Search Results (53)

Service failures remain inevitable in tourism and hospitality, yet complaint behavior is often suppressed, particularly in non-routine, time-bound travel contexts. The Tourist Complaint Constraints (TCC) framework explains this silence through five tourism-specific constraints. However, it does not explicitly account for how platform-based and AI-mediated service environments reshape post-failure behavior. This paper revisits TCC and introduces TCC 2.0, a conceptual extension that reframes complaint constraints as structurally generated within platform-mediated recovery architectures. Drawing on justice theory and emerging research on AI-enabled service systems, the framework positions distributive, procedural, and interactional justice as central mediators linking complaint constraints to behavioral outcomes. It further incorporates platform/AI process constraints and algorithmic trust constraints as additional structural dimensions, while identifying recovery channel and failure magnitude as boundary conditions. A key contribution is the concept of platform-mediated silence, defined as a structurally induced form of non-complaining behavior shaped by constrained agency and recovery system design rather than satisfaction. The paper advances a set of propositions to guide empirical testing and future scale development in AI-mediated tourism contexts. By extending complaint behavior theory into digitally mediated service environments, TCC 2.0 offers a foundation for understanding how platform architectures shape customer voice, silence, and post-failure responses. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

Automated customer service and algorithmic governance are common in digital marketplaces, yet trust can erode when logistics, refunds, and escalation fail. Complaint-based risk and trust narratives in Turkey’s e-commerce marketplaces are analyzed for January 2019–December 2025 using 118,173 de-identified Turkish and English texts from Şikayetvar, a leading Turkish online consumer-complaint portal, and reviews of official marketplace apps on Google Play and the Apple App Store. BERTopic is implemented in Python with multilingual transformer embeddings, UMAP, HDBSCAN, and c-TF-IDF representations. The selected model identifies 35 micro-topics grouped into five macro-themes: fulfillment disruptions, remediation frictions, product-integrity risks, escalation failures, and governance threats. Monthly probability-weighted prevalence is estimated, and marketplace differences are evaluated with divergence measures, permutation tests, and multinomial regression controlling for time and language. Changepoint tests indicate a shift toward fulfillment grievances in April 2020, rising governance threats from June 2022, and increasing escalation failures linked to automated support from February 2023. These patterns suggest that barriers to human escalation convert operational incidents into platform-level trust judgments, offering monitoring signals for service recovery, marketplace governance, and AI oversight. By isolating escalation failures as a distinct complaint domain, the study links service automation to procedural justice mechanisms that translate operational breakdowns into platform-level trust and risk judgments. Full article

Logistics involves planning and managing resources to meet customer demands. Its effectiveness depends not only on time and process coordination but also on the performance of logistics operators, whose actions directly affect customer satisfaction. Although operational risks are inherent to logistics, customer-oriented service failures are often overlooked in traditional risk assessment. To address this gap, this study proposes an integrated approach that combines a Pareto Diagram and Failure Mode and Effects Analysis (FMEA) within the ISO 31000 risk assessment framework. This structured method enables the identification and prioritization of logistics failures based on customer complaints, thereby supporting data-driven decision-making and continuous service improvement. Applied to a real-world case in a ceramic production line specializing in tableware manufacturing, the method identified and evaluated key logistics failures; particularly those related to late deliveries and damaged goods. Based on these findings, improvement actions were proposed to reduce the recurrence of these issues. This study contributes a structured, practical, and replicable approach for organizations to introduce risk assessment practices and enhance the service quality of logistics management. This study advances the literature by shifting the focus from internal production failures to customer-driven service risks, offering strategic insights for improving reliability and operational performance. Full article

This research addresses the ‘cultural blind spot’ in Big Data and AI, where algorithms treat global user-generated content monolithically, fostering biased marketing models. It proposes a dynamic ‘contextual value amplification’ framework, integrating Impression Management and Construal Level Theories. The study argues that service context—luxury versus budget—systematically reconfigures how cultural values are expressed in online customer reviews. A dual-method approach was applied to 284,746 negative hotel reviews. First, a high-dimensional fixed-effects model provided evidence for ‘cultural complaint signatures’ and revealed a novel mechanism: the luxury context amplifies individualists’ focus on relational Service but dampens their focus on transactional Value. Second, an XGBoost model offered computational validation. Including these theoretically derived features improved the model’s ability to classify a reviewer’s cultural orientation by over 220%. The study proposes a dynamic, context-contingent theory of cross-cultural expression, offers a methodological template fusing econometrics and machine learning to mitigate bias, and advances a conceptual framework for ‘Cultural Intelligence’. Full article

Public transport agencies must triage thousands of multilingual complaints every day, yet the cost of training and serving fine-grained sentiment analysis models limits real-time deployment. The proposed “one encoder, any facet” framework therefore offers a reproducible, resource-efficient alternative to heavy fine-tuning for domain-specific sentiment analysis or opinion mining tasks on digital service data. To the best of our knowledge, we are the first to test this paradigm on operational multilingual complaints, where public transport agencies must prioritize thousands of Russian- and Kazakh-language messages each day. A human-labelled corpus of 2400 complaints is embedded with five open-source universal models. Obtained embeddings are matched to semantic “anchor” queries that describe three distinct facets: service aspect (eight classes), implicit frustration, and explicit customer request. In the strict zero-shot setting, the best encoder reaches 77% accuracy for aspect detection, 74% for frustration, and 80% for request; taken together, these signals reproduce human four-level priority in 60% of cases. Attaching a single-layer logistic probe on top of the frozen embeddings boosts performance to 89% for aspect, 83–87% for the binary facets, and 72% for end-to-end triage. Compared with recent fine-tuned sentiment analysis systems, our pipeline cuts memory demands by two orders of magnitude and eliminates task-specific training yet narrows the accuracy gap to under five percentage points. These findings indicate that a single frozen encoder, guided by handcrafted anchors and an ultra-light head, can deliver near-human triage quality across multiple pragmatic dimensions, opening the door to low-cost, language-agnostic monitoring of digital-service feedback. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure to labeled training data, making it valuable for handling emerging issues and dynamic complaint categories in finance. However, this task is particularly challenging, as financial complaint categories often overlap, requiring a deep understanding of nuanced language. In this study, we evaluate the zero-shot classification performance of leading LLMs and reasoning models, totaling 14 models. Specifically, we assess DeepSeek-V3, Gemini-2.0-Flash, Gemini-1.5-Pro, Anthropic’s Claude 3.5 and 3.7 Sonnet, Claude 3.5 Haiku, and OpenAI’s GPT-4o, GPT-4.5, and GPT-4o Mini, alongside reasoning models such as DeepSeek-R1, o1, and o3. Unlike traditional LLMs, reasoning models are specifically trained with reinforcement learning to exhibit advanced inferential capabilities, structured decision-making, and complex reasoning, making their application to text classification a groundbreaking advancement. The models were tasked with classifying consumer complaints submitted to the Consumer Financial Protection Bureau (CFPB) into five predefined financial classes based solely on complaint text. Performance was measured using accuracy, precision, recall, F1-score, and heatmaps to identify classification patterns. The findings highlight the strengths and limitations of both standard LLMs and reasoning models in financial text processing, providing valuable insights into their practical applications. By integrating reasoning models into classification workflows, organizations may enhance complaint resolution automation and improve customer service efficiency, marking a significant step forward in AI-driven financial text analysis. Full article

The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction. Full article

Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management. Full article

Background: The research presented here looks into ongoing problems with the package delivery services of a State parcel company, especially concerning damaged, wrongly delivered, late, and missing packages. These problems greatly affect customer satisfaction, so it is important to understand how they are connected. Methods: Three hundred and seventy-five customer complaints made between 2021 and 2023 were analyzed. Paniotto’s method was used to ensure that the study data accurately represented the situation. Pearson’s correlation coefficients helped find statistical links between different delivery problems. Results: The analysis revealed significant linkages among the core delivery issues. A strong positive correlation was found between damaged shipments and misdelivered shipments (r = 0.93835) and between prolonged delivery delays and lost goods (r = 0.9188). These findings suggest that addressing one issue may reduce the prevalence of others, given their tendency to coexist. Conclusions: The study emphasizes the necessity for the State parcel firm to execute a comprehensive strategy to improve the overall quality of parcel delivery services. Addressing concerns such as poor delivery and delays is critical since they are closely related to package damage and loss, which affects customer satisfaction. Full article

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law. Full article

Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study is a discourse analysis that explores negative online guest complaints made to four luxury heritage hotels in Hong Kong that are classified as cultural heritage sites with rich interpersonal and historical values. We collected authentic guest complaints and responses from hotel managers from April 2012 to October 2022 in online travel forums, and then had GAI draft response letters on behalf of the hotel managers. Our total dataset was 65,539 words and consisted of three subcorpora: guest complaints (Text a of 115 complaints totaling 26,224 words), hotel manager responses (Text b of 115 response letters totaling 14,975 words), and AI-generated responses (Text c of 115 response letters totaling 24,340 words). This study used systemic functional linguistics to explore interpersonal meanings in texts; for example, appraisal resources, verb processes, and personal pronouns were compared between texts. First, we identified the most frequent words of the common themes across the three subcorpora and found significant differences in lexicogrammatical features between hotel managers and AI-generated responses using the log-likelihood ratio. The results suggest that AI-generated texts are able to provide a tailored and empathetic response to guests, but hotel managers may need to introduce some modifications, such as time indicators, sensory verbs used, and complimentary offers. This study explores the differences in word choices and communication strategies, which have implications and insights for the hospitality industry, especially luxury heritage hotels where caring and personalized customer service are considered important. Full article

Companies need to decrease service failures through service recovery and by managing customers’ emotions, which is important. Customer databases are vital to understand needs and service innovation. Utilizing data, companies can identify pain points and innovate services, reducing customer complaints. We examined 140 customer records from an auto maintenance company’s database and found 602 failure descriptions. The main complaints include maintenance performance, service attitude, and professionalism. Employee behavior accounted for most failures. Pain points in the process were the most common, followed by support, financial, and productivity issues. Such results suggest that service innovation is needed. Full article

In today’s digital society, social networks such as Twitter are a preferred place for expressing one’s emotions, especially when they are negative. Despite a growing interest in the variety of linguistic realizations of commuters’ complaints, little attention has so far been paid to writers’ choices, especially when morphologically or syntactically simpler alternative formulations are available. A typical example is the “inference towards the antonym” triggered by the negation of contrary adjectives, an effect that is stronger for positive compared to negative adjectives. In the context of railway transport, a customer could use the negative statement The train is not clean instead of the corresponding affirmative sentence The train is dirty. It remains unclear, in our current state of knowledge, why online customers would prefer more complex constructions to voice their criticisms. Based on a large corpus of tweets sent to the French and Belgian national railway companies by their customers, I have semi-automatically extracted instances of not (very) + adjective (ADJ). Based on previous observations in the literature, I expected positive adjectives to be more frequently used in these negative environments compared to negative ones. As recent research demonstrates that one’s desire to save the interlocutor’s face is not necessarily the only reason why positive adjectives are used in linguistically negative environments, other motivations will also be considered. More precisely, I suggest that in a context where negativity is prevalent, customers using negated positive adjectives kill two birds with one stone: not only do they signal an issue with a product or a service, pointing to expectations that have not been met by the company, but they also mitigate the impact of their negative comments to the positive face of the service managers with whom they are interacting. By offering a quantitative, corpus-based analysis of negative constructions, complemented by a qualitative linguistic analysis of selected examples, this research sheds new light on users’ lexical choices in online negative customer feedback. Full article