Purpose: This study investigates the antecedents of women entrepreneurs’ performance in an emerging economy. Based on the review of extant literature, six antecedents of women entrepreneurs’ performance, namely, motivation, networking, socio-cultural, business environment, training and development, and financials were proposed and subsequently empirically examined.Design/methodology/approach: Gaps in the literature were identified, based on which the theoretical background of the study was formulated. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were applied to confirm the factor-item structure. The impact of explanatory variables was investigated using the structural equation modeling (SEM) based path analysis.Findings: The study concludes that motivation, networking, socio-cultural, business environment, training and development, and financials have a significant positive influence on the performance of the women entrepreneurs.Originality/value: There is a dearth of quality research that holistically investigates the key antecedents of performance among women entrepreneurs. Most existing studies have not considered the possible antecedents of performance concomitantly. Additionally, the relationships have been measured individually rather than at the construct level. Further, a majority of the existing studies investigating the performance of women entrepreneurs have been confined to settings within developed countries. By providing insight into the antecedents of women entrepreneurs’ performance elsewhere, the present study attempts to bridge these identified gaps. This study is expected to advance the knowledge about the factors influencing the performance of women entrepreneurs in emerging economies such as India. These insights will likely be valuable when creating policies related to this crucial aspect of economic development.

Introduction: The fear of emergence of newer strains of SARS-CoV-2 as well as concerns of waning of protection after doses of COVID-19 vaccine has created a degree of global uncertainty surrounding the pandemic. Some of the emerging strains of SARS-CoV-2 have shown potential for causing serious disease and death, a threat that has been ameliorated by ensuring the vaccine coverage in populations. Still, the vaccine coverage remains unsatisfactory in certain populations. Hence, understanding and working on the factors which affect acceptance of the vaccine amongst the public can be considered a priority for public health as much as ensuring availability of the vaccines. Objective: This research work aims to build and validate a scale to assess the public attitude towards COVID vaccination. The proposed scale has been named as COVID Vaccination Attitude Scale (C-VAS). Materials and Methods: A three-stage process was used to develop the C-VAS which includes (1) item generation (deductive and inductive approach); (2) item–refinement (pre-testing and pilot testing, exploratory factor analysis (EFA); and (3) scale validation (confirmatory factor analysis, CFA). The sample size used for this research was 840. In order to overcome the issue of common method bias, the data was collected in two phases. The sample n1 (411) was used for EFA and the sample n2 (429) was employed for undertaking CFA. Common method bias was assessed to check if variations in responses are caused by the instrument instead of the actual dispositions of the respondents. Items of the scale were taken by reviewing the extant literature about vaccination, from the relevant established theories such as health belief model and by interviewing with domain experts. The content validity of the scale was determined. Results: EFA extracted five factors, labelled as “Perceived Benefits”, “Perceived Barriers”, “Perceived Severity”, “Health Motivation” and “Perceived Risk”. To further validate the factor–item structure CFA was performed. Conclusion: The measurement model was assessed by applying CFA to examine the reliability, accuracy and validity of the scale. Development of this scale can help in understanding factors that affect vaccine acceptability behavior. This can be used in promoting COVID vaccine coverage in countries and societies which still have low vaccination rates especially due to lack of acceptance of the vaccine. This scale also has the potential to understand public behavior in relation to similar future outbreaks and the acceptance of the mitigatory vaccines.

Social media has been widely used to design and publish pro-environment campaigns; the goal is to spread awareness about environmental issues and explain how customers can change their consumption behavior to protect the environment. Customers’ engagement with pro-environmental campaigns (CEPC) on social media is crucial to achieving these objectives. Limited studies have examined the factors that could increase CEPC, and its effects, on pro-environmental consumption behavior. The available literature suggests the antecedents of social media engagement, but is restricted to brands/products. Based on these findings, this study explores the effects of advertising attractiveness, advertising-based skepticism, online interaction propensity, and privacy concerns on CEPC. This study also suggests that customers’ engagement with pro-environmental consumption-related content can be defined as a pro-environmental experience that could lead to pro-environmental consumption. The study was based on a sample of 262 responses from online users; the results indicated that CEPC is largely affected by advertising attractiveness, advertising skepticism, and online interaction propensity. The study further established that pro-environment consumption is a potential outcome of CPEC. This study contributes to the existing literature by providing insight into ways in which activist groups can inspire consumers to engage with pro-environment content and exploring whether that engagement translates into a pro-environment intention.

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

Melanoma, an aggressive skin cancer, claims 57,000 lives annually worldwide. Early detection improves survival rates, reduces mortality, enhances treatment outcomes, increases awareness and provides economic benefits. However, detection faces challenges, including limited dermatologist availability, geographical constraints and inaccurate self-assessment. This study proposes an intelligent \(m\)-Health system using Squirrel Search Stochastic Gradient Descent (SSSGD) with Deep Quantum Neural Network (DQNN). The SSSGD is the integration of Squirrel Search Algorithm (SSA) and Stochastic gradient descent (SGD). Here, Type-2 Fuzzy and Cuckoo Search-Based Filter (T2FCS) is used for pre-processing Enhance image quality, resize, and normalize. Spine Generative Adversarial Network (Spine GAN) lesion segmentation, geometric transformations data augmentation, Grey Level Co-occurrence Matrices feature extraction, and DQNN detection trained by SSSGD, providing clinicians with a reliable Decision Support System (DSS) for early diagnosis and effective treatment, enhancing accuracy, computational efficiency, and patient outcomes. Performance evaluation metrics include loss curves, confusion matrix, sensitivity (98.61%), specificity (98.13%) and accuracy (98.42%). Results demonstrate enhanced performance, providing clinicians with a reliable DSS for early diagnosis and effective treatment.