Customer Service Interactions Research Articles

The Role of Sentiment Analysis in Machine Translation- A Review on the Example of ChatGPT

In today’s globalization, machine translation technology has become an indispensable bridge for cross-lingual communication. The integration of sentiment analysis makes machine translation not only able to convey information, but also able to accurately express and understand the emotional color of the original text, which is of great significance in the fields of cross-cultural communication, customer service, and social media interaction. This study overviews the development of machine translation technology, from early rule-based models to modern statistical models to Transformer-based neural network models, with a special focus on the integrated application of ChatGPT in sentiment analysis and machine translation. First, this study introduces the background and research significance of machine translation, and discusses the role of sentiment analysis in improving translation quality and user satisfaction. Subsequently, the historical development of machine translation technology is reviewed in detail, and the basic concepts and methodologies of sentiment analysis and its applications in different fields are elaborated. On this basis, this study analyzes in depth the technical architecture, language model and interaction capability of ChatGPT, as well as its specific application and performance in sentiment analysis. Further, this study explores the importance of sentiment analysis in machine translation, including sentiment recognition, translation strategies for sentiment expression, and the impact of sentiment analysis on improving translation quality and cultural adaptability. Meanwhile, the advantages and limitations of ChatGPT in sentiment analysis are assessed, and future research directions are proposed, including technology convergence, construction of datasets and evaluation criteria, and ethical and bias issues. Finally, this study summarizes the practical application value of sentiment analysis in machine translation and looks forward to the potential areas of future research, aiming to provide reference and inspiration for the further development of machine translation and sentiment analysis.

Investigating factors influencing AI customer service adoption: an integrated model of stimulus–organism–response (SOR) and task-technology fit (TTF) theory

PurposeArtificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.Design/methodology/approachThe study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.FindingsThe PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.Practical implicationsThe study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.Originality/valueThe originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

Do You Like Online Shopping? A Case Study Courier Service Quality and Customer Satisfaction Between Malaysia, Indonesia, and India

The surge in online shopping has led to a growing demand for courier services, making it crucial to understand how service quality impacts customer satisfaction. This study aims to evaluate the effect of five key service quality dimensions—tangibility, reliability, responsiveness, assurance, and empathy—on customer satisfaction in the courier industry across Malaysia, India, and Indonesia. An online survey was conducted with 150 respondents from Universiti Sains Malaysia (USM), Universitas Brawijaya, Indonesia, CMS Business School, India, and IMS Engineering School, India. Regression analysis revealed that only empathy had a significant positive relationship with customer satisfaction, while tangibility, reliability, responsiveness, and assurance did not show significant impacts. These findings suggest that courier companies should focus on improving empathy in their customer service interactions to enhance satisfaction. Understanding and addressing customer needs through personalized service can lead to higher satisfaction and loyalty. This study contributes to the literature on service quality in the courier industry, particularly in the context of online shopping across multiple countries

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.

Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies

Machine learning (ML) algorithms have revolutionized the field of predictive analytics, particularly in understanding and anticipating customer behavior. This review explores how these advanced algorithms are utilized to enhance predictive analytics in customer behavior studies, driving more informed and strategic decision-making processes within businesses. Predictive analytics leverages historical data to predict future outcomes, and when combined with ML, it becomes significantly more powerful and accurate. ML algorithms can analyze vast amounts of data, identifying patterns and trends that would be impossible for humans to discern. These algorithms, including decision trees, neural networks, and support vector machines, can handle complex and nonlinear relationships within data, making them exceptionally well-suited for customer behavior studies. The application of ML in predictive analytics begins with data collection from various sources, such as transaction records, social media interactions, and customer feedback. This data is then preprocessed to ensure quality and relevance before being fed into ML models. Through techniques like clustering, classification, and regression, ML algorithms can segment customers, predict purchasing behaviors, and identify potential churners. One significant advantage of using ML for predictive analytics in customer behavior is the ability to deliver personalized experiences. By predicting individual customer preferences and needs, businesses can tailor their marketing efforts, product recommendations, and customer service interactions, thereby enhancing customer satisfaction and loyalty. For instance, e-commerce platforms use ML-driven predictive analytics to suggest products that a customer is likely to purchase based on their browsing and buying history. Moreover, ML algorithms continuously learn and improve from new data, allowing for real-time updates and more accurate predictions over time. This dynamic nature is crucial in today’s fast-paced market environments where customer preferences and behaviors can change rapidly. In conclusion, utilizing ML algorithms in predictive analytics significantly enhances the ability to understand and predict customer behavior. This integration not only helps businesses optimize their strategies and operations but also fosters deeper customer relationships through personalized and timely engagements. As ML technology continues to evolve, its impact on predictive analytics and customer behavior studies is expected to grow, offering even more sophisticated and actionable insights.

Enhancing customer experience through AI-driven language processing in service interactions

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

When chatbots fail: exploring user coping following a chatbots-induced service failure

PurposeWhile the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.Design/methodology/approachThis study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.FindingsIt identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.Originality/valueOur study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

Air India pee gate case – need for comprehensive SOPs in customer management

Learning outcomes This case study allows students to appreciate the value of standard operating procedures in customer management. This case study emphasises the role of employees in delivering superior customer experience. This case study explores many facets of customer experience, reputation, social class membership and standard operating procedures (SOPs). Students will be able to apply theories of customer experience, behavioural psychology and service dimensions relevant to the airline industry. After completing this case study, students will be able to do the following:1. Evaluate the value of SOPs in Customer ManagementThis case study refers to the need for adhering to SOPs to deal with complex situations. Students will be able to evaluate whether compliance to SOPs could have helped Air India avoid the crisis or was it possible that a culture of absolute commitment to customer wellbeing could have prevented the crisis.2. Apply the theory of defensive attribution in customer grievance handling. Discuss if reducing customer effort in getting their problem solved can result in superior customer service.The victim had attributed the blame for not insisting on filing a complaint to the crew. Air India crew had defended their actions or lack of it by stating that they had followed the rule book. Students will be able to appreciate the need for a swift redressal mechanism to protect the self-image and self-esteem of the person/group involved. They will also understand that customer service interactions designed to solve customer problems swiftly and easily can be a very simple dictum to guide all employees in their decision-making while handling a customer complaint. 3. Evaluate the relationship between customer satisfaction and customer experience and examine the value of net promoter score (NPS) to study customer satisfaction. Air India Airlines was catering to varied customer groups such as the Indian diaspora, large student population pursuing education abroad, first-time flyers and the rising middle class with travel aspirations. Customer expectations vary across segments and change over their lifetime. Airline staff must trace customer corridors and deliver on customer expectation across the touch points that matter to them to ensure meaningful and relevant service delivery. Students will have an opportunity to evaluate the NPS in measuring customer satisfaction and debate whether it is a sufficient metric to guide the organisation on delivering and monitoring customer experience. 4. Examine why reputation risk management and not crisis management should be the focus of Air India in delivering superior customer service because nearly 70%–80% of market value for a company comes from its intangible assets such as brand equity and reputation. Students will discuss crisis management i.e. handling the threat to reputation after it has occurred and reputation risk management i.e. proactively managing potential threats to its reputation by taking timely actions to avoid or mitigate it. There are three factors (reputation reality gap, changing beliefs and expectation and weak internal coordination) that determine reputational risks. Students can evaluate this model to determine if Air India should address these three factors to manage its reputation proactively. Case overview/synopsis This case study is set around an incident that happened on 26 November 2022, on Air India flight bound for Delhi from New York when an inebriated 34-year-old man had peed on a 72-year-old woman. The perpetrator of the crime had walked free, and the victim was left dissatisfied with how the cabin crew had handled her ordeal. Air India Airlines was launched in 1932 by industrialist JRD Tata and nationalised in 1953. In 2021, Tata Group acquired the 90-year-old Air India from the Government of India for $2.4bn (INR 18,000 crore) and appointed Campbell Wilson as chief executive officer and managing director. The incident brought to the fore the customer management issues that Wilson had to address. First on the list of Air India’s turnaround plan was delivering “exceptional customer experience”. How was it going to achieve it because the Indian aviation ecosystem lacked infrastructure such as airports, airspace, competition and customer preference-based services? There was also shortage of pilots, engineers, technicians, air-traffic controllers and technocrats to occupy positions within security agencies and regulatory bodies. With Air India’s acquisition, the Tata Group had to find innovative solutions to deal with decades of internal neglect, non-performance and labour union problems. This case study is relevant to address real issues of customer experience, consumer psychology, reputation risk management and standard operating procedures in service management. Complexity academic level This case is suitable for both undergraduate and postgraduate level students of business management. It can also be used for training service personnel of aviation industry. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing

Study of adoption of artificial intelligence technology-driven natural large language model-based chatbots by firms for customer service interaction

Purpose The purpose of this study was to comprehend the adoption of artificial intelligence (AI) technology-driven natural large language model (LLM)-based chatbots by customers. Design/methodology/approach A qualitative research study method was conducted. This was to explore managerial perspectives towards consumer centric technology adoption of AI plus LLM-based chatbots. This was specifically for AI-driven natural LLM-based chatbots services. The author conducted conducted in-depth personal interviews with 32 experts of digital content AI + LLM chatbot services. Thematic content analysis was undertaken to analyse the data. Findings The advent of natural language processing tools driven by AI technology chatbots has altered human-firm interaction. The research findings indicated that the push-pull-mooring (PPM) factors captured the phenomenon in the most comprehensive way. A total of 15 key factors influencing the adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction were identified in this study by the author. The thematic content analysis unraveled insights regarding transformed consumer adoptions towards AI-driven LLM-based chatbots by means of the PPM framework factors. Research limitations/implications The empirical research investigation contributed to the literature on the PPM theoretical framework. This was specifically in the context of adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction. Practical implications The research study insights would help managers to restructure and reconfigure their organizational processes. This would neccessiated a shift in firm-customer interactions as demanded because of the availability of AI technology-driven natural LLM-based chatbots by customers. Originality/value This research study was based upon the PPM theoretical framework. This study provided a unique analysis of the altered firm customer interaction needs and requirements. This was one of the first studies that applied the framework of PPM theory regarding the adoption of AI technology-driven natural LLM-based chatbots by customers.

A Machine Learning Based Approach to Predict Customer Churn in Airline Industry : The Case of India

This research addresses the challenge of customer churn in the airline industry by leveraging machine learning (ML) techniques to predict and understand the factors influencing customer attrition. Drawing on a comprehensive dataset encompassing customer demographics, flight history, and service interactions, we employed rigorous data preprocessing techniques and evaluated various ML algorithms

Customer Churn Prediction

In today’s highly competitive business landscape, customer retention revenue preservation, customer experience improvement, and marketing optimisation are critical factors for sustained growth and profitability. Customer churn predictionis discontinuing their services or purchases, which presents asignificant challenge for businesses across various industries. This project focuses on developing a predictive model to expect customer churn in the banking sector using machine learning techniques. The project involves the collection and analysis of historical customer data, confined account activity, transaction history, demographic information, and customer service interactions. By extracting the right features from this data, a machine learning model is trained to forecast which bank customers are at the highest risk of churning. A critical step in this study was the selection of relevant features that influence customerchurn. Feature selection was guided by domain knowledge and feature importance analysis. The different classifiers were used and then trained on the training dataset further ensuringthe model’s optimal performance. The model’s performance is assessed through various evaluation metrics, including accuracy, precision, and recall. Additionally, the project explores a model illustration to uncover the influential factors contributing to customer churn within the banking context. This project’s outcomes can empower banks to take proactive measures in retaining customers, enhancing their overall experience, and thereby preserving revenue streams. By addressing customer churn, banks can foster long-term relationships, reduce customer acquisition costs, and boost their competitiveness in the financial industry. The results of this project are expected to assist businesses in proactively retaining customers by targeting those at the highest risk of churning. Ultimately, reducing customer churn can lead to increased customer satisfaction, revenue, and long-term business sustainability.

Is cute AI more forgivable? The impact of informal language styles and relationship norms of conversational agents on service recovery

Conversational agents are increasingly handling a significant volume of service interactions and are projected to handle up to 95% of customer service interactions. However, like any service provider, conversational agents are not immune to service failures, which can result in negative customer experiences. Within the context of interactions between conversational agents and consumers, the informal linguistic strategies employed by conversational agents have been recognized as vital in recovering customer satisfaction after service failures. Nevertheless, research on how these linguistic strategies impact customer responses following service failures is still in its early stages. This study delves into the informal language styles used by conversational agents after service failures, examining the comparative effects of cute language styles (as informal language styles) and literal language styles (as formal language styles) on service recovery. Drawing upon the mind perception theory and the stimulus–organism–response (SOR) model, this research also unveils the underlying dynamics of whimsical and kindchenschema cuteness on customers’ forgiveness, along with literal language styles, in the context of service recovery. Furthermore, this study investigates the moderating influence of exchange and communal relationships, taking into account the boundary effects of relationship norms. To explore these hypotheses, three experiments were conducted. The findings of this research offer valuable theoretical insights and practical implications for designing linguistic strategies employed by conversational agents.

Harnessing customized AI to create voice of customer via GPT3.5

The integration of customer feedback is universally acknowledged as crucial in the product development process. Yet, traditional feedback collection methods employed by companies, such as interviews and surveys, have remained mainly unchanged and come with limitations. Interviews often fail to accurately capture customers' needs due to communication barriers, while surveys prompt only incremental changes instead of inspiring innovation. This challenge is compounded in the service industry, where feedback is intangible and more difficult to quantify. Text analysis presents a promising solution to delve into customer preferences more deeply, providing insights that can guide the development of new products and services. Our research advances the use of generative AI, specifically the GPT engine, beyond its conventional role as a chatbot. We innovate by adapting it to extract actionable insights from customer-service interactions, offering real-time, valuable data for decision-making and representing a significant leap forward in Voice of the Customer (VoC) analysis.

Artificial Intelligence and Machine Learning: A Review of State-of- the-Art Trends, Global Developments, and Practical Implications

This paper presents a comprehensive review of the latest trends, global developments, and practical implications in the field of Artificial Intelligence (AI) and Machine Learning (ML). The introduction highlights the transformative impact of AI and ML across various sectors, including healthcare, finance, transportation, manufacturing, retail, and entertainment. The purpose of the scientific research is to analyze the scientific, knowledge, and practical significance of AI and ML developments, emphasizing their potential impact on diverse domains. The study encompasses three main parts: state-ofthe- art trends, global developments, and practical implications. The scientific and practical importance of this paper lies in its examination of the growth and transformative potential of AI and ML technologies. The research methodology involves a comprehensive literature review, covering academic journals, conference proceedings, and reputable research papers and reports, to provide valuable insights and advancements in the field. The key findings of the paper revolve around significant trends in AI and ML, such as the increasing adoption of deep learning techniques and the integration of AI with big data analytics. Additionally, the study highlights global developments in AI research and investments from countries like the United States, China, Canada, Japan, South Korea, and the European Union. Practical implications include improved disease diagnosis and personalized treatment plans in healthcare, enhanced fraud detection and risk assessment in finance, and the use of AI-powered virtual assistants in customer service interactions. The research brings practical relevance to industries and policymakers, demonstrating the transformative potential of AI and ML in reshaping sectors and improving various aspects of human life. The paper concludes with recommendations for establishing robust frameworks to govern the responsible development and deployment of AI and ML systems, ensuring their long-term viability and beneficial impact on society.

Unintended consequences of humanoid service robots: A case study of public service organizations

Robots guided by artificial intelligence (AI) are transforming service operations in for-profit companies. However, public and non-profit organizations have been slower to pursue these innovative new technologies. We conducted an in-depth single-case qualitative study of one public organization (a public library in Australia) to better understand the unintended consequences of humanoid service robots. Our findings theorize unintended consequences through dimensions of customer value and service interaction locus, yielding four types – service disparities, judgment-free communication, noise pollution and social touch. Our analysis reveals new insights into: 1) the unintended consequences of humanoid robots for customers; 2) the effects of these consequences on the service organization; and 3) the technology capabilities of humanoid robots as potential sources of the consequences. When public and non-profit organizations leverage these unintended consequences, they can use robots more effectively to improve the overall customer service experience.

BEYOND THE SURFACE: A DEEP DIVE INTO CULTURE AND HAPPINESS AT WORK

The present study examines happiness and culture from a cross-cultural and interdisciplinary perspective. Addressing cultural barriers and aligning institutional values with cultural norms is crucial. Developing and precisely measuring a framework for a healthy culture can enhance wellness initiatives at work and foster inclusive workplaces. The cultural viewpoint on employee well-being and the effects of culture on relationships and values must thus be explored through research. Five research questions were designed to answer this purpose: 1) What is the relationship between happiness and culture in various settings? 2) What are the commonalities and differences across the studies? 3) What is the role of culture in promoting employee well-being and happiness? 4) What factors influence this relationship? 5) What are the limitations of the studies and the need for further research? The systematic literature search was conducted using the Web of Science, and the included studies were synthesized using a narrative synthesis approach. The studies demonstrate a complex relationship between happiness and culture in various settings. The research reveals that cultural differences significantly shape employees' perceptions of happiness and the factors impacting their subjective well-being at work. The findings indicate that cultural norms and values influence how employees express and regulate emotions in customer service interactions and the workplace. The research also suggests that the relationship between happiness and culture is complex and can be influenced by factors within and outside the employee. However, the studies have limitations, such as lack of generalizability, and may not fully account for other factors that can impact happiness and culture in the workplace.

“Doesn’t Really Answer My Question . . .”: Exploring Customer Service Interactions on Twitter

This article explores customer service interactions between the Irish airline Ryanair and its passengers on the social networking platform Twitter. Using a corpus linguistic methodology, it investigates a 1-million-word corpus of Twitter threads comprising tweets addressed to and posted by Ryanair between August 2018 and July 2019. Studying the communicative strategies used in the corpus reveals customers’ main concerns and causes for complaint, and how the airline addresses these in their response tweets offering assistance to passengers. In addition, the analysis of customer response tweets to these corporate replies allows insights into customers’ reactions to and perception of the (often generic) answers they receive. The aim of this case study is to gain further understanding of the linguistic and communicative features that characterize customer service interactions online, and the attitudes customers voice toward them, with a view to streamlining customer communication and improving levels of customer satisfaction.

End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization

The automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acoustic features. The conducted experiments on the TIMIT dataset show that the proposed approach outperforms many previous studies on speaker profiling with a mean absolute error (MAE) of 5.18 and 4.91 cm in height estimation and MAE of 5.36 and 6.07 years in age estimation for males and females, respectively, and achieving an accuracy of 99.98% in gender prediction.

Analysis Of The Filipino Brand Of Customer Service In The Accommodation Sector

In recent times, accommodation and hospitality industry is one of the fastest growing sectors because of significant tourism campaigns, government efforts, and development of economy. This industry requires manpower who are proactive and compassionate to their guests. Filipinos manifested such qualities which made the country as the supplier of manpower to other countries. The purpose of this study was to determine the Filipino brands that represent the hospitality manpower and explore the actions that demonstrate these qualities. Thirty-two (32) participants were purposively sampled, 16 as front desk officers in hotels Zamboanga City and 16 as general customers. Findings indicated that there were major characters that Filipino workers in accommodation industry demonstrated. Some of the character include dedication, Filipino warmth, friendly, tactful, and articulate. The natural disposition of Filipinos to be happy and friendly plays a significant role in customer service interactions, making customers feel welcomed and valued. Filipino brand of customer service in the accommodation industry is built on a foundation of hospitality, positive attitudes, and a genuine desire to ensure customer satisfaction. These characteristics, along with the training and proactive management of human resources, contribute to the growth and success of the accommodation industry in the Philippines, attracting more tourists and foreign business travelers to the country.