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Formulating strategic decisions poses a significant challenge for construction organizations, profoundly impacting their overarching strategic management. The success of an organization’s strategy relies on how information is managed and decisions are executed. However, the literature has a limited understanding of the connection between information quality and strategic decision-making, particularly in construction business performance. This study aims to bridge this gap by exploring how information quality mediates the relationship between strategic decision-making and the performance of construction businesses in New Zealand.

This quantitative study aims to fill this gap by assessing how information quality shapes strategic decision-making practices, impacting construction organizations’ performance. Analysing 102 viable responses through partial least squares structural equation modeling structural equation modelling offers partial support to the research framework.

The study used statistical analysis to gauge the impact of adopting strategic management practices on construction business performance, considering the mediation of the quality of information within New Zealand’s context. It affirmed a positive correlation between strategic decision-making management and construction business performance, underpinned by the mediation of quality of information.

This study underscores the critical role of information quality in evaluating strategic decisions for bolstering construction business performance. In essence, it affirms that enhancing the performance of construction organizations via strategic decision-making is intrinsically linked to the quality of information.

This study makes a noteworthy contribution by establishing connections between decision importance, process effectiveness, information quality, intuition in decision-making and model development, providing valuable insights to the field.

In every manufacturing company, irrespective of size, product and other variables, management are constantly involved in quality‐related decisions which have a direct effect on product quality. An analytical method is described for assessing an organisation's approach to quality management. On application, managers are provided with information on the reality of their quality assurance activities. An overview of some of the issues involved in decision making is provided, followed by an outline of the research methodology, and, finally, the “method” is presented with some results arising from its application.

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.

The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.

Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.

This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.

The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

Recently, the increasing development of digital accounting systems has raised their effects on the quality of decision-making. Consequently, this research aims to evaluate the effects of digital accounting systems success factors on the advancement of decision-making quality in Jordanian banks.

The questionnaires were sent to 187 decision-makers who are actual users of digital accounting systems in Jordanian banks. A quantitative research approach was adopted to test the proposed research model based on the partial least squares-structural equation modeling method.

The empirical results of the current research revealed that data and information quality had a significant impact on the overall decision-making quality with the digital accounting systems, whereas system quality had an insignificant impact on it. The results empirical also confirmed that information quality has mediated the relationship between data and system quality and decision-making quality. Eventually, analytical decision-making culture has moderated the relationship between information quality and decision-making quality.

The current research will provide attractive implications and recommendations for practitioners, accounting managers and decision-makers about evaluating the effect of digital accounting systems on improving the decision-making quality in Jordanian banks.

Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.

The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.

The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.

The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

Without a formulated quality management policy and a direct lead from the chief executive, companies are unlikely to be able to effectively co‐ordinate quality‐related decision making; consequently, the approach to quality tends to be inspection orientated. Results of a study on how quality‐related decisions are made in six companies also suggests that the involvement of quality staff in design, purchasing and market feedback is vital, ensuring that quality‐related decision making is effective and consistent with policy.

Strategic decision making influences organizational performance. However, close examination of this relationship reveals a subtle paradox. It appears that the products of strategic decision making, all of which are necessary for enhanced organizational performance, do not peacefully coexist. Conflict seems to be the crux of this conundrum. As such, a better understanding of conflict's effects on strategic decision making is needed This paper integrates a multidimensional conceptualization of conflict Into a model of strategic decision making and organizational performance and develops propositions to guide empirical study of the effects of conflict on strategic decision making.

The purpose of this paper is to explore the system requirements model. According to the concept of loss costs of Type I and Type II errors, it can define the optimal decision line, and reduce overall loss costs. Moreover, it can decrease the probability of Type I and Type II error by the systems thinking, and it can effectively reduce overall loss costs.

The paper proposed a system demand model and constructed a decision‐making system thinking model as well as a decision‐making performance management model using the principle of system demand. Types of decision‐making errors were analyzed to set judgments on the error risk and establish a model of improvement evaluation key factors, in order to reduce decision‐making error risk and enhance decision quality. It also constructed the improved decision‐making to assess the key factors, to reduce the risk of making errors in order to improve the quality of decision‐making.

Optimistic decision‐makers (risk takers) tend to make Type II errors, whereas pessimistic decision makers (conservatives) tend to make Type I errors. Financial depressions are the time for optimistic decision makers (risk takers) and boom periods are the time for pessimistic decision makers (conservatives).

The concept of the loss cost of two decision‐making errors and related cost function models were proposed. Decision makers could make decisions with a more stable model, taking into consideration false alarms and the cost function of errors in order to determine the position of the decision‐making line. It could effectively reduce decision‐making error costs and increase the precision of decision‐making.