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In the COVID-19 outbreak periods, people's life has been deranged, leading to disrupt the world. Firstly, the number of deaths is growing and has the potential to surpass the highest level at any time. Secondly, the pandemic broke many countries' fortified lines of epidemic prevention and gave people a more honest view of its seriousness. Finally, the pandemic has an impact on life, and the economy led to a shortage in medical, including a lack of clinicians, facilities and medical equipment. One of those, a simple ventilator is a necessary piece of medical equipment since it might be useful for a COVID-19 patient's treatment. In some cases, the COVID-19 patients require to be treated by modern ventilators to reduce lung damage. Therefore, the addition of simple ventilators is a necessity to relieve high work pressure on medical bureaucracies. Some low-income countries aim to build a simple ventilator for primary care and palliative care using locally accessible and low-cost components. One of the simple principles for producing airflow is to squeeze an artificial manual breathing unit (AMBU) iterative with grippers, which imitates the motion of human fingers. Unfortunately, the squeezing angle of grippers is not proportional to the exhaust air volume from the AMBU bag. This paper aims to model the AMBU bag by a mathematical equation that enables to implement on a simple controller to operate a bag-valve-mask (BVM) ventilator with high accuracy performance.

This paper provides a curvature function to estimate the air volume exhausting from the AMBU bag. Since the determination of the curvature function is sophisticated, the coefficients of the curvature function are approximated by a quadratic function through the experimental identification method. To obtain the high accuracy performance, a linear regression model and a least square method are employed to investigate the characteristic of the BVM ventilator's grippers angle with respect to the airflow volume produced by the AMBU bag.

This paper investigates the correlation between the exhausting airflow of the AMBU bag and the grippers angle of the BVM ventilator.

The experimental results validated that the regression model of the characteristic of the exhausting airflow of the AMBU bag with respect to the grippers' angle has been fitted with a coefficient over 98% within the range of 350–750 ml.

This chapter explores the use of Lesson Study (LS) as a strategy for co-constructing pedagogical knowledge and draws on data from a series of interviews with student teachers. Sixteen student teachers, undertaking a postgraduate teacher training program in higher education in England, engaged in LS as an official assessment of their ability to jointly plan, deliver, and evaluate a lesson. LS is thus seen to promote an intense collaborative working relationship between participating student teachers that engenders fresh opportunities for learning. It is argued, then, that this approach can challenge the prevalent model of individually led professional development by facilitating a space for the co-construction of pedagogical knowledge. LS is also explored for its potential to bridge the theory-practice divide by enabling participant student teachers to generate theory from practice.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.

This paper aims to measure the intention to use digital marketing strategies to enhance the performance of tourism business as well as the extent of digital renovation applications in tourism for sustainable business in a new normal era.

This paper is an insight from the existing relevant literature on the tourism business from time immemorial. The conceptual framework of this study is designed based on previous studies of digital marketing practices for tourism businesses. Furthermore, data were collected from 270 respondents, of which the valid response rate is 72.97%. Partial least square (PLS)-structural equation modeling (SEM) is used to validate the conceptual framework and hypotheses testing.

Among the nine hypotheses path, seven were supported. This study result shows that perceived usefulness, perceived ease of use, social media marketing and tourism business performance are critical factors for adopting digital marketing in tourism. Thus, tourism service providers' intention has a positive impact to meet the expectation of tourists and adoption of digital marketing.

The study's results will assist tourism researchers and service providers in understanding an authentic relationship between digital practices of tourism business and tourist satisfaction. In addition, the legacy of tourism business through digital marketing empowers the owner and community.

The study is the first to explore the relationship between tourism business performance and digital marketing during the new normal era for the empowerment of local community and expanded business in tourism sector.

With the emergence of coronavirus disease 2019 (COVID-19), the usage of e-money has been reinforced to reach the next level. Therefore, this study aims to examine the mediating role of perceived behavioral control (PBC) on the nexus of customers' innovativeness and continuance intention of electronic money (e-money). This study also explores the moderating roles of perceived risk (PR) and electronic security (e-security) in relationships.

The authors employed a structured questionnaire for data collection and the partial least squares structural equation modeling (PLS-SEM) for empirical estimations.

The authors' findings reveal that customers' innovativeness promotes continuance intention of using e-money and demonstrate that PBC partially mediates the relation between customers' innovativeness and continuance intention of using e-money. The empirical findings also reveal that PR negatively moderates the relationship between customers' innovativeness and continuance intention and the relationship between customers' innovativeness and PBC. The empirical findings also exhibit that perceived e-security enhances the degree of the relationship between customers' innovativeness and continuance intention and the relationship between customers' innovativeness and PBC.

The findings shed light on an important factor that increases the likelihood of repeat e-money usage and has direct managerial implications for customer experience and risk concerns. Hence, the findings imply that e-money service providers should run a promotional advertisement highlighting what additional features are included or offered and how these could be beneficial for the customers. Furthermore, e-money service providers should provide some tutorial videos in order to increase innovative customers' control over e-money services as well as highlight how risk and security are protected.

This paper integrates three key theories: the diffusion of innovation (DOI) theory, the theory of planned behavior (TPB) and the PR theory in post-adoption behavior of e-money usage. The current study also attempts to fill a literature gap by examining the moderating role of PR and e-security, which could be useful within the relationship between customers' innovativeness, PBC and customers' continued intentions of e-money usage.

This study aims to analyze the factors affecting tea-producing farmers' intention to use e-commerce exchanges (ECEs) to sell their products, combining the technology acceptance model (TAM) theory and barrier factors.

The authors use the generalized structural equation modeling (GSEM) to analyze the intermediate model that is built on TAM.

Research results show that perceived usefulness (PU) and perceived ease of use (PEU) significantly influence farmers' intention to use ECEs to sell their products. However, knowledge and information barriers hinder farmers' intention to use such ECEs.

Encouraging farmers to use ECEs is the most helpful solution for agricultural economic development in the context of the Covid-19 pandemic.

From an academic perspective, this is the first study that combines the TAM theory of Davis (1989) and barrier factors to analyze farmers' intention to use ECEs. The findings are valuable references for policymakers to propose strategies for agricultural economic development during the current pandemic. At the same time, the empirical results obtained from this study provide good orientations for agricultural economic development 4.0 in the future.

The current study employs a modified framework of the unified theory of acceptance and use of technology (UTAUT) to predict students' intention to adopt online learning in India. The moderating role of openness to change in influencing the proposed relationships is also assessed.

A structured questionnaire was emailed to 650 students enrolled in various courses in public and private universities in India. In total 424 responses were considered for analysis using structural equation modeling (SEM). Moderation analysis was carried out with multi-group SEM and chi-square difference tests.

The results reveal that there is a significant impact of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FS) and perceived security (PS) on students' intention to adopt online learning. Further, openness to change moderates the impact of PE, FS and PS on intention of students to opt for online learning.

This study is one of the initial efforts to examine the factors affecting students' intention to adopt online learning at the onset of third wave of coronavirus disease 2019 (COVID-19) pandemic in India. Besides the factors of the UTAUT model, this study highlights the importance of PS and openness to change in influencing students' intention to opt for online learning.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).