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This study aims to identify effective behaviors in labor-management negotiation (LMN) and, on that basis, derive overarching psychological principles of successful negotiation in this important context. These empirical findings are used to develop and test a comprehensive negotiation training program.

Twenty-seven practitioners from one of the world’s largest labor unions were interviewed to identify the requirements of effective LMN, resulting in 796 descriptions of single behaviors from 41 negotiation cases.

The analyses revealed 13 categories of behaviors critical to negotiation success. The findings highlight the pivotal role of the union negotiator by illustrating how they lead the negotiations with the other party while also ensuring that their own team and the workforce stand united. To provide guidance for effective LMN, six psychological principles were derived from these behavioral categories. The paper describes a six-day training program developed for LMN based on the empirical findings of this study and the related six principles.

This paper has three unique features: first, it examines the requirements for effective LMN based on a systematic needs assessment. Second, by teaching not only knowledge and skills but also general psychological principles of successful negotiation, the training intervention is aimed at promoting long-term behavioral change. Third, the research presents a comprehensive and empirically-based training program for LMN.

This study examines the case of an initial training, called “Dojo”, invented and implemented at a production site in the Czech Republic. It clarifies the initial training program implementation process and offers a conceptual framework for cooperative management of subsidiary activities at the site and firm.

This study conducts an in-depth analysis of qualitative data from the Czech production site over a five-year period. The theoretical base is the theorization and labeling phase of management innovation (MI), the final phase of which legitimizes a new management practice. Interview data, archival data, pictures and financial data are used for the analysis.

To legitimize the Dojo in the operational flow controlled by the site and firm, the Czech production site acquires validation of the Dojo from employees and board members of the Japanese and European headquarters, helping the site build trustful relationships with them. Training programs, process standardization and skills standardization of the workers offer benefits to the trainees, production site and firm.

The authors offer theoretical insights into MI at the subsidiary-level, which past studies have not differentiated at the firm-level. The authors also provide details of the implementation and management of initial training for newly hired blue-collar workers at the production site. The findings complement related literature on human resource management and operational management.

Open and distance e-learning (ODeL) practices have substantial contributions to make in achieving societal development goals. The challenge however remains with enhancing skilling, training and educating professionals who will contribute to this progress. The purpose of this paper is to illustrate how transformative education and training in global health can be undertaken through ODeL in increasing the quality, quantity and relevance of health professional education and training.

This paper is based on a descriptive qualitative case study of the International Health and Development Course offered by the University of the Philippines Open University and is thus limited in its scope from other courses in the program.

Transformative education and training through ODeL has the potential of increasing the quality, quantity and relevance of health professionals training. However more critical assessment of transformative learning outcomes is needed via rigorous methods of objectifying such outcomes. Achieving transformative health education and training requires rigorous engagement in constructivist-oriented experiential learning that allow learners to be accustomed to significant interactions achieved by involvement in problem-based methods accomplished through small group e-tivities in order to demonstrate applicability in the real work context.

The outcome of this paper is relevant to institutions in Asia that offer ODeL-based global health programs through open knowledge systems in order to produce graduates who are more responsive to the evolving health needs amid twenty-first century global health challenges.

The purpose of this paper is to explore the scope for action of local human resource managers, who are employed in foreign subsidiaries of multinational companies (MNCs), for implementing training activities. These managers are situated in relationships to headquarters and the local environment. Related to this is the question whether MNCs contribute to the local skill base by implementing training activities or whether they exploit the existing skill formation system.

This study focusses on German subsidiaries of MNCs with headquarters in the USA and the UK, France, China and Japan. The study is based on 107 expert interviews with subsidiary managers and representatives of local stakeholder organisations, such as educational organisations, chambers, economic promotion agencies and governmental bodies in Germany.

The study reveals that headquarters introduce general schemes for training. In addition to these MNC-internal trainings, local managers use their information advantage over headquarters to implement dual training activities.

The training activities of subsidiaries are dependent on the institutional settings of the host country.

Albeit dual training activities are laborious and tie the local managers down for the medium and long term, the future need of the subsidiary for adequately skilled workforce prompts local managers’ engagement in implementing dual training activities.

Subsidiaries contribute to the local skill base and do not act in a free-rider position, at least in the German variety of capitalism.

The study deepens insights on distanced relations within and how subsidiaries generate scope for action by using this kind of relationships.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

The purpose of this paper is to conduct systematic reviews on Indonesian papers, to examine the most recent evidence of the efficacy of the combination of cognitive training and physical exercise, and to make recommendations in order to improve prevention, care and treatment services in elderly patients with mild cognitive impairment (MCI).

The databases of Cochrane, Medline, NIH (US National Library Medicine), ProQuest, EbscoHost, Clinical Key, EMBASE, Medical Librarian (TWE) in Ovid, Science Direct, Scopus, The Lancet Global Health, PubMed, Emerald, Indonesian National Library, Google Scholar, Google Indonesia, and Garuda Portal were systematically searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to obtain empirical papers published between June 1976 and January 2018.

Out of the 3,293 articles collected, 10 were included in this analysis. The result of this combined meta-analysis compares the combination therapy group (cognitive therapy and physical exercise) with a control group. It shows that the control group was likely to experience MCI 1.65 times more often than the combination therapy group. According to the result acquired from the synthesized meta-analysis, the control group experienced MCI 1.65 times higher than the combination therapy. The finding is proven to be statistically significant (95% CI= 1.42–1.93).

The research considers only English and Indonesian articles.

It is important to explore the most effective training characteristics in a special combined intervention differentiated by the duration, frequency, intervention, type and combination mode. There is a need for further investigation that focuses on the physiological mechanisms underlying the positive effects, by inserting a more comprehensive neuro-imaging measurement to assess specifically the domain that benefits in terms of cognitive functions and molecular markers. Finally, exploratory studies are definitely required, which will specifically examine maintenance and treatment effects as well as derive theoretical explanations related to the interventions and predictors.

A combination of cognitive training and physical exercise intervention may improve the global health or cognitive functions.

A combination of cognitive training and physical exercise has been found to improve prevention, care and treatment services in elderly patients with MCI. There is an increase in value in comparison to the study of Karssemeijer, which considered five Indonesian articles.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

The purpose of this paper is to explore the work practices of Big 4 firms in Bangladesh with the aim of exploring the extent to which global professional service firms (GPSFs) can be thought of as being genuinely “global”.

Interviews were undertaken with the vast majority of Big 4 partners in Bangladesh. These interviews explored a number of themes related to the professional service work context in Bangladesh and the relationship between local and global firms.

The central finding of this paper is that although the Big 4 have a long-established presence in Bangladesh, local societal factors heavily influence the realities of work for accountants there. In most cases the Big 4 firms establish correspondent firms (instead of full member firms) in Bangladesh and tend to offer restricted service lines. Additionally, the paper identifies professional, commercial and cultural barriers to greater Big 4 involvement in the local market. Conceptually, the chief contribution of this paper is to explore how the effects of globalizing capitalism and standardised “best practices” in global professional service work are mediated through the societal effects of Bangladeshi society, resulting in the Big 4 having only a tentative presence in the Bangladeshi market.

The findings cast doubt on the extent to which self-styled GPSFs are truly “global” in nature. Future work examining the Big 4, or accounting more generally, in the context of globalization, would do well to pay greater attention to the experience of professionals in emerging markets.

Whilst there has been much work looking at accounting and accountants in the context of globalization, this work has tended to privilege “core” western empirical settings. Very little is known about professional service firms in “peripheral” emerging markets. Furthermore, this study extends the application of the system, society and dominance framework by mapping the interactions and dynamics of these three sources of influence in the setting of PSFs.

Information is scarce on healthcare managers' understanding of simulation educators' impact on clinical work. Therefore, the aim of this study was to explore healthcare managers' perceptions of the significance of clinically active simulation educators for the organisation.

Healthcare managers were invited to be interviewed in a semi-structured manner. Inductive thematic analysis was used to identify and analyse patterns of notions describing the managers' perceptions of simulation educators' impact as co-workers on their healthcare organisations.

The identified relevant themes for the healthcare unit were: (1) value for the manager, (2) value for the community and (3) boundaries. Simulation educators were perceived to be valuable gatekeepers of evidence-based knowledge and partners in leadership for educational issues. Their most prominent value for the community was establishing a reflective climate, facilitating open communication and thereby improving the efficacy of teamwork. Local tradition, economy, logistics and staffing of the unit during simulation training were suggested to have possible negative impacts on simulation educators' work.

The findings might have implications for the implementation and support of simulation training programs.

Healthcare managers appreciated both the personal value of simulation educators and the effect of their work for their own unit. Local values were prioritised versus global. Simulation training was valued as an educational tool for continual professional development, although during the interviews, the managers did not indicate the importance of employment of pedagogically competent and experienced staff.

The study provided new insights about how simulation educators as team members affect clinical practice.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.