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“Paying for performance” has been the corporate mantra for ages, but finding the right performance benchmarks continues to be an enigma. Equally significant is the ongoing debate on the superiority of economic value added (EVA) aligned executive incentive plans over traditional financial performance benchmarks to ensure optimal goal congruence between the corporate and the executive performances. Consequently, this paper aims to explore a plausible linkage between executive compensation and EVA for Indian corporates from a social constructivist perspective.

The study uses a mixed method approach where the quantitative analysis of responses from the survey of senior personnel/finance executives of Indian firms is complemented by the qualitative analysis of personal interviews to provide contextual depth to the quantitative data.

Based on the study, the researchers construct an understanding that EVA is a superior concept but has restricted utility primarily owing to its computational complexity and unaudited characteristics. The researchers’ interpretive inference finds mandatory disclosure of an audited EVA figure in the corporate financial statements as a prime requirement for EVA to emerge as an objective and visible performance measure.

Attention of policymakers is sought towards standardising its computation and ensuring its disclosure to bring it at par with the conventional executive financial performance benchmarks.

The narrative on benefits and the challenges of adopting EVA aligned performance management system is provided directly by the top-level executives responsible for designing the “paying for performance” policies.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Business organisations are under serious threat to sustain their business due to globalisation, challenging market and recent economic competitiveness. The aim of this study is to address various pressures to circular supply chain management (CSCM) implementation for sustainability.

The present study is based on two research levels. Initially, extensive literature review has been made to identify 31 pressures to CSCM, and eight categories of pressures have been identified. At the second level, fuzzy analytical hierarchy process (F-AHP) has been applied to rank the identified pressures to CSCM implementation for sustainability.

“Financial Pressures (FP)” has been identified as most significant pressure to CSCM implementation for sustainability. Further, “Lack of support of top management (MP1)”, “Lack of implementation of laws and policies (GP2)” and “Lack of vision for CSCM (GP1)” have been found most critical sub-pressures CSCM implementation for sustainability.

The final results give the prioritised list of all identified 31 sub-pressures and eight main pressure heads, which will be helpful in their removal for achieving the goal of CSCM implementation. It will be helpful for managers to take decisions promoting circular practices in supply chains to achieve truly sustainable supply chains. It will also be help for SC managers to understand the flow of activities and materials in CSC to get good results and remove pressures.

The present study plays an important role in circular activities implementation in supply chain for profit gain, and their pressure ranking may help the mangers to implement the CSCM effectively.

This study aims to test the serial mediation effect of attitude toward socially responsible investing (SRI) and social investing efficacy (SIE) on the relationship of knowledge about SRI with the intention to invest in SRI along with moderating effect of religiosity.

The study uses a quantitative analysis approach, wherein the data has been collected from 569 north Indian retail investors. Partial least square (PLS)-structural equation modeling has been applied in this study using the latest version of SmartPLS (v. 3.2.8) software to examine the complex model of serial and moderated mediation.

Attitude toward SRI and SIE significantly and serially mediate the relationship between knowledge about SRI and intention to invest in SRI. Also, the interaction effect of religiosity with knowledge about SRI is significant only for SIE and not for attitude toward SRI.

The study is cross-sectional in nature conducted only on the north Indian investors. Besides knowledge, there can be many other personal or social aspects that might affect SRI intention that have not been taken into the study.

The results suggested that the companies, financial advisors and governmental bodies can improvise upon social and environmental performance reporting so that investment in SRI can be promulgated.

The paper concludes that religious-minded people are more open to the idea of investing in SRI. India, being is a religious-minded country, the results of this study suggest that there is good potential for the development of SRI in India.

Empirical evidence regarding the relationship of SRI intention with its determinants is limited in Asian countries. Prior literature mainly provides evidence from developed countries where social and governance systems are comparatively stronger. The study provides evidence for the bright future of SRI in India, where investor’s beliefs are dominated by their religious values.

The purpose of this paper is to critically examine the existing literature on sustainable investments and propose an integrated conceptual framework for measuring socially responsible investment intention of investors.

Based on the theory of planned behaviour, the study discusses an integrated conceptual framework by thoroughly analysing the empirical studies of last 18 years, from 2001 to 2018. Some of the important measures of these studies have been reviewed, such as country of study, research methodology applied, sample size and respondents selected, model/theory applied, variables selected and significant findings of the study.

The study posits that collectivism, knowledge about sustainable investment, pro-environmental attitude and perceived risk will have a positive impact on attitude (ATT) towards SRI. Moreover, attitude (ATT) and subjective norms (SN) will be positively related to intention (INT) along with the mediating effect of social investing efficacy (SIE) and moderating effects of religiosity beliefs.

Besides implications for financial managers, various government bodies, prospective investors and other stakeholders, the study will provide impetus to companies for designing more sustainable funds that can promulgate the values and beliefs of investors.

The study incrementally contributes to the literature by way of suggesting a conceptual framework that can be empirically tested by future researchers.

This study aims to investigate the impact of workplace spirituality on citizenship behaviours of high school teachers. In addition, the moderating role of workplace compassion is examined.

This study is quantitative in nature. Data were collected from 232 secondary school teachers using a structured questionnaire. Partial least squares-based structural equation modelling was used to analyse the data.

Results revealed evidence for the positive relationship between spirituality and citizenship behaviours. Compassion was found to be a potential moderator in enhancing the spirituality–citizenship behaviour connection. This study attempts to understand one of the ways in which spirituality affects workplace behaviour by specifying the role of a moderator. The complex nature of the phenomenon is delineated further by identifying the plausible relationship with other variables.

Organisations can leverage the spiritual resources available to their people to compound their proactive behaviours, thus creating a win–win situation for both the parties involved. This is particularly useful for educational institutions where such behaviours are not merely desirable but could significantly elevate the working environment.

This study evaluates the connections between spirituality, compassion and citizenship behaviour, which remain largely unexplored in a non-profit sector. The construct of workplace compassion is not sufficiently mapped, especially in a context where it is thought to be embedded in the work environment.

The purpose of this paper is to measure the impact of financial inclusion on rural development through cooperatives.

The primary data were collected from 540 beneficiaries of Cooperatives banks operating in three northern states of India, i.e., J&K, Himachal Pradesh and Punjab using purposive sampling during January to June 2016. Exploratory factor analysis, confirmatory factor analysis, ANOVA, t-test and structural equation modelling were used for scale purification and data analysis.

The findings of the study revealed that financial inclusion through cooperatives has direct and significant impact on rural development. Further, the results support the notion that financial inclusion is a strategy of inclusive growth, but inclusive growth itself is a subset of a larger set of inclusive development which means that the benefit must reach the all, particularly the women and the children, minority groups, the extremely poor and those pushed below the poverty line by natural and human-made disasters.

The research has certain inescapable limitations. First, the in-depth analysis of the study is restricted to three northern states of India only because of time and resource constraints. Second, the study is confined to the perception of financial inclusion beneficiaries only, which in future could be carried further on the perception of other stakeholders such as SHGs, banking correspondents, etc. Third, possibility of subjective interpretation in some cases cannot be ruled out.

The study makes contribution towards financial inclusion literature relating to sustainable rural development and fulfils the research gap to some extent by assessing the impact of financial inclusion on rural development through cooperatives.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

The purpose of this study is to monitor suspended particulate matter (SPM), PM2.5 and source apportionment study for the identification of possible sources during the year 2018–2019 at Raipur, India.

Source apportionment study was performed using a multivariate receptor model, positive matrix factorization (PMFv5.0) with a view to identify the various possible sources of particulate matter in the area. Back-trajectory analysis was also performed using NOAA-HYSPLIT model to understand the origin and trans-boundary movement of air mass over the sampling location.

Daily average SPM and PM2.5 aerosols mass concentration was found to be 377.19 ± 157.24 µg/m³ and 126.39 ± 37.77 µg/m³ respectively. SPM and PM2.5 mass concentrations showed distinct seasonal cycle; SPM – (Winter ; 377.19 ±157.25 µg/m?) > (Summer; 283.57 ±93.18 µg/m?) > (Monsoon; 33.20 ±16.32 µg/m?) and PM2.5 – (Winter; 126.39±37.77 µg/m³) > (Summer; 75.92±12.28 µg/m³). Source apportionment model (PMF) have been applied and identified five major sources contributing the pollution; steel production and industry (68%), vehicular and re-suspended road dust (10.1%), heavy oil combustion (10.1%), tire wear and brake wear/abrasion (8%) and crustal/Earth crust (3.7%). Industrial activities have been identified as major contributing factor for air quality degradation in the region.

Chemical characterization of aerosols and identification of possible sources will be helpful in abatement of pollution and framing mitigating strategies. It will also help in standardization of global climate model.

The findings provide valuable results to be considered for controlling air pollution in the region.