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In the era of big data, there is doubt about the significance of causal inference as a paramount scientific task in the social sciences. Meanwhile, data-mining techniques rooted in big data and artificial intelligence (AI) have infiltrated numerous aspects of social science research. This study aims to expound the criticality of discerning causal relationships – beyond mere correlations – and scrutinizes the ramifications of big data and AI in the identification of causality.

This study discusses the challenges and opportunities for causality identification in the era of big data under the framework of potential outcomes model and structural causal model.

First, even in the age of big data, correlations that lack interpretability, robustness and feasibility cannot substitute causality. Second, the richness of the sample size does not help solve the problem of systematic bias in the process of causal inference. Furthermore, current AI research targets correlations rather than causality, thus creating difficulties in advancing from observations to counterfactuals.

This study provides insights into the impact of big data era on causal inference in the social sciences, with a view toward enhancing the pool of theoretical concepts available to researchers in relevant fields and accurately guiding the direction of scientific research in these fields.

The current study used the social exchange theory to examine the dimensional impact of total quality management (TQM) on the organisational citizenship behaviour for the environment (OCBE) of employees through the mediatory role of environmental corporate social responsibility (ECSR).

The data were collected from 311 employees of ISO-14001-certified manufacturing firms in Pakistan. Both symmetrical partial least squares structural equation modelling (PLS-SEM) and asymmetrical fuzzy-set qualitative comparative analysis (fsQCA) methods were applied to test the proposed hypotheses to enhance the robustness of the results.

PLS-SEM results discovered that all dimensions of TQM, like process management, leadership, human resource management, customer focus, information and analysis and strategic planning, foster the OCBE through the mediation role of ECSR. The robustness of the findings was manifold when fsQCA results complemented the results by discovering that all six dimensions have been identified as sufficient conditions and some as necessary conditions to drive the OCBE.

The theoretical contribution of this study sheds light on TQM's function in boosting OCBE through the mediation of ECSR. Practically, the business managers may utilise TQM as a strategy to foster the OCBE in order to mitigate environmental damages of their organisations’ operations by instilling OCBE among the employees.

This paper offers a new perspective on the potential motivation for the adoption of employee ownership based on market power. Employee ownership may be linked to market power, either through contributing to firm growth that leads to market power or through industry leaders adopting employee ownership as part of rent sharing or a broader consolidation of market position. Both employee stock ownership plan (ESOP) coverage and product market concentration (PMC) have been increasing in the past two decades, providing a good opportunity to see if and how these are related.

The authors predict ESOP adoption and termination using multilevel regressions based on 2002–2012 firm- and industry-level data from the Census Bureau, Compustat and Form 5500 pension datasets.

The authors find that the top four firms in concentrated industries are more likely to adopt Employee Stock Ownership Plans (ESOPs), while having an ESOP does not predict entering the top four, apart from firm-level predictors. Tests indicate the first result does not reflect simple rent sharing with employees but instead appears to reflect an effort by firms to consolidate market power through the attraction and retention (or “locking in”) of industry talent. Other positive predictors of ESOPs include company size, being in a high-wage industry and having a defined benefit (DB) pension.

To better distinguish among hypotheses, it would be helpful to have firm-level data on managerial attitudes, strategies, networks and monopsony measures. Therefore, future research using such data would be highly useful and encouraged.

The paper includes implications for the potential usefulness of ESOPs in attracting and retaining talent and for the design of nuanced policy to encourage more broadly based sharing of economic rewards.

While prior research focuses on firm-level predictors of employee ownership, this study uses market concentration and other industry-level variables to predict the use of ESOPs. This study makes a unique contribution, broadening the current thinking on firm motives and environmental conditions predictive of firm ESOP adoption.

This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.

The purpose of this study is to investigate the influences of the characteristics of procurement strategies, in this instance labour-only, on project performance concerning health and safety (H&S), a project performance indicator.

Using non-probability purposeful and snowballing sampling methods, questionnaires were used to collect data from construction professionals in Nigeria. This was then analysed using descriptive (frequency and mean scores) and inferential statistics (Mann–Whitney-U and Kendall's Tau_b tests).

The findings indicate a statistically significant negative correlation between ‘the level of client involvement and ‘fatalities' and a positive one with ‘conducting of health and safety risk assessment' and ‘conducting employee surveys on health and safety attitude’. Poor hygiene is found to be the worst lagging indicator, while conducting of inspection is the most adopted leading indicator of project health and safety performance. It also emerged that there is no significant difference in the health and safety performance of projects procured through the procurement strategy in urban and rural areas.

The study provides valuable insight into the complexities in H&S management due to the high level of client involvement in labour-only procurement system (LoPS) projects and the level of diversity in their responsibilities therein. It creates a fundamental direction for developing a detailed framework or guidance notes for client involvement in the integration of H&S into LoPS projects.

This is the first study that examines the influence of the characteristics of procurement strategy on project health and safety performance. Evidence in the literature shows that project delivery outcomes significantly improve if procurement is strategically used, including when it is considered early in projects. However, integrating H&S into procurement strategies has received little attention.

Drawing on the organizational learning and relational governance literature, this study aims to advance a theoretical model to explain the export performance of emerging market export ventures.

This study selects quantitative methodology because the main objective of this study is to explore the role of export ventures’ performance (past) on guanxi networking, co-creation marketing strategies and present performance.

The empirical evidence suggests that guanxi networking and co-creation strategy can mediate the relationship between export venture performance in the preceding year and export venture performance in the following year. In addition, this study also provides some guidance for emerging market export ventures on how to build a strong guanxi networking and create opportunities for collaboration when the effect of export performance in the preceding year on current performance is absent.

The authors propose the inclusion of strategic guanxi networking-related factors (e.g. top executives’ ties with business-to-business customers, such as distributors in the host market) in the prior performance-current performance paradigm. The outcomes of this study also contribute to extant organizational learning theory research by integrating preceding performance research with the co-creation theory. The study offers new insights into organizational learning and relational governance from the emerging market perspective.

The study aims to investigate the nurse job enjoyment and their participation in medical affairs to enchance the quality of patient care in Saudi hospitals. In addition, this study also investigates the management team's role in patient care quality through the mediating effects of nurse job enjoyment and participation in medical affairs.

The present study used a self-administered survey questionnaire to collect data from registered nurses in Saudi hospitals. In this study, 600 survey questionnaires were distributed online (Google Forms) and received 266 valid responses (44.33% response rate). In addition, SmartPLS-4.0 was applied to validate the research constructs and test the hypotheses via partial least squares structural equation modelling (PLS-SEM).

The study's findings indicate that the job enjoyment of the nurses and participation in medical affairs have positive and significant effects on the quality of patient care. In addition, the research findings also suggest that the management team of the hospitals has a significant indirect influence on the quality of patient care through the medicating effects of nurse job enjoyment and participation in medical affairs.

The findings of this study also offer various practical implications. This study showed the direct impact of the management team on job enjoyment, medical affairs and patient care quality. Therefore, hospital authorities and policymakers may emphasise clear communication, collaboration, respect and trust for the effective management team in providing higher-quality patient care. The present study suggests that hospital policymakers should strive to create a positive work environment, provide adequate resources, foster team spirit, offer incentives and allow flexible scheduling to ensure higher job enjoyment and increase nurse participation in medical affairs.

This study adds to the growing body of knowledge by investigating the effects of the management team, job enjoyment and nurses' participation in medical affairs on patient care quality. This study also enhances the theoretical depth by exploring the mediating impact of job enjoyment in predicting the relationships between the management team and the quality of care provided to patients. The present study provides guidelines for healthcare service providers or practitioners to focus on the nurses' job enjoyment and their engagement in medical activities to continuously improve the quality of patient care in the hospitals.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

This paper aims to devise a case-controlled method combined with Bradford Hill criteria for causal inference of contractual disputes in construction projects. It is a genuine attempt in a systematic method from research design to execution for causal issues where only observational data is available.

The authors located insufficient top management support as the putative pathogen of construction disputes based on a literature review, an interview and Delphi surveys. A questionnaire survey was then conducted to collect case-controlled data to ensure comparability, in which for each disputed project put in the experimental group, the authors sought for a dispute-free project of similar characteristics. The incidence rates of insufficient top management support in the experimental and control groups were then examined by Bradford Hill criteria as an alternative to the test of intervention effect.

The association of insufficient top management support and construction disputes was tested to conform with the Bradford Hill criteria with case-controlled data where applicable and logical deduction where statistical tests were not applicable. With a clear, positive, reasonable and statistically significant association, while excluding methodological biases, confounding and chance, the authors reached a causal verdict of insufficient top management support causing contractual disputes.

This paper supports the validity of applying a case-controlled method combined with Bradford Hill criteria in investigating causal issues in project management, especially the verdict of causal inference based on empirical data. In addition, the located root cause of contractual disputes could inform project management personnel with reasoned strategies for dispute avoidance.