Customer Opinions Research Articles

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.

Airline reviews processing: Abstractive summarization and rating-based sentiment classification using deep transfer learning

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics.

A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media

Sentiment analysis is discovering the current ideology opinion of a group of people and their thoughts. The Sentiment analysis based onthe natural reaction of people on social media platform to reflect their mantel status and state. The main poupose of sentiment analysis is to dealing with society's environment and its impact effects towards the media world and surrounding environment. However, this is the key task of understanding every part of the world. The evolution of feeling simulates the sentiment behaviours to shows different direction of reactions and feeling across time. It can help users obtain a more advanced and detailed understanding of the views and attitudes represented in the content provided by users. The development of social media platforms, such as journals, forums, blogs, micro-blogs, Twitter, and social networks, has fostered sentiment analysis. Competitive advantages for organizations are collecting corporate social media and implementing machine learning algorithms to get valuable insights. In this study, our tasks are to show Bag of Words (BoW) and Term-Frequency-Inverse-Document-Frequency (tf_idf) feature-based machine learning prediction models that can help with sentiment analysis and figure out what their customers need and want from company items. Market research is perhaps the most important field for sentiment analysis applications, aside from brand perception and customer opinion surveys and feedbacks. This study results analysis shows the crucial way of classifying social media tweets feedback into positive or negative categories via using the classifier as a baseline to demonstrate in what manner comments are important based on features for any business model and their result.

A Comparative Study of Customer Perceptions: Organized Stores versus Kirana Stores in the Retail Landscape

This research aims to conduct a comprehensive comparative analysis of customer perceptions towards organized retail stores and traditional kirana stores. As the retail landscape undergoes transformation, understanding the factors that shape customer preferences and perceptions is crucial for both types of stores to effectively cater to consumer needs. Using a mixed-methods approach, this study combines qualitative insights gathered through interviews and focus group discussions with quantitative surveys to analyze and compare customer perceptions. Qualitative data will provide an in-depth understanding of the factors influencing customer opinions, while quantitative analysis will identify trends, preferences, and correlations among various aspects of organized and kirana stores. The research aims to uncover key determinants influencing customer choices, including the impact of the shopping experience, product availability, pricing strategies, convenience, and overall retail environment. Additionally, it seeks to identify areas for improvement and innovation within both types of stores by evaluating factors contributing to customer satisfaction and loyalty. The findings from this research are expected to offer valuable insights for retailers, policymakers, and industry analysts. By understanding the nuanced preferences of consumers in both organized and kirana retail settings, retailers can tailor their strategies to better meet customer expectations, fostering a competitive and adaptive retail environment. This study contributes to the broader understanding of consumer behavior in the evolving retail sector, providing a foundation for further research and strategic decision-making in the industry.

A Comparative Study of Customer Perception towards Various Supermarkets: Unraveling Preferences and Influencing Factors

This research delves into the intricate realm of customer perceptions towards supermarkets, aiming to conduct a comparative analysis across diverse retail environments. In a competitive market where consumer preferences shape the retail landscape, understanding the factors influencing customer perceptions is critical for supermarkets seeking to enhance customer satisfaction and loyalty. Employing a mixed-methods research design, this study combines qualitative data gathered through interviews and focus group discussions with quantitative surveys to examine and compare customer perceptions towards various supermarkets. Qualitative insights will provide a nuanced understanding of the factors shaping customer opinions, while quantitative analysis will identify trends, preferences, and correlations among different supermarket attributes. The research aims to uncover the key determinants influencing customer choices, such as the impact of pricing strategies, the role of product quality, the importance of a positive shopping experience, and the influence of brand image. Additionally, the study seeks to identify areas for improvement and innovation within the supermarket industry by evaluating the factors that contribute to customer satisfaction and loyalty. The findings from this research are expected to offer valuable insights for supermarket operators, retailers, and industry analysts. By understanding the nuances of customer perceptions, supermarkets can tailor their strategies to better meet consumer expectations, ultimately fostering a competitive and thriving retail environment. This study contributes to the broader understanding of consumer behavior within the supermarket sector, providing a foundation for further research and strategic decision-making in the retail industry.

Client's perception toward services of smoking cessation clinics in Riyadh, Saudi Arabia.

In Saudi Arabia, approximately 70,000 deaths yearly occur from diseases caused by smoking. Assessing patients' smoking behavior in primary care has apparent benefits. Assisting patients with smoking cessation is one of the essential roles of primary care. This study evaluated the quality of smoking cessation clinic counseling in Riyadh, Saudi Arabia, based on customer opinions of such services. This research employed a cross-sectional approach involving individuals attending smoking cessation clinics located within primary health care centers in Riyadh city. The participants were chosen through random selection. They were informed about the study's objectives, and those who consented to take part were contacted and provided with an online questionnaire to complete. A total of 340 subjects participated (95.6%) of them were males, and 41.2% were in the age group of 25 to 35 years old. The common modality of tobacco use was cigarettes (82.6%), and 87.9% reported consuming tobacco daily. The mean (±SD) total score of the Fagerstrom Test of Nicotine Dependence was 5.03 (±2.36), and the highest percentage (44.4%) of the participants had a moderate level of nicotine dependence. The vast majority (93.8%) of the participants had former quitting experiences. Half (50.9%) of the participants showed complete satisfaction with the cessation clinics, while 13.2% and 9.1% of them were not satisfied or slightly satisfied, respectively. The majority of smoking cessation clinic visitors were satisfied with the services provided, exceeding three-quarters of them, with half totally satisfied.

The importance of communication through social media in building clients satisfaction for hotel institutions

Purpose. This study aims to understand how to build a positive relationship between communication via social media and customer satisfaction for classified hotel establishments, with a particular focus on the Marriott International hotel chain. It explores the importance of extracting and analyzing the opinions of customers of the Marriott International hotel chain on social media platforms, identifying the most common positive and negative aspects in the evaluations of the mentioned hotel series. Results. The results indicate that communication via social media has a positive impact on customer satisfaction, which in turn affects the performance of hotel establishments. Additionally, active interaction with customers and monitoring their electronic reviews on social media platforms assists in identifying areas for improvement and contributes to enhancing the hotel’s reputation, offering insights for hotel managers to improve customer service strategies. Scientific novelty. To the best of the researchers’ knowledge and based on their review, the current study presents its novelty by encompassing all the hotels of the Marriott International chain for the year 2023. It demonstrates that most social media platforms utilized by these entities were explored, and user opinions were extracted and analyzed using tools such as Excel 2022 and Google Analytics. Additionally, the study incorporates the Uses and Gratifications Theory. It contributes to the existing literature by providing insights into how to effectively leverage social media to enhance customer experiences in the hospitality sector. Practical value. The practical value of the study lies in highlighting the importance of utilizing and analyzing user reviews and ensuring the effective use of social media, as it serves as a reflective mirror for hotels. It has the potential to direct businesses in the hospitality sector to improve interactions with clients and increase satisfaction, as observed with the Marriott International hotel chain.

Understanding Customer Opinion Change on Fresh Food E-Commerce Products and Services—Comparative Analysis before and during COVID-19 Pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, non-face-to-face e-commerce has become a significant consumer channel for customers to buy fresh food. However, little is known about customer opinion changes in fresh food e-commerce (FFEC) products and services during COVID-19. This study investigated the changes in expectations and preferences of FFEC customers on products and services before and during the pandemic from online reviews through a text mining approach. We divided the pandemic into two phases, acute and recovery, and found that eight attributes affect customers’ opinions. Some logistic service-related attributes gained customer attention during the acute phase, but product-related attributes gained more attention in the recovery phase. Customers showed a great level of forgiveness on many attributes during the acute phase, but customers’ dissatisfaction was expressed during the recovery phase. Finally, the results of the comparative importance–performance analysis provide improvement strategies for FFEC and help optimize their resource allocation of FFEC and enhance sustainable operation capacity in the case of a crisis.

Sentiment analysis of Arabic social media texts: A machine learning approach to deciphering customer perceptions

Sentiment analysis (SA) is a subfield of artificial intelligence that entails natural language processing. This has become increasingly significant because it discerns the emotional tone of reviews, categorising them as positive, neutral, or negative. In the highly competitive coffee industry, understanding customer sentiment and perception is paramount for businesses seeking to optimise their product offerings. Traditional methods of market analysis often fall short of capturing the nuanced views of consumers, necessitating a more sophisticated approach to sentiment analysis. This research is motivated by the need for a nuanced understanding of customer sentiments across various coffee products, enabling companies to make informed decisions regarding product promotion, improvement, and discontinuation. However, sentiment analysis faces a challenge when it comes to analysing Arabic text due to the language's extraordinarily complex inflectional and derivational morphology. Consequently, to address this challenge, we have developed a new method designed to improve the precision and effectiveness of Arabic sentiment analysis, specifically focusing on understanding customer opinions about various coffee products on social media platforms like Twitter. We gathered 10,646 various coffee products' Twitter reviews and applied feature extraction techniques using the term frequency-inverse document frequency (TF-IDF) and minimum redundancy maximum relevance (MRMR). Subsequently, we performed sentiment analysis using four supervised learning algorithms: k-nearest neighbor, support vector machine, decision tree, and random forest. All the classification statements derived in the analysis were aggregated via ensemble learning to convey the final results. Our results demonstrated an increase in prediction accuracy, with our method achieving over 95.95% accuracy in the Hard voting and soft voting at 94.51 %.

A Model for Customer Opinion Mining and Sentiment Classification using a Mixture of Experts Machine Learning Model

This paper presents customer opinion mining technique based on a Mixture of Experts (MoE) machine learning model. The approach allows a corpus from open source data repositories to be classified into positive, negative and neutral sentiments as the case may be and in a predictive manner. The results of simulations showed that the proposed MoE approach can effectively be used as a core tool in opinion mining and also serve in decision making by appropriate categorizations. In particular, it was found that the use of higher epoch sizes greatly enhances the performance of the MoE by reducing perplexity and error cost margins to appreciable levels. Thus, the MoE presents a promising candidate for customer opinion mining particularly in business product development environments.

A novel deep learning model for detection of inconsistency in e-commerce websites

On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product’s merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product’s quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model’s performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures.

The Role of Human Resources Management Bractices In Enhancing The Effectiveness of Comprehensive Quality An exploratory study of a sample of employees at the College of Pure and Human Education/University of Tikrit

This study aimed to determine the role of the most important practices of human resources, represented by their dimensions (training and development, career development, recruitment, and selection) in enhancing the effectiveness of the following comprehensive quality management practices (continuous development, customer focus, and continuous pursuit of customer opinions). in the Colleges of Education for Humanities and Pure Sciences. This goal was translated into a hypothesis drawing that included the study variables, the dependent variable represented by total quality management, according to the exploratory descriptive approach, and its hypotheses were formulated, and then the research hypotheses were formulated and tested using appropriate mathematical statistical methods and using the statistical package program (SPSS). Thus, the theoretical and then analytical framing was used to test the research hypotheses, where the study population consisted of employees in the College of Education for Pure Sciences and Humanities at the three administrative levels, and the sample size was (94) individuals from directors, directors of departments, divisions, units, and administrative employees. The research reached several conclusions, the most important of which was the existence of a significant correlation to the role of human resources management practices, including their dimensions, and the effectiveness of total quality management in the sample studied. Finally, the research recommendations came out with the need to pay attention to providing an appropriate basis for human resources practices for new operations by making structural changes, enhancing cooperation processes, and using appropriate sources in order to advance, maintain survival, continuity, and develop the effectiveness of total quality management

An experimental study for identifying customer prominent viewpoints on different flight classes by topic modeling methods

The airline industry has a wide range of customers with different tastes and expectations of airline services. Therefore, knowing the different attitudes of customers and consequently addressing their opinions is paramount in this business. Airlines offer different flight classes to customers to provide passenger transportation services and use different methods to evaluate customer satisfaction. One of the methods used to analyze customer opinions is the use of text analysis with machine learning techniques. In this study, to analyze the textual data, we used topic modeling to identify passengers' attitudes. In the first stage, we evaluated the performance of three different topic modeling methods: Latent Semantic Indexing (LSI), Hierarchical Dirichlet Process (HDP), and Latent Dirichlet Allocation (LDA). As a result of the modeling process, LDA had the best performance with an average coherence value of 0.42. Eventually, after determining the optimum number of topics as 10 topics, the prominent topics were interpreted and explained. The results of this study demonstrate the common and distinct attitudes of customers of economy class, business class, and first class. As appeared in the results of the economy flight class, the most important topic that appeared in comments was "passenger care". However, in whole reviews and reviews of business and first flight classes, the topic of "time" was more repetitive. Given the wide application of the presented methodology, the findings of this research can provide airline managers with a broader insight into passenger preferences and guide them on how to provide quality service for their customers.

THE MAGNIFICENCE OF THE BRAND AND ITS IMPACT ON WORD OF MOUTH (FIELD RESEARCH AT PEPSI COMPANY BAGHDAD

The current research aims to reveal the nature of the relationship between brand coolness and Word-of-Mouth, as the variables of the current research are considered critical variables in the literature of marketing management as well as their importance to any company, as all companies seek to have their brands characterized as coolness, which is reflected in the opinions of customers about those companies. It may lead to coolness about the companies, and in order to achieve the goal of the research, the researcher distributed (350) questionnaires to the employees of Pepsi Baghdad Company.(325) of them were obtained, and the number of valid questionnaires for analysis was (302) questionnaires, and they were analyzed using the statistical program SPSS v.26 Discussions between customers about the company, so the research recommends that the company, the research sample, and Iraqi companies in general should pay attention to their products in order to live up to being described as wonderful, which will benefit those companies and improve their image from the point of view of customers

Movie Review Sentiment Analysis: Supervised Learning versus Large Language Model

Sentiment analysis is frequently used to derive insights from natural language. Examples include analysis of textual data to measure brand perception, social media trends, or customer opinion about products. This paper evaluates the performance of three supervised machine learning methods and compares them with the next-generation large language model (LLM), which recently gained popularity with the release of OpenAI ChatGPT. Specifically, we explore the application of Decision Tree, Random Forest, and Support Vector Machine classifiers to a representative sample of 100K movie reviews collected by a well-known website, IMDb.com. Reviews are tagged with numeric ratings, allowing the formulation of a supervised learning problem and exploring the ability to differentiate sentiment between strongly opinionated positive and negative reviews and also, a more challenging problem of differentiating between weakly opinionated positive and negative reviews. Models are tuned to optimize recall and precision in this application, achieving an accuracy score of 0.89 for strong reviews and 0.63 for weak reviews. We then compare the results with ChatGPT, without specialized training, which reaches a perfect accuracy score of 1.00 for strongly opinionated reviews and 0.75 for weakly opinionated reviews, concluding that it outperforms supervised learning approaches but is also imperfect in distinguishing more subtle sentiment in weakly opinionated reviews.

The Influence of Online Platforms on Decision-Making Process and Behavioural Traits of International Travelers

Marketing communications in the international travel and hospitality industry greatly depend on online platforms. Various goals can be achieved by using digital technologies: advertising, PR, sales, branding, customer relationship management (CRM), data analytics, and online reputation management (ORM). At the same time, international travelers actively use online channels on various stages of behaviour and decision-making process, for different purposes: pre-departure information search, evaluation of alternatives, booking and purchasing services, and post-travel actions. Besides, the peculiarities of Web 2.0 give customers wide opportunities to disseminate their reviews concerning a service. It may significantly impact potential travelers’ decisions as opinions of other customers are regarded as more reliable. Hence, digital platforms must be considered one of the central issues in tourism marketing. The importance of the issue has increased in the post-pandemic conditions when competition in the international tourism industry worldwide has moved to a new level. The paper, employing a combination of literature review, observation, and analysis of primary and secondary data, provides a discussion and comprehensive analysis of various aspects of using online platforms for marketing purposes: opportunities, decision-making process under the influence of online platforms, behaviour peculiarities of international travelers, and strategies for using online channels to increase influence efficiency. Recommendations and solutions are presented for managing various online platforms. The conclusion briefly summarizes the issues discussed in the paper.

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.

Using Innovative Technologies in Hotels as a Factor of Sustainable Development of Tourism Industry

In today’s turbulent times, several factors are necessary for the sustainable development of the tourism industry.Among them, modern technologies take place among the main factors. Ensuring sustainable growth of the tourismindustry and the hotel industry requires systemic reforms. In the hotel industry, the role of innovative technologies is veryimportant. Today’s competitive environment is such that every hotel and every tourism business must constantly growwith the times or else the market will squeeze it. Customer opinion is especially important in an era when marketing andmanagement need to focus heavily. This article provides a thorough analysis of the status and role of technology use inthe hospitality and tourism industry.