This research explores the “transformation” ideas of Japanese Lesson Study (LS) and Open Approach (OA) to create and sustain a Thailand LS incorporated OA (TLSOA) model to…
Abstract
Purpose
This research explores the “transformation” ideas of Japanese Lesson Study (LS) and Open Approach (OA) to create and sustain a Thailand LS incorporated OA (TLSOA) model to successfully adapt to the local contexts. Although LS is spreading globally, previous studies have identified several challenges to its implementation.
Design/methodology/approach
The researcher employed a longitudinal research design that involved repeated investigations of a group of participants: from their fourth year as bachelor's degree students until they became eligible coordinators to practice the TLSOA model for teachers' professional development (PD). Data were collected using reflective journals, two types of survey questionnaires, and records of periodical reflective meetings over three cohorts.
Findings
As results reveal, the participating teachers' active engagement in the TLSOA model has made a positive impact on their teaching practices, collegiality, and professional self-identification. Students perceived themselves as having enormous changes in their learning behaviors. Those changes are linked to establishing a positive, student-centered, and active learning-based school culture with teachers' beliefs for innovations.
Research limitations/implications
Further studies should focus on the possible conflicts emerging between the different cultures of teaching.
Practical implications
The idea of the TLSOA model is to ensure teachers are well trained to possess sufficient skills.
Originality/value
The findings could be of value for the leaders, educators, policymakers to advocate the TLSOA model as a systematic approach to whole-school improvement and as a channel for spreading effects at the national, the APEC, and the CLMV regional levels.
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En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…
Abstract
Purpose
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.
Design/methodology/approach
A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.
Findings
Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.
Originality/value
In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.
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Syed Haroon Rashid, Mohsin Sadaqat, Khalil Jebran and Zulfiqar Ali Memon
This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of…
Abstract
Purpose
This study aims to investigate the market timing strategy in different market conditions (i.e. up, down, normal and in-financial-crisis situation) in the emerging market of Pakistan over the period 1995 to 2015. Furthermore, this study tests the validity of the capital asset pricing model (CAPM) and Fama and French model.
Design/methodology/approach
This study considers monthly stock returns of 167 firms and constructs six different portfolios on the basis of different size and book to market ratio. The Treynor and Mazuy model is used to capture the market timing strategy.
Findings
The results indicate evidence of the market timing in normal market conditions. However, there is less supportive evidence of market timing in up-market, down-market and in-financial-crisis situations. This study also confirms the validity of the capital asset pricing model and Fama and French three-factor model with strong support of value premium and size premium in the stock market.
Practical implications
The findings of this study are helpful to companies in estimating the cost of issuing equity more accurately. The investors can use market timing to make their investment in a more better and profitable manner.
Originality/value
Unlike other previous studies, this study considers an extended period to test the validity of the capital asset pricing model and Fama and French model. In addition, this study is novel in testing the marketing timing of the firms in the context of emerging economy of Pakistan.
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Babak Lotfi and Bengt Ake Sunden
This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice…
Abstract
Purpose
This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice structure topology (simple cubic, body-centered cubic, z-reinforced body-centered cubic [BCCZ], face-centered cubic and z-reinforced face-centered cubic [FCCZ] lattice structures) and porosity value ( ) – on the thermal-hydraulic characteristics of the novel trussed fin-and-elliptical tube heat exchanger (FETHX), which has led to a deeper understanding of the superior heat transfer enhancement ability of the PCL structure.
Design/methodology/approach
A three-dimensional computational fluid dynamics (CFD) model is proposed in this paper to provide better understanding of the fluid flow and heat transfer behavior of the PCL structures in the trussed FETHXs associated with different structure topologies and high-porosities. The flow governing equations of the trussed FETHX are solved by the CFD software ANSYS CFX® and use the Menter SST turbulence model to accurately predict flow characteristics in the fluid flow region.
Findings
The thermal-hydraulic performance benchmarks analysis – such as field synergy performance and performance evaluation criteria – conducted during this research successfully identified demonstrates that if the high porosity of all PCL structures decrease to 92%, the best thermal-hydraulic performance is provided. Overall, according to the obtained outcomes, the trussed FETHX with the advantages of using BCCZ lattice structure at 92% porosity presents good thermal-hydraulic performance enhancement among all the investigated PCL structures.
Originality/value
To the best of the authors’ knowledge, this paper is one of the first in the literature that provides thorough thermal-hydraulic characteristics of a novel trussed FETHX with high-porosity PCL structures.
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Martina Neri, Federico Niccolini and Luigi Martino
Cyberattacks are becoming increasingly widespread, and cybersecurity is therefore increasingly important. Although the technological aspects of cybersecurity are its best-known…
Abstract
Purpose
Cyberattacks are becoming increasingly widespread, and cybersecurity is therefore increasingly important. Although the technological aspects of cybersecurity are its best-known characteristics, the cybersecurity phenomenon goes beyond the detection of technological impacts, and encompasses all the dimensions of an organization. This study thus focusses on an additional set of organizational elements. The key elements of cybersecurity organizational readiness depicted here are cybersecurity awareness, cybersecurity culture and cybersecurity organizational resilience (OR). This study aims to qualitatively assess small and medium enterprises’ (SMEs) overall level of organizational cybersecurity readiness.
Design/methodology/approach
This study focused on conducting a cybersecurity organizational readiness assessment using a sample of 53 Italian SMEs from the information and communication technology sector. Informed mixed method research, this study was conducted consistent with the principles of the explanatory sequential mixed method design, and adopting a quanti-qualitative methodology. The quantitative data were collected through a questionnaire. Qualitative data were subsequently collected through semi-structured interviews.
Findings
Although many elements of the technical aspects of cybersecurity OR have yielded very encouraging results, there are still some areas that require improvement. These include those facets that constitute the foundation of cybersecurity awareness, and, thus, a cybersecurity culture. This result highlights that the areas in need of improvement are exactly those that are most important in fighting against cyber threats via organizational cybersecurity readiness.
Originality/value
Although the importance of SMEs is obvious, evidence of such organizations’ attitudes to cybersecurity are still limited. This research is an attempt to depict the organizational issue related to cybersecurity, i.e. overall cybersecurity organizational readiness.
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Sheila Namagembe and Shamim Nantumbwe
Environmental emissions are increasing in the urban areas. Much of the emissions arise from public procurement activities given that public sector firms are major customers to…
Abstract
Purpose
Environmental emissions are increasing in the urban areas. Much of the emissions arise from public procurement activities given that public sector firms are major customers to many supplying firms. Given the tremendous contribution, this study aims to examine the adoption of environmentally friendly urban freight logistics practices among public sector firms through assessing the impact of urban environmental governance, government environmental communication and organizational environmental governance.
Design/methodology/approach
Data for the study were collected in a single time period from central procuring and disposing entities (public sector firms) in the urban areas. A sample of 105 public sector firms in were used. One procurement officer and one member of the contracts committee were the key informants in the study. AMOS SPSS version 26 was used to obtain the results for the structural model and measurement model, respectively.
Findings
The findings indicate that the adoption of environmentally friendly urban freight logistics practices among public sector firms is significantly influenced by government environmental communication, organizational environmental governance and urban environmental governance. Urban environmental governance significantly influences organizational environmental governance. Urban environmental governance fully mediates the relationship between government environmental communication and public sector firms’ adoption of environmentally friendly urban freight logistics practices. Also, urban environmental governance and organizational environmental governance mediate the relationship between government environmental communication and adoption of environmentally friendly urban freight logistics practices.
Research limitations/implications
This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. However, the study was conducted in a public procurement setting rather than a private sector procurement setting. Also, the study examined the impact of government environmental communication on public sector firms’ adoption of environmentally friendly urban freight logistics practices ignoring the impact of internal communications made within the public sector firms on environmental issues.
Originality/value
This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. Freight logistics in public sector procurement has not been given significant attention in earlier research. Emphasis is placed on sustainable public sector procurement ignoring other aspects that would help curb environmental emissions that may arise during and after the delivery of public procurement requirements.
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Syed Ali Raza, Komal Akram Khan and Bushra Qamar
The research analyzes the influence of three environmental triggers, i.e. awareness, concern and knowledge on environmental attachment and green motivation that affect tourists'…
Abstract
Purpose
The research analyzes the influence of three environmental triggers, i.e. awareness, concern and knowledge on environmental attachment and green motivation that affect tourists' pro-environmental behavior in the Pakistan’s tourism industry. Furthermore, this study has analyzed the moderating role of moral obligation concerning environmental attachment and green motivation on tourists' pro-environmental behavior.
Design/methodology/approach
Data were gathered via a structured questionnaire by 237 local (domestic) tourists of Pakistan. Furthermore, the data were examined by employing SmartPLS.
Findings
Findings demonstrate that all three environmental triggers have a positive and significant relationship with environmental attachment and green motivation. Accordingly, environmental attachment and green motivation promote tourists' pro-environmental behavior. Furthermore, the moderating role of moral obligations has also been incorporated in the study. The finding reveals a strong and positive relationship among environmental attachment and tourists' pro-environmental behaviors during high moral obligations. In contrast, moral obligations do not moderate association between green motivation and tourists' pro-environmental behavior. Therefore, competent authorities should facilitate tourists to adopt environmentally friendly practices; which will ultimately promote pro-environmental behavior.
Originality/value
This study provides useful insights regarding the role of tourism in fostering environmental attachment and green motivation that sequentially influence tourist pro-environmental behavior. Secondly, this research has employed moral obligations as a moderator to identify the changes in tourists’ pro-environmental behavior based on individuals' ethical considerations. Hence, the study provides an in-depth insight into tourists' behavior. Lastly, the present research offers effective strategies for the tourism sector and other competent authorities to increase green activities that can embed the importance of the environment among individuals.
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Charles Alba and Manasvi M. Mittal
Over the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care…
Abstract
Purpose
Over the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.
Design/methodology/approach
This is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.
Findings
The results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.
Research limitations/implications
The authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.
Practical implications
The authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.
Originality/value
The results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.
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Morné Owen, Stephen V. Flowerday and Karl van der Schyff
Researchers looking for ways to change the insecure behaviour that results in phishing have considered multiple possible reasons for such behaviour. Therefore, the purpose of this…
Abstract
Purpose
Researchers looking for ways to change the insecure behaviour that results in phishing have considered multiple possible reasons for such behaviour. Therefore, the purpose of this paper is to understand the role of optimism bias (OB – defined as a cognitive bias), which characterises overly optimistic or unrealistic individuals, to ensure secure behaviour. Research that focused on issues such as personality traits, trust, attitude and Security, Education, Training and Awareness (SETA) was considered.
Design/methodology/approach
This study built on a recontextualized version of the theory of planned behaviour to evaluate the influence that optimism bias has on phishing susceptibility. To model the data, an analysis was performed on 226 survey responses from a South African financial services organisation using partial least squares (PLS) path modelling.
Findings
This study found that overly optimistic employees were inclined to behave insecurely, while factors such as attitude and trust significantly influenced the intention to behave securely.
Practical implications
Our contribution to practice seeks to enhance the effectiveness of SETA by identifying and addressing the optimism bias weakness to deliver a more successful training outcome.
Originality/value
Our study enriches the Information Systems literature by evaluating the effect of a cognitive bias on phishing susceptibility and offers a contextual explanation of the resultant behaviour.
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Roberto De Luca, Antonino Ferraro, Antonio Galli, Mosè Gallo, Vincenzo Moscato and Giancarlo Sperlì
The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment �…
Abstract
Purpose
The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.
Design/methodology/approach
In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.
Findings
The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.
Research limitations/implications
A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.
Practical implications
The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.
Originality/value
The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.