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This study investigates the impact of corporate social responsibility (CSR) on corporate innovation. We selected the listed nonfinancial firms of South Asian Economies. The sample of the study comprised a total of 426 listed manufacturing firms of South Asian Countries for period spans 10 years from 2012 to 2021. In this study, descriptive statistics, multicollinearity diagnostic tests, correlation analysis and two-step dynamic panel system generalized method of moments (GMM) were applied to analyze the data. CSR measured with three proxies' social indicators, environmental indicators, and CSR composite index of social and environmental indicators. However, corporate innovation is captured with number of citations received in a year and number of patents filed in the year. Overall, findings of the study using all measures of CSR shows that CSR significantly and positively related with corporate innovation. Our results find support for CSR-innovation view with all measures of CSR. The findings suggest that the current study is helpful for managers, regulators, policymakers, and researchers. For managers, the study helps them to make the CSR and innovation decision. The policymakers should take appropriate innovative decision while considering factors such as CSR. This study can also be extended by considering this study for developed and emerging economies sample.

In recent years, increased use of all-aspect infrared (IR)-guided missiles based on the long-wave infrared (LWIR; 8–12 µm) band has lowered the probability of aircraft survival in warfare. The lock-on of these highly sensitive missiles is difficult to break, especially from the front. Aerodynamically heated swept-back leading edges (SBLE), because of their high temperature and large area, serve as a prominent LWIR source for aircraft detection from the front. This study aims to report the influence of sweep-back angle (Λ, based on the Mach number [M_∞]) on aerodynamic heating and the LWIR signature of SBLE.

The temperature along SBLE is obtained numerically as radiation equilibrium temperature (T_w) by discretizing the SBLE length into “n” number of segments, and for each segment, emission based on T_w is evaluated. IR radiance due to reflected external sources (sky-shine and Earthshine) and radiance due to T_w are collectively used to determine the IR contrast between SBLE and its replaced background in the LWIR band (i_{cont-SBLE,LWIR}).

The results are obtained for low subsonic turboprop aircraft (Λ = 3°, M_∞ = 0.44); high subsonic strategic bombers (Λ = 35°, M_∞ = 0.8); fifth-generation stealth aircraft (Λ = 40°, M_∞ = 1.6); and aircraft with supercruise/supersonic capability (Λ = 50°, M_∞ = 2.5). The aircraft with supersonic capability (Λ = 50°, M_∞ = 2.5) reports the maximum LWIR signatures and hence the highest visibility from the front. The results obtained are compared with values at Λ = 0° for all cases, which shows that increasing Λ significantly reduces aerodynamic heating and LWIR signatures.

The novelty of this study comes from its report on the influence of Λ on the LWIR signatures of aircraft SBLE in the frontal aspect for the first time.

Research and experience show that evidence-based practice (EBP), i.e. using the best available knowledge in daily professional work, is difficult to achieve in social services. The purpose of this study is to understand the development of organizational EBP learning processes in daily work through workplace education for staff and managers of supported homes for people with cognitive disabilities. The authors examine how the EBP model and new knowledge are understood and made actionable in the workplace, applying theories of organizational learning.

The authors used empirical material collected from an EBP workplace education pilot in Sweden, as well as documents on national EBP implementation in Swedish social services. Before the pilot, a focus group interview was conducted with regional senior managers. Participating managers and staff were individually interviewed two to three years after the pilot.

The study illustrates how knowledge-based action emerged from education where EBP was interpreted, understood, reflected on, and tested, supported by codified EBP tools in the work context. The participants, when supervised, and when observing and questioning their own behaviors in practice, contributed to double-loop learning (DLL) processes. Codification of EBP knowledge into useful tools and socialization processes during education and workplace meetings was crucial in developing individual and group DLL and knowledge-based actions.

The bottom-up approach to EBP development and the adaptive contextual learning at the workplace gave new insights into organizational learning in social service workplaces.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

This paper improves the evaluation index system of green building operation effect and establishes the evaluation model of green building operation effect. It is expected to promote energy saving and emission reduction and provide a more scientific evaluation method for green building operation effect evaluation.

First, 20 key evaluation indexes are selected to establish the operation effective evaluation index system. Then, the combined weight method is proposed to determine the weight of each evaluation index. Next, the gray clustering-fuzzy comprehensive evaluation method is used to construct the green building operation effective evaluation model. Finally, the feasibility and validity of the selected model were verified by taking Shenzhen Bay One green building in Shenzhen as an example.

This paper establishes the evaluation system of green building operational effect, and evaluates green building from the angle of operational effect. Taking Shenzhen Bay One project as an example, the rationality and applicability of the model are verified.

In this paper, for the first time, relevant indexes of user experience are included in the evaluation system of green building operational effect, which makes the evaluation system more perfect. In addition, a more scientific fuzzy gray clustering method is used to evaluate the operational effect of green building, and a new evaluation model is established.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Service robots present a new frontier in the provision of services, with far-reaching implications for customers and managers alike. The purpose of this chapter is to examine how service robots impact service providers' current marketing strategies. For this, the authors perform an integrative, nonsystematic review of international gray and academic literature to understand how both practitioners and academics perceive the impacts of the technology. Based on this analysis, the present work identifies three key themes that emerge from the current state of practitioner and academic research, namely (1) service robots demand new core business capabilities and competencies, (2) service robots offer new value propositions, and (3) service robots impact not only service providers' cost structures but also revenue streams. These insights are combined into the Service Robot Innovation Canvas, a visual tool for service providers to identify the impact of service robot implementations on a company's marketing strategy. In addition, based on the analyzed literature, the most pressing questions for researchers are laid out in a research agenda.

The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.

The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.

Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.

A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.