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In light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.

Multivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.

According to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.

This study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

This study aims to assess the effect of corporate lobbying power on fraud and money laundering in listed firms on the Tehran Stock Exchange. For the study, the information of 173 firms is assessed during 2013–2020, and a total number of 1,384 year-companies are analysed.

In this paper, the Beneish model is used for fraud detection, and the clause of the auditor’s report on money laundering is used for the variable of money laundering. The multivariate regression, Logistic regression, the fixed effects of panel data, additional random effects tests, Hausman, least generalised squares and T + 1 are used by using the Stata Software.

The obtained results indicate a direct and significant relationship between lobbying and fraud and lobbying and money laundering. Suppose the board members of firms are among the parliament members or the government cabinet (politicians) and/or major shareholders affiliated with state-owned and/or quasi-governmental institutions. In that case, the likelihood of corporate lobbying will be increased.

The outcomes of the current study give great insight to developing countries due to the high volume of money laundering to reduce such a financial crime.

The purpose of this study is to empirically examine how the voluntary formation of a specialised investment committee (IC) and IC characteristics affect financial distress risk (FDR) and whether such impact is influenced by the level of investment inefficiency.

The authors use a large sample of Gulf Cooperation Council (GCC) non-financial companies during 2006–2016. A principal component analysis is done to aggregate and derive a factor score for IC characteristics (i.e. independence, size and meeting) as a proxy for the effectiveness of IC. This study also uses three measurements of FDR to corroborate the findings and partitions sample firms into overinvesting and underinvesting companies to examine the potential impact of investment inefficiency on the IC–FDR nexus.

Using feasible generalised least square estimation method, the authors document that the likelihood of financial distress occurrence decreases for firms with separate ICs. The authors also find that firms with effective ICs enjoy lower FDR. In other words, the probability of financial distress minimises if the IC is large, meets frequently and has a high number of independent directors. However, the authors find neither any moderation nor any mediation effect of investment inefficiency for the impact of IC and IC attributes on FDR. The additional analysis indicates the expected benefits of an actively performing IC are amplified for firms with risk of both over- and underinvestment. These findings are robust to alternative measures of FDR and investment inefficiency, sub-sample analysis and endogeneity concerns.

This study, to the best of researchers’ knowledge, is the first to provide evidence in GCC firms’ perspective, suggesting that the existence of an effective IC is associated with a lower risk of financial distress, and to some extent, the economic benefits of IC are aggrandised for companies with a high probability of over- and underinvestment problems. These results are unique and contribute to a small but growing body of literature documenting the need for effective ICs and their economic consequences on investment efficiency in the FDR environment. The findings of this study carry valuable practical implications for regulatory bodies, policymakers, investors and other interested parties in the GCC region.

This paper examines the impact of founder ownership concentration (FOC) on risk disclosures. It further investigates the moderating role of risk governance in the association between FOC and risk disclosures.

We use data from the top 200 Indian listed firms as our sample and rely on ordinary least squares (OLS) for our results. In addition, we use the propensity score matching, Heckman selection model and instrumental variable estimates for robustness checks.

We find that FOC decreases the risk disclosures. However, the effectiveness of risk management committee composition (risk governance) mitigates the negative influence of FOC on risk disclosures.

The paper is built on the agency theory. Based on the agency theory, the ownership concentration has two implications: first, it reduces the conflicts between managers and shareholders. Here, the managers act in favour of shareholders and therefore, brings more risk disclosers. Second, it invites conflicts between controlling and minority shareholders. The study is, therefore, interesting to see the cost and benefits of FOC on risk disclosures.

The study has practical implications for the regulatory bodies to encourage risk disclosures and benefit the outsiders of the firm. It also has implications for the companies to see the benefits of risk management committee as improved risk governance.

It contributes to the literature of risk disclosures and risk governance in emerging economies. It is the first study to investigate the role of risk governance in mitigating the adverse effects of founder’s ownership on risk disclosures in developing economies. It also contributes to the theory of agency cost and information asymmetry.

This study aims to evaluate the impact of board characteristics on bank performance at the commercial bank in Vietnam.

By running the pool OLS, fixed-effect and random-effect models with a panel data set of 294 observations from 2008 to 2021, the authors have examined determinants of bank performance.

The research results show that bank size, governance efficiency, capital adequacy ratio and economic growth have a positive effect while credit risk has a negative relationship with the commercial bank’s performance.

In particular, the result shows the relationship between chief executive officers’ (CEOs) gender and bank performance. Commercial banks led by female CEOs have lower bank performance than that led by male CEOs. However, this impact magnitude is not significant. The research results are the basis to propose recommendations to improve the Vietnamese commercial bank’s performance.

The agriculture industry has a considerable impact on Malaysia’s economy, as seen by its contribution of roughly 8.2% of gross domestic product in 2018 and its potential to absorb 11.09% of Malaysian labor in the same year. This study aims to simulate rising output in a system model of sustainable and profitable crude palm oil (CPO) supply chain management (SCM) and to formulate policy solutions to build sustainable and profitable SCM of Malaysian CPO.

This research included both primary and secondary data. This study used the dynamic system model to simulate palm oil land expansion, replanting policies and environmentally friendly growing techniques.

This study’s findings suggest that the dynamic system model of Malaysia’s CPO’s sustainable and profitable SCM is valid when its structure and performance are tested. The fifth scenario provides the best results, with the most significant net benefit value compared to the other scenarios.

The ideal policy alternative is replanting sustainable agricultural practices without burning technologies during new land clearing to achieve the best net advantages.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

This study aims to identify the intentions of immigrant entrepreneurs to start new projects by investigating the role of influence of institutional support, social context, cultural intelligence, self-efficacy, optimizing personality traits and hierarchy legitimacy on intentions to start new ventures. In addition, the strength of the relationship for such factors and intentions to start new ventures was determined through the moderator role of easy access to venture capital.

To this end, this study complements the academic literature by integrating the structural equation modeling (SEM) and multiple-criteria decision-making (MCDM) techniques. Thus, the MCDM (i.e. analytic hierarchy process and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) is an effective approach to solving the problem of complexity and evaluation (i.e. multiple evaluation criteria, important criteria and data variation). Hence, to complete the strategic guideline solution, this study uses a survey for collecting data from 202 immigrants in Malaysia, Pakistan, Nigeria and Singapore.

The results from SEM prove several critical factors of immigrants’ entrepreneurs. These factors of immigrants’ entrepreneurs can be vital for academics and host countries. By focusing on these aspects and by developing some personality traits (such as self-efficacy and optimal personality traits), these factors can contribute a good deal to increasing the capabilities of immigrant’s entrepreneurs toward entrepreneurial intentions. In the validation, the statistical objective method indicates that the immigrants' prioritizations in all countries are supported by the systematic ranking. Thus, entrepreneurial intentions for immigrants can pursue the order proven by the VIKOR results.

This study has some significant practical and theoretical implications. Practically, the study findings will enable managers to develop strategies to support immigrants for entrepreneurial intentions to start new ventures.

The novelty of the context under given circumstances of global environment adds to the originality of this study. Several previous studies have also emphasized the need for this type of study in other contexts. The findings can call managers’ attention toward a critical issue of immigrants’ entrepreneurial intentions to start new ventures.

The purpose of this study is to investigate the moderation effects of religiosity in the relationship between service quality (SQ), customer satisfaction (CS), relationship quality (RQ) and Islamic banking (IB) loyalty in a non-Islamic country.

This study used a quantitative approach and a cross-sectional research design. The data was collected by a closed-ended questionnaire from a systematic sample of 267 customers of full-fledged Islamic banks in Tanzania. Structural equation modeling and regression analysis techniques were used to analyze the data.

Results indicate that religiosity is a significant (p < 0.05) moderator of the relationship between SQ and loyalty (β = 0.176), and between CS and loyalty (β = 0.263). However, religiosity sharpens the impact of CS on loyalty (β increases from 0.170 to 0.263, p < 0.05) and does not sharpen the impact of SQ on loyalty (β decreases from 0.338 to 0.176, p < 0.05). The results further indicate that religiosity is not a significant moderator (p > 0.05) in the link between RQ and loyalty (β = −0.112).

This study used only full-fledged Islamic banks in Tanzania. Therefore, future research could be undertaken by including conventional banks that have introduced an IB window.

To build and maintain a loyal customer base, Islamic banks should take advantage of religiosity by providing a wide range of Sharia-based products and services that truly distinguish them from conventional banks. Accordingly, policymakers should establish an appropriate legal framework to enable Islamic banks to leverage religiosity to sharpen the impact of CS on loyalty and hence maintain loyalty in non-Islamic countries like Tanzania.

This study proposes and validates the theoretical model of loyalty in IB by showing the role of religiosity as a moderator in a non-Islamic country. This knowledge strengthens the overall understanding of loyalty in IB. To the best of the authors’ knowledge, this study is the first to examine the moderation effects of religiosity in the link between RQ and loyalty, and thus between SQ, CS, RQ and loyalty in a single study.

The most common reaction to an acute physical illness is anxiety, which may be followed by depression. In patients with chronic diseases, the prevalence of anxiety disorders and depression is almost twice as high as in other diseases. This study aims to extract prominent components in the design of treatment spaces on reducing hospitalized patients’ depression from both experts and patients/users’ point of views. A final model is also presented based on the findings.

This research used an exploratory mixed method. The effective components were extracted through the administration of two Likert-scale researcher-made questionnaires in two phases. Q factor analysis was conducted to reach the components. A total of 205 patients were admitted to Namazi Hospital in Shiraz, and 20 architecture and psychology experts participated in the survey. Final modeling of the data was done through path analysis.

Six factors were found to be effective by experts in reducing depression in therapeutic spaces: nature-oriented space, targeted social space, diverse space, visual comfort, logical process and safe space. On the part of patients, seven components were deemed to be effective: visual perception, naturalism, functionalism, physical security, logical process, psychological safety and diversity. Also, four main cycles were extracted from the final model with the direct effect of diversity and the other five cycles were mediated by naturalism.

A total of 15 interviews with architects and psychologists, who were available at the time of the study, were conducted in January 2018. The only general question during interviews was “In your opinion, what factors are effective in reducing the level of depression of patients in the design of treatment spaces?” This may have limited the range of factors that could be surveyed in the study. After collecting the effective factors from the aforementioned expert’s points of view, the questionnaire of experts was designed (Appendix). The expert questionnaires were distributed and edited in two stages in January 2019 among 20 architect experts who were available at the time of the study. The one-year interval between designing and administering the questionnaires occurred because of the limitations posed by the COVID-19 pandemic situation. However, the interval did not pose methodological obstacles for the study.

Evidence-based investigation of the effectiveness of proper design components of therapeutic spaces in reducing the symptoms of patients with chronic secondary depression has received little attention in the literature. Using a “conceptual model,” the present study brought the issue into its focus so as to find effective components in the design of treatment spaces that can alleviate depression symptoms in chronically hospitalized patients.