Enhanced relationship marketing anchored on digital live selling behaviors and Uses and Gratification Theory.

Designing and delivering services with stories: how service stories and consumer transportability affect the customer experience and outcomes

This study presents a developmental research project entitled Uncle Brew Online Ordering System, a web-based solution developed to improve sales monitoring, product management, and customer feedback for Uncle Brew Coffee Shop located in Binaobao, Bantayan, Cebu. The system was designed to address issues encountered in manual operations, such as delays in order processing, inaccurate sales recording, and difficulty in managing inventory and customer transactions. The developed system includes key features such as an online ordering platform, an administrator dashboard for sales and inventory tracking, and a customer feedback module to enhance user interaction. The system was evaluated using the ISO/IEC 25010 Software Quality Model, focusing on functional suitability, performance efficiency, compatibility, reliability, and security. In addition, usability was assessed using the USE Questionnaire in terms of usefulness, ease of use, ease of learning, and user satisfaction. Results showed that the system achieved an overall mean rating of 4.94 (Very Satisfactory) in software quality and 4.94 (Strongly Agree) in usability. These findings indicate that the system performs efficiently, is reliable, and is highly acceptable to users. Furthermore, the system functionality evaluation also showed a high overall mean of 4.93 (Excellent), confirming that the system effectively performs its intended features such as order processing, product management, and dashboard monitoring. The study demonstrates that the implementation of a web-based ordering system significantly improves business operations by reducing manual errors, enhancing transaction accuracy, and providing a more convenient ordering experience for customers. The system proves to be an effective digital solution for supporting the growth and efficiency of small coffee shop businesses.

Personal AI refers to artificial intelligence systems designed to assist individuals in their daily lives by providing smart and personalized support. It helps users manage tasks such as scheduling, reminders, emails, and communication more efficiently. Personal AI systems learn from user behaviour and preferences to deliver customized recommendations and solutions. They enhance productivity by automating repetitive and time-consuming activities, allowing individuals to focus on more important work. Common examples include virtual assistants, smart home devices, and personalized mobile applications. Personal AI can also support education by offering tailored learning experiences and instant access to information. In healthcare, it can help monitor fitness, track health data, and provide basic medical guidance. It improves decision-making by analysing data and suggesting better options based on patterns. Privacy and data security remain important concerns, as these systems often rely on personal information. With rapid advancements in technology, personal AI is becoming more accurate, efficient, and widely accessible. It enables seamless interaction through voice, text, and even visual inputs. Personal AI also plays a role in entertainment by recommending music, movies, and content based on user interests. Businesses use personal AI to improve customer experience and engagement. As development continues, it is expected to become an essential part of everyday life. Overall, personal AI aims to make life easier, smarter, and more convenient for individuals.

Purpose This study aims to investigate how artificial intelligence (AI) memory expression influences customer attitudes during service interactions. Drawing on uniqueness theory, it examines whether AI’s ability to recall customer preferences enhances perceived uniqueness and reduces AI aversion. Additionally, it explores whether embarrassing service contexts moderate these effects. Design/methodology/approach Three scenario-based experiments were conducted. Study 1 used a 2 × 2 between-subjects design (memory expression × agent type) to examine whether memory expression attenuates AI aversion. Study 2 examined the mediating role of perceived uniqueness using moderated mediation analysis. Study 3 tested the boundary condition of embarrassment using a 2 × 2 × 2 between-subjects design. Findings The results show that AI memory expression improves customer attitudes by enhancing perceived uniqueness, thereby reducing AI aversion. However, in embarrassing service contexts, memory expression produces the opposite effect, leading to lower customer evaluations regardless of agent type. Practical implications Service providers can leverage memory expression to personalize customer experiences and mitigate AI aversion. However, in embarrassing contexts, memory expression should be used cautiously withheld to maintain customers’ psychological comfort. Originality/value This study introduces memory expression as a novel dimension of AI behavior and demonstrates its dual role in shaping customer evaluations. While memory expression enhances customer attitudes by increasing perceived uniqueness, it can also trigger negative reactions in embarrassing service contexts. These findings extend theoretical understanding of AI aversion and provide practical insights for managing AI-enabled service interactions.

Customer satisfaction represents a critical indicator of service performance and organizational sustainability in service-based industries. In the electric vehicle charging services, customer satisfaction not only reflects customers evaluative judgments of service encounters but also determines their intention to continue usage and maintain long-term relationships with the provider. This study examines the interconnections among service quality, perceived value, customer experience, trust, and customer satisfaction at SPKLU UB Disyan PLN Batam. The population of this study consists of the people of Batam City, particularly users of the SPKLU UB Disyan PLN Batam service. The study aims to analyze the influence of service quality, perceived value, and customer experience on customer satisfaction, with trust serving as a mediating variable. This research employed a quantitative approach using survey data collected from 155 respondents selected through nonprobability purposive sampling, namely customers who had used the SPKLU service within the last six months. This sampling technique is appropriate because the study requires respondents who have direct experience with SPKLU services, ensuring that the data collected are relevant and reflective of actual service evaluations. Purposive sampling is widely used in service research where specific criteria are necessary to obtain valid and meaningful insights. Data were analyzed using Structural Equation Modeling–Partial Least Squares. The results indicate that service quality, perceived value, and customer experience have positive and significant effects on customer satisfaction. These findings emphasize the importance of enhancing service performance and building customer trust to improve customer satisfaction in electric vehicle charging services in Batam City.

This study aims to explore the fashion retail experiences of Generation Z (Gen Z) consumers by conceptualizing first-store models as integrated experience design systems. Utilizing a brand-personality-based persona development method within a service design (SD) framework, the investigation identified five archetypal personas (Sincerity, Ruggedness, Excitement, Sophistication, and Competence), and from 109 respondents, the top scorer was selected for each type of persona as the participant. Adopting a service design approach, this research examined how experiential dimensions were constructed and orchestrated across the first-store service ecosystem, providing insights into the orchestration of the holistic customer journey rather than isolated experience attributes. Specifically, this investigation used multiple tools as data collection methods: service safaris, mobile ethnography, and contextual interviews. Three fashion first-stores were selected as case studies in Beijing, China. The findings propose three interconnected design-oriented frameworks: experience dimensions embedded in fashion first-stores, key touchpoints along the customer journey, and differentiated experiential effects generated by these touchpoints within the service system. Overall, the study contributes to understanding experience-driven fashion retail systems and offers design insights into how service-oriented first-stores can enhance meaningful engagement with Gen Z consumers, extending existing customer experience research from a service and experience design perspective.

Purpose This study aims to explore how consumers engage with climate change issues through their interactions with the Too Good To Go application, an online marketplace designed to combat food waste at restaurants. Design/methodology/approach Drawing on customer engagement (CE) theory and the norm activation model (NAM) and using a mixed-methods approach, this research uses large language model-assisted thematic analysis to explore the key motivational drivers of engagement. Building on these insights, the authors used a survey and conducted structural equation modeling to test the conceptual model. Findings The results reveal that perceived sustainability, novelty, sense of community and value for money significantly foster affective engagement, which in turn drives behavioral engagement outcomes. Research limitations/implications This research deepens understanding of pro-environmental consumer behavior by integrating CE theory with the NAM, thereby explicating the moral activation mechanisms underlying sustainable dining behaviors. This study also makes a methodological contribution by combining mixed methods with large language model-assisted thematic analysis to examine CE at scale. Practical implications By focusing on restaurant-based climate action, the study provides actionable insights for hospitality businesses seeking to embed sustainability into their operations and customer experience strategies. Originality/value This research makes a significant contribution to the field through its innovative methodological approach, its targeted application within the hospitality sector and its examination of how digital transformation facilitates sustainable behavioral change. The study provides valuable insights for designing digital platforms that simultaneously promote environmental action and enhance consumer loyalty.

The rapid expansion of e-commerce in Indonesia has encouraged platforms to adopt AI-based personalization to enhance customer experience and loyalty. However, privacy concerns and service quality remain critical factors influencing customer retention. This study examines the effects of AI-based personalization, perceived privacy risk, and service quality on customer loyalty in Indonesian e-commerce platforms. Novelty: This study offers a novel contribution by empirically integrating AI-based personalization, perceived privacy risk, and service quality into a single explanatory model of customer loyalty within the Indonesian e-commerce context. A quantitative explanatory approach was employed using a cross-sectional survey of Indonesian e-commerce users (n = 260). Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that AI-based personalization and service quality positively and significantly affect customer loyalty, while perceived privacy risk has a negative effect. Service quality demonstrates the strongest influence on customer loyalty. Customer loyalty in Indonesian e-commerce platforms depends on effective AI personalization supported by high service quality and careful management of privacy risks.

The retail industry is undergoing a significant transformation in 2026. Despite the surge in e-commerce, the physical retail store has experienced a "renaissance," repositioning itself as a hub for "Experience Centers" rather than just points of sale. In this high-stakes service environment, the frontline employee is the most critical asset. They are the face of the brand, the primary point of contact for the consumer, and the ultimate architects of customer loyalty. Consequently, employee motivation has shifted from being a "soft" HR topic to a "hard" strategic necessity. A motivated retail workforce does more than stock shelves; they engage in "Emotional Labor," converting a routine transaction into a memorable brand experience. The retail sector is notoriously characterized by high pressure, long hours, and physically demanding tasks. Historically, these factors have led to high attrition rates and low engagement. However, in the current economic climate, where "Service Quality" is the primary differentiator between competitors, retailers cannot afford a disengaged workforce. Motivation in retail is complex; it requires a balance between transactional rewards (salary) and transformational experiences (purpose and growth). This study explores the "Motivation-Performance Gap"—the difference between what an employee can do and what they actually do based on their internal drive.

Fake reviews are deceptive evaluations that mislead customers rather than reflect genuine customer experiences. These reviews can damage the business's reputation by deceiving the customers, which then causes them to make poor decisions about what to buy and diminishes the trust that e-commerce platforms can have. Detecting fake reviews is crucial for e-commerce platforms to maintain their integrity, protect consumers, and uphold business reputations. Despite its importance, there is a paucity of comprehensive research addressing fake review detection through the lenses of Sentiment Analysis (SA) and imbalanced data handling. To bridge this gap, a systematic literature review uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review analyzed 43 studies from the Scopus and Web of Science databases, covering the period from 2019 to 2024. Three primary themes emerged: SA levels, detection methods, and techniques for handling imbalanced data, which further branched into 28 sub-themes. The analysis revealed key trends such as a predominant focus on document-level SA, the application of machine learning approaches, and data resampling techniques to address imbalanced datasets. The review underscored the necessity for more research on aspect-level analysis and the development of combinational approaches, such as hybrid models, to enhance the accuracy and reliability of fake review detection. These insights provide valuable guidance for researchers, data scientists, and developers seeking to advance the field.

Purpose In today’s burgeoning platform-based business landscape, firms frequently outsource major components of their services to third-party providers. This shift makes it crucial to understand how a firm’s actions impact customer experiences at these third-party touchpoints. This research aims to explore the impact of a firm’s signaling interventions, through badges, on the spillover of customer experience evaluations from third-party providers to the firm, within the context of access-based services. Design/methodology/approach This study used a multiphased experimental approach through four distinct studies, whereby the authors used between-subjects designs. Studies 1A and 1B, for example, examine the impact of satisfying versus dissatisfying service encounters on platform evaluations, manipulated through scenarios within an on-demand hair salon app context. Study 2 investigates the underlying mechanisms of spillover effects, while Study 3 focuses on the moderating roles of consumers’ risk attitudes and perceived provider-platform independence. Finally, Study 4 replicates the findings of Studies 1A and 1B, albeit in a different context (on-demand laundry platform services). Findings Through the experimental studies, the authors observe that negative experiences with signaled providers result in significantly lower evaluations of the platform, and demonstrates a greater spillover effect than experiences with nonsignaled providers. However, these differential spillover effects are not observed in cases of positive experiences. In addition, our results reflect that perceived cognitive consistency mediates these effects between the platform’s claims and the third-party provider’s service. Broadly, the findings suggest that these effects are moderated by consumers’ risk aversion and their perception of provider-platform independence. Research limitations/implications Future studies could examine spillover effects in multiplatform scenarios, the impact of diverse signaling types (provider credentials, platform guarantees, customer reviews) and the role of information transparency on consumer evaluations. In addition, understanding the long-term impacts of service experiences on platform evaluations could offer insights into whether observed effects are transient or enduring, guiding strategic platform management. Practical implications Findings recommend a cautious approach in promoting specific providers to avoid potential negative effects on consumer perceptions after unsatisfactory service experiences. Highlighting the importance of effective service recovery strategies, this research underscores their necessity in addressing negative experiences with highlighted providers. Furthermore, it suggests platforms should work to lessen the perceived overlap between the platform and its providers, improving consumer perceptions through consistent branding and joint initiatives. For new ABS platforms, building a strong overall reputation is emphasized as critical to mitigating the impact of early negative consumer experiences. Originality/value This study reveals the asymmetric consequences of a firm’s signaling efforts, and provides crucial insights for firms in their choice of badges as signaling strategies to enhance customer experience and maintain firm reputation.

Mobile reviews act as a key indicator of customer satisfaction, with over 90% of smartphone users referring to reviews before purchasing a device, and 72% of consumers indicating that positive feedback enhances their trust in a brand. However, manual examination of customer reviews and ratings is inefficient, susceptible to errors, and often fails to capture the subtle emotions present in unstructured text. To address these issues, this study proposes a framework based on Natural Language Processing (NLP) using an iPhone 14 dataset that includes user reviews, titles, and ratings. The workflow begins with NLP preprocessing and Exploratory Data Analysis (EDA) to clean, standardize, and visualize the data distribution. Next, Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) is utilized for contextual feature extraction, enabling effective semantic representation of textual data. To handle class imbalance in review categories, K-Means Synthetic Minority Over-Sampling Technique (K-Means SMOTE) is applied to generate synthetic samples. Unlike existing approaches such as Adaptive Boosting Classifier (ABC) and Tao Tree Classifier (TTC), the proposed framework integrates an Extra Trees Classifier (ETC) to ensure more robust and scalable classification. The model predicts bivariate outputs Review Title and Rating thereby improving both sentiment understanding and rating consistency. By automating the analysis of reviews, the system provides valuable insights into customer satisfaction, product performance, and brand perception, ultimately supporting better decision-making and enhancing the overall customer experience.

Customer reviews are a vital component of modern e-commerce platforms, offering meaningful insights into customer satisfaction, product quality, and overall user experience. With the rapid growth of online shopping, a massive volume of textual feedback is generated daily. Traditional evaluation methods, such as average ratings and review counts, provide only a general overview and fail to capture the deeper sentiments expressed in textual reviews. As a result, a large portion of valuable customer feedback remains underutilized. Moreover, manual analysis of large-scale review data is time-consuming and inefficient, emphasizing the need for automated analytical solutions. To address these challenges, this research proposes a machine learning-based framework for analysing customer review data and predicting both recommendation outcomes and product ratings. The system incorporates data preprocessing, exploratory data analysis, and text representation using Term Frequency–Inverse Document Frequency (TF-IDF). Multiple machine learning models are implemented, including Restricted Boltzmann Machine (RBM) combined with Logistic Regression (LR) for classification, RBM with Ridge Regressor (RR) for regression, Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Multi-Task Neural Network with Extra Trees (MTNN-ET) these models are works with Classification and Regression Tree (CART) Model. Experimental results demonstrate that the proposed MTNN-ET-CART model achieves superior performance, with a classification accuracy of 0.9640 and a regression R² score of 1.0000. The framework effectively processes large-scale review datasets and generates reliable predictions, enabling enhanced decisionmaking and improved customer experience in e-commerce platforms

The objective of the study was to demonstrate how Process Management (BPM) influences the improvement of service quality in an outsourcing firm located in Miraflores. This research was aligned with SDG 9, which focuses on fostering innovative and efficient processes. The study employed a quantitative approach, implementing a pre-experimental design to assess the impact of the independent variable (BPM) on service quality. The sample population comprised 25 clients, and data were collected through biweekly measurements prior to and following the intervention. In the preliminary phase, the quality of the service was evaluated at 64.18% in the pre-test phase, while in the post-test phase, the percentage increased to 79%. The percentage increased by 23.87%, reaching 50%. In dimensional terms, there was an increase of 3.92% in reliability, and a 19% increase in performance. The compliance rate was found to be 84%, while the noncompliance rate was 16.21% and perceived quality 21.09%. Furthermore, a considerable decline in service capacity was observed, resulting in a notable reduction in service times. This decline ranged from 40 minutes for each individual requirement, thereby enhancing the overall customer experience.

Abstract - This study examines the influence of supply chain reliability on customer satisfaction and retention, with emphasis on order accuracy, delivery reliability, information quality, and information sharing. The results indicate that these factors play a crucial role in shaping customer satisfaction, which subsequently impacts customer retention. The study highlights the importance of consistent service delivery, effective communication, and coordination among supply chain partners in enhancing overall customer experience. It further suggests that improving supply chain performance contributes to better service quality and organizational effectiveness. The findings support the need for continuous evaluation and proactive management of supply chain practices to meet customer expectations and maintain competitiveness. Keywords: Supply Chain Reliability, Customer Satisfaction, Customer Retention, Service Quality, Information Sharing, Operational Efficiency, Supply Chain Management

Abstract This study investigates how customer perception influences brand loyalty in the Nigerian pension industry, using the Brand Equity Theory. Focusing on trust, service quality, and communication, a descriptive survey was conducted among 400 RSA holders in Lagos, Abuja, and Calabar. Findings from descriptive and inferential statistics show a strong positive correlation between customer perception and brand loyalty (r = 0.702, p < 0.01). Regression analysis confirms all three variables significantly predict loyalty, with trust as the strongest predictor. ANOVA further shows significant loyalty differences between clients of Trustfund and competitors such as Stanbic IBTC and ARM Pensions. The study suggests Trustfund strengthen digital communication, improve customer experience, and benchmark industry leaders. It concludes that customer perception is a strategic differentiator in the competitive pension environment. Keywords: Customer Perception, Brand Loyalty, Pension Fund Administrators, Service Quality, Communication, Brand Equity Theory.

Customer service via telephone began in the mid-20th century with human operators manually handling customer inquiries and requests. At that time, companies relied entirely on staff to resolve problems, take orders, or provide information, resulting in long wait times and limited service capacity. With technological advancements, automated switchboards and interactive voice response (IVR) systems were implemented in the following decades, allowing calls to be routed without human intervention. More recently, the integration of advanced automated systems such as artificial intelligence, chatbots, and voice recognition has revolutionized telephone service. It is now possible to offer 24/7 support, personalized responses, and immediate solutions to common problems, all without the need for a human agent. This development has optimized business efficiency, reduced operating costs, and significantly improved the customer experience. The evolution of telephone customer service demonstrates how technology can transform and refine the way organizations interact with their customers.

The article provides an in-depth analysis of the digitalization process within the insurance market, highlighting its role in transforming the financial sector and advancing the digital economy. It defines digitalization as a crucial element in enhancing the operational efficiency of insurance companies and in establishing new business models. The study examines modern technologies being integrated into insurance practices, focusing on big data, artificial intelligence, machine learning, blockchain, and the Internet of Things (IoT), and how these technologies influence automation, underwriting, risk assessment, and insurance claim settlement. The development of digital technologies in insurance is largely based on the implementation of innovative solutions for collecting, analyzing, processing, transmitting and securely storing data. In this direction, modern tools are actively used, covering a wide range of capabilities. In particular, an important place is occupied by interactive platforms for online insurance, mobile applications for simplifying access to services, as well as specialized software designed to increase the efficiency of business processes. Particular attention is paid to the implementation of network solutions for automating operations and the use of artificial intelligence technologies, which allow analyzing large volumes of data and offering personalized approaches for each client. Significant emphasis is placed on how the digital environment alters interactions between insurers and clients, fostering the growth of online insurance, mobile apps, and digital platforms. The study explores how digitalization increases financial inclusion, makes insurance services more accessible, and enables the personalization of insurance products. It identifies key benefits of digital transformation in the insurance market, such as enhanced operational efficiency, lower administrative costs, faster data processing, and improved customer experiences. However, it also outlines major challenges and risks associated with digitalization, including cybersecurity threats, personal data protection concerns, technological dependencies, and the necessity for robust regulatory frameworks. The article argues for a comprehensive strategy for the digital transformation of insurance companies to maintain their competitiveness and ensure sustainable growth in the digital economy.

Retail banking occupies a central place in the financial ecosystem, serving millions of individuals and small businesses with a wide array of products and services including savings accounts, personal loans, credit cards, mortgages, and digital banking platforms. This report investigates the multifaceted relationship between retail banking product offerings and customer relationship management (CRM), examining how banks design, deliver, and continuously improve their services to attract and retain customers in an increasingly competitive marketplace. The study analyses the role of personalized banking offers, digital innovation, and data-driven insights in reshaping customer expectations and banking behaviour. It further explores how effective CRM strategies, omnichannel service delivery, loyalty programmes, and customer satisfaction mechanisms contribute to sustainable business growth. Through case analysis and review of current banking practices, this report highlights the challenges banks face—including regulatory compliance, customer data privacy, digital disruption, and rising service expectations—and offers actionable recommendations for enhancing the retail banking customer experience. Keywords: Retail banking, customer relationship management, CRM, banking services, digital banking, personalized offers, customer satisfaction, loyalty programmes, omnichannel banking, financial products, customer retention, service quality, NPA, banking innovation, regulatory compliance.