Search Results (61)

This study examined the relationships between norm perceptions about innovation, innovation source and customer satisfaction with sample data from small-scale hospitality businesses in Ghana. We adopted the quantitative approach and correlational survey design using sample data from 465 small-scale hospitality firms. Partial Least Squares Structural Equation Modelling was used to analyse the data. Measurement model classification and validation procedures comprised construct specification, indicator reliability assessment, internal consistency reliability, convergent validity (AVE), discriminant validity (HTMT and Fornell–Larcker), and collinearity diagnostics within the PLS-SEM framework. Results showed that a significant negative relationship exists between subjective norms about innovation adoption and customer satisfaction. This finding diverges from the Theory of Planned Behaviour because, contrary to its assumption that subjective norms foster positive behavioural outcomes, socially driven innovation in small-scale hospitality settings may encourage conformity-based decisions that undermine customer-oriented value creation. However, a significant positive relationship was found to exist between subjective norm perceptions about innovation adoption and innovation source. A significant positive relationship was also found to exist between innovation source and customer satisfaction. Innovation source positively mediated the relationship between subjective norm perceptions about innovation adoption and customer satisfaction. The study’s findings are relevant for owners and managers of small-scale hospitality firms seeking to align innovation decisions with customer needs, as well as for policymakers aiming to strengthen industry support systems. It offers insights into how social influences and innovation sources can be leveraged to enhance service quality and customer satisfaction in small hospitality businesses. Full article

Sentiment classification plays a crucial role in analyzing customer feedback to identify market trends, enhance product recommendations, and improve customer satisfaction. This study focuses on sentiment analysis of Amazon reviews using two major datasets—Fine Food Reviews and Unlocked Mobile Reviews—which exhibit label imbalance. To address this challenge, both oversampling and undersampling techniques were applied to balance the datasets. Various machine learning (ML) algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Gradient Boosting Machine (GBM), as well as deep learning (DL) models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer-based models like RoBERTa, were implemented. After data cleaning and preprocessing, models were trained, and performance was evaluated. The results indicate that oversampling significantly enhances classification accuracy, particularly for the Fine Food dataset. Among ML models, Random Forest achieved the highest accuracy due to its ensemble approach and robustness in handling high-dimensional data. DL models, particularly RoBERTa, also demonstrated superior performance owing to their capacity to capture contextual dependencies. The findings emphasize the importance of data balancing for optimal sentiment analysis and contribute valuable insights toward advancing automated opinion classification in e-commerce applications. Full article

Despite the growing use of machine learning (ML) for analyzing service quality and customer satisfaction, empirical studies based on Brazilian broadband telecommunications data remain scarce. This is especially true for those who leverage publicly available nationwide datasets. To address this gap, this study investigates customer satisfaction with broadband internet services in Brazil using supervised ML and explainable artificial intelligence (XAI) techniques applied to survey data collected by ANATEL between 2017 and 2020. Customer satisfaction was operationalized using the Net Promoter Score (NPS) reference scale, and three modifications in the scale were evaluated: (i) a binary model grouping ratings ≥ 8 as satisfied and ≤7 as dissatisfied (portion of the neutrals as satisfied and another as dissatisfied); (ii) a binary model excluding neutral responses (ratings 7–8) and retaining only detractors (≤6) and promoters (≥9); and (iii) a multiclass model following the original NPS categories (detractors, neutrals, and promoters). Nine ML classifiers were trained and validated on tabular data for each formulation. Model interpretability was addressed through SHAP and feature importance analysis using tree-based models. The results indicate that Histogram Gradient Boosting and Random Forest achieve the most robust and stable performance, particularly in binary classification scenarios. The analysis of neutral customers reveals classification ambiguity, showing scores of “7” tend toward dissatisfaction, while scores of “8” tend toward satisfaction. XAI analyses consistently identify browsing speed, billing accuracy, fulfillment of advertised service conditions, and connection stability as the most influential predictors of satisfaction. By combining predictive performance with model transparency, this study provides computational evidence for explainable satisfaction modeling and highlights the value of public regulatory datasets for reproducible ML research. Full article

This study examined stakeholders’ satisfaction with current student classification and progress monitoring systems and explored their perceptions of the potential role of artificial intelligence (AI) in enhancing these processes. A cross-sectional survey was administered to 313 stakeholders, including teachers, administrators, decision-makers, and educational service providers. Descriptive statistics, multiple regression analysis, and group comparisons were employed to examine satisfaction levels, predictors of satisfaction, and expectations regarding AI integration. Despite high satisfaction with the current systems (85%), nearly 80% of stakeholders rated AI integration as essential. The most frequently expected functions of an AI-enabled system were predicting student challenges (33.2%), generating detailed analyses and reports (32.9%), customizing individual learning plans (22.7%), and providing immediate feedback (11.2%). Anticipated challenges focused on acceptance and adaptation by teachers and students (40.9%) and concerns about privacy and system integration. Regression analysis revealed that perceptions of classification practices (β = 0.473, p < 0.001) were a stronger predictor of satisfaction than perceptions of progress monitoring practices (β = 0.315, p < 0.001). Demographic analyses revealed greater dissatisfaction among non-teaching staff, females, and mid-career professionals. The findings show that stakeholders are broadly satisfied with existing systems while simultaneously demanding AI-driven innovation, suggesting satisfaction reflects acceptance rather than alignment with stakeholders’ needs and expectations. Full article

The COVID-19 pandemic reshaped travel patterns and customer expectations, generating profound challenges for the hotel industry. This study analyzes 50,000 TripAdvisor reviews of New York hotels to examine how customer satisfaction with hotel selection attributes shifted before and during the pandemic. BERTopic was applied to extract eight key attributes, while VADER, PRCA, and Asymmetric Impact–Performance Analysis (AIPA) were used to capture asymmetric effects and prioritize improvements. Comparative analyses by hotel classification, travel type, and customer residence reveal significant shifts in food and beverage, location, and staff, particularly among lower-tier hotels, business travelers, and international guests. The novelty of this study lies in integrating BERTopic and AIPA to overcome survey-based limitations and provide a robust, data-driven view of COVID-19’s impact on hotel satisfaction. Theoretically, it advances asymmetric satisfaction research by linking text-derived attributes with AIPA. Practically, it offers actionable guidance for hotel managers to strengthen hygiene, expand contactless services, and reallocate resources effectively in preparation for future crises. In addition, this study contributes to sustainability by showing how data-driven analysis can enhance service resilience and support the long-term socio-economic viability of the hotel industry under global crises. Full article

This study addresses the ongoing challenge of optimizing chatbot interactions to significantly enhance customer experience and satisfaction through personalized, empathetic responses. Using advanced NLP tools and strong statistical methodologies, we developed and evaluated a multi-layered analytical framework to accurately identify user intents, assess customer feedback, and generate emotionally intelligent interactions. With over 270,000 customer chatbot interaction records in our dataset, we employed spaCy-based NER and clustering algorithms (HDBSCAN and K-Means) to categorize customer queries precisely. Text classification was performed using random forest, logistic regression, and SVM, achieving near-perfect accuracy. Sentiment analysis was conducted using VADER, Naive Bayes, and TextBlob, complemented by semantic analysis via LDA. Statistical tests, including Chi-square, Kruskal–Wallis, Dunn’s test, ANOVA, and logistic regression, confirmed the significant impact of tailored, empathetic response strategies on customer satisfaction. Correlation analysis indicated that traditional measures like sentiment polarity and text length insufficiently capture customer satisfaction nuances. The results underscore the critical role of context-specific adjustments and emotional responsiveness, paving the way for future research into chatbot personalization and customer-centric system optimization. Full article

This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation. Full article

Customer sentiment analysis plays a pivotal role in the digital economy by offering comprehensive insights that inform strategic business decisions, optimize digital marketing initiatives, and improve overall customer satisfaction. We propose a large language model-based topic-level sentiment analysis framework. We employ a BERT-based model to generate contextualized vector representations of the documents, and then clustering algorithms are automatically applied to group documents into topics. Once the topics are formed, a GPT model is used to perform sentiment classification on the content related to each topic. The simulations show the effectiveness of this approach, where selecting appropriate clustering techniques yields more semantically coherent topics. Furthermore, topic-level sentiment polarization shows that 31.7% of all negative sentiment concentrates on the shopping experience, despite an overall positive sentiment trend. Full article

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing. Full article

This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. Full article

This study explores the development and implementation of an intelligent incident management system leveraging artificial intelligence (AI), knowledge engineering, and mathematical modeling to optimize enterprise operations. Enterprise incident resolution can be conceptualized as a complex network of interdependent systems, where disruptions in one area propagate through interconnected decision nodes and resolution workflows. The system integrates advanced natural language processing (NLP) for incident classification, rule-based expert systems for actionable recommendations, and multi-objective optimization techniques for resource allocation. By modeling incident interactions as a dynamic network, we apply network-based AI techniques to optimize resource distribution and minimize systemic congestion. A three-month pilot study demonstrated significant improvements in efficiency, with a 33% reduction in response times and a 25.7% increase in resource utilization. Additionally, customer satisfaction improved by 18.4%, highlighting the system’s effectiveness in delivering timely and equitable solutions. These findings suggest that incident management in large-scale enterprise environments aligns with network science principles, where analyzing node centrality, connectivity, and flow dynamics enables more resilient and adaptive management strategies. This paper discusses the system’s architecture, performance, and potential for scalability, offering insights into the transformative role of AI within networked enterprise ecosystems. Full article

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a method for fabric type classification and object detection using the YOLOv5 architecture. The model was trained on a diverse dataset containing images of different fabrics, including cotton, hanbok, dyed cotton yarn, and a plain cotton blend. We conducted a dataset preparation process, including data collection, annotation, and training procedures for data augmentation to improve model generalization. The model’s performance was evaluated using precision, recall, and F1-score. The developed model detected and classified fabrics with an accuracy of 81.08%. YOLOv5s allowed a faster performance than other models. The model can be used for automated quality control, inventory tracking, and retail analytics. The deep learning-based object detection method with YOLOv5 addresses challenges related to fabric classification, improving the abilities and productivity of manufacturing and operations. Full article

The article aims to identify the effects and determinants of implementing digital customer service tools in Polish SMEs in terms of digital customer requirements. Quantitative research was conducted among Polish SMEs using a survey. The following statistical methods were used to analyze the survey data: Dunn’s post hoc tests, ANOVA Kruskal–Wallis test, Kendall’s rank correlation coefficient, and Multivariate Adaptive Regression Splines (MARSplines). Research results showed that Polish SMEs demonstrating better preparedness to serve digital customers achieve higher financial results, an increase in the rapidity and agility of customer service, increased customer satisfaction, and improved image. In addition, they gain sustainability benefits in the form of reduced emissions of hazardous substances or waste, recycling of waste, and reduced consumption of water, electricity, and other raw materials. The main determinants of digital transformation in customer service are the type of business (Polish Classification of Activities—PKD), the age of the company, and the educational level of its manager. The article contributes to promoting digitization among SME managers and motivates them to support customer service with digital tools. The identified effects and determinants provide practical guidance and encourage the implementation of digital technologies to meet the demands of digital customers. Using this approach, SMEs can increase their satisfaction and loyalty, resulting in better financial performance and improved competitiveness. This article identifies the economic and sustainability effects and determinants of implementing digital customer service tools in Polish SMEs in the context of digital customer requirements. This study has an original approach to the issue of digital transformation in the SME sector in Poland. Full article

Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization’s reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations. Full article

Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embeddings from large language models and logistic regression to accurately identify churners. We conducted extensive experiments using a range of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, bidirectional encoder representations from transformers (BERT), Sentence-Transformers, Sent2vec, and Doc2vec, to extract meaningful features. Additionally, we tested various classifiers, including logistic regression, support vector machine, random forest, K-nearest neighbors, multilayer perceptron, naive Bayes, decision tree, and zero-shot classification, to build a robust model capable of making accurate predictions. The best-performing model in our experiments is the logistic regression classifier, which we trained using the extracted feature from the OpenAI Text-embedding-ada-002 model, achieving an accuracy of 89%. The proposed model demonstrates a high discriminative ability between churning and loyal customers. Full article