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The regulatory landscape surrounding international construction projects presents significant challenges, and contractors are still struggling to pay a painful price for their performance in the project. While existing research has identified various causes of contractor compliance, the intricate interplay of these factors and their impact on compliance remain largely elusive. The motivation-opportunity-ability (MOA) framework may hold the key to determining what factors can foster induced contractor compliance in international projects.

This study collected 124 valid data samples from practitioners involved in large-scale international contracting projects through expert interviews and questionnaire surveys. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to analyze the diverse combinations of contractor compliance factors.

The study identifies seven key factors that contribute to compliance behavior among international construction contractors: economic motivation, social motivation, normative motivation, legal completeness, deterrent sanctions, organizational learning and compliance management ability. The interplay of these factors promotes compliance in the following ways: When international construction contractors are influenced by both social and normative motivations, they exhibit a higher level of compliance. In situations where regulatory systems are relatively weak, the ability to manage compliance becomes the primary driver of compliance behavior for businesses. A comprehensive legal framework creates a conducive environment for contractors to improve their compliance through organizational learning.

The findings offer guidance for international construction contractors in enhancing compliance by considering factors such as motivations, legal frameworks, organizational learning and compliance management. This can lead to improved risk management and performance in international projects.

This research enhances fair and ethical practices in international construction by identifying compliance drivers, fostering positive social impact, mitigating negative consequences and empowering local communities. It informs legal and regulatory reform, encourages improved business practices and contributes to knowledge advancement in the field. Overall, the findings have the potential to positively impact the social fabric of international construction projects.

This study has made an important contribution to the field of compliance theory by integrating theories from multiple disciplinary domains and constructing a new theoretical framework from the perspectives of motivation, opportunity and capability. By elucidating how these factors interact and influence compliance behavior among international construction contractors, this research aids in understanding the complex dynamics of contractor compliance behavior and provides theoretical reference for compliance governance within the construction industry.

This paper aims to examine the role of corporate governance (CG) in the earnings management (EM) of affiliated companies in family business groups (FBGs) listed on the Pakistan Stock Exchange (PSX), using principal–principal agency theory.

The sample of 327 nonfinancial firms of the PSX, consisting of 187 group-affiliated firms and 140 nonaffiliated firms has been used in this study for the period of 2010 to 2019. The study uses different regression models for analysis, with robustness tests of various alternative measures of EM and FBG affiliation. In addition, endogeneity is controlled with the propensity score matching method.

The findings show that EM is less prevalent in affiliated firms compared to nonaffiliated companies. The results show a negative and significant relationship between FBGs affiliated firms and EM. Moreover, the results also show a positive relationship between EM and the interaction term of the CG index and group affiliation. It refers to the fact that effective governance cannot reduce EM in affiliated companies of FBGs as well as in the nonfinancial companies of the PSX. In addition, the quality of CG is higher in affiliated companies compared to its counterpart in nonaffiliated firms. The findings support the principal–principal agency theory that CG cannot mitigate the expropriating behavior of controlling shareholders against minority shareholders by reducing EM in emerging markets due to the ownership concentration phenomenon.

This research study has implications for small investors, government agencies and regulators. The findings of the study show that CG code should make it mandatory for companies to reveal information about their complex ownership structure and ownership information about affiliated companies and directors. Furthermore, it is suggested to revisit the code of CG in the Pakistani context of principal–principal conflict instead of the agent–principal explanation of agency theory based on Anglo–Saxon countries.

This research study has contributed to the CG and FBG literature in relation to EM in idiosyncratic settings of Pakistan. One of the prime contributions of the paper is the development of a comprehensive CG index. This research study used detailed, manually collected novel data on affiliated firms of FBGs in Pakistan.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.