Search results

This study explores the awareness (AWN) levels of good governance amongst Thai credit union cooperatives' (CUCs) members and the factors hindering good governance practice in Thai CUCs.

This study used a survey questionnaire from 629 members of 36 selected CUCs in Thailand. This study analysed the determinants of governance AWN levels of Thai CUCs' members using the ordered probit model. The study also employs OLS estimation to investigate the factors hindering good governance practices.

The study shows that members of different CUC types and sizes have different levels of governance AWN. Members' characteristics, experiences, and perceptions significantly influence CUC members' AWN of governance issues. The findings also suggest that a lack of morality, transparency, participation, responsibility and accountability are key obstacles that hinder good governance practices of Thai CUCs.

This is the first study that attempts to assess the level of AWN amongst Thai CUCs' members in different CUC sizes and types. This is also the first research that identifies the factors that hinder good governance practice in Thai CUCs based on members' evaluations. The study's findings provide important reference and implications for Thai policy makers and CUCs' board of managers to enhance members' AWN and CUCs' governance performance, and thus increase income and living standard of CUCs' members in the long term.

Focusing on micro-level indicators, we investigate financial inclusion levels in rural China, examining its determinants and impact on household welfare. We construct a financial inclusion index of four essential financial services: savings, digital payments, credit and insurance. We identify factors influencing financial inclusion among Chinese rural households and assess the effects of financial inclusion on household welfare.

With the entropy method, we use data from the 2019 China Household Finance Survey to assess financial inclusion levels in rural China. Determinants and their impact on welfare are analyzed through probit and ordinary least squares models, respectively. Propensity scoring matching is applied to address potential endogeneity.

We reveal that rural households exhibit limited usage of formal financial services, with notable regional disparities. The eastern region enjoys the highest financial inclusion and the central region lags behind. Household characteristics such as family size, education level of the household head, income, employment status and financial literacy significantly influence financial inclusion. Financial inclusion positively impacts household welfare as indicated by household consumption expenditure. The use of different types of financial services is crucial with varying but significant effects on household welfare.

This study offers valuable insights into China’s rural financial inclusion progress, highlighting potential barriers and guiding government actions.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

Violence toward frontline health-care workers (HCWs) from patients and visitors is a pervasive issue that ranges from verbal and psychological abuse to physical assault. The emergence of the COVID-19 pandemic has led to increased reports of escalated verbal workplace aggressions (VWPAs); however, most studies have been conducted internationally. Studies based in the USA have focused on physical violence experienced by nurses and paramedics in emergency situations. The purpose of this study is to learn about the experiences of different levels of frontline HCWs with VWPA from patients and visitors and discover ways to address this issue.

This qualitative descriptive study asked registered nurses, licensed practical nurses and patient care technicians from one health-care system about their experiences with patient and visitor VWPA using an anonymous, voluntary open-ended survey and in-person interviews. In all, 31 participants completed the survey and 2 were interviewed. Data were analyzed using content analysis.

Three themes emerged from the data: the experience, moving through and moving forward. Frontline HCWs described experiences of VWPA, indicating its forms, frequency and conditions. They used coping, along with personal and professional measures, to manage and move through the situation. Moving forward was captured as suggestions for the future and conveyed hope for a perfect state.

The experiences of frontline HCWs offered insight into how they perceive and cope with difficult encounters. Recommendations relate to not only implementing interventions that support frontline HCWs but also creating a culture where aggression is not tolerated and addressing perpetrator behavior is a priority.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

This study adopts the perspective of dynamic capabilities to investigate influencing factors and proposes improvement strategies of supply chain resilience of prefabricated construction.

The structural equation model (SEM) is used to identify and verify the relationship between factors influencing supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. The system dynamics (SD) model is constructed to dynamically simulate the specific effects of different influencing factors.

Results indicate that: (1) An evaluation index system for supply chain resilience of prefabricated construction containing five first-level indicators and 36 second-level indicators is constructed; (2) Ability to anticipate, ability to respond, ability to adapt, ability to recover and ability to learn are positively correlated with the supply chain resilience of prefabricated construction and (3) ANT3 (information system), RES1 (quick response), ADA3 (buffer stock) and LEA4 (trust) are the most leading factors influencing supply chain resilience of prefabricated construction over time.

This study fulfills the need for an in-depth exploration of the various influencing factors on supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. Furthermore, this study provides improvement strategies to enhance supply chain resilience of prefabricated construction in China.

This paper aims to discuss innovations in the training and development practices of companies and delineate a new approach to training and development in the context of the hybrid workplace using the ADDIE and Kirkpatrick training models.

This paper discusses innovations in training and development in modern times and builds on the instructional training design approach or the ADDIE Model and the Kirkpatrick Model of training evaluation.

The paper presents new approaches to training and development in the context of the hybrid work model applying the ADDIE Model and the Kirkpatrick Model. These new approaches are both necessitated and also made possible due to the technological advancements of modern times.

With the rapid transition of companies to the hybrid model of work in recent times, several human resource management practices need to be transformed to suit the requirements of the new work model. Training and development is one function that needs to change in the hybrid work model to ensure its effectiveness. This paper analyses innovations in the training and development practices of companies and discusses new approaches while applying existing training models, the ADDIE and Kirkpatrick Models, to adapt to the changes associated with the hybrid work model.

The onset of a health crisis has substantially crippled the hotel industry, causing employees' fears of an imminent job loss. This study investigates how hotel employees' perceived job insecurity affects work engagement and psychological withdrawal behavior. Additionally, it explores the mediating role of work engagement between job insecurity and psychological withdrawal behavior, along with examining the moderating effects of employee resilience on the links between job insecurity, work engagement and psychological withdrawal behavior.

Adopting simple random sampling, a total of 357 completed questionnaires by Thai frontline hotel employees in 36 four- and five-star international hotel chains in Phuket, Thailand, were analyzed. Data analyses were undertaken by SPSS version 25.0 and partial least squares structural equation modeling (PLS-SEM) version 4.0.9.1.

Results indicate that perceived job insecurity diminishes work engagement and leads to psychological withdrawal behavior. Work engagement is found to partially mediate the connection between job insecurity and psychological withdrawal behavior. Furthermore, employee resilience lessens the impact of job insecurity on work engagement while reinforcing the link between work engagement and psychological withdrawal behavior.

The study findings offer valuable practical implications, illustrating how Thai hospitality firms can cultivate effective talent management practices to develop and enhance employees' skills, engagement and enthusiasm in their work. These practices can assist employees in coping with and managing their perceptions of job insecurity during turbulent times and uncontrollable crises.

This study creates a compelling framework to elucidate the connections among COVID-19-intensified job insecurity, work-related outcomes and personal factors. It introduces a previously underexamined perspective that enriches the authors' theoretical comprehension of how personal resources, like employee resilience, serve as protective factors, shaping employee behavior and performance amidst job insecurity. Moreover, the study advocates for a synthesizing approach, emphasizing the integration of various theoretical perspectives and past literature, particularly when research gaps cannot be sufficiently addressed by a single theory.

This paper aims to straighten out the research progress in the field of maker education, summarize the research hotspots and frontiers of maker education at home and abroad and provide path optimization suggestions for the research and development of this field.

In total, 751 pieces of domestic and the foreign maker education research literature from 2014 to 2021 are retrieved and screened, and literature analysis methods such as keyword analysis and clustering map analysis are used to quantitatively analyze the quantity distribution, published journals, core authors, research institutions and subject keywords of the maker education literature.

It is found that research in this field is still in the development stage, but the pandemic has severely inhibited maker education and related research. Frontiers at home and abroad have begun to pay attention to the impact of humanistic care on maker education. Strengthening the dialog between multidisciplinary theories requires cross-disciplinary research. Regional and cross-field cooperation and fully grasping the actual situation and constraints of the development of maker education are the cornerstones of bold innovation in maker education research.

This paper uses bibliometric analysis to reveal the severe challenges to the development of maker education due to the normalization of the epidemic. By excavating the research hotspots and research frontiers in this field, it fills the gap that the current research in the field of maker education has not yet formed a complete theoretical framework and evaluation system.