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Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.
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The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China's commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks' digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance.
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Wastewater surveillance has been widely used to track the prevalence of SARS-CoV-2 in communities. Although some studies have investigated the decay of SARS-CoV-2 RNA in wastewater, understanding about its fate during wastewater transport in real sewers is still limited. This study aims to assess the impact of sewer biofilms on the dynamics of SARS-CoV-2 RNA concentration in naturally contaminated real wastewater (raw influent wastewater without extra SARS-CoV-2 virus/gene seeding) using a simulated laboratory-scale sewer system. The results indicated that, with the sewer biofilms, a 90% concentration reduction of the SARS-CoV-2 RNA was observed within 2 h both in wastewater of gravity (GS, gravity-driven sewers) and rising main (RM, pressurized sewers) sewer reactors. In contrast, the 90% reduction time was 8–26 h in control reactors without biofilms. The concentration reduction of SARS-CoV-2 RNA in wastewater was significantly more in the presence of sewer biofilms. In addition, an accumulation of c.a. 260 and 110 genome copies/cm2 of the SARS-CoV-2 E gene was observed in the sewer biofilm samples from RM and GS reactors within 12 h, respectively. These results confirmed that the in-sewer concentration reduction of SARS-CoV-2 RNA in wastewater was likely caused by the partition to sewer biofilms. The need to investigate the in-sewer dynamic of SARS-CoV-2 RNA, such as the variation of RNA concentration in influent wastewater caused by biofilm attachment and detachment, was highlighted by the significantly enhanced reduction rate of SARS-CoV-2 RNA in wastewater of sewer biofilm reactors and the accumulation of SARS-CoV-2 RNA in sewer biofilms. Further research should be conducted to investigate the in-sewer transportation of SARS-CoV-2 and their RNA and evaluate the role of sewer biofilms in leading to underestimates of COVID-19 prevalence in communities.
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The COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities have all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chain. As a result, we examine the food supply chain, which is one of the key industries COVID-19 has detrimentally affected, impacting, indeed, on the entire business process from the supplier all the way to the customer. Retail businesses are thus facing supply issues, which affect consumer behavior by creating stress regarding the availability of food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. This study examines the impact of COVID-19 on the United Arab Emirates food distribution systems and how consumer behavior changed in reaction to interruptions in the food supply chain and the food security problem. Hypothesis testing was used in the study's quantitative methodology to assess consumer behavior, and participants who were consumers were given a descriptive questionnaire to ascertain whether the availability and security of food had been impacted. The study used JASP 0.17.2 software to develop a model of food consumption behavior and to reveal pertinent connections between each construct. Results show that consumer food stress and consumption behavior are directly impacted by food access, food quality and safety, and food pricing. Furthermore, food stress has an impact on how consumers behave when it comes to consumption. Food stress, however, is not significantly influenced by food supply.
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The multi-generational workforce presents challenges for organizations, as the needs and expectations of employees vary greatly between different age groups. To address this, organizations need to adapt their development and learning principles to better suit the changing workforce. The DDMT Teaching Model of Tsing Hua STEAM School, which integrates design thinking methodology, aims to address this challenge. DDMT stands for Discover, Define, Model & Modeling, and Transfer. The main aim of this study is to identify the organization development practices (OD) and gaps through interdisciplinary models such as DDMT and design thinking. In collaboration with a healthcare nursing home service provider, a proof of concept using the DDMT-DT model was conducted to understand the challenges in employment and retention of support employees between nursing homes under the healthcare organization. The paper highlights the rapid change in human experiences and mindsets in the work culture and the need for a design curriculum that is more relevant to the current and future workforce. The DDMT-DT approach can help organizations address these challenges by providing a framework for HR personnel to design training curricula that are more effective in addressing the issues of hiring and employee retention. By applying the DDMT-DT model, HR personnel can better understand the needs and motivations of the workforce and design training programs that are more relevant to their needs. The proof-of-concept research pilot project conducted with the healthcare nursing home service provider demonstrated the effectiveness of the DDMT-DT model in addressing the issues of hiring and employee retention. The project provides a valuable case study for other organizations looking to implement the DDMT-DT model in their HR practices. Overall, the paper highlights the importance of adapting HR practices to better suit the changing workforce. The DDMT-DT model provides a useful framework for organizations looking to improve their HR practices and better address the needs of their workforce.
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While technology factors are the main driver of the booming real estate APP platforms with important implications for user behavior pattern during and post-pandemic contexts, there is a lack of adequate research. In response, this study explores the user behavior pattern of real estate APP platforms to promote user mental health by taking the real estate APP platforms users as the participants based on theory of technology readiness and acceptance model. Data collected from offline surveys are analyzed using PLS-SEM. The results reveal the technology readiness index positively affects individuals' perceived usefulness and satisfaction, ultimately positively affects individuals' continuance intention with real estate APP platforms;satisfaction with real estate APP platforms mediated the relationship between technology readiness index, perceived ease of use, perceived usefulness, and individuals' continuance intention with real estate APP platforms. However, the group comparison finds no significant difference in user behavior patterns by gender. The contribution of this study is to reveal the influence mechanisms of digital technology on users' behavioral patterns toward real estate APP platforms, which can help guide the sustainable development of real estate APP platforms and promote user mental health and wellbeing in the post-COVID era.
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Education plays a critical role in promoting preventive behaviours against the spread of pandemics. In Japan, handwashing education in primary schools was positively correlated with preventive behaviours against COVID-19 transmission for adults in 2020, during the early stages of COVID-19. The following year, the Tokyo Olympics were held in Japan, and a state of emergency was declared several times. Public perceptions of and risks associated with the pandemic changed drastically with the emergence of COVID-19 vaccines. We re-examined whether the effect of handwashing education on preventive behaviours persisted by covering a longer period of the COVID-19 pandemic than previous studies. A total of 26 surveys were conducted nearly once a month for 30 months from March 2020 (the early stage of COVID-19) to September 2022 in Japan. By corresponding with the same individuals across surveys, we comprehensively gathered data on preventive behaviours during this period. In addition, we asked about the handwashing education they had received in their primary school. We used the data to investigate how and to what degree school education is associated with pandemic-mitigating preventive behaviours. We found that handwashing education in primary school is positively associated with behaviours such as handwashing and mask wearing as a COVID-19 preventive measure but not related to staying at home. We observed a statistically significant difference in handwashing between adults who received childhood handwashing education and those who did not. This difference persisted throughout the study period. In comparison, the difference in mask wearing between the two groups was smaller but still statistically significant. Furthermore, there was no difference in staying at home between them. Childhood hygiene education has resulted in individuals engaging in handwashing and mask wearing to cope with COVID-19. Individuals can form sustainable development-related habits through childhood education.
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In the post-epidemic era, the labor market has become increasingly complex, making it even more crucial to incorporate sustainability into employment demand. As we enter the post-pandemic era, a globalization trend has become more apparent. It is crucial to modernize employability through educational reform in order to assist employees in enhancing their professional skills. This study began by analyzing the importance of financial engineering practice instruction and graduate employability in the post-epidemic era. Second, the study proposed the content and a plan for inter-disciplinary teaching reform to address talent cultivation needs based on labor market requirements. Third, a face-to-face survey and interview were conducted with students affected by changes in teaching, and the results were analyzed and summarized. On this basis, the impact of education reform was evaluated using both the expert scoring method and the analytic hierarchy approach. The results indicated that the suggested financial engineering teaching reform program improved the school's discipline strength, enrollment rate, employment rate, and competition awards, especially discipline strength. This research can be used to inform the teaching of financial engineering majors in various countries, assist job candidates in enhancing their professional skills, and build a formidable talent pool for the labor market.
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In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.
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With destinations steadily ‘opening back up for business' (while COVID-19 cases are still high in many areas), there is an increasing need to consider residents. Integrating the cognitive appraisal theory and the affect theory of exchange, this work tests a structural model examining the degree to which residents' perceptions of COVID-19 precautionary measures explain emotions directed toward visitors, and ultimately their willingness to engage in shared behaviors with tourists. Data were collected from 530 residents in 25 U.S. counties with the highest percentages of historical COVID-19 cases per population. A total of 10 of the 12 tested hypotheses were significant, contributing to 60% and 85% of the variance explained in contending and accommodating emotions, and 53% and 50% of the variance explained in engaging in less intimate–distal and more intimate–proximal behaviors with tourists. The implications highlight the complementary use of the two frameworks in explaining residents' preference for engagement in less intimate–distal interactions with tourists.
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Under the Chinese "Zero-COVID” policy, many laborers were forced out of work. Participating in educational compensation can effectively help unemployed laborers achieve re-employment. This paper selected Jining, Shandong Province, as the research area, analyzed the data using a questionnaire survey and key interviews, and observed factors that affect and change willingness to receive educational compensation (WTEC) and the willingness to accept the training duration (WTTD) of unemployed laborers. The study found that 77.78% of unemployed laborers are willing to receive educational compensation, and the WTTD is 12.05 days. Among them, eight factors affect WTEC, such as the years of education, the duration of unemployment, whether there are dependents in the family, and the family's size. Nine factors such as gender, age, education, unemployed duration, dependents, and other supportable incomes affect WTTD. Based on this, by taking measures from the government and laborers, WTEC and WTTD can be improved, and then the rate of re-employment can be enhanced, and finally, the employment problem can be ameliorated.
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The COVID-19 pandemic has recently caused the loss of millions of lives, and billions of others have been deeply affected. This crisis has changed the way people live, think about life, and perceive happiness. The aim of this study is to reveal differences between geographical regions by investigating the effect of the happiness variable on different countries during the international COVID-19 pandemic. The primary purpose is to demonstrate how such a pandemic may affect different countries in terms of happiness at the individual level and to identify possible strategies for the future. With this aim, both static and dynamic panel data models were used while applying fixed effects, random effects, and the generalized method of moments (GMM). A basic assumption in panel data models is that the coefficients do not change over time. This assumption is unlikely to hold, however, especially during major devastating events like COVID-19. Therefore, the piecewise linear panel data model was applied in this study. As a result of empirical analysis, pre- and post-COVID differences were seen between different geographical regions. Based on analysis conducted for three distinct geographical regions with piecewise linear models, it was determined that the piecewise random effects model was appropriate for European and Central Asian countries, the piecewise FGLS model for Latin American and Caribbean countries, and the piecewise linear GMM model for South Asian countries. According to the results, there are many variables that affect happiness, which vary according to different geographical conditions and societies with different cultural values.
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In December 2019, a novel coronavirus broke out in Wuhan City, Hubei Province, and, as the center of the coronavirus disease 2019 (COVID-19) epidemic, the economy and production throughout Hubei Province suffered huge temporary impacts. Based on the input–output and industrial pollution emissions data of 33 industrial industries in Hubei from 2010 to 2019, this article uses the non-parametric frontier analysis method to calculate the potential production losses and compliance costs caused by environmental regulations in Hubei's industrial sector by year and industry. Research has found that the environmental technology efficiency of the industrial sector in Hubei is showing a trend of increasing year-on-year, but the overall efficiency level is still not high, and there is great room for improvement. The calculation results with and without environmental regulatory constraints indicate that, generally, production losses and compliance costs may be encountered in the industrial sector in Hubei, and there are significant differences by industry. The potential production losses and compliance costs in pollution-intensive industries are higher than those in clean production industries. On this basis, we propose relevant policy recommendations to improve the technological efficiency of Hubei's industrial environment, in order to promote the high-quality development of Hubei's industry in the post-epidemic era.
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Despite an increased emphasis on improvement in airline service quality concerning consumer behavior, such as passenger repurchasing as a result of their behavioral intention over the last several decades, there is still much less concern with the nature of airline service quality than should exist in the so-called "logistics service quality” and less concern with examining the specific behavioral intention preceding repurchasing behavior together with the theory of planned behavior. As such, this study aims to explore these issues, along with the psychological factors of the theory of planned behavior, that can lead to repurchasing behavior via word-of-mouth intention (WOMI). With an online survey of 383 respondents experienced with flying, the results reveal that the logistics service quality and each determinant in the theory positively influence a passenger's repurchasing behavior through WOMI. Accordingly, service marketers can implement service design and apply integrated marketing communication by learning from repurchasing behavior that was formed by the given factors to retain their existing customers. Moreover, this study is the first to empirically and explicitly validate dimensions of airline services through the lens of logistics that are deemed fit with the nature of the airlines. It advances the understanding of theory approaching and connects what has hampered its advancement in a body of knowledge, simultaneously in a context of airline context where it should not be relegated to transportation and consumer and service orientation.
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Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.
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This study examined psychological health and coping strategies among faculty and staff at a Saudi Arabian university. A web-based self-administered survey was used to assess probable anxiety, depression, post-traumatic stress disorder (PTSD), and coping strategies by using the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Impact of Event Scale-Revised (IES-R), and Brief-COPE scale, respectively. Of 502 participants (mean age 36.04 ± 10.32 years, male: 66.3%), 24.1% (GAD-7 ≥ 10) had probable anxiety. Anxiety score was significantly higher in females (p < 0.001), those with a history of COVID-19 infection (p = 0.036), and participants with less work experience (p = 0.019). Approximately 40% of participants met the criteria of probable depression, with females (p < 0.001) and participants with less experience having more depressive symptoms. Around one-fourth (27.7%) of study participants indicated probable PTSD (score ≥ 33), with higher symptoms in females (p <0.001), less experienced staff (p < 0.001), and academic staff (p = 0.006). Correlation analysis indicated a significant positive correlation between anxiety and depression (r = 0.844, p < 0.001), anxiety and PTSD (r = 0.650, p < 0.001), and depression and PTSD (r = 0.676, p < 0.001). Active coping, religious/spiritual coping, and acceptance were common coping strategies, while substance use was the least adopted coping method among the study participants. This study indicated a high prevalence of probable psychological ailments among university staff.
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With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.
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There was a change in the environment and food security threat during the COVID-19 pandemic. Many countries, including Indonesia, are forced to allocate funds to reduce the risk of this disaster. The Government Republic of Indonesia, through the Ministry of Social Affairs, has launched a Social Cash Assistance Program for 10 million families affected by COVID-19. This study aims to identify how families affected by COVID-19 take advantage of this social cash assistance. The study was conducted on the beneficiaries of social assistance, in cash transfer of IDR 600,000 (USD 40), per month, for three months. This study involved 2290 beneficiaries as respondents spread across 12 provinces. The sampling technique was the Cohen Manion Morrison Table by proportional stratified random sampling. The findings show that (1) 99% of cash assistance is used for basic needs, especially for food, and (2) cash assistance could be used for basic needs for around two to three weeks, thereby strengthening food security. Recommendations are submitted based on the results of this study related to social cash assistance and food security. The first is that this assistance still needs to be continued until the COVID-19 pandemic is over. It is to help families affected by the COVID-19 pandemic meet their daily needs. Second, most respondents do not have a fixed income during the pandemic, so providing capital and business startups are needed to increase family income sustainably to maintain food security.
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Evaluation and selection of eco-innovation strategies is a significant and complex strategic decision, and despite the relevance and interest in the field of eco-innovation, the area of eco-innovation strategies has not been explored in depth in the scientific literature. Therefore, in this study, we propose an integrated approach to evaluating eco-innovation strategies from the perspective of strategic green transformation that helps decision-makers evaluate and select eco-innovation strategy aiming to achieve a competitive advantage. For this study, we adopted a validated multi-criteria decision-making methodology (MCDM) by combining Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The reliability of the proposed framework was tested and applied in the context of the Lithuanian furniture industry. This study offers three contributions and provides a comprehensive and profound insights into eco-innovation strategies. First, this study conceptualizes eco-innovation strategy from the perspective of strategic green transformation and proposed a novel definition and classification of eco-innovation strategies leading to competitive advantage. Second, this study proposes a novel approach to the evaluation of eco-innovation strategies taking into account micro-, meso-, and macro-level environmental factors. Third, the findings of this study provide implications for scholars and decision-makers in the field of eco-innovation strategy and set an agenda for future research.
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Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions;the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies;the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.