For one company to have a competitive advantage and sustainability over others, its human resource management is of the utmost importance to secure competent employees. As job satisfaction plays a critical role in securing excellent manpower and enhancing corporate performance, it is essential to identify factors that would affect employees’ job satisfaction. Recently, writing reviews with integrity on job portal sites by former and current employees has become prevalent as such websites have guaranteed the reviewers’ anonymity. For this reason, we collected a vast amount of review data over nine industries, such as IT web communication, from one of the representative job portal sites in South Korea, Job Planet, and investigated factors that affect one’s job satisfaction based on the two-factor theory. As a result, it was found that (1) both motivation and hygiene factors had a substantial effect on job satisfaction over all industries; (2) the moderating effect between former and current employees was different for each industry; and (3) there was no moderating effect on job satisfaction between motivation and hygiene factors.

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.

Purpose: Because the food service industry is more dependent on customer contact and human resources than other industries, it is crucial to understand the factors influencing employee job satisfaction to ensure that employees provide satisfactory service to customers. However, few studies have incorporated employee reviews of job portals into their research. Many job seekers tend to trust company reviews posted by employees on job portals based on the information provided by the company itself. Thus, this study utilized company reviews and job satisfaction ratings from employees in the food service industry on a job portal site, Job Planet, to conduct mixed-method research.Design/methodology/approach: For qualitative research, we applied the Latent Dirichlet Allocation (LDA) model to food service industry company reviews to identify 10 job satisfaction factors considered important by employees. For quantitative research, four algorithms were used to predict job satisfaction ratings: regression tree, multilayer perceptron (MLP), random forest and XGBoost. Thus, we generated predictor variables for six cases using the probability values of topics and job satisfaction ratings on a five-point scale through LDA and used them to build prediction algorithms.Findings: The analysis showed that algorithm accuracy performed differently in each of the six cases, and overall, factors such as work-life balance and work environment have a significant impact on predicting job satisfaction ratings.Originality/value: This study is significant because its methodology and results suggest a new approach based on data analysis in the field of human resources, which can contribute to the operation and planning of corporate human resources management in the future.

This study explores the determinants of job satisfaction among IT industry employees in the U.S. and South Korea, focusing on how cultural and socio-economic contexts influence employee well-being and organizational sustainability. Given the high turnover rates in the IT industry, understanding the key factors affecting job satisfaction and dissatisfaction is critical for promoting sustainable organizational practices. By comparing reviews from Glassdoor and Jobplanet, this study uncovers cultural and organizational differences that directly affect employee retention and job satisfaction, offering actionable insights for multinational IT companies seeking to align their strategies with sustainable HR practices. The research utilizes Contextualized Topic Modeling (CTM), a cutting-edge method leveraging Sentence-BERT embeddings, to analyze user-generated reviews. CTM identifies key topics such as attendance management, career development, organizational culture, welfare support, salary, and job autonomy, revealing distinct cultural influences. For example, attendance management and organizational culture positively influenced job satisfaction in U.S. companies, while welfare benefits were more significant in the U.S. than in South Korea. Salary had a positive effect in South Korea but a negative effect in the U.S. The comparative analysis also highlights the higher satisfaction levels among current employees, underscoring factors critical for long-term retention. This study contributes to the literature by leveraging user-generated content to reveal context-specific factors, including new elements such as “technological capabilities” and “location and accessibility,” which are often overlooked in traditional survey-based research. The findings provide actionable insights for IT companies to refine HR practices in ways that enhance employee satisfaction, contributing to both organizational sustainability and the well-being of employees.

The COVID-19 pandemic reshaped travel patterns and customer expectations, generating profound challenges for the hotel industry. This study analyzes 50,000 TripAdvisor reviews of New York hotels to examine how customer satisfaction with hotel selection attributes shifted before and during the pandemic. BERTopic was applied to extract eight key attributes, while VADER, PRCA, and Asymmetric Impact–Performance Analysis (AIPA) were used to capture asymmetric effects and prioritize improvements. Comparative analyses by hotel classification, travel type, and customer residence reveal significant shifts in food and beverage, location, and staff, particularly among lower-tier hotels, business travelers, and international guests. The novelty of this study lies in integrating BERTopic and AIPA to overcome survey-based limitations and provide a robust, data-driven view of COVID-19’s impact on hotel satisfaction. Theoretically, it advances asymmetric satisfaction research by linking text-derived attributes with AIPA. Practically, it offers actionable guidance for hotel managers to strengthen hygiene, expand contactless services, and reallocate resources effectively in preparation for future crises. In addition, this study contributes to sustainability by showing how data-driven analysis can enhance service resilience and support the long-term socio-economic viability of the hotel industry under global crises.