ORGANIZATIONAL FACTORS AND ETHICAL DECISION-MAKING PROCESS DURING THE COVID-19

Aim. The researcher's goal is to investigate how organizational characteristics affect the process of making ethical decisions. Methods. The study's focus was on certified accountants working in Kosovo during the Covid19 era. The task is qualitative in nature;SPPS is used to process the data after Excel has done so. This study used descriptive and regression analysis. Results. The study demonstrated that organizational criteria like size, industry, the presence of an ethics code, and ethical climate have an impact on how decisions are made. To identify organizational elements and, as a result, improve the ethical decision-making process, the paper's findings may have policy implications. Conclusions. This study provided insight into how to increase the influence of organizational elements in ethical decision-making, particularly in the accounting field. A sound foundation for an ethical decision-making process that is sustainable is provided by the recommendations at the end of the paper. © 2023, Institute for Human Rehabilitation. All rights reserved.

Chinese travellers' mobility decision-making processes during public health crisis situations: a Bayesian network model

Travellers' mobility decisions are fraught with uncertainty and instability during public health crises. However, existing studies have not revealed the internal mechanism of travellers' mobility changes in a public health crisis. This paper established and trained a Bayesian network model from multiple data to analyse Chinese travellers' mobility decision-making processes under COVID-19 and simulated the changes in mobility decisions in different scenarios. The results show that travellers reformulate mobility decisions in response to various information and negotiate between social customs and personal needs. Mobility can be modified through risk communication and habits adaptation. Bayesian network models provide a methodological contribution to causal exploration and scenario prediction.

Comparison of indoor air quality and thermal comfort standards and variations in exceedance for school buildings

Addressing indoor air quality (IAQ) and thermal comfort issues in school buildings is challenging but relevant. Firstly, their primary occupants are more vulnerable than adults. Secondly, school buildings are often inadequate being too old or designed to prioritise energy-efficiency targets. Thirdly, occupants have often little control over the indoor environmental quality (IEQ). Lastly, the SARS-CoV-2 pandemic highlighted the complexity and vulnerability of existing decision-making processes in relation to making timely and well-informed decisions about IEQ threats. Standards and guidelines vary over time and among similar countries despite targeting similar occupants, evaluate IAQ and thermal comfort independently, and do not include any specific adaptations to children. Thus, the aim of this research is to compare different available standards to evaluate IAQ and thermal comfort in school buildings. By analysing with different standards (EN16798, BB101, and ASHRAE 55 and 62.1) the data collected in schools in northern Italy, this research evaluated the consequences of different limits and approaches, and proposed improvements. The conclusions are that (i) thresholds and methods inconsistency within the same standard should be avoided;(ii) upper- and lower-bounded operative temperature scales are the most appropriate means to design and verify thermal comfort in classrooms;(iii) IAQ metrics that give an upper limit per a certain amount of consecutive time might prevent the build-up of indoor pollutants, even with high emissions from the building fabric;(iv) no standard proposes a combined IAQ and thermal comfort analysis which could enable more informed trade-off decisions considering IAQ, thermal comfort, and energy targets. © 2023 The Authors

The Impact of Social Media Usage on Customer Decision Making-Process in Holiday Travel Planning Context, Applied Study Among Petra Visitors

The study aimed to explore the impact of social media usage on customer decision-making process among Petra visitors in holiday travel planning context. In order to achieve the objectives of the study, the analytical descriptive approach methodology and convenience sample technique were adopted, the researcher designed a questionnaire based on previous studies, which consisted of (33) items to gather the information from the study sample. The Statistical Package for Social Sciences (SPSS) program was used to analyze and examine the data and the hypotheses. The research hypotheses were tested using descriptive statistical methods and simple linear regression test. The main results were: There is a moderate level of using social media in customer purchase decision process among Petra visitors, where the average of using social media was (3.05) in search information phase, (3.15) in evaluation of alternatives phase, (3.54) in purchase decision phase and (3.64) in post-purchase evaluation phase. There is statistically significant impact of the using social media on customer decision making process in all phases at the significance level (α ≤ 0.05). Also, the study found that the information provided by friends, relatives or other travelers on social media tools is more trusted in comparison to other source of information. The study recommends for companies and agents engaged in tourism industry to adopt social media as a marketing strategy due to its great advantages. Specially, After the end of the COVID-19 pandemic, so companies operating in the field of tourism are in dire need of promotional and advertising campaigns in line with the modern technologies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessment of tourists perceived without travel risks and intention to visit night market aftermath the COVID-19 pandemic: An empirical approach

Prior studies on night market tourism have mainly investigated by governments and scholars on the current situation products and development strategies with descriptive research. There are no empirical studies that have explored the perceptions of tourists in the development of night market tourism. More importantly, because of the COVID-19 pandemic, issues of night market products and services, and tourists' opinions about night markets have become a challenge. This study aims to examine the relationships between attitude, subjective norm (SN), perceived behavioural control (PBC), perceived without travel risk, and visit intention towards night markets aftermath of the COVID-19 pandemic. A quantitative approach was used using an online survey method involving 387 respondents followed by subsequent empirical testing of the proposed hypotheses, which was performed using SPSS and AMOS. The results indicate that attitude, SN, PBC, and perceived without travel risk positively influence intention. Furthermore, SN also displayed a significant positive influence on attitude, PBC, and perceived without travel risk. Finally, the theoretical and practical implications, as well as limitations were discussed. © by the author.

COVID-19: End-of-life care decision-making and preferences in older people

Elderly patients, frail, and with underlying many chronic comorbidities or severe illness are most at risk from COVID-19 pandemic. Recent data from the Italian Istituto Superiore di Sanità (ISS) showed that COVID-19 is more lethal in older subjects. In Italy, on the date of March 17, 2020, the overall case-fatality rate was 7.2%, and 96.4% of died patients had more than 60 years. When age groups stratified data, individuals aged 70 years or older represent 35.5% of cases, while subjects aged ≥80 years were 52.3% [1]. With respect to the severe context of widespread world mortality, the main aims of the palliative care (quality of life, discernment of patient goals, advance care planning, pain and symptom management, and support for caregivers over protracted trajectories) may appear not essential [2]. The COVID-19 pandemic showed, conversely, the limits of the healthcare system on managing elderly patient's wishes even and expectations during the dreadful COVID-19 disease. During epidemic such as that of SARS CoV 2, the necessity of intensive care unit (ICU) beds could be not sufficient for the patients with severe respiratory distress (ARDS). Many recommendations suggest, in these contests, that the physicians should guarantee the healthcare resources to the patients with a higher life expectancy. The evaluation for the need for intensive care should include the severity of the disease, the comorbidity and the presence of multi-organ failure. Have still, in this period, the insight of patient goals such as wishes, advance care planning and even end of life preferences a central value? This chapter would analyse this prerogative in light of the severity of COVID pandemic. © Springer Nature Switzerland AG 2020.

The Impact of Artificial Intelligence on Financial Institutes Services During Crisis: A Review of the Literature

Artificial Intelligence (AI) is the general field that covers everything related to importing "intelligence” to computer systems in order performing tasks which require human intelligence, this is achieved by using algorithms that can detect patterns, generate insights from the data presented to them, to apply them to future decision-making processes and predictions, Artificial intelligence is a new concept of technological innovation where different technologies, processes and methods have been combined to create alternative solutions which are precise and to the point to enhance the economies as well as the competitive edge of the organization. The implications of the AI technology have been seen in various fields of life including medical automotive as well as financial industries. Financial institutes with crisis face challenges to deal with during to improve it quality and efficiency, so now days the financial services are under strain and challenges due to the wake up of the Covid-19 pandemic, Therefore, many financial institutes shift to implementing AI in its services to enhance it services, satisfy their customer and increase productivity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Antecedents to bounce forward: A case study tracing the resilience of inter-organisational projects in the face of disruptions

Inter-organisational projects depend on stakeholder interactions and joint decision-making to perform and continually adjust to variations. This paper examines the emergence of transformative resilience (i.e., dynamic project capabilities to pursue fundamentally new strategies and practices) when facing external disruptions. A process-orientated case study was conducted within a culturally diverse project network of disaster risk management actors from Sweden and four Asian countries during the COVID-19 pandemic. The study found three crucial interactional considerations in the premise of project resilience during challenging times. These considerations concern contextual (through proactivity for a common picture and centralisation of linkages), behavioural (through stakeholders' willingness to engage, commit and distribute agency), and cognitive embeddedness (through appreciation of diversity and reflexivity of actions). The findings enrich our understanding of resilience with new insights into the sequential and antecedent role of social embeddedness in projects' organisational transformation and the complexity of inter-organisational relationships in uncertain times. © 2023 The Author(s)

Policy Making Based on Real-Scale Social Simulations

In this paper, we discuss decision-making processes for our communities using social simulation tools using some machine learning or artificial intelligence techniques. We take an example of considering preventive measures based on simulation results during COVID-19 pandemic. To avoid explosive infection in Japan, several preventive measures were considered. Among them, short-time business for restaurants, tourism support policies and vaccination schedules are included. I am involved in Covid-19 AI & Simulation Project Team (AISP) of Cabinet Secretariat, Japanese government. My contribution is to provide synthetic population data for real-scale social simulations for specific areas. In those simulations, we do not aim to predict a precise number of infected or severe patients by COVID-19 but to show several simulation results under various scenarios with different simulation parameters. After their simulation results are compared with each other, common outcomes are extracted from their results, and finally they are provided to the government. In that decision making process, their simulation results under several scenarios are shown to government officers, and final decision makings are left for politicians to decide. Since experts who are not elected are not able to take political responsibilities for their decision making, the AISP team shows several scenarios using their simulation models to support government officers and politicians to make their decisions. © 2022 IEEE.

Application of Artificial Neural Network to Estimate Students Performance in Scholastic Assessment Test

The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance. © 2022 IEEE.

Factors Influencing Customer Grocery Shopping Behaviour Amid Covid-19 Pandemic

Purpose: The paper attempts to understand and analyze the factors that influence different types of end consumer's behavior while shopping for groceries both online and offline, considering the Covid-19 pandemic situations. Design/ Method/ Approach: A sample size of 145 members of age varying from 18 to 60 were surveyed through a digital form of a questionnaire. Few factors which generally impact the customer's shopping behavior were categorized into six different factors: Purchase, Personal, Behavioral, Operational, Customer Engagement, and Pandemic factors. The respondents were asked to rank the elements according to their preferences. Findings: Factors like Quality of the Product, Hygiene practices followed by the delivery person, representation of products on website/application, Accessibility and Availability of the products were given priority amid pandemic, over other factors which influence customers shopping behavior. We have also found that FMCG advertisements that promise hygiene and safety during product delivery convinced the respondents, convinced the respondents. Practical Implications: The study gives an understanding of the factors influencing the retail customers amid the pandemic. This study can develop marketing strategies to target the middle-class and age group of 18-45.Originality/ Value: Factors influencing the customer's decision to buy groceries online amid the Covid-19 pandemic induced consumer shopping behavior and preferences. Furthermore, the results show how the respondents were affected by the Hygiene promotions made by FMCG companies during the lockdown.

Policy Making Based on Real-Scale Social Simulations

In this paper, we discuss decision-making processes for our communities using social simulation tools using some machine learning or artificial intelligence techniques. We take an example of considering preventive measures based on simulation results during COVID-19 pandemic. To avoid explosive infection in Japan, several preventive measures were considered. Among them, short-time business for restaurants, tourism support policies and vaccination schedules are included. I am involved in Covid-19 AI & Simulation Project Team (AISP) of Cabinet Secretariat, Japanese government. My contribution is to provide synthetic population data for real-scale social simulations for specific areas. In those simulations, we do not aim to predict a precise number of infected or severe patients by COVID-19 but to show several simulation results under various scenarios with different simulation parameters. After their simulation results are compared with each other, common outcomes are extracted from their results, and finally they are provided to the government. In that decision making process, their simulation results under several scenarios are shown to government officers, and final decision makings are left for politicians to decide. Since experts who are not elected are not able to take political responsibilities for their decision making, the AISP team shows several scenarios using their simulation models to support government officers and politicians to make their decisions. © 2022 IEEE.

Effective Analysis and Investigation in COVID-19 Prevention Policies Based on Population Dynamics Modeling and Simulation

The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.

Decision-making process about prenatal genetic screening: how deeply do moms-to-be want to know from Non-Invasive Prenatal Testing?

BACKGROUND: Prenatal information may be obtained through invasive diagnostic procedures and non-invasive screening procedures. Several psychological factors are involved in the decision to undergo a non-invasive prenatal testing (NIPT) but little is known about the decision-making strategies involved in choosing a specific level of in-depth NIPT, considering the increased availability and complexity of NIPT options. The main aim of this work is to assess the impact of psychological factors (anxiety about pregnancy, perception of risk in pregnancy, intolerance to uncertainty), and COVID-19 pandemic on the type of NIPT chosen, in terms of the number of conditions that are tested. METHODS: A self-administered survey evaluated the decision-making process about NIPT. The final sample comprised 191 women (Mage = 35.53; SD = 4.79) who underwent a NIPT from one private Italian genetic company. Based on the test date, the sample of women was divided between "NIPT before COVID-19" and "NIPT during COVID-19". RESULTS: Almost all of the participants reported being aware of the existence of different types of NIPT and more than half reported having been informed by their gynecologist. Results showed no significant association between the period in which women underwent NIPT (before COVID-19 or during COVID-19) and the preferences for more expanded screening panel. Furthermore, regarding psychological variables, results showed a significant difference between perceived risk for the fetus based on the NIPT type groups, revealing that pregnant women who underwent the more expanded panel had a significantly higher level of perceived risk for the fetus than that reported by pregnant women who underwent the basic one. There was no statistically significant difference between the other psychological variables and NIPT type. CONCLUSIONS: Our findings indicate the paramount role of gynecologist and other health care providers, such as geneticists and psychologists, is to support decision-making process in NIPT, in order to overcome people's deficits in genetic knowledge, promote awareness about their preferences, and control anxiety related to the unborn child. Decision-support strategies are critical during the onset of prenatal care, according to the advances in prenatal genomics and to parent's needs.

The Impact of Virtual Reality Toward Telemedicine: A Qualitative Study

Telemedicine is quickly becoming an essential asset in the healthcare industry today. After COVID, the combination of virtual reality (VR) technology and telemedicine is quickly becoming a safe and effective solution for patients. Despite the advantages of employing VR in medical education and treatment, various problems and limits lead to the technology’s ineffectiveness or misuse. As a result, addressing potential problems associated with VR could be beneficial in the strategic decision-making process for implementing and developing this technology in the healthcare industry. This research used case study method to identify current issues of VR technology adoption at a large US hospital system. The findings of this qualitative study explore potential concerns and limitations of current VR technology. Suggestions and insights are highlighted to benefit researchers and practitioners. © 2022, Springer Nature Switzerland AG.

Information-Functional Modeling of the Resource Support for the Management Decision-Making Process in the Conditions of COVID-19

The main purporse of the study is to determine the key aspects of resource support for the management decision-making process in the context of COVID-19. The negative impact of COVID-19 has significantly affected the management system of the socio-economic system and therefore requires a new methodological approach to solving this problem. The methodology implies the application of the functional modeling method to better display the results of the study. Key steps were demonstrated to implement resource provision for making key management decisions in a pandemic. As a result of the study, we have formed a model of resource support for managerial decision-making in the socioeconomic system under the negative impact of COVID-19. The study has a number of limitations, and they lie in the fact that the model is purely theoretical in nature of the demonstration. Prospects for further research suggest an expanded analysis of the main possibilities of resource support for managerial decision-making in the socio-economic system under the negative impact of COVID-19. © 2022 IEEE.

Surveillance of SARS-CoV-2 in Urban Wastewater in Italy

The presence of SARS-CoV-2 RNA in wastewaters was demonstrated early into the COVID-19 pandemic. Data on the presence of SARS-CoV-2 in urban wastewater can be exploited for different aims, including: i) description of outbreaks trends, ii) early warning system for new COVID-19 outbreaks or for the spread of the virus in new territories, iii) study of SARS-Co V-2 genetic diversity and detection of its variants, and iv) estimating the prevalence of COVID-19 infections. Therefore, wastewater surveillance (known as Wastewater Based Epidemiology, WBE) can be a powerful tool to support the decision-making process on public health measures. Italy was among the first EU countries investigating the occurrence and concentration of SARS-Co V-2 RNA in urban wastewaters, virus detection being accomplished at an early phase of the epidemic, between February and May 2020 in north and central Italy. The present study reports on the methodological issues, related to sample data collection and management, encountered in establishing the systematic, wastewater-based SARS-CoV-2 surveillance, and describes the results of the first six months of surveillance. © 2022 IEEE.

Three-way decision theory based on interval type-2 fuzzy linguistic term sets

This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach. © 2022 - IOS Press. All rights reserved.

The benefits of Nature Based Systems in a changing and uncertain world

Multi-functional nature-based solutions (NBS) can help urban areas become more climate proof, adaptable, and provide a range of societal goals. Alongside chronic impacts from climate change, the Covid-19 pandemic has illustrated the disruption that unexpected and acute shocks can bring to society. Measures like NBS can help reduce the vulnerability of urban areas and increase resilience. Traditional infrastructure planning relies on strong business cases to demonstrate the economic value of a scheme. Numerous approaches assign economic value to the benefits from using NBS. However, this value is more than can be accounted by traditional finance methods as there are many different perspectives on 'value'. Decision making processes for selection of NBS measures require stronger integration of these value perspectives. This paper considers these perspectives in the business models that are being used in the decision processes regarding use and selection of NBS. Examples are drawn from case studies in the EU BEGIN project and also from the Living With Water (LWW) partnership in the UK illustrate how value perspectives can be included in business cases for NBS, also signposting the need to account for potential future changes using scenario planning. © 2022 ICE Publishing: All rights reserved.

When Edge Computing Meets Agile Software Development

Agile development has been a common methodology in software development. In response to the covid-19, most software development teams choose to work remotely. As a result of the different network environments, the company cloud center network load cannot meet the requirements of remote development and fault tolerance requirements of the agile development process. We designed a mixed-method called the Edge Development approach for improving Agile software development during the decision-making process. The extensive literature review provided us with three categories of challenges as well as solutions to support Edge Development's decision-support process. In the light of the survey, Five main software development decision-making challenges were identified in this study. In addition, we made a series of recommendations to improve the decision-making process of Edge Development from a variety of perspectives. © 2022 IEEE.