Customer Experience Research Articles

Comparative analysis of M/G/K queueing model with artificial neural networks for service system dynamics

Abstract Queues emerge when the demand for service exceeds the available capacity to provide it. The M/G/K queuing model is the typical and theoretical framework to model and analyze the system when arrival rates are not in constant mode and service rate follow exponential distribution. This study investigates traffic congestion at fuel stations, focusing on queue length, customer waiting time distribution, and operational efficiency. A system with four servers were modelled to evaluate the system's behavior. The governing differential equations using M/G/K model were developed and solve by Runge–Kutta (RK4) method to derive initial state probabilities. Additionally, an Artificial Neural Network (ANN) was implemented to predict these state probabilities using simulated data. A comparison of results revealed a close approximation between the RK4 solutions and ANN predictions, demonstrating the ANN's robustness and accuracy in replicating traditional queuing dynamics These findings emphasize the potential of artificial neural networks (ANNs) as a practical tool for planning and optimizing fuel station operations, allowing for better congestion management and customer experience. Future applications may involve the extension of this methodology to other service-oriented systems.

Exploratory Analysis of Geo-locational Data

Abstract: Geospatial data has emerged as a crucial component in the digital age, encompassing geographic coordinates and location-based information related to people, vehicles, objects, and natural phenomena. Its rapid growth is fuelled by the widespread use of smartphones, GPS, social media, and various location-based applications, leading to a significant shift in how data is applied across industries. As a result, geospatial data has become indispensable in understanding complex spatial relationships, driving development, and fostering transformation in various sectors. This article delves into the multifaceted role of geospatial data in modern society, emphasizing its importance in shaping industries and addressing contemporary challenges. As a fundamental element of the Internet of Things (IoT), geospatial data enables seamless navigation and supports industries in optimizing resource allocation, enhancing decision-making, and improving overall operational efficiency. From monitoring traffic patterns to tracking environmental changes, geospatial data provides real-time insights that empower organizations to respond swiftly and adapt to evolving needs. The report further explores the expanding role of geospatial data in critical domains, including urban planning, transportation, environmental monitoring, marketing, and public safety. In urban planning, it aids in identifying infrastructure needs and optimizing land use, while in transportation, it helps streamline logistics, reduce congestion, and improve route planning. Environmental monitoring benefits from geospatial data through its ability to track climate changes, deforestation, and pollution, providing valuable information for sustainability initiatives. In marketing, businesses leverage geospatial data to understand consumer behavior, target advertisements, and enhance customer experiences. Lastly, in public safety, geospatial data is instrumental in disaster response, crime mapping, and emergency management. In conclusion, geospatial data is a powerful tool driving innovation and efficiency in today’s world. As technology continues to evolve, the applications and importance of geospatial data will only grow, shaping the future of industries and society. Keywords: Geolocational Analysis, Geospatial Data, Environmental Monitoring, Streamline Logistics, Disaster Response, Crime Mapping, Emergency Management, Industries And Society.

Exploring the drivers of hospitality and tourism customer loyalty for generative artificial intelligence (AI): a multi-analytic approach

ABSTRACT Generative artificial intelligence (AI) is transforming the hospitality and tourism industry by offering innovative solutions to enhance customer experiences when planning holidays. The drivers of customer loyalty towards generative AI have not been thoroughly examined despite its potential. This study applies a multi-analytic approach, which incorporates both sufficiency and necessity logics in order to investigate the relationships among the risk variables, such as the cognitive and emotional responses in regards to shaping loyalty. We examine the impact of performance, financial, time, psychological, and privacy risks using the theory of planned behaviour and emotional variables by utilising the cognitive-emotional-behavioural framework. We also explore the moderating role of customer innovativeness and employ a fuzzy-set qualitative comparative analysis (fsQCA) in order to uncover causal recipes for loyalty. The findings reveal that cognitive and emotional variables, such as perceived behavioural control and emotional well-being are important and necessary conditions for customer loyalty to manifest, and customer innovativeness has an interaction effect on these relationships. This study offers valuable insights for academia and practitioners in regards to leveraging generative AI technologies in order to enhance the customer experience in the hospitality and tourism sector.

Telecommunications Solutions for E-commerce: Enhancing Customer Engagement and Sales

The integration of telecommunications solutions into e-commerce platforms has revolutionized customer engagement and sales strategies in the digital marketplace. Modern contact centers leveraging advanced technologies enable seamless omnichannel communication, personalized customer experiences, and efficient service delivery. The convergence of artificial intelligence, machine learning, and social media integration has transformed how businesses interact with customers, manage relationships, and optimize operations. Implementation of these solutions, supported by strategic planning and technical considerations, has led to enhanced customer satisfaction, improved operational efficiency, and increased revenue growth across the e-commerce landscape.

The healthy aging and service firms: the promise of smart technologies

PurposeThe world is getting older but healthier, making the over-65s healthy aging the only age-based growth segment in developed countries. This article offers a new perspective on healthy aging consumers and examines how service firms can use smart technologies, such as intelligent automation (IA), artificial intelligence (AI), and service robots, to improve their customer experience (CX).Design/methodology/approachThis conceptual article draws insights from the literature on healthy aging in consumer behavior, psychology, and medicine, amongst others, and the literature in service management and marketing on technology.FindingsThis article first demonstrates the attractiveness of the healthy over-65s segment to service firms. Second, it addresses the previously overlooked healthy aging segment in consumer research, connecting it to the evolving physical, cognitive, and socio-emotional responses of older adults in service settings. Third, it advances how service firms can use smart technologies to improve the healthy aging’s CX in the contexts of hedonic and utilitarian services delivered in physical and digital service environments.Practical implicationsThis paper focuses service firms on their fastest-growing segment, that is, healthy older consumers. It provides recommendations on how service firms can use smart technologies to serve this segment better.Originality/valueThis article opens a new stream of service research on healthy aging and technology.

Smart Secure Locker System

The paper's focus is on revolutionizing the delivery of parcels by creating a streamlined delivery approach that emphasizes customer experience, precision, data safety, and altering the delivery framework. Automated locker allocations and modern delivery tracking, as well as automated notifications, all contribute to heightened efficiency for delivery partners. This efficiency is also translated to the customers who can now enjoy convenient self-service kiosks, secure parcel storage, an automated customer dashboard, and unprecedented control over the delivery process. Customer data is protected within the system through encryption, firewalls, and strict access guidelines which block all outside paths to sensitive customer information. The strategy focuses on the integration of new age technologies and design elements focused on the end user to ensure easy and unbreachable eco-delivery systems.

CONSUMERS’ ATTITUDE TOWARD AI-DRIVEN E-COMMERCE ADOPTION IN BANGLADESH: AN EXTENSION OF PLANNED BEHAVIOR

In recent years, the online shopping sector in Bangladesh has witnessed a tremendous transition driven by technological advancements and changing consumer habits. Artificial intelligence (AI) technologies, including chatbots, AI-enhanced Personalization, and intelligent recommendations, have further developed this sector. However, research indicates that many consumers in Bangladesh still favor offline shopping. This situation highlights the necessity of identifying the factors affecting consumers' AI-driven online shopping behavior. Therefore, this study employs an extended Theory of Planned Behavior (TPB) framework to investigate the factors determining consumer preferences for AI-enabled e-commerce platforms in Bangladesh. Data was gathered using a stratified random sampling method from 384 online shoppers in Rajshahi City Corporation. Structural Equation Modeling (SEM) assessed the affinities between the key variables. The findings reveal that consumers' perceptions of promotional discounts and perceived behavioral control significantly influenced their attitudes toward AI-driven online shopping. Factors such as promotional discounts, perceived benefits, and AI-based Personalization notably influence consumers' purchase intentions. These results underscore the importance of competitive pricing strategies, value-added services, and personalized experiences in encouraging consumer adoption of AI-powered e-commerce platforms, providing valuable insights for improving the online shopping environment nationwide. This research offers actionable strategies for online retailers, suggesting that prioritizing AI integration, promotional offers, and tailored customer experiences can better position e-commerce businesses to meet evolving consumer expectations and sustain long-term market growth in Bangladesh's competitive retail landscape. JEL Classification Codes: O33, L81, M31, M37, D12, D91.

Human less Banking System

The rapid evolution of technologies has fundamentally reshaped the financial services landscape, establishing branchless and digital banking as key innovations that are transforming the industry. In this study we explore the implementation and impact of a human-less banking system powered by AI, automation, and digital technologies. Analyse the efficiency, security, and customer experience of human-less banking systems compared to traditional banking models and Investigate the challenges and opportunities associated with adopting a fully automated banking model. Used questionnaire method to collect data and used Percentage and ANOVA for analysis. Despite the strong support for automation, many users still prefer a hybrid banking model that includes human support for complex transactions. This indicates that financial institutions should balance automation with accessible human interaction to meet diverse customer needs

Assessing Risk Mitigation Strategies and Their Impact on the Efficiency of Micro-Insurance Products

Microinsurance, aimed at providing affordable and accessible risk protection to low-income and disadvantaged groups, has become a crucial tool for strengthening financial resilience. Despite its potential, it faces significant challenges such as limited awareness, access difficulties, and operational inefficiencies that restrict its reach and impact. This study evaluates the viability of microinsurance by examining key factors like demand, operational efficiency, customer satisfaction, perceived value, and financial sustainability. Findings reveal strong positive relationships between adoption rates, customer satisfaction, and perceived value, indicating that aligning product benefits with customer expectations is essential for boosting uptake. Operational efficiency also significantly enhances both satisfaction and perceived value. However, issues such as enrollment barriers and financial sustainability remain pressing concerns. The weak link between enrollment barriers and other variables suggests a need for targeted interventions, including awareness campaigns, innovative distribution strategies, and tailored product designs. Overall, the study highlights that improving operational processes, customer experience, and perceived product value is critical to expanding the adoption of microinsurance and advancing broader financial inclusion and poverty alleviation efforts.

A Thematic Analysis on Emerging Trends in Marketing Management: A Conceptual Perspective

Abstract: The thematic analysis adopted in this paper is a method that examines the evolving nature of marketing management field conceptually. Basic marketing principles has remained essential but the need for digital technological adoption coupled with artificial intelligence and human focused methods and moral standards has brought dynamics in marketing. This paper highlights several modern trends in marketing management which includes customization for customers together with digital changes and eco- friendly progressions and innovative patterns of practices. The research used theoretical frameworks for evaluation because it has revealed the exact operational hindrances and focus on current modern strategic development opportunities. In this paper is also highlighted that organizations must implement agile principles alongside data driven decisions and a compressive understanding of customer experiences for sustainable market competition. Recommendations in the analysis includes direct practitioners to adapt their marketing methods or processes to contemporary marketplace needs together with suggestions for researchers to continue with their studies into new fields of study in the marketing management perspectives.

The Customer Journey in Purchasing Counterfeit Fashion Among Generation Z

Research aim: The study aims to explore how digital technologies redefine the customer journey of generation Z, particularly on their experiences purchasing counterfeit fashion products. Design/Method/Approach: This study employs the quantitative descriptive analysis technique. A survey was administered generation Z who have prior experience in purchasing counterfeit fashion items. Research Finding: The findings reveal that online marketplace and social media play an important role in generation Z’s journey for counterfeit fashion. These two platforms not only increase awareness and appeal stages through advertisement and recommendation but also enable direct interaction and digital transactions. Theoretical contribution/Originality: This study contributes to a relatively unexplored topic in developing countries like Indonesia, especially about customer journey and counterfeit purchases. Practitioner/Policy implication: This study offers insights to policymakers and fashion industry stakeholders. An understanding of customer journey can help policymakers to advocate for policy regarding the ethics of counterfeit purchases. Additionally, the findings can assist fashion industry stakeholders in promoting the value of authentic products. Research limitation: This study primarily focused on generation Z, hence limits its applicability to other demographic groups.

Evolution and future agenda of service robot research: a computer-assisted qualitative data analysis approach

PurposeGiven the rapid proliferation of service robots in the hospitality and tourism industry and the increasing complexity of stakeholder interactions, this study aims to systematically analyze stakeholder perspectives and interactions in service robot implementation in hospitality and tourism studies.Design/methodology/approachThe review analyses 175 articles published between 2019 and 2023 in Hospitality and Tourism Management (HTM) and Business Management (BM) journals, combining industry-specific insights and broader business contexts to examine service robot implementation and theoretical development. The study employs Computer-Assisted Qualitative Data Analysis (CAQDA) to systematically analyze research trends and theoretical developments.FindingsOur analysis highlights three main points. First, service robot research has evolved from technological implementation to more meaningful human-robot interaction dynamics, emphasizing psychological aspects, social responses, and relationship building in service environments. Second, limited research on stakeholders' viewpoints on organizational impacts suggests a need for more comprehensive stakeholder analysis in service robot studies. Third, HTM and BM journals exhibit different research trends and focus areas, HTM focuses primarily on operational applications, customer experience, and service efficiency. In contrast, BM journals emphasize theoretical development, strategic implications, and organizational transformation.Originality/valueThis review maps the knowledge structures across HTM and BM journals within hospitality and tourism contexts. This study makes dual contributions. Theoretically, it reveals the current research landscape and guides future research directions. Practically, it provides valuable insights for industry practitioners, including managers implementing service robots, engineers designing robot functionalities, and designers developing human-robot interfaces by offering stakeholder perspectives and evidence-based implementation strategies.

The Transformational Impact of AI Across Financial Services

Artificial Intelligence has emerged as a transformative force across the financial services landscape, fundamentally reshaping how institutions operate and deliver value to customers. This article explores the multifaceted impact of AI technologies across banking, insurance, valuation, and payment systems, highlighting the significant advancements in each domain. From enhanced customer service through intelligent chatbots to sophisticated fraud detection systems, from improved underwriting processes to innovative portfolio optimization techniques, AI applications are revolutionizing every aspect of financial operations. The integration of these technologies presents substantial opportunities for efficiency gains, risk reduction, and customer experience enhancement, while simultaneously raising important considerations regarding algorithmic transparency, data privacy, and ethical implementation. As financial institutions continue to navigate this technological evolution, understanding both the practical applications and governance requirements of AI becomes essential for maintaining competitive advantage in an increasingly digital marketplace.

Driving Industry Transformation with Advanced Data Engineering: Scalable Solutions for Healthcare, Retail, and Agriculture

The rapid acceleration of data-driven decision-making across multiple sectors has catalyzed significant demand for sophisticated data engineering solutions. This article examines how data engineering transforms three critical industries—healthcare, retail, and agriculture—through the implementation of advanced technologies, including Apache Spark, Hadoop, and cloud platforms such as AWS. Data engineers are revolutionizing these sectors by developing robust data pipelines, implementing both SQL and NoSQL database architectures, and leveraging containerization strategies to create scalable solutions. The integration of REST and GraphQL APIs facilitates seamless data exchange between disparate systems, while orchestration tools like Airflow and Kubernetes enable complex workflow management. Through the exploration of industry-specific applications, this article demonstrates how strategic data engineering delivers measurable value in improving patient care, enhancing retail customer experiences, and optimizing agricultural productivity in an increasingly data-dependent business landscape.

Emirates' AI Innovation Challenge: Enhancing Customer Experience through Personalized In-Flight Services

Purpose: This research explores the efforts of Emirates Airline in addressing a challenge to create personalized in fight amenities through an open artificial intelligence (AI) innovation. It is the central aim to investigate how AI can be strategically leveraged to raise the passenger experience in line with the airline’s long term business goals. Methodology: The way a study is framed is within a theoretical framework pertaining to Porter’s (1990) Competitive Strategies framework and Schilling’s (2005) Innovation Funnel model. These frameworks are utilized to examine the correspondence between AI-based offers and Emirates’ pronounced goal, customer inclinations, along with its working adaptability. Findings: Based on our implementation of the AI personal service package, it was revealed that there were three major advantages. The first was that it substantially increased the customer satisfaction because of its services they offer that is caters to individual preferences. Secondly, the service delivery system improvement increased the operational performance of the airline by reducing inefficiency. Unique Contribution to Theory, Practice and Policy: The research based on the findings recommends that airlines would increasingly adopt use of AI based personalization as a core part of their innovation strategy. With each new AI capability, the aviation sector has to realize AI as ’stuff to do better operations’, but also ’fuel to extend markets and to build customer delight’..

Application of Cluster Analysis in Optimizing Financial Product Recommendation System

A financial product recommendation system is an important tool for financial institutions in financial services to enhance customer experience and satisfaction. Such systems provide personalized recommendations, assisting users in identifying and selecting appropriate financial products. However, traditional recommendation methods, including collaborative filtering and content-based recommendation, frequently suffer from limited personalization capabilities due to challenges like sparse data and user cold-start problems. This study investigates the application of cluster analysis in financial product recommendation systems and the role of various clustering algorithms (e.g., K-means, DBSCAN, and hierarchical clustering) in customer segmentation and personalized recommendations and evaluates their advantages and challenges in optimizing recommendation strategies. The results show that clustering analysis can effectively group users and products, significantly improving product matching and, thus, recommendation accuracy and customer satisfaction. In addition, this paper discusses the main challenges of applying clustering techniques to financial recommendation systems, such as ensuring the scalability of clustering models and accurately evaluating validation clustering results.

Factors Affecting Customer Satisfaction of Last-Mile Delivery Logistics

The study aims to determine the factors affecting customer satisfaction in last-mile delivery logistics, focusing on Delivery Riders’ Sense of Direction, Timeliness, Politeness, and Parcel Condition. It also covers satisfaction levels across select profiles. A descriptive research design was employed, using a researcher-made online questionnaire. A total of 385 participants were selected through purposive sampling. Data were analyzed using frequency, percentage, mean, and standard deviation. Findings revealed that participants were satisfied with all four factors. Delivery Riders’ Sense of Direction received the highest satisfaction rating, followed by Parcel Condition and Politeness, while Timeliness ranked lowest. Based on the results, the study concludes that these factors significantly influence overall customer satisfaction with last-mile delivery. The researchers recommend programs targeting the four key factors to enhance service quality. These initiatives aim to improve customer experience and satisfaction, helping delivery providers build a stronger competitive edge in the rapidly evolving last-mile delivery sector.

The transformative power of artificial intelligence in entrepreneurship: exploring AI’s capabilities for the success of entrepreneurial ventures

This study examines the influence of artificial intelligence (AI) capabilities on the success of entrepreneurial ventures by enhancing firms’ decision making, automation, customer experience, innovation, risk mitigation and competitive advantage. A quantitative approach was used to gather data from a sample of 327 entrepreneurs through a questionnaire. The collected information was analyzed using structural equation modeling with the SmartPLS software. Additionally, snowball sampling was employed to ensure a diverse sample of entrepreneurs with existing projects. The analysis reveals significant effects of AI capabilities in decision making, innovation, risk mitigation and competitive advantage, all contributing to the success of entrepreneurial ventures. However, automation and customer experience did not show statistically significant effects. The results indicate that entrepreneurs can leverage AI to strategically adopt this technology. Policymakers and investors can use the insights from this work to foster a supportive ecosystem for AI-driven entrepreneurial ventures. Furthermore, the new conceptual framework introduced in this study enhances understanding of how AI can revolutionize entrepreneurship. It also provides valuable insights for entrepreneurs and industry stakeholders operating in AI-driven environments. This study underscores the importance of decision making strategies and the integration of risk reduction practices, ultimately enriching the body of knowledge on AI implementation across various business settings.

Digital Transformation at Bandung Wholesale Center: The Impact of Information Technology on Sales Performance and Experience

This study examines the impact of digital transformation on sales performance and customer experience in wholesale centers in Bandung, specifically focusing on the adoption of digital inventory management systems, electronic payment platforms, and mobile applications, including the use of TikTok and Instagram for digital marketing. The study aims to analyze how these technologies improve operational efficiency, transaction speed, and customer satisfaction in the wholesale sector. A mixed-method approach was used, combining qualitative and quantitative data. Qualitative data was obtained through in-depth interviews with 10 wholesale center owners and managers, while quantitative data was collected through a survey distributed to 200 customers. Purposive sampling was used to select wholesale centers that had adopted digital technology. The findings show significant improvements in operational efficiency, with inventory checking time reduced by 66.67% (from 12 hours to 4 hours), stock errors reduced by 66.67% (from 15% to 5%), and order fulfillment speed increased by 50% (from 2 days to 1 day). The use of TikTok and Instagram in digital marketing has expanded market reach, increased customer engagement, and raised brand awareness. In addition, electronic payment systems and mobile applications have accelerated transactions, reduced queues, and increased sales volumes. Customers reported higher satisfaction, with ease of transaction increasing by 40.63% and overall satisfaction increasing by 31.43%. This study supports existing literature on the benefits of digitalization in retail and highlights challenges such as investment costs and staff training. These findings offer important insights for the wholesale sector to embrace digital transformation to increase competitiveness in an increasingly digital marketplace.

Marketing 5.0: Adaptation of Technology and Artificial Intelligence in Marketing Strategies

The digital revolution has significantly transformed marketing strategies, particularly with the emergence of Marketing 5.0, which integrates advanced technology and artificial intelligence (AI) to create more personalized and efficient customer experiences. This study aims to analyze how companies adapt technology and AI in their marketing strategies to enhance competitiveness in the digital market. The research employs a qualitative method using a case study approach on several companies that have implemented AI-based technologies in their marketing strategies. Data was collected through in-depth interviews, observations, and document analysis. The findings indicate that AI in marketing enables content personalization, automation of customer interactions, and improved efficiency in data-driven decision-making. However, challenges such as limitations in digital infrastructure and organizational resistance to technological changes remain significant barriers. This study provides insights for marketing practitioners to better understand the role of technology in developing more innovative and adaptive strategies in the Marketing 5.0 era.