeys for underserved communities not only improve customer satisfaction but also drive business growth and social impact. By prioritizing equity and inclusion, businesses can enhance their brand reputation, foster customer loyalty, and contribute to the economic empowerment of these communities. In conclusion, a commitment to understanding and addressing the unique needs of underserved communities through personalized customer journeys is essential for fostering inclusive growth and creating lasting positive change.

AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention

In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to analyze customer behaviors, preferences, and feedback. By aggregating data from various sources—such as online interactions, purchase histories, and social media activity—AI can generate personalized recommendations, offers, and communication strategies that resonate with individual customers. The framework facilitates dynamic customer segmentation, allowing SMEs to tailor marketing efforts and enhance service delivery. The personalization process also extends beyond marketing by optimizing customer support through AI-powered chatbots and recommendation systems, which provide real-time solutions and advice. This level of personalization fosters stronger emotional connections, increasing customer satisfaction and brand loyalty. Additionally, AI-driven insights enable SMEs to anticipate customer needs, predict churn rates, and proactively address potential issues, thereby boosting retention rates. For SMEs, the implementation of an AI-driven personalization framework is not only cost-effective but scalable, making it accessible even to businesses with limited resources. As SMEs increasingly adopt digital tools, the integration of AI-based personalization becomes essential for staying competitive in a rapidly evolving market. This review highlights the potential of AI in transforming customer engagement strategies for SMEs by offering a tailored, efficient, and sustainable approach to enhancing customer experiences.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic

Introduction: With an emphasis on predictive and prescriptive analytics, this study examines the revolutionary implications of AI-driven analytics on small and medium-sized organizations (SMEs). SMEs play a crucial role in the global economy and require advanced solutions to improve decision-making and operational efficiency. The research aims to explore how AI-powered analytics, particularly in predictive and prescriptive forms, can add value to SMEs by enhancing demand forecasting, customer behavior insights, and financial planning. To determine how AI-driven analytics might affect SMEs, a thorough assessment of the literature was undertaken. The study reveals that SMEs implementing predictive analytics experience notable improvements in areas such as inventory management, revenue generation, and overall operational efficiency. Furthermore, businesses that leverage prescriptive analytics benefit from optimized resource allocation, enhanced marketing strategies, and better risk management practices. These findings highlight the potential for AI to overcome key challenges faced by SMEs, including budget constraints and limited data availability. AI-driven analytics can provide valuable insights that allow SMEs to streamline operations and foster growth. With future trends pointing to greater accessibility and developments in machine learning, natural language processing, and the integration of AI with other cutting-edge technologies, like blockchain, AI-powered analytics offers substantial prospects for small and medium-sized enterprises.

Tracing applications for the control of virus dispersal - COVID-19 experience

Abstract Tracing apps have been promoted as an effective tool for the monitoring and control of the spread of COVID-19. Although the correlation between the use of mobile tracking apps and reducing the spread of the virus has been demonstrated, the success of these apps depends on a number of factors, such as the number of app users, the rate at which new systems get adopted, as well as concerns related to anonymity and privacy. The aim of the research was to analyse the pattern of adaptation and use of a COVID-19 tracing app in the context of Polish society. The research used a qualitative approach, data were collected through focus group and individual interviews. The sample consisted of 34 respondents, aged 23-61, with different educational levels and backgrounds. The study followed two phases. First, a model with a set of questions was proposed on the basis of the unified theory of technology acceptance and use. Secondly, using the set of questions, interviews were conducted. In order to examine the data, thematic analysis was applied. To summarise the results of the survey, the respondents have clear expectations towards the app, which are not fulfilled. They are afraid of new technologies. However, they use them frequently and willingly without thinking about the security of their personal data. They expect applications to provide reliable information, but at the same time do not want to enter necessary data that they consider sensitive. They are negative about government actions without losing trust in government applications. It can also be speculated that a lack of information and understanding of the app may have contributed to some of the objections. In conclusion, we need to better understand users and be able to accommodate often conflicting characteristics in order to design more effective tracking applications in the future. Key messages • Technology offers ways to successfully prevent viruses from spreading. • Getting the technology right, based on customer behaviour, is key to the effectiveness of tracking applications.

Determinants of halal food customer behaviour in the context of Indonesia and Malaysia: a systematic literature review

Determinants of halal food customer behaviour in the context of Indonesia and Malaysia: a systematic literature review

Development of a Green Banking and Green Financing Practice Model for Enhancing Sustainability Development Goals (SDGS)

Objective: This study is to investigate a green banking model based on green banking practices, green financing, and green environment to enhance sustainability development goals (SDGs). Theoretical Framework: The research focuses on developing green banking and financing practices to improve environmental performance in the Indonesian banking sector. Green Banking emphasizes sustainability in banking operations and products, while Green Financing promotes funding environmentally friendly projects. The study also draws on Stakeholder Theory, highlighting that banks are accountable to a wide range of stakeholders. Method: The research stages include the development of instruments, questionnaire distribution, data collection, and data processing using a sample of 54 respondents, selected using convenience sampling. Data testing and processing were carried out using SmartPLS software. Results and Discussion: The analysis of banking employees and practitioners revealed that Banks' Customer-Related Practices (BCRP), Employee-Related Practices (BERP), and Operation-Related Practices (BORP) positively impact Green Environment Performance (GEP), whereas Banks' Policy-Related Practices (BPRP) do not. Additionally, while BCRP, BERP, and BORP showed no significant influence on Green Financing (GF), BPRP demonstrated a notable positive effect on Green Financing (GF). This result also show that Studies on Bank’s customer behavior change towards sustainability and its influence on the SDGs. Research Implications: Banks can implement specific green practices to improve their sustainability development goals (SDGs). The study reinforces Sustainability Theory by highlighting the importance of balancing environmental, social, and economic concerns in banking operations, confirming the need for a "Triple Bottom Line" approach in business models. Originality/Value: The originality of the research is demonstrated through its Studies on Bank’s customer behavior change towards sustainability and its influence on the SDGs by integrating multiple theoretical perspectives.

Evaluation of Quality Online Banking Services Using WASPAS Method

The rapid expansion of the Internet and advances in digital technology have revolutionized traditional banking methods, leading to the emergence of online banking. Online banking enables customers to conduct financial transactions anywhere, anytime, with unprecedented convenience and accessibility. This paper provides a comprehensive analysis of online banking, examining its benefits, challenges and impact on both banks and consumers. Key aspects such as security concerns, customer satisfaction, cost effectiveness and complexity in implementing online banking systems are discussed. Also, how online banking is reshaping the banking landscape, promoting greater competition, financial inclusion and digital transformation in the global economy. The advent of the Internet and digital technologies has fundamentally changed the way financial institutions operate, paving the way for online banking. Online banking, also referred to as internet or e-banking, allows customers to access a wide range of financial services through digital platforms. These services include checking account balances, transferring funds, paying bills and managing investments all from the convenience of a personal device. As customers increasingly demand fast, convenient and secure banking services, financial institutions have responded by embracing digital transformation. The shift to online banking has brought significant benefits such as reduced operational costs, improved customer experience and increased efficiency in financial services. However, along with these advantages, banks also face challenges related to cyber security, customer trust and the need for continuous technological upgrades to meet evolving consumer needs. The importance of research related to online banking lies in its profound impact on the global financial system and its growing relevance in the digital age. Understanding online banking is important for financial institutions, regulators and customers as it changes traditional banking practices and introduces new opportunities and challenges. Technological Advances and Innovations: Online banking is at the forefront of financial technology (FinTech), creating innovations in service delivery. Exploring this area allows for a deeper understanding of how technological advances such as mobile banking, artificial intelligence (AI) and block chain are improving efficiency, customer experience and the competitive landscape of banking institutions. Customer Behavior and Satisfaction: As more consumers shift to digital platforms, research on online banking provides insights into changing customer expectations and behavior. It highlights key factors influencing customer satisfaction, including usability, convenience and trust, helping banks refine their services to meet growing demands for seamless, personalized experiences. Cost Efficiency and Operational Impact: The financial sector has experienced significant cost reductions and operational improvements through the adoption of online banking. Understanding how banks can use online platforms to optimize resource allocation, reduce transaction costs and improve efficiency is critical for stakeholders looking to stay competitive and increase profitability. Security and risk management: As online banking systems become more pervasive, so do the associated risks—especially those related to cyber security. Research in this domain is essential for developing effective security protocols, understanding fraud prevention strategies, and managing data privacy concerns. It ensures continuous improvement of security measures to protect both financial institutions and consumers. Customer Satisfaction (Benefit), Transaction Speed (Benefit, sec), Implementation Cost (Non-Benefit, $000), Security Rating (Non-Benefit). System A, System B, System C, System D, System E.

Digital Disruption in Indian Retail: Trends, Opportunities, And Challenges

This research paper examines the significant growth and transformation of the Indian Retail Industry, now ranked as the fifth-largest globally. This expansion is driven by rising incomes, shifting middle-class lifestyles, and improved digital connectivity. In this competitive environment, Indian consumers increasingly demand personalized and exceptional services. Digital transformation is identified as a key success factor, enabling retailers to adapt to changing customer behaviors. The surge in online shopping, propelled by smartphone usage, has reshaped the consumer-retailer dynamic, necessitating robust online presences and investments in data analytics for better customer insights. Transparency in supply chains and real-time inventory management have become essential for effective retail operations. The paper highlights key digital transformation trends, such as digitized in-store experiences and workforce empowerment, while acknowledging challenges like change management, skill shortages, and data security risks. Retailers are advised to implement change management strategies, develop talent acquisition plans, and invest in cybersecurity to ensure sustainable growth.

The Future of Food Health-Focused Brands in the Fast-Food Industry Based on the Analysis of Chipotle

Abstract: Healthy and sustainable food eating has become one of the significant trends in customer behavior. Academics and media research have found the significance of embracing new consumption habits, food choices on nutritionally adjusted meals, and increasing consumer health awareness. However, more research is still needed on the development opportunities of the fast-food industry in response to this new consumer behavior. Therefore, this article will use Chipotle as an example to demonstrate the financial opportunities of the fast-food industry's change toward healthy eating. This article uses research on healthy eating development, trends in the fast-food industry, balance sheets, and financial ratios. We have employed the analysis of Chipotle's financial statement and forecast the development of the fast-food restaurant from 2024 to 2028 after it accepts healthy eating. Research has found that customers' attitudes are changing. Consumers now prefer natural, high-quality fast food; introducing healthy products is financially rewarded with higher sales growth. This kind of brand will gain traction and boost profit. Various brands should also launch new products that focus on healthier alternatives.

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.

Personalized Data Insights: How­­ Machine Learning is Revolutionizing Everyday Business Decisions

The rise of machine learning has fundamentally transformed the way businesses approach decision-making, particularly in the realm of data analytics. In today’s data-driven landscape, organizations are not just gathering information—they’re using advanced algorithms to turn raw data into personalized, actionable insights that drive business strategy. Machine learning enables businesses to go beyond surface-level trends and dig deeper into customer behavior, operational inefficiencies, and market dynamics. This technology is revolutionizing everyday business decisions by delivering real-time, data-driven intelligence that is specifically tailored to each business's unique needs. By leveraging machine learning, businesses can analyze massive datasets, uncover hidden patterns, and make predictions that were previously unimaginable with traditional analytics methods. One of the key benefits of machine learning is its ability to personalize data insights, offering businesses a clearer, more detailed understanding of their customers and operations. It empowers companies to make more accurate decisions in marketing, product development, customer service, and beyond, leading to improved efficiency, customer satisfaction, and overall business performance. This article explores the transformative role of machine learning in generating personalized data insights and its profound impact on everyday business decisions. It highlights how this technology can improve decision-making processes, enhance customer experiences, and drive more effective business strategies through real-time analysis and prediction. Through practical examples, we will demonstrate how businesses across various sectors are already using machine learning to revolutionize their operations and improve outcomes.

A Study on Work Environment and its Impact on Employees Performance at Boston Business Solutions

This research presents a thorough examination of the supermarket's sales data, focusing on customer behaviour, regional performance, and payment preferences. The supermarket has successfully cultivated a diverse customer base, with a nearly equal split between normal customers and members. This balanced customer distribution suggests a stable foundation for growth, particularly through strategic initiatives aimed at increasing membership and enhancing customer loyalty. The supermarket's regional presence is well-balanced across major cities, including Mandalay, Naypyitaw, and Yangon. Sales are dispersed equally among these regions, indicating that the supermarket has established a strong foothold in each market. This balanced distribution provides an opportunity to tailor strategies to the specific characteristics and preferences of each city, potentially driving increased market share and customer satisfaction through localized approaches. Analysing supermarket sales data is necessary for selecting wisely in business situations and ensuring long-term success. Sales data can also uncover issues in the supply chain, helping supermarkets run more smoothly and efficiently. By examining sales in different regions, supermarkets can adjust their products and promotions to better fit local preferences, improving sales in specific areas. Financially, analysing sales data helps with accurate budgeting and pricing strategies, leading to higher profits. Additionally, it allows supermarkets to evaluate their performance in relation to rivals and quickly adapt to changes in the market, helping them stay ahead. Analysing sales data helps supermarkets understand their customers, operate more efficiently, tailor strategies to different regions, and make better financial decisions, all of which contribute to their growth and success

An Innovative Perspective for Constructing Cultural and Creative e-Commerce Platforms: Integrating Insights from ICDT Theory and Brand Personality

Thriving in competitive business environments requires innovation, especially within cultural and creative-based e-commerce platforms. This study examines the application of two prominent theories, ICDT theory and brand personality, in assessing new business models. Focusing on 479 respondents from Taiwan, this research explores how the implementation of ICDT and brand personality in cultural and creative e-commerce influences customer behavior. Statistical analyses and the Smart PLS method are adopted to identify the significance of and relationships between the variables in the structural equation model. The profound influence of the ICDT model is evident in the emergence of virtual information space, virtual communication space, and virtual transaction space. Brand personality traits underscore the significance of intellectual excellence, M-type traits, seeking innovation and change, and stable reliability in shaping customer values. This research enriches the literature by emphasizing the essential role of innovative technologies and brand issues integrated with core product attributes for successful consumer acceptance in the fiercely competitive landscape of e-commerce platforms.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

A Study on Customer Behavior in Online Dating Platforms: Analyzing the Impact of Perceived Value on Enhancing Customer Loyalty.

Customer loyalty is critical for organizations to gain market share and maintain a sustained competitive advantage. However, no study has yet explored customer loyalty in online dating platforms. Perceived value theory suggests that perceived value is a key predictor of customer loyalty. Accordingly, this research constructed a conceptual model drawing on a multidimensional perspective of perceived value to explore customer loyalty in online dating platforms and investigated the mediating role of satisfaction. By quantitatively analyzing 352 customers who had experienced online dating platforms and utilizing structural equation modeling (SEM) to examine the relationships among propositions, the research demonstrated a strong positive correlation between perceived value, satisfaction, and loyalty, and a notable indirect impact on loyalty through satisfaction. In addition, the experiential value dimension of perceived benefits had the most positive and substantial influence on perceived value, while the perceived risk dimension of perceived sacrifice had the most negative and notable impact on perceived value. The results of the study provide designers, managers, and vendors of online dating platforms with valuable insights into customer behavior and practical recommendations for improvement, helping them to develop more effective strategies to enhance market competitiveness and ensure the sustainability of their platforms.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

Virtual Tasting in the Metaverse: Technological Advances and Consumer Behavior Impacts

Product tasting is a key element in improving customer satisfaction in the commercial environment. This study looks at the notion of traditional tasting and its effect on customer behavior and explores emerging tasting techniques, shedding light on the contribution of digital tasting. Indeed, the advent of the metaverse has made it possible to offer new virtual tasting experiences. However, this experience does not yet involve a sense of taste. Our manuscript highlights the potential of tasting in the metaverse through a descriptive study of various concrete cases of international brands that have included it in their marketing strategies. In light of the results, practical and theoretical recommendations are provided for professionals interested in leveraging virtual tools to improve consumer satisfaction.

Assessment of Factors Affecting Customer Preferences and Customer Satisfaction on Online Shopping Experiences Case of Delhi NCR

This study is on assessment of factors affecting customer preferences and customer satisfaction on online shopping experiences in Delhi NCR. It is mandated to look into the different aspects of e-shopping, from factors that determine the individual preferences, to those that influence satisfaction and some pain points that the customers experience. The objectives of the research are to look into what influences consumers' choice of e-commerce platforms, evaluate and assess the different aspects contributing to customer satisfaction in the online shopping experience, and explore common challenges faced by customers while shopping online. The study offers important insights into the dynamics of e-commerce platforms and consumer preferences through a thorough examination of these goals. A number of factors, such as frequency of purchases, price competition, app use, return policies, and delivery reliability and speed, affect customers' choice of e-commerce platforms. According to the analysis, the majority of users give priority to platforms that provide easy-to-use payment methods including E-payments and Cash on Delivery (COD) (71%). One important feature that stood out was price competitiveness (58%); consumers favored platforms that provided a large selection of products at competitive prices. Customers also emphasized the importance of the user-friendliness of apps, the return policy, and the dependability and timeliness of delivery while choosing e-commerce platforms. The majority of consumers (77%) are satisfied with their online purchasing experiences, according to the study. Customer satisfaction is largely influenced by the usability of websites and mobile apps, the reliability and speed of deliveries, and the correctness and quality of the products. Platforms that provide high-quality products, timely and dependable delivery services, and user-friendly interfaces are highly valued by customers. These elements have a big impact on how satisfied consumers are with e-commerce sites overall. Although consumers are generally satisfied, there are a number of difficulties when they shop online. Common pain points experienced are poor product quality, complicated or nonexistent return processes, worries about payment security, language barriers, and improper item deliveries. These difficulties highlight how critical it is to respond to consumer complaints and improve the online shopping experience by implementing stronger security measures, clear return procedures, and better-quality products. Several recommendations are put out in light of the findings in order to boost e-commerce platform efficacy and raise customer satisfaction. The study further acknowledges limitations, which could affect how broadly the results can be applied, such as its limited sample size and resource limits. In order to offer more thorough insights into the tastes and behaviours of e-commerce customers, future research projects should address these shortcomings. The study concludes by highlighting the multifaceted nature of client preferences and issues in the e-commerce sector. E-commerce platforms may promote stronger client loyalty and drive sustainable growth in the cutthroat online marketplace by comprehending the variables impacting platform choice, improving components contributing to customer satisfaction, and addressing frequent issues experienced by customers.