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This paper presents a realist evaluation of leadership within an integrated care system (ICS) in England. This paper aims to examine which aspects of leadership are effective, for whom, how and under what circumstances.

Realist evaluation methodology was used, adopting prior realist review findings as the theoretical framework to refine explanations of how and why leadership within an ICS is effective. Between January and November 2023, 23 interviews with ICS leaders took place, alongside 7 meeting observations and documentary analysis. The Realist And Metanarrative Evidence Syntheses: Evolving Standards (RAMESES) guidance informed the study design, conduct and reporting.

The findings highlight two overarching infrastructural contexts influencing leadership in ICSs: the impact of the post-COVID-19 pandemic legacy and the differences between health and social care regulatory and financial environments. Findings demonstrate that ICS leaders identified a strong sense of purpose as crucial for guiding decisions and creating a psychologically safe environment for open, honest discussions, fostering calculated risk-taking. Whilst a shared vision directed priority setting, financial pressures led to siloed thinking. Leadership visibility was linked to workforce morale, with supportive leadership boosting morale amidst evolving ICS landscapes and confidence in data-driven decisions supported prevention activities. However, financial constraints hindered responsiveness and innovation in addressing health inequalities.

By examining ICS leadership post-COVID-19 pandemic and amidst varying regulatory and financial environments, this study contributes to the emerging literature on systems leadership and offers practical guidance for leaders navigating the complexities of integrated care.

In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.

This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.

Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.

This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.

This research proposes a viable method of slab and shore load computation for the partial striking technique utilized in high-rise construction projects to optimize the use of horizontal formwork. The proposed Partial Striking Simplified Method (PSSM) is designed to be utilized by industry practitioners to schedule the construction operations of casting floors in order to control the formwork costs incurred throughout the completion of a project.

The article presents the PSSM for calculating slab and shore loads in multi-story building construction. It introduces the concept of “clearing before striking,” where shore supports are partially removed after a few days of pouring fresh concrete. The PSSM procedure is validated through numerical analysis and compared to other simplified approaches. Additionally, a user-friendly Python program based on the PSSM procedure is developed to explore the capability of the PSSM procedure and is used to study the variations in slab load, shoring level, concrete grade and cycle time.

The study successfully developed a more efficient and reliable method for estimating the loads on shores and slabs using partial striking techniques for multi-story building construction. Compared to other simplified approaches, the PSSM procedure is simpler and more precise, as demonstrated through numerical analysis. The mean of shore and slab load ratios are 1.08 and 1.07, respectively, which seems to have a slight standard deviation of 0.29 and 0.21 with 3D numerical analysis. The Python program developed for load estimation is effective in exploring the capability of the proposed PSSM procedure. The Python program's ability to identify the floor under maximum load and determine the specific construction stage provides valuable insights for multi-story construction, enabling informed decision-making and optimization of construction methods.

High-rise construction in Indian cities is booming, though this trend is not shared by all the country's major metropolitan areas. The growing construction sector in urban cities demands rapid construction for efficient utilization of formwork to control the construction costs of project. The proposed procedure is the best option to optimize the formwork construction cost, construction cycle time, the suitable formwork system with optimum cost, concrete grade for the adopted level of shoring in partaking and many more.

The proposed PSSM reduces the calculation complexity of the existing simplified method. This is done by considering the identical slab stiffness and identical shore layout for uniform load distribution throughout the structure. This procedure utilizes a two-step load distribution calculation for clearing phase. Initially, the 66% prop load of highest floor level is distributed uniformly over the lower interconnected slabs. In the second step, the total prop load is removed equally from all slabs below it. This makes the load distribution user-friendly for the industry expert.

This paper aims to outlines a model for water main rehabilitation in Kitchener, Ontario, using a machine-learning approach. Water main networks are vital infrastructure, requiring regular condition assessments to ensure consistent service. Budgets are often allocated for nondestructive testing methods, but using machine learning to predict network conditions offers cost benefits.

The study focuses on a prediction approach that includes the rehabilitation requirement model. The Decision Tree machine learning method was applied to predict water main pipe breaks in 2024. Based on the predictions, 24 pipes were identified for rehabilitation, and the appropriate Trenchless Rehabilitation Method was selected accordingly.

The model, applied to data from Kitchener, successfully predicted 24 water main pipe breaks for 2024. The largest pipe diameter was 1200 mm, and the longest length was 6977 m. A cost comparison, factoring in Environmental and Social (E&S) costs, showed that open-cut methods were 25% more expensive than Cured-in-Place Pipe (CIPP). When E&S costs were included, the total cost of the open-cut method increased by approximately 300% compared to sliplining.

Based on the pipe characteristics, CIPP lining and sliplining are recommended for rehabilitation by the City of Kitchener. This study presents a novel approach using Decision Tree machine learning techniques to predict pipe breaks, with a 97% prediction accuracy, making it a promising alternative to traditional models.

This chapter proposes a framework explaining the evolution of property rights in land, assuming two unequal groups of actors: elites possessing means of violence and nonelite land cultivators. It then shows that all intermediary groups – those acting between the chief violence holders (i.e., rulers) and cultivators – are in effect (greater or lesser rulers and cultivators). Using this framework, this chapter explains most of the developments in the evolution of land rights in 19th century colonial Bengal. The proposed theoretical framework explains how different, hierarchically arrayed claims over land and the resulting allocation of rights was a function of asymmetries in power and information between three groups: rulers, direct cultivators, and intermediaries without their own coercive means. It explains inter alia why private property in land was not likely to emerge in this configuration, and that the (non-private) property rights of the other two groups wouldn't attain stability as long as rulers perceived an information asymmetry. In such a situation, land rights would attain neither “private,” nor “public” character.

This study aims to examine the applicability of Hartmann et al.’s (2018) service ecosystem framework to the day-to-day management of the modern sales force. The authors provide a review of the framework, acknowledging its strengths, while also indicating areas for advancement. The authors conclude with recommendations to the framework and indicate opportunities where future research could advance sales theory.

A review of the theoretical underpinnings of the service ecosystem framework is weighed against the established roles and responsibilities of the modern sales force in the literature.

The ability of the framework to capture the multi-level, multi-actor and dynamic aspects of sales represents an improvement in the conceptualization of selling is critical. Suggestions around the refinement for meso-level sales interactions and a more pliant application of service dominant-logic are offered.

The suggested extensions of the framework continue the advancement of novel theorization for the field of sales. Priorities for future research include consideration of ethical implications of the framework and formulations of new management strategies reflective of the broad and dynamic properties of the ecosystem conceptualization.

This paper provides managerial guidelines and implications tied specifically to the thick and thin crossing points and how they may impact employee decision-making.

To the best of the authors’ knowledge, this study is the first to pointedly examine the service ecosystem framework with respect to established principles of managing a modern sales force.

In this chapter, I will outline the labels of giftedness and underachievement and present the theoretical debates surrounding these labels. A historicist examination of these labels follows, highlighting how the gifted underachievement (GUA) label emerges through the negation of “giftedness.” Subsequently, I explore the concept of GUA and its negative connotations, stemming from the positive valuation inherent in the term “giftedness” and its implications for what is considered “normal.” This chapter also reviews perspectives on shifting the focus away from the individual within the current paradigm of labeling giftedness and explores insights from systemic thinking and symbolic interactionism (SI). The conclusion underscores the necessity of a symbolic interactionist perspective to address the gaps in research on the labeling of giftedness and underachievement. Finally, I propose a generic definition that can be used in GUA research in the light of SI.

This review identifies low self-concept clarity (SCC) as a source of consumer vulnerabilities and explains how the uncertainty associated with low SCC leads to processes that result in materialistic behaviors and overspending, product dissatisfaction, and potential self-harm. Processes include uncertainty reduction efforts through symbolic self-completion and social comparison, responses to everyday self-concept threats that result in feelings of deficiency and reduced consumption constraints, and susceptibility to interpersonal and marketer influences. In addition, the negative association between SCC and materialism is explained, risk factors for low SCC are described, and the need for research to help low SCC consumers deal with their vulnerabilities is explored.

To argue current calls to address grand challenges like income inequality are unlikely to succeed until the academy acknowledges how accounting is constitutive of these problems. We demonstrate how accounting is part of the problem because of its adherence to a legal model of the corporation erected on false suppositions.

Using multiple disciplines, e.g. history, economics, law and philosophy, pertaining to the nature of the corporate form, we present a logical argument that the official telos of accounting obstructs any fruitful effort to address grand challenges.

The global legal concept governing corporations (an aggregate of members) makes corporations a major cause of the grand challenges humans face. Adherence to a legal theory of the corporation leads accounting policy to rationalize income and wealth inequality by subsuming the legal powers of corporations to expropriate wealth into a singular maximand labeled “earnings.”

Though accounting is essentially “of” law, scholarly efforts to understand accounting’s social role are based on an information metaphor. We provide reasons for skepticism of any efforts addressing grand challenges until accounting acknowledges the legal nature of its social role as a regulator of business conduct. There are no accounting solutions to grand challenges without acknowledging how the accepted legal nature of the corporate form makes the corporation the cause of the grand challenges we face.

The purpose of this research is to establish a robust numerical framework for the calibration of macroscopic constitutive parameters, based on the analysis of polycrystalline RVEs with computational homogenisation.

This framework is composed of four building-blocks: (1) the multi-scale model, consisting of polycrystalline RVEs, where the grains are modelled with anisotropic crystal plasticity, and computational homogenisation to link the scales, (2) a set of loading cases to generate the reference responses, (3) the von Mises elasto-plastic model to be calibrated, and (4) the optimisation algorithms to solve the inverse identification problem. Several optimisation algorithms are assessed through a reference identification problem. Thereafter, different calibration strategies are tested. The accuracy of the calibrated models is evaluated by comparing their results against an FE2 model and experimental data.

In the initial tests, the LIPO optimiser performs the best. Good results accuracy is obtained with the calibrated constitutive models. The computing time needed by the FE2 simulations is 5 orders of magnitude larger, compared to the standard macroscopic simulations, demonstrating how this framework is suitable to obtain efficient micro-mechanics-informed constitutive models.

This contribution proposes a numerical framework, based on FE2 and macro-scale single element simulations, where the calibration of constitutive laws is informed by multi-scale analysis. The most efficient combination of optimisation algorithm and definition of the objective function is studied, and the robustness of the proposed approach is demonstrated by validation with both numerical and experimental data.