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With growing concern about sustainable development and increased awareness of environmental issues, digital technologies (DTs) are gaining prominence and becoming a promising trend to improve productivity, sustainability and project performance in the construction industry. Nonetheless, the uptake of DTs in the construction industry has been limited and plagued with roadblocks. This study aims to identify critical barriers for construction organisations to adopt DTs and to demonstrate relationships between organisational characteristics and the perceived DTs adoption barriers.

This study adopted an explanatory sequential design by combining the advantages of quantitative and qualitative data. Data collection methods include literature review, a pilot study, questionnaire survey, and semi-structured interviews. Questionnaire data were analysed by using SPSS and multivariate regression technique. The interview data were processed by using content analysis to validate and supplement findings from the questionnaire.

Based on the survey and interview results, eight critical barriers were identified: the three top critical barriers are (1) “status quo industry standards”, (2) “lack of client interest” and (3) “lack of financial need/drive for using DTs”. The eight critical barriers were further classified into technical, environmental, and social dimensions to determine the major constructs that hinder DTs adoption. A theoretical framework articulating critical barriers with underlying components and root causes was also proposed. Furthermore, by using multivariate regression analysis, a model was developed to link the organisational characteristics with barriers to DTs adoption.

By referring to the framework and the model developed, academics, industry practitioners, and decision makers can identify pivotal areas for improvement, make informed decisions and implement remedial measures to remove the barriers to digitalisation transformation.

This study contributes to the literature on construction innovations by investigating barriers to DTs adoption holistically as well as perceptions of the impact of organisational attributes on these barriers. It establishes the groundwork for future empirical research into the strategic consolidation of movement of DTs adoption and diffusion.

Financial reports are the basis of economic decisions that affect organizational interests and shareholders. However, there is a severe research gap concerning the factors affecting the quality of financial information (such as audit report readability and tone). Therefore, considering the importance of presenting high-quality financial information, this study aims to investigate the impact of intellectual capital (IC) and its components on the audit report's readability and tone.

The multivariate regression model tests research hypotheses. Then, hypotheses are tested via a sample of 824 observations of the listed companies on the Tehran Stock Exchange (103 companies) from 2014 to 2021, using the multivariate regression model based on pooled data and fixed effects.

Results determine that customer capital (CC) and structural capital (SC) are likely to influence the audit report tone positively. In general, the IC and human capital (HC) negatively impact auditors' tone. More analyses also document that IC and its CC, HC and SC components positively and significantly affect audit report readability based on two readability indices, including FOG and text length. Finally, findings pertaining to the third readability index (Flesch index) reveal that only HC and SC are robust based on this measurement, whereas the IC and CC have a negative and significant impact on the readability of auditors’ reports.

To the best of the authors’ knowledge, this study is the first to address this issue in emerging markets, and it provides helpful insights for users, analysts and legal institutions regarding IC, which significantly affects audit report readability and tone.

This study investigates the relationship between management's psychological characteristics (managers' narcissism, overconfidence and managers' myopia) and earnings management in the pre-Islamic State of Iraq and Syria (ISIS) and post-ISIS eras.

A multivariate regression model was used to test the hypotheses. The research hypotheses were tested using a sample of all companies listed on the Iraqi Stock Exchange from 2014 to 2020.

Findings indicate a positive and significant relationship between managers' narcissism, overconfidence and myopia with accrual and real earnings management. According to the results, the ISIS weakens the relationship between managers' narcissism, managers' overconfidence and managers' myopia with accrual and real earnings management.

Because no study has addressed this issue in Iraq so far, the results of this research can provide helpful information for its users and improve the knowledge and science in this area.

This study examined the impact of the Glasgow Climate Pact on the abnormal returns of global clean energy stocks. Further, this study examines which country-specific and firm-specific variables drive the cumulative abnormal returns (CARs) of clean energy stocks.

The authors used the event study method and cross-sectional multivariate regression model. The clean energy stocks in this study are limited to 81 constituent firms of the S&P Global Clean Energy Index across 17 nations. The final sample includes 80 firms and the sample period ranges from January 26, 2021, to December 07, 2021.

The study finds that the Glasgow Climate Pact negatively affects the stock returns of clean energy firms. Moreover, the climate change performance index (CCPI) positively impacts cumulative abnormal returns (CARs), signifying that clean energy investors react positively to firms in nations with good CCPI scores. The environmental, social and governance (ESG) measure for the shorter window (−1, +1) exhibited a negative relationship with CARs. The firm-specific variables (BTM, stock liquidity, size and past returns) exhibit a negative relationship with CARs in different event windows.

The authors use the CCPI as a proxy for the stringency of environmental policies in any nation. The authors extend the existing literature by employing firm-specific variables and supporting previous findings. Their findings have policy implications for clean energy investors, policymakers and other market participants.

Climate risks impact the global financial market, so the findings have implications for global regulatory bodies. Currently, there are bankruptcy cases due to climate risks. Because financial markets must play a critical role in shifting the economy toward a green one, regulators can use the cross-sectional drivers of this study to shape policy. It is also critical for regulators to reduce stock price volatility in the event of the implementation of environmental regulations and improve environmental disclosures by publicly traded companies. Furthermore, governments are interested in researching the effects of environmental regulations to protect stakeholders' interests. These regulations significantly impact emerging markets because they lack the same solid institutional frameworks as developed markets.

The authors provide evidence that firms with better ESG scores and larger firm sizes have experienced fewer abnormal returns, as these firms have stable financial and non-financial fundamentals. This timely study on the ongoing regulatory shift in environmental policy will help investors, policymakers, firms and other stakeholders make relevant decisions.

A perusal of extant literature suggests asymmetric devotions in empirical research that overlooks unique social and industrial dynamics of married working women in highly competitive male-dominated industries. Drawing on the social role theory, the study addressed this asymmetry by proposing a multivariate regression model that examined performance drivers of married female real estate brokers, as mediated by organizational, cognitive and social influences.

The study employed a descriptive design. A structured electronic questionnaire was used to collect data from a sample of 256 married female brokers who are active on online messaging platforms. Snowball sampling was used to reach the research participants. Collected data were subjected to multivariate regression analysis.

The results infer that social influences were the most significant drivers of married female brokers in the real estate industry (r = 0.932, p < 0.05, F = 556.581). Significant interventions of diversity management, work-life balance, family support and access to economic opportunities were found.

In acknowledging the societal stereotype and culturally imposed burdens married women face in emerging economies, the study advanced theoretical, practical and policy initiatives for a more inclusive, supportive and learning-oriented work environment.

The novelty of the paper lies in its exposition of the unregulated real estate brokerage market in an emerging economy and the dynamic organizational, cognitive and societal influences of married female brokers.

This study aims to examine the timing of corporate disclosure in the context of Georgia, an emerging market where a recent reform of corporate financial transparency mandated about 80,000 private sector entities to publicly disclose their annual financial statements.

The main analysis covers more than 4,000 large, medium, small and micro private sector entities, for which the data is obtained from the Ministry of Finance of Georgia. This paper builds an empirical model of logit/probit regression, with industry fixed and random effects to investigate the drivers of the corporate disclosure timing.

Findings suggest that the mean reporting time lag is 279 days after the fiscal year-end, that is nine days after the statutory deadline. Almost one-third (30%) of the entities miss the nine-month statutory deadline, while the timely filers almost unexceptionally file immediately before the deadline. Multivariate tests reveal that voluntarily filing entities completed the process significantly faster than those mandated to do so; audited financial statements take more time to be filed, whereas those with unqualified audit opinion or audited by large/international audit firms are filed faster than their counterparts. The author concludes that despite the overall high filing rates, the timing of corporate disclosure is not (yet) efficiently enforced in practice (but is progressing over time), whereas regulatory incentives prevail over market incentives among the timely filers.

To the best of the author’s knowledge, this is the first study that explores corporate disclosure timing incentives in the context of Georgia. This study extends prior literature on the timing of financial information from an emerging country’s private sector perspective, with juxtaposed market and regulatory incentives.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.

Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.

The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.

It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.

The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.

This study aims to determine whether the stock holdings of equity mutual funds are informative for predicting future stock performance in the Chinese market. It is a puzzle that actively managed mutual funds underperform passive benchmarks, whereas retail investors still delegate investment decisions to the fund managers. The present study sheds light on whether mutual fund managers possess security selection skills in their top ten holdings.

By regression analysis and portfolio sorting, this study focuses on 830 Chinese A-share stocks in the industry research reports from the Guotai Junan Securities Company. It collects mutual fund's top ten holdings data from the Wind Financial Terminal between 2019Q1 and 2021Q1. As robustness checks, the result holds for the fixed-effect model, an additional measure of ranks in the top ten holdings, the predictability test based on the confusion matrix and two stage least square (2SLS) regression.

The authors find that the top ten holdings by equity mutual funds are informative for predicting stock performance and can provide valuable information for investors to support their decision-making.

The findings of this study provide insightful guidance for retail investors in making investment decisions and support the hypothesis that active fund management adds value.

Firstly, the authors find that the top ten holdings of Chinese mutual funds show significantly positive signals for future stock excess returns, indicating the selection skills of fund managers. Secondly, the above positive relationship exhibits a diminishing marginal effect with more funds holding this stock. Thirdly, the authors find that the predictability horizon of the number of overweighing funds is up to three quarters and then diminishes in the fourth quarter. Finally, investors have a 59% prediction accuracy for the whole stock sample and an 85% precision conditional on the predicted positive subsample to outperform the market. The authors also address the endogeneity and reverse causality issues.

The paper identifies the influence of socio-economic factors and livelihood training in stimulating micro-entrepreneurship among women self-help group (SHG) members.

The study is based on a sample of 416 women SHG members drawn from all the four districts of Sikkim using cluster sampling procedure. A multivariate binary logistic model is used to find the impact of socio-economic factors, and a Poisson regression has been used to find the impact of training on fostering micro-entrepreneurship. The result is validated using a propensity score matching approach which corrects for the potential self-selection bias in the sample. Subsequently, a covariate adjustment estimator verifies the robustness of the approach.

The study finds that “size of landownership”, “amount of loan borrowed”, “member's age”, “number of earning and dependent members”, “number of years of SHG enrolment” as well as the “district to which the member belongs to” have a statistically significant influence on the graduation of SHG members to micro-entrepreneurs. Furthermore, it is found that members attending the livelihood training programmes had a significantly higher number of microenterprises.

The study differentiates itself by providing empirical evidence on how socio-economic factors and livelihood training stimulate micro-entrepreneurship among SHG women of Sikkim, which has so far remained unexplored. Moreover, advanced econometric method has been used to eliminate the possible self-selection bias involved with training participation and thereby provides reliable and robust results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0070