Search results

The purpose of this paper is to detail the measurement development of a set of indices that manifest culture in the context of business decision making.

Survey research was conducted eliciting opinion data from managers of firms in two countries, the USA and Hungary, for cross‐cultural comparison. Critical issues of measurement equivalency are assessed using item response theory (IRT) prior to the comparison of the US and Hungarian manager samples.

The following sub‐indices were identified as a result of the analysis: individualism in decision making; individualism and authority; logic/emotion; equality as people, equality of authority, tolerance for ambiguity in decision making; tolerance for ambiguity and flexibility, and time orientation.

While the framework provides a broad representation of the cultural value dimensions (CVD) in a business context, it cannot be said that it encompasses the entire dimension or where the true endpoints will lie for the each of the five continuums. As additional culture groups are tested, it will be possible to ascertain where the true midpoint and endpoints of the scale lie.

The framework was utilized to place the five CVDs in a business context, resulting in a very even representation of the manifestation of cultural values across decision‐making processes in a firm. As a result, a more complete picture of current CVD scores is provided by the data for the two populations sampled. The sub‐indices identified not only aid in the establishment of equivalence and allow for comparison of the two groups, but also provide deeper insight into the similarities and differences between the two groups that were masked in the original CVD structure.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem.

The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively.

The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units.

This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

Despite intense debates about the outcomes of large-scale agricultural investment in developing countries, gender issues and employment effects of such investment on women empowerment are very thin in the literature. The main purpose of this article is, therefore, to examine the effect of women's access to income-earning jobs on their empowerment.

The authors used a quasi-experimental research design and a mixed research approach. The authors collected primary data from 65 employed and 85 unemployed women and ten key informants. Principal component analysis (PCA) and propensity score matching (PSM) techniques were used to construct indexes and assess employment effect.

The article finds that consistent with the argument of the resource in culture theory, women's employment in large-scale commercial farms does not have an effect on their empowerment level.

The result implies that the government should strengthen gender sensitization programs and facilitate the formation of well-organized and profitable alternative income generating activities for women in such ways that improve their empowerment.

This article assesses the effect of women employment on their empowerment and contributes to filling gaps in the empirical and theoretical literature. It could also contribute to academic and policy debates in the area of large-scale commercial farming based on the actual evidence.

This article explores the relationship between teachers' perceptions of administrative practice in high schools and students' perceptions of the school, teachers and their own performance. It was found that where decision making is perceived by teachers as collegial, collaborative, co‐operative and consultative and providing adequate opportunities for participation it will be more likely to lead to positive student perceptions about their school and teachers, as well as perceptions about relationships and their own performance, than where decision making is more top‐down, executive or does not foster widespread involvement. Reinforcing these findings it was found that where teachers identify the main sources of stress in their schools as lack of support from management, poor leadership and ineffective decision‐making processes, students are much less favourably disposed towards their teachers or their own engagement and performance. Previously reported results have shown that such inclusive decision‐making practices may not be widespread in high schools.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

This study aims to explore whether there is an association between women's empowerment and the utilization of maternal healthcare facilities.

This study considered four indices for measuring women's empowerment – labor force participation index (LFPI), decision-making power index (DMPI), attitude toward partner’s violence index (ATPVI) and knowledge level index (KLI) – and three healthcare facilities – number of antenatal visits, delivery with healthcare facilities and postnatal checkup after delivery. Data extracted for this study were from the Bangladesh Demographic and Health Survey 2011 and 2014. A chi-square test was used for bivariate analysis, and a three-level logistic regression model was applied for multivariate analysis.

An increment was observed in the practice of all considered healthcare facilities, and the percentage of highly empowered women in DMPI decreased from 2011 to 2014. This study found that higher empowerment of women in DMPI, KLI and ATPVI significantly (p-value < 0.05) increases the utilization of healthcare facilities. High empowerment of mothers in LFPI was found negatively associated with facility delivery and positively associated with the postnatal checkup.

Women's empowerment was found significantly associated with the utilization of maternal healthcare facilities. This study is seeking the attention of corresponding authority to come up with a more effective intervention program to empower women to utilize maternal healthcare facilities.

Many nations in Sub-Saharan Africa, including Nigeria have initiated programmes targeted at addressing food insecurity, but without any major significant breakthrough. This necessitates the call for inclusion of social network institutional framework into the food and nutrition security policy of many developing countries. This is touted as an important social safety net needed for farming households’ economic advancement and welfare. Consequent on this, the purpose of this paper is to investigate the importance of social networks in building resilience against food insecurity among farmers in Oyo State, Nigeria.

Data collected from a sample of 297 smallholder farmers during 2015/2016 agricultural production season were analysed using descriptive statistics to describe farmers’ notable characteristics. However, Foster–Greer–Thorbecke indices vis-à-vis food consumption expenditure-metrics technique was used to evaluate the food security profile of the respondents. Similarly, ordinary least square and two-stage least square (2SLS) models were applied to investigate the effects and “reverse causality” in the social networks food security model.

The study findings indicated that about 48.15 per cent of the smallholder farmers are food insecure. Similarly, the study revealed that age (p<0.01), years of formal education (p<0.05), marital status (p<0.05), number of persons in the family (p<0.01), executive status in social groups (p<0.05), aggregate social networks (p<0.1) and non-food consumption expenditure (p<0.01) made significant contributions to food security status of farming households. Importantly, 2SLS estimation indicated non-existence of reverse causality effect of social networks components and households’ food security status.

There is an urgent need for pragmatic policies that support systematic and organized social groups and networks formation in the rural/agrarian areas across Nigeria.

A widely accepted view of economic theory posits that traditional capitals (physical, economic/financial and human) drive households’ welfare including food security status. However, this study has established the relevance of social networks as an important component of human well-being. Apparently, this study also established the exogeneity of social networks in the framework of households’ food security status.

Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran.

During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors.

Results of the first phase show that “supervision technical weaknesses for overcoming technical and executive workshop problems” and “Inaccurate estimation of workload, required equipment and project completion time” are the most significant delay factors. In contrary, “non-use of new engineering contracts” has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like “Inaccurate estimation of workload, required equipment and project completion time” “weakness of laws and regulations related to job responsibilities” and “lack of foreseen of fines and encouragements in the contracts” are the most significant root factors of delay in construction projects.

This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts’ opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model.

The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers.

First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes.

The effectiveness of the model is proved by an example of carrier aircraft selection.

The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields.

In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.