Mobile Social Smart Technologies to Optimise Customer Support Services

PurposeThis study aims to develop an empathetic and user-centric customer support service design model. Though service design has been a critical research focus for several decades, few studies focus on customer support services. As customer support gains importance as a source of competitive advantage in the present era, this paper aims to contribute to industry and academia by exploring the service design model.Design/methodology/approachThe study adopted a theories-in-use approach to elucidate mental models based on the industry’s best practices. In-depth interviews with 62 professionals led to critical insights into customer service design development, supported by service-dominant logic and theory of mind principles.FindingsThe ensuing insights led to a model that connects the antecedents and outcomes of empathetic and user-centric customer service design. The precursors include people, processes and technology, while the results are user experience, service trust and service advocacy. The model also emphasises the significance of the user’s journey and the user service review in the overall service design.Research limitations/implicationsThe model developed through this study addresses the critical gap concerning the lack of service design research in customer support services. The key insights from this study contribute to the ongoing research endeavours towards transitioning customer support services from an operational unit to a strategic value-creating function. Future scholars may investigate the applicability of the empathetic user service design across cultures and industries. The new model must be customised using real-time data and analytics across user journey stages.Practical implicationsThe empathetic and user-centric design can elevate the customer service function as a significant contributor to the overall customer experience, loyalty and positive word of mouth. Practitioners can adopt the new model to provide superior customer service experiences. This original research was developed through crucial insights from interviews with senior industry professionals.Originality/valueThis research is the original work developed through the key insights from the interview with senior industry professionals.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

Knowledge management plays an important role in customer support processes such as incident management and problem management. Knowledge-oriented approach on customer support provides an IT organization with several benefits including faster incident resolution times and better identification of systematic incidents. Unfortunately, little attention has focused on examining challenges in customer support from the knowledge management perspective. In this study, the research question is: what kind of knowledge management challenges can arise in customer support. A case study method was used as a research method. Several knowledge management-related challenges were found, such as challenges in documenting incident resolutions and classifying incidents, a lack of public knowledge base and the missing knowledge flow for urgent incidents.

This study investigates an important newly implemented type of customer support in major e-commerce web sites, which is the live service chat feature. The study provides a benefit analysis of live customer support chat. It further reviews solid theoretical frameworks of service quality including the Integrated Gaps Model of Service Quality and the service quality model. The study introduced new dimensions of success for Eservice quality (system quality, system reliability, system availability, information quality, consistency of service quality and online customer feedback). The study also provided a new theoretical framework for e-service quality success (particularly live customer service chat), which provides a solid ground for further research in this new and critical area of customer support.

Providing customer support through social media channels is gaining popularity. In such a context, predicting customer satisfaction in an early stage of a service conversation is important. Such an analysis can help personalize agent assignment to maximize customer satisfaction, and prioritize conversations. In this paper, we show that affective features such as customer's and agent's personality traits and emotion expression improve prediction of customer satisfaction when added to more typical text based features. We only utilize information extracted from the first customer conversation turn and previous customer and agent social network activity. Thus, our customer satisfaction classifier outputs its prediction in an early stage of the conversation, before any interaction has taken place between the customer and an agent. Our model was trained and tested on a Twitter conversations dataset of two customer support services, and shows an improvement of 30% in F1-score for predicting dissatisfaction.

Customer support process has seen a transformational shift in recent years. With the rapid proliferation of Artificial Integrating (AI), the human driven customer support has leapfrogged into automated customer services & support. The field of Artificial Intelligence (AI) has seen phenomenal rise in the development of agents' capability right from autonomous decision-making and problem-solving abilities to adapt to changing environments. Their proficiency in learning from experience and adaptation greatly influences their effectiveness. They both drive improvement over time and insure response to dynamic customer needs and conditions. Adaptation and learning also permit strategies to be revised in light of new data provided to them. This paper explores the bases and techniques of adaptation and learning in AI agents, focusing on reinforcement learning, supervised learning, unsupervised learning, and the combination. In addition, it also addresses challenges like overfitting, exploration-exploitation tradeoffs and computational economics. The role of these processes in various AI applications, including robotics, natural language processing and autonomous systems help industry reduce dependency on manual effort and improve efficiency to drive growth. Keywords-Adaptation, Learning, AI agents, Reinforcement learning, Supervised learning, Autonomous systems.

Antecedents of customer satisfaction with online banking in China: The effects of experience

The relevance of studying customer attitudes towards AI-driven support systems has grown significantly amid the expanding integration of artificial intelligence in service industries. This research aims to explore the perceptions, trust levels, and preferences of Polish consumers regarding AI in customer support, testing hypotheses related to trust, demographic influences, and user experience. The study was conducted through a structured online survey in Poland during 2025, involving 103 adult respondents representing diverse demographics, including age, gender, and education level, with both AI-experienced users and novices. Results revealed that 65% of respondents showed skepticism towards trusting AI for sensitive tasks, and only 34% endorsed full reliance on AI support, confirming limited trust. Despite a general preference for human interaction (82%), over half of participants supported AI as an initial contact with fallback options for human agents. The survey further indicated that younger respondents and those with prior AI experience exhibited higher acceptance levels. Additionally, 47% reported that positive AI interactions increased their future willingness to use such systems, while concerns over data security persisted among 55%. These findings underscore the necessity of developing hybrid, transparent, and personalized AI support models. Future research should focus on enhancing trust factors, ethical considerations, and adaptive learning to foster wider acceptance and efficacy of AI in customer service environments.

Live support chat (LSC) is a relatively new type of online customer support that is growing significantly in e-commerce websites. LSC bypasses the traditional types of customer support like phone, e-mail and web forms and it provides instant answers to customers' questions and concerns. Few researchers have empirically examined the factors affecting LSC at the individual level, however there is no evidence of research that explored the determinates of LSC in an organizational context and how it affects employees. This study attempts to fill this research gap by empirically examining the factors that affect LSC in the context of the organizational Intranets. Survey data from 389 employees at private and public organizations in Kuwait were collected to test the study hypotheses. The study findings have important implications at both the theoretical and practice levels.

This paper examines the critical role of service desks in modern organizations, focusing on their functionalities, challenges, and best practices. As a central hub for customer support and IT service management, service desks facilitate communication between users and service providers, ensuring prompt resolution of issues and enhancing user satisfaction. Here we discuss with an example of opening a ticket in a Service Desk for Technical Issues. The findings emphasize the importance of adopting best practices and technologies to meet the evolving demands of customers in an increasingly digital landscape.

Dialogue Systems are becoming ubiquitous in various forms and shapes - virtual assistants(Siri, Alexa, etc.), chat-bots, customer sup-port, chit-chat systems just to name a few.The advances in language models and their publication have democratised advanced NLP.However, data remains a crucial bottleneck.Our contribution to this essential pillar isMATILDA, to the best of our knowledge the first multi-annotator, multi-language dialogue annotation tool. MATILDA allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of inter-annotator disagreement. We evaluate the tool on ease of use, annotation speed and interannotation resolution for both experts and novices and conclude that this tool not only supports the full pipeline for dialogue annotation, but also allows non-technical people to easily use it. We are completely open-sourcing the tool at https://github.com/wluper/matilda and provide a tutorial video1.

Live streaming platforms such as Twitch and YouTube Live now play a central role in digital engagement, offering real-time interaction that grows increasingly complex with larger audiences. Our study leverages the exogenous viewer influx from Twitch’s Raid function to examine how increases in group size affect user engagement. Analyzing chat histories from more than 7,000 playbacks using a difference-in-differences approach, we find that although attracting more viewers, larger group sizes also lead to reduced engagement among existing participants. This decline is linked to increased topic incoherence and heightened emotional volatility—or comment polarity—in live chats. Importantly, our results highlight that targeted moderation strategies can mitigate these negative effects. Bot moderators are particularly effective in maintaining coherence during large-scale raids, whereas human moderators better manage emotional surges when the incoming group is smaller. These findings reveal a congestion effect in synchronous digital environments and offer clear practice- and policy-oriented implications: Online synchronous platforms should consider the scalability of viewers and commenters and use flexible moderation strategies to sustain user engagement and foster healthier, more constructive real-time interactions.

Sentiment analysis, in the context of digital banking reviews, aims to assess customer satisfaction and support service enhancement. Despite increasing attention to sentiment analysis across domains, Arabic banking reviews remain underexplored. To bridge this gap, we introduce a dataset of 4922 Arabic reviews from three major Saudi digital banks with three sentiment categories positive, negative, or conflict—providing actionable insights for banks. We evaluate the dataset using several machine learning models and four large language models (LLMs)—GPT 3.5, GPT 4, Llama-3-8B-Instruct, and SILMA—using zero-shot (no labeled examples) and few-shot (a few labeled examples) learning strategies. Our results show that GPT 4 performs best among LLMs in few-shot settings, while traditional models still outperform LLMs, with a Voting Classifier achieving 90.24% accuracy. This study contributes a domain-specific dataset and comparative analysis to support research and practical improvements in Arabic digital banking services.

Salesforce Service Cloud allows your customer service representatives and support representatives (users) to manage, track, and resolve customer inquiries and issues. The Service Cloud application uses the Case object to capture all the details needed to help them to resolve the inquiries and issues. Your users can manually create cases for requests via phone and in person or accept cases from multiple channels (e.g., web forms, email, real-time web chat, online communities, and social media outlets). You can allow your users to efficiently manage these channels by setting up case assignment rules.

In today’s information economy, information technology customer service managers require increasingly technologically advanced, but cost effective operations. These managers have a Byzantine array of standards, methodologies and best practice frameworks that offer hundreds of pages of detailed, but often confusing, guidance. This article attempts to offer a simpler guide for IT managers beginning a formal program to increase the effectiveness and efficiency of the day- to-day operation of their customer support services. The paper’s goal is to summarize and clarify important key concepts from the literature, and also present lessons learned from consulting experiences with several large organizations. Finally, the paper offers ideas for future empirical confirmation of the recommendations summarized here.