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This study aims to investigate the moderating role of sociodemographic factors, specifically age and education level, in the knowledge-attitude-behavior (KAB) model concerning information security awareness (ISA) amid growing technological threats.

This study uses a survey methodology, collecting data from 400 working individuals in Vietnam, to test the applicability of the KAB model and evaluate the moderating effects of age and education on the model’s established relationships. In addition, the theoretical model and hypotheses were evaluated using the partial least squares structural equation model (PLS-SEM) approach.

This research confirms the relationships posited in the KAB model. Notably, it shows that younger employees showcase a more positive attitude and behavior toward information security compared with their older counterparts. In addition, higher education levels strengthen the positive association between information security knowledge and attitude. The findings underscore the imperative for organizations to consider sociodemographic variables when formulating strategies to enhance ISA.

This study extends the KAB model by exploring the impact of sociodemographic factors, focusing on age and education in ISA. Overcoming the oversight in current literature, particularly in the context of technological threats, the research uses PLS-SEM and targets a specific demographic in Vietnam.

The purpose of this study is to show the presence of market discipline and provide an explanation for bank risk nondisclosure behavior, specifically market risk (MR), credit risk (CR), operational risk (OR) and counterparty credit risk (CCR). The response of market discipline when banks comply with Basel III capital and liquidity restrictions is also investigated in this study.

The study used the Lasso regression method to give accurate results with the lowest error when using small observational data with a large number of features.

First, theoretically, the study points to the presence of market discipline and its sensitivity to the risks disclosed by the bank, especially when applying capital regulations under Basel III. In addition, the study also shows differences between the developed and emerging countries in the sensitivity of market discipline to factors when considering banking regulations. Finally, an interesting result that the study shows is that the higher the index of economic freedom, the weaker the market discipline is, especially for emerging countries.

The study’s findings have several important implications: (1) help regulators devise policies to manage banks' risk and meet liquidity and capital requirements according to the Basel III framework. The effectiveness of market discipline is reduced, and banking regulators need to compensate by strengthening their supervisory functions. (2) Showed the reasons why banks ignore the disclosure of bank risks according to the provisions of the third pillar of the Basel III framework. Because when following the Basel III framework, depositors demand higher interest rates or increase market discipline towards riskier banks.

This study is the first attempt to assess market discipline under the new capital and liquidity regulations using the Lasso regression model as suggested by Tibshirani (1996, 2011), Hastie et al. (2009, 2015). This is also the first study to look at the impact of four different forms of risk on market discipline (as required by the Basel regulatory framework to improve disclosure).

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

This study investigates the role of social media brand posts on customer response and whether said impacts foster engagement in brand co-creation behaviors, especially in the higher education sector. The study further explores the moderating role of a university's reputation in strengthening the effects on student response and co-creation behaviors.

The authors conducted this research by using the dual processes of the heuristic–systematic model to understand the effects of brand post‐characteristics on student’s responses and behaviors. A dataset obtained from a survey of 755 students was employed to estimate the proposed research model.

The results illustrated two key characteristics of brand posts, namely argument quality (systematic processing) and quantity of posts (heuristic processing), positively affect cognitive and affective responses, thus encouraging students to co-create value for a university brand. Moreover, our study also found that university reputation plays a significant moderating role in strengthening the relationship between recipients’ responses and co-creation behavior.

Online brand posts not only enable institutions to exchange brand information but also allow students to contribute their own resources to co-create brand value. Thus, the study findings can help brand managers successfully implement co-branding efforts and foster students in the co-creation process.

This study aims to ascertain the impact of data collecting awareness on perceived information security concerns and information-sharing behavior on social networking sites.

Based on communication privacy management theory, the study forecasted the relationship between information-sharing behavior and awareness of data collecting purposes, data collection tactics and perceived security risk using structural equation modeling analysis and one-way ANOVA. The sample size of 521 young social media users in Vietnam, ages 18 to 34, was made up of 26.7% men and 73.3% women. When constructing the questionnaire survey method with lone source respondents, the individual’s unique awareness and experiences with using online social networks (OSNs) were taken into account.

The results of the investigation demonstrate a significant relationship between information-sharing and awareness of data collecting, perceptions of information security threats and behavior. Social media users have used OSN privacy settings and paid attention to the sharing restriction because they are concerned about data harvesting.

This study was conducted among young Vietnamese social media users, reflecting specific characteristics prevalent in the Vietnamese environment, and hence may be invalid in other nations’ circumstances.

Social media platform providers should improve user connectivity by implementing transparent privacy policies that allow users to choose how their data are used; have clear privacy statements and specific policies governing the use of social media users’ data that respect users’ consent to use their data; and thoroughly communicate how they collect and use user data while promptly detecting any potential vulnerabilities within their systems.

The authors ascertain that the material presented in this manuscript will not infringe upon any statutory copyright and that the manuscript will not be submitted elsewhere while under Journal of Information, Communication and Ethics in Society review.

This study aims to map existing literature on customer brand co-creation (CBC) in the context of social media and highlight multiple avenues for future research.

A systematic literature review (SLR) was used to identify relevant articles in Scopus and ISI Web of Science (WoS) databases to get access to the articles. The final sample of 59 articles on CBC on social media published from 2009 to 2021 was selected for review.

The findings clarified the conceptualization of CBC and revealed three groups of theories, seven groups of antecedents and two groups of consequences. Then, these components were synthesized into an integrative framework as a basis for further developing this research area.

The findings contribute to the implementation of branding strategies aimed at involving customers in co-creating value for the brand.

Kapok was well-known for its oleophilic properties, but its mechanical properties and morphology impeded it from forming suitable absorbent materials. This study aims to demonstrate the process of creating an oil-absorbent web from a blend of treated kapok and polypropylene fibers.

Kapok fibers were separated from dried fruits, then the wax was removed with an HCl solution at different concentrations. The morphological and structural changes of these fibers were investigated using scanning electron microscopy images. The blending ratios of kapok and polypropylene fibers were 60/40, 70/30 and 80/20, respectively. The fiber blends were fed to a laboratory carding machine to form a web and then consolidated using the heat press technique. The absorption behavior of the formed web was evaluated regarding oil absorption capacity and oil retention capacity according to ASTM 726.

The results showed that the HCl concentration of 1.0% (wt%) gave the highest wax removal efficiency without damaging the kapok fibers. This study found that oil absorbency is influenced by the fiber blending ratio, web tensile strength and elongation, porosity, oil type and environmental conditions. The oil-absorbency of the web can be re-used for at least 20 cycles.

This study only looked at three types of oils: diesel, kerosene and vegetable oils.

When the problem of oil spills in rivers and seas is growing and causing serious environmental and economic consequences, using physical methods to recover oil spills is the most effective solution.

This research adds to the possibility of using kapok fiber in the form of a web of non-woven fabric for practical purposes.

This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.

The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.

The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.

This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.

Traditionally health care research is often valued on the evidence‐based basis, which is dominated by statistically motivated quantitative research. However, the increasingly popular use of qualitative methods in general and health research, in particular, has provided a different kind of research evidence, which reflects real‐life issues in social contexts. Though health care qualitative research has provided health care workers, policymakers and the public valuable insights into various aspects of population health, there are issues and problems regarding conducting qualitative research in an intercultural discourse. Sociolinguistic and cultural factors are the main causes of problems facing researchers and policymakers. Concepts such as confidentiality and security are culturally constructed. This paper examines some intercultural awareness issues that need to be considered in the conduct of health care qualitative research and some potential problems in the treatment of health qualitative data. Attention will be given to the Vietnamese health discourse.