Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques.

In recent years, the turnover phenomenon of new college graduates has been intensifying. The turnover of new employees creates many difficulties for businesses as it is difficult to recover the costs spent on their hiring and training. Therefore, it is necessary to promptly identify and effectively manage new employees who are inclined to change jobs. So far previous studies related to turnover intention have contributed to understanding the turnover phenomenon of new employees by identifying factors influencing turnover intention. However, with these factors, there is a limitation that it has not been able to present how much it is possible to predict employees who are actually willing to change jobs. Therefore, this study proposes a method of developing a machine learning-based turnover intention prediction model to overcome the limitations of previous studies. In this study, data from the Korea Employment Information Service's Job Movement Path Survey for college graduates were used, and OLS regression analysis was performed to confirm the influence of predictors. And model learning and classification were performed using a logistic regression (LR), k-nearest neighbor (KNN), and extreme gradient boosting (XGB) classifier. A novel finding of this research is the diminished or reversed influence of certain traditional factors, such as workload importance and the relevance of one's major field, on turnover intention. Instead, job security emerged as the most significant predictor. The model's accuracy rates, highest with XGB at 78.5%, demonstrate the efficacy of applying machine learning in turnover intention prediction, marking a significant advancement over traditional econometric models. This study breaks new ground by integrating advanced predictive analytics into turnover intention research, offering a more nuanced understanding of the factors influencing the turnover intentions of new college graduates. The insights gained could guide organizations in effectively managing and retaining new talent, highlighting the need for a focus on job security and organizational satisfaction, and the shifting relevance of traditional factors like job preference.

Online reviews, customer Q&As, and product sales: A PVAR approach.

Online reviews and customer Q&As have emerged as two vital forms of electronic word-of-mouth (eWOM) that significantly influence consumer decisions in e-commerce. Yet, a comprehensive understanding of the individual and combined roles of these eWOM types in shaping market dynamics remains elusive. This study addresses this research gap by tracking and analyzing three months of eWOM and sales data for 120 laptops on Amazon, comprising 7,205 online reviews, 6,365 customer Q&A questions, and 7,419 answers. Leveraging the Panel Vector Autoregression (PVAR) model and STATA16.0 software, we unravel the intricate dynamics between online reviews, customer Q&As, and laptop sales. The empirical results reveal distinctive influence mechanisms of online reviews and customer Q&As on product sales, with review volume and answer valence positively affecting sales. Importantly, answer volume was found to stimulate online reviews and enhance their valence. Our study elucidates the interplay among online reviews, customer Q&As, and product sales, underscoring the need for future research on multi-type eWOM. Further, the insights gleaned offer valuable guidance for online platforms and retailers to strategize their eWOM management.

Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with collaboration software.

Background: In today's digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user satisfaction after the introduction of collaboration software, there are gaps in predicting user satisfaction before its implementation. To address this gap, this study offers a machine learning-based forecasting method. Methods: We utilized national public data provided by the national information society agency of South Korea. To enable the data to be used in a machine learning-based binary classifier, we discretized the predictor variable. We then validated the effectiveness of our prediction model by calculating feature importance scores and prediction accuracy. Results: We identified 10 key factors that can predict user satisfaction. Furthermore, our analysis indicated that the naive Bayes (NB) classifier achieved the highest prediction accuracy rate of 0.780, followed by logistic regression (LR) at 0.767, extreme gradient boosting (XGBoost) at 0.744, support vector machine (SVM) at 0.744, K-nearest neighbor (KNN) at 0.707, and decision tree (DT) at 0.637. Conclusions: This research identifies essential indicators that can predict user satisfaction with collaboration software across four levels: institutional guidance, information and communication technology (ICT) environment, company culture, and demographics. Enterprises can use this information to evaluate their current collaboration status and develop strategies for introducing collaboration software. Furthermore, this study presents a novel approach to predicting user satisfaction and confirm the effectiveness of the machine learning-based prediction method proposed in this study, adding to the existing knowledge on the subject.

What is holding back business process virtualization in the post-COVID-19 era? Based on process virtualization theory (PVT).

The post-pandemic COVID-19 has been influential in accelerating the digital transformation of enterprises and business process virtualization. However, in a virtual working environment with no physical interaction, the psychological requirements of the communication between teleworkers and the negative impact of information systems are hindering the business process virtualization. Studying the relationship between the interaction between organizational members and job performance is an important part of organizational psychology. For an enterprise to maintain high-efficiency output, it is necessary to study psychological factors related to business process virtualization. This paper verified the factors hindering business process virtualization based on process virtualization theory (PVT). The research was implemented on a sample of 343 teleworkers in China enterprises. The structure of the model of this study includes two aspects that hinder the business process virtualization: the psychological requirements of teleworkers (Sensory requirements, Synchronism requirements, and Relationship requirements) and the negative effects of information systems (Information overload and Communication overload). The results show that teleworkers' sensory requirements, synchronism requirements, and communication overload negatively impact business process virtualization. However, unlike the results in the existing literature, the relationship requirements and information overload do not affect the business process virtualization. The results will help business managers, teleworkers, and information system developers develop strategies to address the negative factors hindering business process virtualization. In the so-called new "normal era," our research will help companies to create a successful virtual work environment.

Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning.

RESEARCH MOTIVATION: Recently, the digital divide problem among elderly individuals has been intensifying. A larger problem is that the level of use of digital technology varies from person to person. Therefore, a digital divide may even exist among elderly individuals. Considering the recent accelerating digital transformation in our society, it is highly likely that elderly individuals are experiencing many difficulties in their daily life. Therefore, it is necessary to quickly address and manage these difficulties. RESEARCH OBJECTIVE: This study aims to predict the digital divide in the elderly population and provide essential insights into managing it. To this end, predictive analysis is performed using public data and machine learning techniques. METHODS AND MATERIALS: This study used data from the '2020 Report on Digital Information Divide Survey' published by the Korea National Information Society Agency. In establishing the prediction model, various independent variables were used. Ten variables with high importance for predicting the digital divide were identified and used as critical, independent variables to increase the convenience of analyzing the model. The data were divided into 70% for training and 30% for testing. The model was trained on the training set, and the model's predictive accuracy was analyzed on the test set. The prediction accuracy was analyzed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and eXtreme gradient boosting (XGBoost). A convolutional neural network (CNN) was used to further improve the accuracy. In addition, the importance of variables was analyzed using data from 2019 before the COVID-19 outbreak, and the results were compared with the results from 2020. RESULTS: The study results showed that the variables with high importance in the 2020 data predicting the digital divide of elderly individuals were the demographic perspective, internet usage perspective, self-efficacy perspective, and social connectedness perspective. These variables, as well as the social support perspective, were highly important in 2019. The highest prediction accuracy was achieved using the CNN-based model (accuracy: 80.4%), followed by the XGBoost model (accuracy: 79%) and LR model (accuracy: 78.3%). The lowest accuracy (accuracy: 72.6%) was obtained using the DT model. DISCUSSION: The results of this analysis suggest that support that can strengthen the practical connection of elderly individuals through digital devices is becoming more critical than ever in a situation where digital transformation is accelerating in various fields. In addition, it is necessary to comprehensively use classification algorithms from various academic fields when constructing a classification model to obtain higher prediction accuracy. CONCLUSION: The academic significance of this study is that the CNN, which is often employed in image and video processing, was extended and applied to a social science field using structured data to improve the accuracy of the prediction model. The practical significance of this study is that the prediction models and the analytical methodologies proposed in this article can be applied to classify elderly people affected by the digital divide, and the trained models can be used to predict the people of younger generations who may be affected by the digital divide. Another practical significance of this study is that, as a method for managing individuals who are affected by a digital divide, the self-efficacy perspective about acquiring and using ICTs and the socially connected perspective are suggested in addition to the demographic perspective and the internet usage perspective.