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The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

Correct registration for the value-added tax (VAT) is a key aspect of tax compliance; it is vital in ensuring adequate tax revenue collection in all countries but particularly in developing countries such as South Africa. Non-registration hinders sufficient tax revenue collection, stifles economic growth and causes unfair competition with formal businesses. The purpose of this study is to determine whether changes in the VAT rate affect the registration decisions of businesses, ultimately impacting upon tax compliance behaviour and tax revenue collection.

An online 2 × 2 between-subjects field experiment was conducted, as part of a broader study, to consider compliance with registration requirements by small business entities in South Africa, specifically when there are changes in the VAT rate.

Although the study establishes that changes in the VAT rate tend not to have a significant impact on the registration decisions of such taxpayers, it nonetheless indicates that the magnitude of the change in the VAT rate may be influential on registration decisions, whether relating to compulsory or voluntary registration. More particularly, the greater the magnitude of the VAT rate decrease (increase), the more likely it is that taxpayers will register (deregister) for VAT purposes, indicating that the magnitude of changes in the VAT rate do have an impact on VAT registration decisions and therefore on tax compliance more generally.

Not only does the study add to the limited knowledge available on registration decisions of small businesses, but also gives valuable guidance to policymakers in terms of determining the VAT rate for the country.

Not only does the study add to the limited knowledge available on registration decisions of small businesses, but it also gives valuable guidance to policymakers in terms of determining the VAT rate for the country.

Despite being a viable source of funds, African sovereign bond markets are relatively underexplored. The empirical literature fails to consider the impact of exclusively macroeconomic factors and the volatile contexts in which African markets operate. The purpose of this paper is to fill the vacuum by proposing a context-sensitive theoretical framework. The study targets, specifically, macroeconomic factors and assesses the extent to which they affect bond market development.

Using panel data on sovereign bond markets from 26 African economies, the study extends previous methodologies used in similar studies by accounting for downside risk in a generalized method of moments (GMM) framework and employing tighter robustness measures.

This study finds that inflation, domestic debt, external debt, GDP at PPP, fiscal balance and exports are important macroeconomic drivers of sovereign bond market development in African emerging economies.

While GMM estimation is beneficial in the presence of endogeneity between the dependent variables that are instrumented with lagged independent variables, it guarantees consistency but, not unbiased estimations.

Market-oriented government funding with well-defined debt management strategies must be implemented to support the development of sovereign bond markets. External debt must be set at a sustainable level, and government should be dedicated to the confirmation of this. Furthermore, inflation rates must be kept low and stable.

If policymakers are to take this study seriously, bond markets may begin to be viable sources of funds for African emerging economies.

This study introduces a methodology for measuring bond market development that considers the systemic volatility in emerging markets and proposes a theoretical framework for African emerging economies. In addition, the authors identify a new macroeconomic determinant of bond market development in the region.

Successful firms are important sources of productivity, employment and economic stability in societies. As the micro-level origins of firm innovations are increasingly attracting attention amongst innovation scholars, the purpose of this study is to investigate the role of managerial innovativeness, i.e. small firm managers' innovative behaviour for firm performance. Specifically, the present study investigates managerial innovativeness as a predictor of small firms' product innovativeness and market performance.

This research model suggests that managerial innovativeness is positively linked to firms' market performance and that product innovativeness partially mediates the relationship between managerial innovativeness and market performance. The model was tested using partial least squares structural equation modelling (PLS-SEM) with a dataset (N = 93) collected from small logistics firms in South-Eastern Finland.

The findings support the authors' hypotheses and show that managerial innovativeness had a direct effect on firms' product innovativeness and market performance. The authors also found that firms' product innovativeness mediated the relationship between managerial innovativeness and firms' market performance.

This is one of the few studies that shed light on and show that managerial innovativeness is significantly and positively related with small firms' product innovativeness and market performance, whereas earlier research tended to focus on managers' personalities, traits, characteristics or managerial actions, leaving managerial innovativeness unexplored.

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.

Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.

The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.

By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

Foodbank use in the UK is rising but, despite high levels of poverty, Pakistani women are less likely to use food banks than white British women. The purpose of this paper is to understand the lived experience of food in the context of poverty amongst Pakistani and white British women in Bradford, including perspectives on food aid.

A total of 16 Pakistani and white British women, recruited through community initiatives, participated in three focus groups (one interview was also held as a consequence of recruitment difficulties). Each group met for two hours aided by a moderator and professional interpreter. The transcripts were analysed thematically using a three-stage process.

Women in low-income households employed dual strategies to reconcile caring responsibilities and financial obligations: the first sought to make ends meet within household income; the second looked to outside sources of support. There was a reported near absence of food insecurity amongst Pakistani women which could be attributed to support from social/familial networks, resource management within the household, and cultural and religious frameworks. A minority of participants and no Pakistani respondents accessed charitable food aid. There were three reasons for the non-use of food aid: it was not required because of resource management strategies within the household and assistance from familial/social networks; it was avoided out of shame; and knowledge about its existence was poor.

This case study is the first examination of varying experiences of food insecurity amongst UK white British and Pakistani women. Whilst the sample size is small, it presents new evidence on perceptions of food insecurity amongst Pakistani households and on why households of varying ethnicities do not use food aid.

The entrepreneur is the main decision-maker in small and medium-sized enterprises and is the principal force in the implementation of a firm's international strategy. Research has paid limited attention to the intangible aspects of human capital, namely behavioural competencies that may have an impact on the entrepreneur's ability to take advantage of international opportunities. This study addresses this gap, identifying the behavioural competencies that distinguish entrepreneurs who pursue a stronger internationalisation expansion beyond the European market.

A competency modelling process is implemented for a sample of Italian entrepreneurs. Data on behavioural competencies are obtained through the coding of behavioural event interviews administrated to the entrepreneurs. Export intensity is adopted as a performance criterion to classify the entrepreneurs.

Three behavioural competencies (change catalyst, teamwork and organisational learning orientation) emerged as more significantly activated by entrepreneurs who show a higher export intensity in the global market.

The exploratory nature of the study, conducted in a small sample and in a specific geographical area, may reduce the generalisability of the findings.

Entrepreneurs can become aware of the behavioural competencies needed for the implementation of internationalisation processes. Additionally, training programmes can be designed to promote the development of these behaviours.

Bridging the literature on international entrepreneurship, cross-cultural studies and competency-based research, this study highlights the role of behavioural competencies in the internationalisation process from a micro level of analysis. This article proposes a competency framework that can be adopted to assess a broader portfolio of entrepreneurs' behaviours.

Purpose: Previous research identified a measurement gap in the individual assessment of social misconduct in the workplace related to gender. This gap implies that women respond to comparable self-reported acts of bullying or sexual discrimination slightly more often than men with the self-labeling as “bullied” or “sexually discriminated and/or harassed.” This study tests this hypothesis for women and men in the scientific workplace and explores patterns of gender-related differences in self-reporting behavior.

Basic design: The hypotheses on the connection between gender and the threshold for self-labeling as having been bullied or sexually discriminated against were tested based on a sample from a large German research organization. The sample includes 5,831 responses on bullying and 6,987 on sexual discrimination (coverage of 24.5 resp. 29.4 percentage of all employees). Due to a large number of cases and the associated high statistical power, this sample for the first time allows a detailed analysis of the “gender-related measurement gap.” The research questions formulated in this study were addressed using two hierarchical regression models to predict the mean values of persons who self-labeled as having been bullied or sexually discriminated against. The status of the respondents as scientific or non-scientific employees was included as a control variable.

Results: According to a self-labeling approach, women reported both bullying and sexual discrimination more frequently. This difference between women and men disappeared for sexual discrimination when, in addition to the gender of a person, self-reported behavioral items were considered in the prediction of self-labeling. For bullying, the difference between the two genders remained even in this extended prediction. No statistically significant relationship was found between the frequency of self-reported items and the effect size of their interaction with gender for either bullying or sexual discrimination. When comparing bullying and sexual discrimination, it should be emphasized that, on average, women report experiencing a larger number of different behavioral items than men.

Interpretation and relevance: The results of the study support the current state of research. However, they also show how volatile the measurement instruments for bullying and sexual discrimination are. For example, the gender-related measurement gap is considerably influenced by single items in the Negative Acts Questionnaire and Sexual Experience Questionnaire. The results suggest that women are generally more likely than men to report having experienced bullying and sexual discrimination. While an unexplained “gender gap” in the understanding of bullying was found for bullying, this was not the case for sexual discrimination.