Salesforce Service Cloud

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.

Today almost all organizations have changed their traditional systems and have improved their performance using web-based applications. This process will make more profit and at the same time will increase the efficiency of their activities through customer support services and data transactions. Usually, web application take inputs from users through web form and send this input to get the response from database. Modern web-based application use web database to store all critical information such as user credentials, financial and payment information, company statistics etc. However error in validation of user input can cause database vulnerable to Structured Query Language Injection (SQLI) attack. By using SQLI attack, the attackers might insert malicious code in the user input and trying to gain access to the confidential and sensitive data from database. Security tester need to identify the appropriate test cases before starting exploiting SQL vulnerability in web-based application during testing phase. Identifying the test cases of a web application and analyzing the test results of an attack are important parts and consider as critical issues that affects the effectiveness of security testing. Thus, this research focused on the developing a framework for testing and detecting SQL injection vulnerability in web application. In this research, test cases will be generated automatically based on SQLI attack pattern and then the results will be executed automatically based on generated test cases. The primary focus in this paper is to develop a framework to automate security testing based on input injection attack pattern. To test our framework, we install a vulnerable web application and test result shows that the proposed framework can detect SQLI vulnerability successfully.

When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, "Whisper", for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild.

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.

With over 1.7 billion individuals engaged in social media, patients and consumers share more about their lives than ever before through wearable devices, smartphone applications, and social media outlets. This cornucopia of data offers significant opportunity for health researchers and clinicians to track and explore how digital presence contributes to patients' health outcomes and use of health care resources. While patients readily share their information with online communities, it is imperative that they maintain a sense of autonomy over who has access to such data. Recent data breaches of major insurance companies and retailers illustrate the challenges and vulnerabilities related to information safety and privacy. Many Web sites and mobile apps require users to agree to data policies, but how those data are mined, protected, used, and externally shared is frequently nontransparent, resulting in a climate of fear and distrust around all forums of digital information sharing. Although such skepticism is perhaps justified, it should not deter health researchers from attempting to collect and analyze these novel data for the purpose of designing unique health interventions. By clarifying intent around digital data acquisition, simplifying consent procedures, and affirming a commitment to privacy, the authors contend that health researchers can partner with patients to transform the boundaries of scientific inquiry.

This study explores the evolution of messaging platform preferences in Ukraine over the past decade and presents the development of a Telegram-based chatbot aimed at improving customer support services. Using Google Trends data from January 2014 to March 2024, we analysed the relative popularity of major messaging applications, revealing a significant increase in Telegram usage and a decline in platforms such as Viber. These insights informed the choice of Telegram as the primary channel for deploying a customer service bot. The chatbot was developed using Python and the aiogram framework, selected for its asynchronous capabilities and efficient handling of real-time user interactions. The bot supports two user roles – customers and administrators – and includes functionalities such as live chat, manager allocation, scheduling, and user management, all backed by an SQLite database. The system is designed for scalability, ease of maintenance, and potential integration with additional services. The results demonstrate the effectiveness of combining trend analysis with targeted software development to address evolving communication needs. The chatbot provides a practical, scalable solution for businesses seeking to automate and improve their customer support processes. This project highlights the value of data-driven decision-making and modern development tools in creating responsive and efficient digital services.

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.

PurposeThe purpose of this paper is to present the current status of digital reference services (DRS) among selected academic libraries in the Philippines – the University Library of the University of the Philippines Diliman, the Rizal Library of the Ateneo de Manila University and the University Library of the De La Salle University Manila – by figuring out how information service is provided using instant messaging (IM) and social networking sites.Design/methodology/approachA survey was conducted to examine the nature, practice and impact of DRS and to ascertain users' preferences and level of awareness of the availability of Ask‐a‐Librarian – an information service offered through IM, and the Facebook fan page of the university libraries under study.FindingsInterestingly, the findings reveal that the full potential of Reference 2.0 has not yet been maximized. When it comes to reference services in academic libraries, students and faculty members have chosen to go online: Ask‐a‐Librarian, web forms, e‐mail and Facebook. A significant increase in the number of reference transaction was observed after integrating Web 2.0 and digital reference tools to its information services. IM and Facebook were found to be the most useful tools in finding relevant information. A majority of the queries received were brief and factual information inquiries and questions on how to use online databases.Practical implicationsThe paper further explores the different aspects of IM and Facebook reference service: statistics, content of the questions and quality of the answers, reference interview, and the user's awareness and preferences. The results of this study may prescribe practices integrating Web 2.0 applications to beef up reference service, in which academic librarians in the Philippines may benchmark.Originality/valueThis paper is the first systematic evaluation of the nature and practice of Reference 2.0 in the Philippine setting; it makes a valuable contribution that could boost academic libraries in the country as they endeavor to provide world‐class service to many.

Objective –This study investigates the role of customer service support (CSS) in co-creating brand value and enhancing corporate performance in the fintech industry. The research focuses on analyzing the impact of CSS on the customer journey from registration to first deposit. Methodology – The study utilizes historical data analysis and A/B testing. Client data from a leading fintech company is analyzed to compare conversion rates between those who used CSS versus those who did not. An A/B test is then conducted using a specialized support team targeting clients who haven’t made a deposit and had less than 2 chats with CSS. Findings –Clients who used CSS before depositing had 5 times higher conversion rates. The A/B test found a 3.1 percentage point (9.3% relative) increase in conversion from using targeted support. Novelty –This study provides new empirical evidence on the impact of CSS as a brand value co-creator in fintech, with a novel methodology to enhance corporate performance. Type of Paper: Empirical JEL Classification: M14, M31. Keywords: Brand value; Brand co-creation; Fintech; Customer service; Performance Reference to this paper should be made as follows: Syropyatov, V.V; Arenkov, I.A; Sajid, S; Mahar, D.H. (2023). Leveraging Customer Service Support to Enhance Brand Value and Corporate Performance: Evidence from the Fintech Industry, J. Fin. Bank. Review, 8(3), 116 – 124. https://doi.org/10.35609/jfbr.2023.8.3(1)

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.

Understanding the Design of Mobile Social Networking

Knowledge management (KM) is a combination of several techniques and processes for creation, sharing and dissemination of knowledge within an enterprise. According to published reports, KM is a key ingredient in any successful customer support service business. It impacts positively the productivity of support personnel, improves the quality of their decision making, enables the extraction of knowledge from various repositories, and represents the core of automatic problem solving. The majority of the known KM systems deliver services in an open-loop fashion, where the impact of the system deployment on users or on the application domain is not known or controlled. In this paper, we report a knowledge-based system (KBS) that was deployed within Hewlett Packard Corporation to deliver customer support services. This system differs from others in that the monitoring loop is closed. This enables the support system to adapt based on observed usage patterns and evolving needs.

The elastic computing power and the pay-as-you-go model of the cloud offer an attractive platform to deploy software as a service applications. The large number of applications expected to heavily take advantage of the cloud will result in an explosive growth of various cloud services. As many cloud services may compete to offer similar functionalities, it is desirable to consider user preferences on the nonfunctional service properties (aka, quality of service, or QoS) when delivering cloud services to the end users. Unfortunately, current approaches primarily rely on the descriptions from the cloud service providers or expert-provided rankings, which are completely orthogonal to the open and distributed nature of the cloud. We present a novel framework (referred to as CloudRec) that exploits a user-centric strategy to achieve personalized QoS assessment of cloud services. CloudRec integrates a novel community-based QoS assessment model with an iterative algorithm to accurately discover a set of homogenous user and service communities from scarce and large-scale QoS data. The communities can serve as a bridge to relate users and services and hence provide an effective means to estimate the QoS of unknown cloud services. The effectiveness of the proposed framework is demonstrated through a rigorous theoretical analysis and an extensive empirical study on real QoS data.