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Organizations are confronted with problems and political risks to which they have to respond, presenting a need to develop tools and frames of understanding requisite to do so. In this article, we argue for the necessity of cultivating “political judgment” with a “sense of reality,” especially in the upper echelons of organizations. This article has two objectives: First to highlight how a number of recent interlinked developments within organizational analysis and practice have contributed to weakening judgment and its accompanying “sense of reality.” Second, to (re)introduce some canonical works that, although less in vogue recently, provide both a source of wisdom and frames of understanding that are key to tackling today’s problems. We begin by mapping the context in which the need for the cultivation of political judgment within organizations has arisen: (i) increasing proliferation of political risks and “wicked problems” to which it is expected that organizations adapt and respond; (ii) a wider historical and contemporary context in which the exercise of judgment has been undermined – a result of a combination of economics-inspired styles of theorizing and an associated obsession with metrics. We also explore the nature of “political judgment” and its accompanying “sense of reality” through the work of authors such as Philip Selznick, Max Weber, Chester Barnard, and Isaiah Berlin. We suggest that these authors have a weighty “sense of reality”; are antithetical to “high,” “abstract,” or “axiomatic” theorizing; and have a profound sense of the burden from exercising political judgment in difficult organizational circumstances.

The present research sought to analyze the effects of customer delight on both internal and external financial structures of publicly traded, service firms.

Primary (i.e. survey) and secondary (i.e. financial records) data sources were gathered. A total of 685 participants responded to one questionnaire focusing on hotels and another one focused on restaurants, both of which measured levels of customer delight and satisfaction. Financial data were gathered from Center for Research in Security Prices, CRSP/COMPUSTAT.

Results of MANOVA revealed that there was a significant difference in the net profit margin (NPM) based on customer delight. Canonical correlation results exposed a significant correlation between satisfaction and delight combined and the financial performance measures (net profit margin, cash flow margin, return on assets and b-beta) combined.

By delighting their customers, managers will achieve higher profit margins. However, these are not likely to result in improved cash flow margin or return on assets. The effects of COVID-19 can alter yearly returns; thus, longitudinal research is needed to continue testing for the effects on delight on financial performance.

The relationship between delight and financial measures had not been previously determined (notwithstanding a few studies using substitute measures for financial performance). The present study uses actual data from the financial filings to empirically test their relationship to customer delight.

Organizational sociology and organization studies have a long history together, while also sharing a proclivity to self-diagnose crises. Instead of taking these assessments at face value, this paper treats them as an object of study, asking what conditions have fueled them. In the case of organizational sociology, there are indications of a connection between rising levels of discontent and community building: self-identified organizational sociologists have progressively withdrawn from general debates in the discipline and turned their attention to organization studies, which, they suspect, has seen dramatic levels of growth at their expense. Organization studies, on the other hand, are still haunted by “a Faustian bargain”: leaning heavily on the authority of the social sciences, business school faculty were able to facilitate the emergence of a scholarly field of practice dedicated to the study of organizations, which they control. However, in doing so, they also set organization studies on a path of continued dependence on knowledge produced elsewhere: notably, by university disciplines such as sociology.

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.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

The primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.

This study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.

The study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.

The research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.

For policymakers and stakeholders in the housing market, this study highlights the tangible effects of renovation subsidies on property values. It provides insights into how such financial incentives can shape the housing market dynamics.

The research underscores the role of government policies in potentially influencing equitable access to housing. It suggests that subsidies like ROT can have broader social implications, including the distribution of housing benefits among different income groups and regions.

This study contributes original insights into the field of applied real estate economics by quantitatively analysing the impact of a specific government subsidy on the housing market. It offers a unique perspective on how fiscal policies can affect property values and renovation activities at the municipal level in Sweden.

Literature on teacher leadership highlights a significant gap in understanding the role of teacher leaders (TLs) as entrepreneurs. This research aims to bridge this gap by examining the multifaceted entrepreneurial dimension of teacher leadership. It specifically focuses on providing a comprehensive profile of these leaders and assessing their perceived influence on teachers’ outcome, which are important for improving school performance.

A two-step clustering procedure was utilized to discern profiles of teacher leaders’ entrepreneurial behaviours, sampling 586 participants in a teacher leader training program. To assess mean differences in relation to perceived influence on teacher outcomes (i.e. job satisfaction, intrateam trust and innovative teaching practices) among these clusters, two-way contingency table analysis and MANOVA were conducted.

We identified three teacher-leader profiles: congenial facilitators, champion-leaders and executors. Our findings reveal the unique strengths and weaknesses of each profile and their contributions to job satisfaction, intrateam trust and innovative teaching practices.

This study is innovative in its detailed examination of teacher leadership through the lens of Teacher Entrepreneurial Behaviour (TEB), providing new perspectives on the intricate relationships between teacher leaders' TEB and their perceived influences. This deeper insight emphasizes the important role of entrepreneurial behaviours within teacher leadership, suggesting new directions for further research and development in educational leadership practices.

This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of regional green development acts as a moderator regarding this relationship.

Using a dataset composed of annual observations from 57 Chinese commercial banks between 2008 and 2021, this study employs both piecewise and curvilinear models.

Our results indicate that when the scale of green credit is low (<0.164), it increases the risk-taking of commercial banks. Conversely, when the scale of green credit is high (>0.164), it reduces the risk-taking of commercial banks. Moreover, this nonlinear relationship impact exhibits bank heterogeneity. Furthermore, the results show that the level of regional green development and local government policy support negatively moderate the relationship between green credit and commercial bank risk-taking. Furthermore, we find that green credit can directly enhance the net interest margin of commercial banks.

This study is the first to provide evidence of a nonlinear relationship between green credit and risk-taking in commercial banks, and it identifies the significant roles of regional green development level and local government policy support in the Chinese context.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

The study aims to determine the long and short-term causal relationships between the variables associated with the adjustment of monetary policy and the stock market in India in the presence of structural breaks.

The study employed the autoregressive distributed lag (ARDL) bounds test and the Error Correction Model to assess long- and short-term causal relationships. The study also used non-frequentist Bayesian inferences for the validity of estimation robustness. The Bai–Perron test is used to identify breakpoint dates for the Indian stock market index, and the Granger Causality test is employed to ascertain the direction of causality.

The F-bounds test reveals cointegration among the variables throughout the examined period. Specifically, the weighted average call money rate (WACR), inflation (WPI), currency exchange rate (EXE), and broad money supply (M3) exhibit statistical significance with precise signs. Furthermore, the study identifies the negative impact of the COVID-19 outbreak in March 2020 on the Indian stock market.

Although the study provides significant insights, it is not exempt from constraints. A significant limitation is selecting a relatively limited time period, specifically from April 2008 to September 2023. The limited time frame of this study may restrict the applicability of the results to more comprehensive economic settings, as dynamics between the monetary policy and the stock market can be influenced by multiple factors over varying time periods. Furthermore, the utilisation of the Weighted Average Call Money Rate (WACR) rather than policy rates such as the Repo rate presents an additional constraint as it may not comprehensively account for the impacts of particular policy initiatives, thereby disregarding essential complexities in the connection between monetary policy variables and financial markets.

The findings of the study suggest that investors and portfolio managers should consider economic issues while developing long-term investing plans. Reserve Bank of India should exercise prudence to prevent any discretionary measures that may lead to a rise in interest rates since this adversely affects the stock market. To mitigate risk, investors should closely monitor the adjustment of monetary policy variables.

The study has important social implications, especially regarding the lower levels of financial literacy among investors in India. Considering the complex nature of the study’s emphasis on monetary policy adjustments and their impact on the stock market. Investors face the risk of significant losses due to unexpected adjustments in monetary policy. Many individuals may need help understanding how policy changes impact their investments. Therefore, RBI must consider both price and financial stability when formulating monetary policies. Furthermore, market participants should consider the potential impact of fluctuating monetary policy variables when devising their long-term investment strategies. Given that adjustments in interest rates can markedly affect stock market dynamics, investors must carefully assess the implications of monetary policy decisions on their portfolios.

The study uses dummy variables in the ARDL model to represent structural breaks that emerged from the COVID-19 pandemic (as determined by the Bai–Perron multiple breakpoint test). The study also used the Perron unit root test to find out the stationary of the series in the presence of structural breaks. Additionally, the study also employed Bayesian inferences to affirm the robustness of the estimates.