Search results

Increased dietary salt content is one of the effective factors of hypertension and a major public health challenge globally. Although the positive effects of dietary salt reduction on health are universally accepted, people can hardly reduce their salt intake. The purpose of this study is to identify the inhibitory factors of dietary salt reduction among 20–65-year-old women in Yazd City, Iran.

This study was conducted using a deductive content analysis approach based on the communication for the behavioral impact (COMBI) framework. The purposeful sampling method was applied with maximum variation in terms of different educational levels, age groups, occupational status and residential areas to select the participants. Snowball sampling was used to select health-care professionals. Furthermore, semi-structured interviews and focus-group discussions were conducted with 31 local women and 11 health-care professionals working in the City until data saturation was achieved. Data were analyzed using Graneheim and Landsman’s method.

After data analysis, 617 initial codes were extracted over the perceived barriers. After merging similar codes, 223 codes were extracted. The barriers were classified into five main categories of family, personal, organizational, educational and socio-cultural barriers.

Based on the COMBI framework, the results demonstrated that the most important barriers for reducing salt intake were negative attitude toward restrictions on dietary salt intake, insufficient and incorrect beliefs about the health risk of salt, lack of family support, inadequate health literacy and low self-efficacy in Yazd City. Among these barriers, lack of family support was considered as the most effective factor in reducing salt consumption. So, by focusing on this area and providing the community with the required education, the amount of salt consumed by families can be reduced.

Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.

We use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.

Our findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.

This study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

The purpose of this study paper is to identify and prioritize the factors affecting the knowledge flow in high-tech industries.

This research is applied in terms of purpose and descriptive-survey in terms of data collection method. This research has been done in a qualitative–quantitative method. In the qualitative part, due to the nature of the data in this study, expert interviews have been used. The sample studied in this research includes 35 managers and expert professors with experience in the field of knowledge management working in universities and high-tech industries who have been selected by the method of snowball. In the quantitative part, the questionnaire tool and DANP multivariate decision-making method have been used.

In this study, a multicriteria decision-making technique using a combination of DEMATEL and ANP (DANP) was used to identify and prioritize the factors affecting the knowledge flow in high-tech industries. In this study, the factors affecting the knowledge flow, including 8 main factors and 31 subfactors, were selected. Human resources, organizational structure, organizational culture, knowledge communication, knowledge management tools, knowledge characteristics, laws, policies and regulations and financial resources were effective in improving knowledge flow, respectively.

By studying the research, it was found that the study area is limited, and the previous work has remained at the level of documentation and little practical use has been done. In previous research, the discussion of knowledge flow has not been very open, and doing incomplete work causes limited experiences and increases cost and time wastage, and parallel work may also occur. Therefore, to complete the knowledge management circle and fully achieve the research objectives, as well as to make available and transfer the experiences of people working in this field and also to save time and reduce costs, the contents and factors of previous models have been counted. It is designed for high-tech industries, a model for the flow of knowledge.

Health is one of the most basic human rights. This study aims to examine the pattern of unmet health services needs and barriers to use health services among people with disabilities.

This cross-sectional study was conducted among people with disabilities in Sanandaj, the capital of a less economically developed province in western Iran in 2020. In total, 548 samples were selected using random sampling, and data were collected using a questionnaire. Chi-square test and Fisher's exact test were used to examine the relationship between independent variables including demographic variables and dependent variables. Statistical tests were performed using STATA software package.

About 64% and 23% of participants needed outpatient and inpatient services, respectively. The gap between the perceived need and using health services for outpatient and inpatient services was reported to be 55% and 30%, respectively. The main reasons for not seeking the required health care were “insufficient coverage of costs by insurance” and “lack of physical access.” Having supplementary insurance and better economic status were significantly correlated with using outpatient care and having supplementary insurance and type of disability had a significant relationship with using inpatient care.

There was a big gap between the perceived need, seeking for and receiving health services in people with disabilities. Designing and implementing appropriate interventions to reduce barriers on the way of transforming, the need for health services into demand in the population of people with disabilities should be on the agenda of policymakers and relevant managers in the country.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best response plan for all crises.

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Information security is a critical issue in all organizations. The success of information security in libraries depends, to a large extent, on the effective behavior of administrators, librarians, users and all human staff. Accordingly, this study aims to design a model for identifying human factors affecting information security in libraries.

This study is applied in terms of research objectives and is a survey in terms of data collection. Moreover, it goes under the rubric of structural equation modeling in terms of the relationship between variables. The statistical population consisted of 100 managers and librarians of academic and public libraries of Hamadan in Iran. A questionnaire was used for data collection. The face and content validity of the questionnaire were examined using the expert’s opinions in the field of Iranian libraries. Also, the reliability of the questionnaire was calculated through Cronbach’s alpha coefficient. Data were analyzed using SPSS 16 and Smart PLS 2.

The results showed that among the components of information security, the highest score was designated to self-esteem (4.11 ± 0.57) and level of skill (4.07 ± 0.59), whereas the lowest score belonged to the level of education (3.51 ± 0.74). Ranking human factors affecting information security showed that experience with Rank 1 had the most impact, whereas the level of skill with Rank 6 had the least impact on information security.

In this study, for the first time, a model was designed and tested for human factors affecting information security in libraries. Information security professionals, librarians and library and information science researchers can exploit this model in the future.

This study aims to examine the reuse of plastic and fly ash (FA) to improve the soil and achieve sustainable development goals.

Sand from the Anzali port was reinforced with Geopet (GP) and stabilized with FA plus 3% sodium hydroxide. The GP was placed in FA-stabilized soil and the California bearing ratio (CBR), and unconfined compressive strength (UCS) tests were performed on samples at the optimum moisture content.

The results showed that the improvement in the optimum CBR was 174.9%. The UCS increased 15.25% and 48.65% in soil reinforced with three layers of GP plus 15% FA over those containing 10% and 5% FA, respectively. Additionally, the current analysis used response surface methodology (RSM) to investigate the impact of FA percentage, GP layers and their interaction on CBR. The results highlight the efficacy of the used RSM model, as evidenced by the significantly low p-value (<0.0001).

This demonstrates the suitability and effectiveness of RSM for evaluating CBR in this scientific study.