Customer Opinions Research Articles

Optimalisasi Kualitas Layanan Transportasi Publik: Pendekatan Model P-Transqual dalam Mengevaluasi Kinerja di PO. Moedah

Purpose: This study aims to quantify and scrutinize the caliber of public transportation services through meticulous application of the Public Transportation Quality (P-TRANSQUAL) framework. Research methodology: The proposed research method involves a qualitative approach that integrates the use of Public Transportation Service Quality (PTSQ) and fishbone diagrams. The research population consisted of users of PO Moedah services, selected randomly based on predetermined inclusion criteria, including diverse age groups, gender, occupation, and monthly income range. Results: The empirical findings suggest that, while functionally proficient, the public transportation services administered by PO Moedah are not immune to deficiencies, warranting remedial intervention. Limitations: The study was limited by subjective customer opinions and potential sample bias due to inclusion criteria such as age and income. Additionally, the findings are specific to PO Moedah's area and offer only a snapshot in time, limiting the generalizability and long-term relevance. Contribution: This study offers practical insights for PO Moedah to improve services and demonstrates the use of the P-TRANSQUAL framework to assess public-transportation quality. It highlights the importance of customer satisfaction and promotes sustainable urban transportation by addressing key issues, such as fuel consumption and traffic congestion.

Harnessing the Power of Web Scraping and Machine Learning to Uncover Customer Empathy from Online Reviews

In today's information-driven world, companies need to grasp customer sentiment and pinpoint key product features to make informed choices and improve customer satisfaction. The abundance of online customer feedback provides businesses with a rich source of information about customer perceptions and preferences. Yet manual analysis of large volumes of reviews is a tedious and resource-intensive task. It describes a web scraping application that sorts reviews into positive, negative, or neutral sentiments that businesses can use for insight. By assigning weights to reviews based on perceived impact, the approach offers a nuanced understanding of customer opinions. Experimental results demonstrate its effectiveness in accurately categorizing and analysing reviews, distinguishing between genuine and fake feedback. Additionally, AI-powered examination of product reviews employs NLP methods to extract important information from customer feedback, identifying particular elements that customers like or find troubling. After the successful identification of gain points, they can be used to improvise product development and marketing. So, product-specific focused crawler is developed to extract customer reviews from the most trusted websites. As a side chain, this can also be used as a tool for analysing the competitors’ products to understand their pain as well as gain points. The approach can uncover the most discussed aspects of products, enabling businesses to better understand customer perceptions and preferences to make better decisions thereby saving time and money.

Factors Affecting Towards Decision to Use Services of Luang Prabang Night Garden

The purpose of this research is to: 1) to study the situation of using restaurant services in Luangprabang Night gardenrestuarant, 2)to study the opinions of customers about Luangprabang Night garden restuarant 3) to find out the factors affect the decision to use services of Luangprabang Night garden restaurant. It wasa survey researchby using a questionnaire as a tool to collect and compile data from a sample group of 385 people.Use the SPSS version 20 program to find the frequency, percentage, mean value, standard deviation, and multiple regression equation to analyze the factors that affect the decision touseservicesthis restaurant. The results of the research found that the situationof the Luangprabang Night garden restuarantare that most of the consumers go to useservice 1-2 times permonth, and some go to there every day for aweek. The busiest time is between 6 o'clock and 8 o'clock in the evening. The opinion from the service users about Lat Lat Restaurant in the capital city is that after the customers have eaten at Luangprabang Night garden restuarant, they have the idea of recommending, persuading, and rewarding people they know to use the service and they think that they will come back to this placeagain. Also, the custommershave taken photos and record videos of Luangprabang Night garden restuarant to shareon Facebook (Facebook), Page, Website, etc. in order to persuade others to come toservice. At the same time, they also think that Luangprabang Night garden restuarant is the best place for having dinneroutside ofhome and they are very satisfied after the customers decide to use Luangprabang Night garden restuarant. The factors affect the decision to use Luangprabang Night garden restuarant are product factors, price factors, marketing promotion factors, operational process factors, physical characteristics factors and location factors with a statistical significance level of Sig <0.05.

Sentiment Analysis of Product Reviews Using Transformer Enhanced 1D-CNN and BiLSTM

Abstract The rapid growth of Internet-enabled applications, such as social media platforms, e-commerce sites, and blogs, has led to a surge in user-generated content. This vast amount of data has made sentiment analysis increasingly valuable. Modern Aspect-Based Sentiment Analysis (ABSA) offers a more detailed approach by identifying sentiment trends related to specific aspects within the text. However, the challenge lies in analyzing reviews that are often short, unstructured, and filled with slang and emotive language, making it difficult to gauge customer opinions accurately. To address these issues, we proposed an effective hybrid approach “RoBERTa-1D-CNN-BiLSTM” for ABSA. Initially, the pre-trained Robustly Optimized BERT approach (RoBERTa) and One Dimensional Convolutional Neural Network (1D-CNN) models are used to extract features at the aspect level from the context of the review, following which classification is performed using Bidirectional Long Short-Term Memory (BiLSTM). The approach is evaluated on three cross-domain standards datasets, yielding an accuracy of 92.33%. The results of the experiments show that it surpasses the current leading methods in sentiment analysis and product recommendation.

Impact of Technological Progress on Consumer Trust and Adoption of E-Banking

Abstract: In this research paper, I have thoroughly described the topic “Impact of Technological Progress on Consumer Trust and Adoption of E-Banking”. The financial services sector has seen a transformation due to the advent of technology improvements, especially with the increasing use of electronic banking, or e-banking. Traditional banking methods have been transformed by the advancement of digital technology, which has made it easier to create complex financial systems and services that are available via a variety of electronic devices. But even as these developments provide potential, a serious worry emerges about the complex relationship that exists between e-banking adoption, customer trust, and technical improvement. Building effective banking relationships requires a lot of trust, particularly in the digital sphere where fraud, privacy, and data security are major problems. The increasing dependence of customers on e-banking for financial operations necessitates a high degree of trust between the technology and the organizations providing these services. However, customer opinions of security, dependability, usability, and overall value are just as important in determining whether or not e-banking is adopted as a banking option as technical developments. To create an environment that is favorable for digital financial services, financial institutions and regulators need to understand the subtleties of customer trust and how it affects the adoption of e-banking in the context of technical improvements. In order to shed light on the opportunities, problems, and consequences in the rapidly changing field of digital finance, this paper will examine the complex relationship that exists between technological advancement, consumer confidence, and acceptance of e-banking. The abstract emphasizes the symbiotic link between trust and adoption, indicating that as trust in e-banking services grows, so does the adoption rate, underscoring the importance of technology innovation in reshaping the banking landscape.

Enhancing Service Quality—A Customer Opinion Assessment in Water Laboratories through Artificial Neural Networks

Enhancing Service Quality—A Customer Opinion Assessment in Water Laboratories through Artificial Neural Networks

Environmentally sustainable apparel merchandising of recycled cotton-polyester blended garments: Analysis of consumer preferences and purchasing behaviors

This research work examines how consumer behavior and purchasing pattern are affected by recycled cotton-polyester blended clothing, with a particular emphasis on sustainable apparel merchandising. The use of recycled materials has drawn a lot of attention as environmental sustainability in the fashion business is growing attention. In order to investigate customer behavior, perceptions, preferences and purchasing decisions regarding recycled cotton-polyester blended clothing.This research employed a mixed-methods design that included both qualitative and quantitative methods. For a deeper understanding of the customer attitudes, opinions and motives towards sustainable fashion recycled materials in particular, this phase qualitatively involves a one-on-one interview and focus group discussions based on age, gender social class as well as the level of education. In addition, the study considers aspects such as longevity in use, wear resistance, quality, ease of use and clothing comfort that influence buying decisions. Using data from the qualitative phase as a foundation, the quantitative phase uses surveys given to a broad consumer sample to measure consumer preferences and purchasing pattern for recycled cotton-polyester clothing. This research aims at identifying the key factors that impact the preference of customers and buying behavior for recycled cotton and polyester blends based apparel. Additionally, it aims to assess how well sustainable clothing merchandising techniques work to promote these goods. This study indicates that individuals aged 25–45 from higher social strata and with higher educational attainment exhibit greater awareness and preference for recycled apparel with more than 60 % demonstrating a marked inclination towards these products.

Electricity Sales Package Decision Making Using Two-Stage Density Clustering and Minimum Adjustment Distance Consensus

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers’ fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper.

Optimized Deep Learning Model for Sentimental Analysis to Improve Consumer Experience in E-Commerce Websites

Sentiment analysis plays a pivotal role in deciphering customer sentiments from vast amounts of unstructured data, particularly in the context of e-commerce where customer reviews are prolific. The evolution of e-commerce reviews toward a multimodal format, including images, videos, and emojis, introduces new dimensions to sentiment analysis. Traditional text-based models may struggle to effectively capture sentiments expressed through non-textual elements. This paper proposed an effective sentiment analysis model for the E-Commerce Platform to improve the user consumer experience. The proposed method comprises Fejer Kernel filtering for data points estimation in the E-commerce dataset points. Within the estimated data points fuzzy dictionary-based semantic word feature extraction is performed for the estimation of features in the E-Commerce dataset. The dataset for the analysis is computed with the Optimized Stimulated Annealing for the feature extraction and selection. The classification of customer opinion is classified with the BERT deep learning model. The feature extracted from the model is the opinion of consumers in the E-Commerce dataset. The classification of consumer preference experience is based on opinion of customers in the E-commerce dataset. Simulation results demonstrated that proposed model achieves the higher classification accuracy for the E-Commerce platform.

Orientation on internal customer satisfaction in public administration

In the modern world, there is a constant increase in the role and importance of the customer who is achieving a dominant position in mutual relations in the organization. The situation is also noticeable in the public administration sector, where activities related to improving the functioning of the office, customer service and customer satisfaction are an important element of the development. The subject of the article is the analysis of the internal customer's opinion as an expression of building an orientation focused on the customer’s satisfaction in the public administration sector. The aim is to present the surveys on internal customer satisfaction that were conducted within the years 2012-2021 in a selected public administration unit. The conceptual basis of the terms public administration as well as the external and internal customer was also presented. The results of the research over the years and their discussion have been presented in the conclusion. The considerations undertaken in the elaboration lead to the conclusion that the orientation towards internal customer satisfaction in public administration is justified because it ultimately affects the level of public services provided, the quality of which is guaranteed, among others, by: stable and job-satisfied staff.

Impact of Preservatives on Food Preservation and their Effect on the Bacteria

The Meat, milk, fruits, and vegetables are vital nutrients for humans because they include protein, essential amino acids, vitamin B groups, and minerals. Because of its high-water activity and nutrient content, it also promotes deterioration and food-borne disorders. Thus, food preservation is essential for guaranteeing food safety and security. This paper examines the critical significance of animal origin natural preservatives in food preservation systems. This review article explains how these preservatives extend the shelf life of food products in a variety of categories by thoroughly examining their efficacy, methods of action, and diverse applications. Furthermore, it explains the inherent benefits of using animal origin natural preservatives, demonstrating their superiority to synthetic counterparts in terms of safety, sustainability, and environmental effect. Regulatory frameworks and customer opinions are also investigated, providing useful information about the uptake and acceptance of various preservatives in the food business. This analysis emphasises the importance of using natural and animal-derived solutions to promote a safer, more sustainable, and healthier food supply chain.

Unravelling tourism destination's competitiveness using big data analytics: a comparative analysis

PurposeThe competitiveness of destinations is crucial for tourism. In this context, the study aims to uncover customer satisfaction, experiences, feelings, and thoughts by conducting a comparative analysis of social media comments from various competitive tourism destinations.Design/methodology/approachBig data research was conducted to answer the research questions. The data was collected on a social media platform focusing on three destinations in the Mediterranean region. Three methods were employed to analyse the data: sentiment analysis, topic modelling, and named-entity recognition.FindingsThis study addressed traveller satisfaction levels. It identified the topics concerning each destination, examined the emotions expressed by travellers about these topics, explored the potential impact on future behaviour, and investigated the features of the destinations and satisfaction levels about these features. It also identified the prominent food and beverage names in destinations and explored tourists’ preferences regarding these foods and beverages.Research limitations/implicationsThe limitations of this study relate to the sample. The data used in this study was solely obtained from a single social media platform and focused on English-only comments. Further research that includes different social media platforms for hotel categories and considers reviews in local languages could capture a broader range of customer opinions and experiences.Practical implicationsPolicymakers can gain insight into a destination’s position in the competitive landscape. This study has numerous implications for policymakers in the relevant destinations and managers in the design and implementation of services.Social implicationsThe findings of this study can have broader societal implications if considered and implemented by decision-makers and tourism businesses in the context of competitiveness.Originality/valueThe study’s originality lies in integrating multiple disciplines and comparing tourism destinations using big data. This study improves the understanding of competitiveness in three specific Mediterranean destinations. Previous research has focused on different contexts in these Mediterranean destinations. Therefore, the study fills this gap by focusing simultaneously on all three destinations in the context of competitiveness.

THE REUSABLE COFFEE CUP SYSTEM “BILLIE CUP” IN A UNIVERSITY SETTING: A PILOT-STUDY

500 billion disposable cups are consumed every year and contribute to polluting the natural environment. Reusable cups offer a reasonable alternative, especially in work or study places with efficient and sustainable washing facilities. In one coffee bar at a university building, disposable cups were completely banished and a reusable polypropylene cup, the so-called “Billie Cup”, was introduced together with automated deposit and washing facilities. Quantitative and qualitative analyses were performed to measure financial differences, waste reduction, and customer opinion. After introduction of the Billie Cup, we reduced approximately 2.5 kg of waste per week and did not report significant changes in weekly sales [median: 336 (interquartile range: 291–359) vs. 337 (297–349) drinks, respectively, P=1.000] or revenue [578 (492–612) vs. 576 (492–586) euro, respectively, P=0.833]. Furthermore, we reported significantly higher numbers of refilling a cup with a median of 21 (16–25) vs. 43 (38–59) drinks per week, respectively, P=0.017. Resulting from our questionnaires, we found that all respondents believed that disposable cups produce unnecessary waste on campus. Our pilot study demonstrates that a reusable coffee cup system along with banishing all disposable cups can be successfully implemented without decreasing monthly revenue or sales.

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations

Improving customer repurchase intention constitutes a key activity for maintaining sustainable business performance. Returning customers provide many economic and other benefits to businesses. In contrast, attracting new customers is a process that is associated with high costs. This work proposes a novel counterfactual explanations methodology that utilizes textual data from electronic word of mouth to recommend business changes that can improve customers' repurchase behavior. Counterfactual explanation methods gained considerable attention because their logic aligns with human reasoning and the fact that they can recommend low-cost actions on how to turn an unfavorable outcome into a favorable. Most counterfactual explanation methods however recommend actions that can change the outcome of individual instances (i.e. one customer) rather than a group of instances. Therefore, this work proposes a multi-instance counterfactual explanation method that recommends optimum changes to an organization's practices/policies that increase repurchase intention for many customers or customer segments.The proposed methodology utilizes topic modeling to extract customer opinions from online reviews' text and use topics as features to train a binary classifier that predicts customer revisit intention. Multi-instance counterfactual explanations are computed for all or different groups of non-revisiting customers, recommending optimum business changes that can increase revisit intention. The proposed methodology is empirically evaluated through a case study on the restaurant revisit problem and compared against a prominent alternative from the literature. The results show that the method has better performance to the alternative method and produces recommendations that are actionable and abide by the customer-repurchase literature.

A STUDY ON ADVERTISEMENT EFFECTIVENESS ON BRAND IMAGE: A CASE STUDY ON HONDA MOTORS

In this study, we shall be investigating the types of advertising that Honda resorts to and the role that advertisements play in the establishment of customer opinions regarding the brand. The primary objectives of the study are threefold: However, I Start With the Important Elements of Honda such as H.O.N.D.A. and Ultimate Benefit. It can be viewed as a sign of how the ad campaigns of Honda help to create and keep a positive brand image. This means adopting advertising methods of different forms, but which are not so conspicuous, therefore, making customers notice them and finally, their reactions to them. First, the author investigates the efficiency of advertisements in the formation of a brand image with account taken of such factors as brand association, brand familiarisation, and personal customer relationship with the Honda car. The research outcome will likely be in line with the advertisement which could be employed with consumer perception as well as brand awareness. On the contrary, however, the outcome is associated highly with a brand that has been acknowledged positively. With this study, the final (decisive) instrument that is most responsible for the advertising effectiveness of Honda will be identified as well. Hence evaluating the performance of each part it will be possible to reach the best one and identify the determining feature that is the main reason for the positive image of Honda in the highly competitive automotive industry. The outcomes of this investigation would constitute a critical source of information that would be extremely beneficial to the marketing and brand managers of Honda, as they can now apply the aforementioned principles in their ads to improve their brand positioning and buyers' engagement level. Keywords: advertisement effectiveness, brand image

Identification and Classification of Bilingual Language using Natural Language Processing

This paper presents an efficient approach to language detection and sentiment analysis using Naive Bayes classifiers. The research involves training classifiers on a diverse dataset of text samples in multiple languages. Our method employs vectorization techniques and separate classifiers for language detection and sentiment analysis tasks. Experimental results demonstrate the effectiveness of the classifiers, with high accuracy in identifying the language and sentiment of input text. The approach showcases notable efficiency in processing language detection and sentiment analysis, addressing the growing need for automated text processing solutions. The findings have implications for various domains, including customer feedback analysis, market research, and opinion Mining

THE IMPACT OF SERVICE QUALITY ON CUSTOMER LOYALTY AT KIM THAI HOTEL

Every hotel's satisfaction and loyalty depend on the quality of its service. As a result, the question of service quality has long been a key research topic in the hospitality business. The study used methods such as secondary data collection and processing, expert interviews to build evaluation models, customer opinion collection, and quantitative data processing to clarify the impact of service quality aspects on loyalty. The study used correlation analysis and linear regression to show how six service quality parameters influenced client loyalty when using various Kim Thai hotel services. Among them, the security element has the most influence, while the empathy factor has the least influence on consumer loyalty to the hotel service. Among them, the security element has the most influence, while the empathy factor has the least influence on consumer loyalty to the hotel service. This research serves as the scientific foundation for the hotel to use when implementing solutions that improve service quality in order to promote customer return intentions.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0791/a.php" alt="Hit counter" /></p>

The factors impact on the cashless payment behavior: the IPA model approach

Purpose In the context of the Covid-19 pandemic and the trend in industrial development 4.0, the problem is how to conduct transactions of goods and services in the market using cashless payment in Vietnam, as in developed countries. This study aims to investigate, analyze and evaluate the factors affecting cashless payment behavior in Ho Chi Minh City, Vietnam. Design/methodology/approach The QR codes are used for payments because many factors affect the behavior of cashless payments made using electronic payment tools, including QR code tools. To achieve this goal, this study applied behavioral theory and the importance-performance analysis (IPA) model to measure service quality based on the difference between customer opinions on the importance and performance level of service providers’ targets. Survey results were obtained from 111 people living in HCMC, Vietnam, including 47 men and 64 women, which was a survey for 15 criteria. Findings According to the IPA results of the first quadrant, managers should focus on allocating their resources toward improving their performance across five key criteria. These criteria are crucial for meeting customers’ expectations and include factors such as product quality, responsiveness to customer queries or complaints, delivery times, pricing and customer service. In addition, the second quadrant of the IPA highlights another set of five criteria that perform well and are essential to the success of the business. These criteria could consist of customer loyalty, employee satisfaction, profitability, market share and innovation Originality/value These results provide a basis for solutions and recommendations for managers to refer to and apply consistent practices. Therefore, this study examines the cashless payment in Vietnam. Empirical results offer solutions to financial technology policy, marketing policy, cashless payment services and technology, which can help managers provide online payments using QR codes and contribute to monetary policy solutions.

CONSUMER BEHAVIOURL ANALYSIS IN E COMMERCE AS MYNTRA USING DATA ANALYTICS

CONSUMER BEHAVIOURL ANALYSIS IN E COMMERCE AS MYNTRA USING DATA ANALYTICS

Spam Review Detection using Machine Learning

Customer opinions play a vital role in buying decisions. These days most customers post their opinions about products on blogs, e-commerce sites, review sites, and social networking sites. The above information is consumed by business or corporate organizations, as they are eagerly interested in analyzing consumer views about their products, services, and support. As people buy products after reading the reviews, the kind of reviews that a product attracts are of concern to the sellers. This means that a positive review on the product would bring in sales and a negative one would reduce them. The project leverages Natural Language Processing (NLP) techniques and supervised learning algorithms to build a robust spam review detection system. The system is trained on a dataset comprising genuine and spam reviews, and it extracts various features from the textual content of reviews, such as sentiment analysis, linguistic patterns, and semantic meaning.