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The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This study aims to investigate the accounting role’s deficiencies in managers’ decision-making processes.

The current research applies a critical review method, which along with a deductive approach – based on a library review of existing sources – examines the underlying causes for the deficiencies of accounting role in the decision-making process of managers; moreover, based on the results obtained, the current study proposes a structural model to explain the issue.

The results exhibit the inadequacies of the accounting role in the decision-making process of managers into three sections: “dilution of financial reporting information content,” “malpractice of accounting information providers” and “managers’ unwillingness to use accounting information.”

This research provides a new perspective on critical accounting studies for the accounting profession, policymakers and managers and invites them to examine the roles of accounting information in more depth and breadth.

This article is the first study that critically expounds upon the literature on the deficiencies of accounting role in the decision-making process of managers and presents these deficiencies in the form of a structural model from three different perspectives.

This paper aims to explore the common ethical decision-making challenges faced by financial advisers and how they meet these challenges. The purpose is to identify successful decision-making tools used by investment advisers in doing business ethically. Additionally, the authors uncover common challenges and offer decision-making tools to provide support for supplemental ethics training in the future.

Questions were analyzed through a qualitative approach using individual interviews to examine a range of experiences and attitudes of active financial advisers. The sample was represented by 11 practicing financial advisers affiliated with US independent broker-dealers: six women and five men, each with 10 or more years of experience, ranging in age from 35 to 75. Grounded in four ethical decision-making models, this research examines individual ethical decision-making using individual (internal, personal) and organizational (external, situational) factors.

The method used uncovered struggles and revealed strategies used in making ethical decisions. Two research questions were examined: what are the common ethical decision-making challenges faced by financial advisers in the US financial industry? How do financial advisers handle ethical decision-making challenges? Four themes emerged that impacted ethical decision-making: needs of the individual, needs of others, needs of the firm and needs of the marketplace. Financial advisers identified moral obligation, self-control and consulting with others as major considerations when they contemplate difficult decisions.

A limitation of this review is its small sample size. A more robust sample size from investment advisers with a broader range of experiences could have widened the findings from the study.

Investment advisers can use the findings of this study as a tool for improving their own ethical decision-making or designing training for their employees to be better decision-makers.

The study explores the decision-making experiences of investment advisers to reveal multifaceted, often private struggles that qualitative methods can uncover. The study provides support for the development of additional training in ethical decision-making specific to investment advisers.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

This paper aims to critique the rationalist theoretical framework of international mediation, which ignores emotions in analyzing the decision by conflict parties to pursue a negotiated settlement or continue fighting, and to present an alternative framework that integrates emotions.

The paper draws on psychology research on emotions and conflict to develop an emotionally informed framework for analyzing conflict parties’ decision-making regarding a settlement. It demonstrates the framework’s validity and value through a case study of the 2000 Camp David mediation to resolve the Israeli–Palestinian conflict.

A rationalist approach to mediation does not have adequate explanatory and predictive power theoretically. In practice, it can reduce the prospect of success.

The paper highlights the necessity for mediation researchers to study the effects of emotion, draw on psychology studies on conflict and explore the emotional implications of different mediation strategies and tactics.

The framework highlights the challenge of designing and conducting mediation in a way that cultivates emotions favorable to a settlement and lessens emotions unfavorable to a settlement.

This is the first study, to the best of the authors’ knowledge, to critique the rationalist framework of international mediation studies and develop an alternative framework that integrates emotions.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.