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Virtual idols have entered the golden period as the main form of future digital people. However, existing studies only focus on a single idol type and partial role relationships related to virtual idols, lacking synthesized insights. To address these gaps, this paper summarizes different types of virtual idols and all role relationships to achieve a comprehensive literature review.

Based on the business ecosystem theory, this paper constructs a business role ecosystem framework for virtual idols from the two subsystems of value co-creation and value realization.

Firstly, we extract common characteristics and the generalized definition applicable to diverse idol types. Secondly, we find that there are commonalities and differences in the significant characteristics of virtual idols in different application fields. Thirdly, literature in the value co-creation subsystem mainly focuses on co-creation mechanisms in the role relationship between idols and demanders (RRID). A few focus on virtual idols’ constructions in the role relationship between producers and idols (RRPI) and co-creation phenomena in the role relationship between demanders and producers (RRDP). Finally, literature in the value realization subsystem mainly focuses on consumer attitudes and realization mechanisms in RRID. A few focus on realization phenomena in the role relationship between producers and tripartite enterprises (RRPT) and RRPI.

This paper points out future implementing directions of industry practitioners, gives strategies to promote economic value realizations and emphasizes the importance of cultural communication.

This paper discusses the existing theoretical gaps and possible future research directions regarding characteristics, applications and role relationships.

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.

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.

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.

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.

The stock market plays a crucial role in driving economic growth and maintaining economic vibrancy. A key factor shaping the stock market’s dynamics is investor attention (IA). With the rapid growth of behavioral finance, which offers insights into investor behavior, choices and their impact, there is growing concern among scholars about the influence of IA on global stock markets. This underscores the importance of understanding the intricate relationship between IA and market fluctuations on a global scale.

This study employs the Toda-Yamamoto Granger Causality test and Wavelet Analysis, to investigate the time-frequency varying causal relationships. The study analyzes closing price data for 26 Emerging Stock Markets from January 2004 to June 2022, with IA measured using Google search volume indices based on the highest intensity keywords sourced from Bloomberg, Wordstream and Google Trends.

The study identifies numerous instances of strong co-movements between IA and stock returns, predominantly occurring over the medium to long term. This suggests that IA plays a significant role in shaping stock market performance, particularly in driving sustained trends that impact long-term returns.

The originality of our study lies in its comprehensive analysis of the varying time–frequency relationships between IA and stock returns across 26 emerging markets, using a robust data set and precise measurement techniques. The results establish the predictive power of IA on market returns covering six different types of crisis, offering novel insights for investors and policymakers in emerging economies.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.