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This study investigated the mediating role of job satisfaction and the moderating role of career growth opportunity in the relationship between public service motivation (PSM) and public employees' turnover intention.

The authors recruited 587 public employees from Yunnan Province, China to test moderation and mediation hypotheses. The authors conducted confirmatory factor analysis to determine the discriminant and convergent validity of the measures of PSM, turnover intention, job satisfaction and career growth opportunity. Finally, the authors carried out bootstrapping to ascertain direct, indirect and conditional indirect effects.

PSM had a negative effect on public employees' turnover intention, but this relationship was partially mediated by job satisfaction. Career growth opportunity moderated the association between job satisfaction and turnover intention. In particular, the indirect effect of PSM on turnover intention through job satisfaction weakened under high career growth opportunities.

The results highlighted the significance of PSM and career growth opportunity in shaping public employees' work-related attitudes and behaviors. Public organizations should consider PSM a key criterion in recruitment and selection and pay more attention to the significance of intervening in career growth to satisfy public employees' psychological needs related to individual career development.

This study contributes to the literature on the disputed link between PSM and turnover intention and uncovered the underlying mechanism through which PSM affects public employees' turnover intention by proposing job satisfaction and career growth opportunity as a mediator and moderator, respectively.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

In a study of 587 public employees from China, researchers found that public service motivation (PSM) made workers less likely to leave their jobs. But the relation to turnover intention was partially mediated by job satisfaction. Career growth opportunity moderated the association between job satisfaction and turnover intention.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

Question-answering (QA) systems are being increasingly applied in learning contexts. However, the authors’ understanding of the relationship between such tools and traditional QA channels remains limited. Focusing on question-answering learning activities, the current research investigates the effect of QA systems on students' learning processes and outcomes, as well as the interplay between two QA channels, that is, QA systems and communication with instructors.

The authors designed and implemented a QA system for two university courses, and collected data from questionnaires and system logs that recorded the interaction between students and the system throughout a semester.

The results show that using a QA system alone does not improve students' learning processes or outcomes. However, the use of a QA system significantly improves the positive effect of instructor communication.

This study contributes to the literature on learning and education technology, and provides practical guidance on how to incorporate QA tools in learning.

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

This chapter examines the relationship between directors and officers (D&O) liability insurance and stock-price synchronicity by testing competing corporate governance-related monitoring and moral hazard-related agency conflict hypotheses. Testing a sample of stocks listed on the Taiwan Stock Exchange and the Taipei Exchange for 2008–2020, the empirical results of this study indicate that D&O insurance in Taiwan is negatively correlated to stock-price synchronicity. This negative relation is robust to a battery of tests, including those of fixed-effects regression models, alternative sample periods, alternative synchronicity measures, and alternative insurance measures. Further evidence indicates that this negative relationship is more pronounced among firms with greater agency problems, especially during periods of high market uncertainty. Overall, these findings support the corporate governance-related monitoring hypothesis, which posits that firms with greater D&O insurance are likelier to be characterized by better governance structures and information transparency. Additionally, their stock prices are more likely to reflect firm-specific information in a timely and precise manner, and they are more likely to have lower synchronicity with the industry and market.

This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique.

Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC-based real-time ILC.

Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers.

The proposed IPSO-ILC-PID controller overcomes the shortcomings of conventional ILC-PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO-ILC-PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO-ILC-PID algorithm is effective and achieves better control in real-time precision positioning.

This study aims to examine university students’ acceptance of e-learning systems in Pakistan. A Web-based learning system is a new form of utilizing technological features. Although, developed countries have initiated and established the concept for e-learning, developing countries require empirical support to implement e-learning.

This paper further explains a conceptual model that is based on the technology acceptance model (TAM). Earlier theories such as the theory of reasoned action (TRA), theory of planned behaviour (TPB) and decomposed theory of planned behaviour (DTPB) have been conducted on user behavioural intention (BI). TAM is considered as the most relevant framework in a Web-based context. To analyse the present study’s hypothesized model, structural equation modelling (SEM) has been used to statistically analyse self-reported sample data from 424 university students.

The results revealed that TAM, with the combination of new constructs, explains university students’ acceptance of the e-learning system reasonably well. Additionally, work life quality (WLQ) and facilitating conditions (FC) have a greater influence on the BI and the actual use (AU) of the e-learning system, respectively.

The study has also provided valuable implications for academics and practitioners for ways to enhance the acceptance of the e-learning system in the higher education of Pakistan.

This paper aims to explore the relationship between human resource attributes and the operating performances of accounting firms by sampling data from the 2012-2013 Survey Report on Accounting Firms, as compiled by the Financial Supervisory Commission in Taiwan.

Multiple regression analysis is conducted to measure operating performances with various measurements, such as operating profits and business diversification. The independent variables include male to female ratio, percentage of senior executives, percentage of employees with higher education backgrounds, organizational vitality, human resource diversity, percentage of employees with certified public accountant (CPA) qualifications and human resource costs (HRCs). The control variables are the firm history, market shares and ownership structures since the inception of the firms.

The empirical results regarding the operating profits model suggest that the higher the male to female ratio, the percentage of employees with higher education backgrounds, organizational vitality, human resource diversity, percentage of employees with CPA qualifications and HRCs, the greater the operating profits. Meanwhile, the findings regarding the business diversification model indicate that the higher the male to female ratio, percentage of senior executives and human resource diversity, the greater the business diversification.

It is intended that the research findings can assist the management of accounting firms to understand the human resource attributes critical to operating performances, which will help to enhance the competitiveness of employees, mitigate the operating risks and improve the operating performances of the firms.