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The purpose of this study is to present the relationship between family support factors and Chinese college students’ online-startup thinking on live streaming platforms. Considering China's specific online entrepreneurial environment, this paper divides Chinese college students’ online-startup thinking according to the liberal–conservative thinking theory. This study classifies family support factors based on the tangible–intangible resource division theory. Different tangible and intangible factors have different impacts on their online-startup thinking.

This study tests 588 samples based on the partial least squares path modelling and variance-based structural equation modelling. This study promotes importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for Chinese college students and related departments.

Tangible family support factors, such as labour resources support, and intangible family support factors, such as verbal encouragement, can positively enhance Chinese college students’ liberal thinking to online-startup and decrease their conservative thinking. Meanwhile, according to importance-performance map analysis results, verbal encouragement from the intangible unit instead of financial resource support from the tangible unit has a higher total effect and performance on Chinese college students’ liberal thinking and conservative thinking.

This study draws on psychology research based on Chinese college students’ unique entrepreneurial mentality. This paper divides Chinese college students’ thinking in online-startups into liberal thinking and conservative thinking based on the liberal–conservative thinking theory. Meanwhile, according to the feature of Chinese family support factors, this paper classifies various elements based on the tangible–intangible resource division theory, which is helpful for scholars to understand that the student perceptions of the value of family support are critical to the success of the online-startup.

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 current study examines a novel model that examines how the online and offline or general personality of the same person predicts social identification with the endorser in a message and their subsequent online behaviors (e.g. ad-skipping) on social media, both differentially and simultaneously.

Real-time ad-skipping behaviors were tracked and analyzed across three online experiments.

The results supported the model explicating the dual and simultaneous influence of offline and online personalities on ad-skipping behaviors. Specifically, in response to a skippable video ad, online and offline personalities respectively increase and decrease viewers’ identification with the endorser. Consequently, the higher or lower the identification, the lower or higher the rate of ad-skipping behaviors.

The current study will benefit from a larger set of real-world data (i.e. big data) to enhance the generalizability of the findings, supporting the model.

With the growing prevalence of the gap between online and offline self-identities driven by social media usage, this paper suggests that the ad message needs to address the dual influence of both online and offline identities on ad-skipping behaviors.

The current study tests a novel model that shows that the online and offline personalities of the same person concurrently influence one’s behavior on the Internet, rather than separately.