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Human or machine, which one is more intelligent and powerful for performing computing and processing tasks? Over the years, researchers and scientists have spent significant amounts of money and effort to answer this question. Nonetheless, despite some outstanding achievements, replacing humans in the intellectual tasks is not yet a reality. Instead, to compensate for the weakness of machines in some (mostly cognitive) tasks, the idea of putting human in the loop has been introduced and widely accepted. In this paper, the notion of collective hybrid intelligence as a new computing framework and comprehensive.

According to the extensive acceptance and efficiency of crowdsourcing, hybrid intelligence and distributed computing concepts, the authors have come up with the (complementary) idea of collective hybrid intelligence. In this regard, besides providing a brief review of the efforts made in the related contexts, conceptual foundations and building blocks of the proposed framework are delineated. Moreover, some discussion on architectural and realization issues are presented.

The paper describes the conceptual architecture, workflow and schematic representation of a new hybrid computing concept. Moreover, by introducing three sample scenarios, its benefits, requirements, practical roadmap and architectural notes are explained.

The major contribution of this work is introducing the conceptual foundations to combine and integrate collective intelligence of humans and machines to achieve higher efficiency and (computing) performance. To the best of the authors’ knowledge, this the first study in which such a blessing integration is considered. Therefore, it is believed that the proposed computing concept could inspire researchers toward realizing such unprecedented possibilities in practical and theoretical contexts.

As a relatively new computing paradigm, crowdsourcing has gained enormous attention in the recent decade. Its compliance with the Web 2.0 principles, also, puts forward unprecedented opportunities to empower the related services and mechanisms by leveraging humans’ intelligence and problem solving abilities. With respect to the pivotal role of search engines in the Web and information community, this paper aims to investigate the advantages and challenges of incorporating people – as intelligent agents – into search engines’ workflow.

To emphasize the role of the human in computational processes, some specific and related areas are studied. Then, through studying the current trends in the field of crowd-powered search engines and analyzing the actual needs and requirements, the perspectives and challenges are discussed.

As the research on this topic is still in its infancy, it is believed that this study can be considered as a roadmap for future works in the field. In this regard, current status and development trends are delineated through providing a general overview of the literature. Moreover, several recommendations for extending the applicability and efficiency of next generation of crowd-powered search engines are presented. In fact, becoming aware of different aspects and challenges of constructing search engines of this kind can shed light on the way of developing working systems with respect to essential considerations.

The present study was aimed to portrait the big picture of crowd-powered search engines and possible challenges and issues. As one of the early works that provided a comprehensive report on different aspects of the topic, it can be regarded as a reference point.

The purpose of this paper is to investigate the relationship between cash holdings, investment opportunities and financial constraint with audit fees in Iran.

In order to collect data, all manufacturing companies listed on the Tehran Stock Exchange are used to test the hypotheses during 2008–2015. Panel data and combined data regression model were used for data analysis. Tests were performed using R statistical software.

The results obtained from the statistical analysis of research hypotheses indicated that there is a significant relationship between cash holdings and audit fees. Furthermore, the relationship between cash holdings, financial constraints and audit fees was significant. In addition, there was no significant relationship between cash holdings, investment opportunities and audit fees.

The current study employed a unique topic in terms of a developing country, and the results may give strength to other developing nations.

This study investigates the influence of corporate culture on financial reporting transparency within Iranian firms.

Leveraging a dataset of 1,480 firm-year observations from the Tehran Stock Exchange spanning from 2013 to 2022, the study employs text mining to quantify linguistic features of corporate culture and transparency, specifically readability and tone, within annual financial statements and Management Discussion and Analysis (MD&A) reports.

Our results confirm a positive and significant relationship between corporate culture and financial reporting transparency. The distinct dimensions of corporate culture — Creativity, Competition, Control, and Collaboration — each uniquely enhance financial transparency. Robustness tests including firm fixed-effects, entropy balancing, Generalized Method of Moments (GMM), and Propensity Score Matching (PSM) validate the profound influence of corporate culture on transparency. Additionally, our analysis shows that corporate culture significantly affects the disclosure of business, operational, and financial risks, with varying impacts across risk categories. Cross-sectional analysis further reveals how the impact of corporate culture on transparency varies significantly across different industries and firm sizes.

The study’s scope, while focused on Iran, opens avenues for comparative research in different cultural and regulatory environments. Its reliance on text mining could be complemented by qualitative methods to capture more nuanced linguistic subtleties.

Findings underscore the strategic importance of cultivating a transparent corporate culture for enhancing financial reporting practices and stakeholder trust, particularly in emerging economies with similar dynamics to Iran.

This research is pioneering in its quantitative analysis of the textual features of corporate culture and its impact on transparency within Iranian corporate reports, integrating foundational theoretical perspectives with empirical evidence.

The purpose of this study is to present a comprehensive knowledge mapping and an in-depth analysis of pro-environmental travel behaviour research to better understand the global trend in this field that have emerged between 2000 and 2021.

In this study, a visual analysis of 187 scholarly articles between the year 2000 and 2021 related to pro-environmental travel behaviour (PETB) is presented. Using the knowledge mapping based on CiteSpace it presents the current research status, which contains the analysis of collaboration network, co-citation network, and emerging trends.

The results revealed that the PETB is an emerging topic, which has an increased number of publications in recent years. Though the collaboration network between scholars is dispersed, some countries exert stronger collaboration network. Researchers from England, USA and China have worked more on this topic comparatively. “Pro-environmental norm” is found to be the major concern in regard to PETB, and the theory of planned behaviour (TPB) is the most common theory used by the scholars around the world. Ten articles with the highest citations are found to be the most valuable articles. COVID-19, value orientation, negative spillover, carbon footprints, biospheric and adolescent are some of the latest keywords based on the past two years' literature review, all of which have huge research potential in the future.

This study is among the pioneers to shed some light on the current research progress of PETB by using a bibliometric analysis to provide research directions for scholars. Moreover, this study utilized latest data from 2000 to 2021. The studies which are published before and during the pandemic are also incorporated.

To enhance the loan repayment performance of microfinance institutions (MFIs) in Pakistan, this study aims to analyze the direct impact of social capital and loan credit terms on loan repayment performance and microenterprises’ business performance while considering the mediating role of microenterprises’ business performance on the relationship between social capital, loan credit terms and loan repayment performance.

The analysis was conducted based on the data gathered via a questionnaire distributed to 316 microenterprises owners. The respondents were selected using the stratified sampling technique by dividing the target population into three influential groups of manufacturing, trading and services microenterprises. The reliability and validity of the constructs were established using (1) factor loading, (2) Cronbach’s alpha, (3) composite reliability, (4) average variance extracted, (5) the variance inflation factor, (6) the Fornell–Larcker criterion and (7) the heterotrait–monotrait ratio. The structural equation modeling technique was then applied, and the hypotheses were tested based on the structure model generated through bootstrapping by using partial least squares structural equation modeling.

The results confirm the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance. It also supports the mediating role of microenterprises’ business performance toward the relationship between social capital, loan credit terms and loan repayment performance while considering the direct impact of microenterprises’ business performance on loan repayment performance.

To date, the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance has been hardly investigated in the context of Pakistan. This study also examines the mediating role of microenterprises’ business performance toward social capital, loan credit terms and loan repayment performance. The findings will enable both MFIs and microenterprises to improve their business performance and loan repayment performance through enhanced social ties and the development of more flexible credit products that protect the borrowers’ interests and the interest of lenders.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

This paper aims to examine the effect of financial restatement on changing the auditor in the following years.

The study uses data of 105 companies (735 company-years) listed on the Tehran Stock Exchange collected during the period 2008-2014. Logistic regression is used to test the hypotheses.

The results of hypotheses present that restatement does not cause auditor changes and that as the severity of a restatement increases, the auditor change in the following year of restatement also does not increase. Restating companies having strong governance do not go for auditor changes as compared with other companies. In addition, in companies that are restating, non-big auditor changes are not more likely than a big auditor. Also, in companies restating simultaneous with a CEO turnover, there is no possibility of auditor change. Furthermore, multinomial logistic regression showed that the adjustments resulting from the correction of errors and changes in procedures and the amount of adjustments do not cause auditor change in the following year. So, the results have shown that the restatement is not an important factor in changing auditor the next year.

The current study analyses the impact of financial restatement on auditor changes in a deep manner in a developing country like Iran.