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A growing literature shows that (integral) emotions arising in response to firm transgressions may influence consumer punishment. However, incidental emotions (which are unrelated to the decision at hand) can also be powerful drivers of consumer decision-making and could influence responses to firm transgressions. This paper aims to examine the role of incidental gratitude, as compared to incidental pride and a control condition, in shaping the acceptance of questionable consumer behavior toward a transgressing firm and the mediating role of self-righteousness in this regard.

Four experimental studies are conducted to examine the effect of gratitude, as compared to pride and a control condition, on the acceptance of questionable consumer behavior against a transgressing firm. Further, this research tests the underlying mechanism and a boundary condition of the predicted effect.

The results show that consumers experiencing gratitude, as compared to pride and a control condition, judge a questionable consumer behavior directed against a transgressing firm as less acceptable. These different emotion effects are found to be explained by self-righteousness. The findings also demonstrate that an apology by the firm attenuates the effect of emotions on consumer response toward the transgressing firm.

The present research contributes to the literature on consumer punishment by identifying the role of incidental emotions in determining self-righteousness and ethical judgments. The research focuses on and contrasts the effects of two specific positive emotions – gratitude and pride.

This paper offers managerial implications for firms involved in a transgression by highlighting the potential of gratitude. Notably, the findings of this research suggest that gratitude activation via marketing communications may help firms mitigate the negative effects of transgression events.

The present research provides a novel perspective on when and how positive emotions, such as gratitude and pride, can differentially and systematically influence ethical judgment toward a transgressing firm.

Dyeing and printing are important steps in textile manufacturing. After the process completion, these dyes are released in the effluent. These dyes impart an unacceptable appearance but are also toxic to the soil and water bodies. The present research has been carried out to study the rate of photocatalytic degradation of an azo dye, namely, CI Direct Green 26, using titania nanoparticles under ultra violet (UV) irradiation as a function of temperature and time. Azo dyes account for the majority of all dyestuffs are produced and extensively used in the textile, paper, food, leather, cosmetics and pharmaceutical industries. Titania nanoparticles have been found to successfully degrade these dyes in the presence of UV light. The purpose of the present paper was to study the photodegradation of azo dyes using titania nanoparticles at different temperatures and time periods.

Titania nanoparticle concentration of 0.1% (w/v) was dispersed in distilled water by sonication for 1 h in sonication bath. The of rate of degradation of Direct Green 26 dye in the titania nanoparticle dispersion, under UV-A exposure was studied at different temperatures ranging from 25°C to 65 °C for time periods ranging from 1 h to 6 h. Photocatalytic degradation tests were performed in a specially designed UV reactor chamber. Raman spectroscopy of Titania nanoparticles, dye and titania/dye mixture before and after UV exposure was carried out using Confocal Laser Dispersion Raman Microscope (Renishaw, UK) with 785 nm excitation laser.

Titanium dioxide is an efficient photocatalyst for decolourisation of direct dye. The photodegradation of the direct Green dye was found to follow the pseudo first-order reaction. The Arrhenius activation energy was found to be 24.8 kJ/mol with A value of 0.0013 for the photocatalytic degradation of the dye. Raman spectroscopy also confirmed the adsorption of dye on titania nanoparticle and its complete degradation on exposure to UV light.

This research highlights the application of titania nanoparticles for the effective degradation of dye in the effluent from textiles, clothing, paper and any kind of dyeing process. Azo dyes account for the majority of all dyestuffs are produced and extensively used in the textile, paper, food, leather, cosmetics and pharmaceutical industries. Titania nanoparticles have been found to successfully degrade these dyes in the presence of UV light which can be very beneficial for the effluent treatment plants in textile and other industries.

Azo dyes are one of the harmful pollutants released in textile waste water. The degradation and removal of the coloured waste in the textile effluent is an important environmental concern and needs to be investigated. The research is one of the first to investigate and understand the mechanism of the degradation of an azo dye in the presence of titania nanoparticles by Raman spectroscopy.

Efficacy of selected commercially available fluorocarbon-based finish agents, such as Oleophobol (Huntsman) Fluoroguard SX and Fluoroguard VOX (Britacel Silicones) Nuva HPU (Clariant) TG-581 (Resil) Aquaphobe-SNT (Akkshata), on cotton and polyester fabrics was studied with respect to water and oil repellency. All the finish agents exhibited excellent water- and oil-repellent properties on both kinds of fabrics. The contact angle of water on the finished cotton and polyester fabrics was found to be ≥ 140° Nuva HPU and Oleophobol gave cotton very good protection against rain. Except Aquaphobe SNT, all other finishes imparted excellent oil-repellent property to cotton. In the case of polyester, maximum oil repellency was achieved with Aquaphobe SNT and Fluorogard SX. The finished polyester fabric showed minimal absorption and better resistance to penetration of water as compared to cotton when subject to the Bundesmann water repellency test. There was a slight yellowing of the cotton fabric after the application of Oleophobol finish. There was no effect on the strength of the fabrics. The effect of these finishes on the crease recovery angle and the bending length of cotton was not significant, However, the polyester fabric became slightly stiffer after the application of the finishes. The crease recovery angle of polyester also increased after the application of these finishes. The finished fabrics showed good water repellency up to 15 washes. Oil repellency showed deterioration after only 5 washes and was partially retained up to 15 washes. Both water and oil repellency became negligible after 30 washes.

Women-owned enterprises (WOEs) are one of the fastest-growing entrepreneurial sectors in the world. Therefore, this study aims to identify and develop the structural cause and effect relationship among the various strategic dimensions that affect the performance of WOEs in the Indian context.

With the help of the questionnaire survey, the data has been gathered. Further, the experts’ opinions are considered, which is followed by interpretative structural modeling and the impact matrix cross-reference multiplication applied to a classification (MICMAC) approach to explore the 13 dimensions associated with the development of WOEs such as work–life balance (WLB), entrepreneurial learning (EL), competencies, social identity (SI) and culture.

WLB and support of financial institutions are the dominant independent dimensions and EL and competencies have emerged as dominant dependent dimensions, which impact the development of WOEs. The other linkage dimensions are experience, entrepreneurship education and training, SI, government, non-government organizations, family, friends, culture, role models, etc.

This conceptual model can be validated for any type of WOEs in India and in other countries.

Through this study, the academicians, decision-makers and policymakers must become aware of the importance of each dimension and their relatedness with each other, which provides the direction for designing and implementing appropriate policies to enhance the growth of WOEs.

This is the foremost research that provides an interpretive structural conceptual model to develop a map of the complex relationships and magnitude among the identified dimensions of WOEs.

The study examined the relationship between workplace toxicity and turnover intentions among Indian healthcare employees. It also explored the role of gratitude as a moderator in the workplace toxicity–turnover intentions relationship.

The study is based on a cross-sectional research design. The sample comprises 315 employees from the Indian healthcare sector. Approximately, 400 employees are approached both through email and office visits. Responses were received from 336 participants, and 21 incomplete questionnaires were discarded. The relationships between four variables of workplace toxicity and turnover intentions are examined using correlation and hierarchical regression. The moderation effect of gratitude is studied using the PROCESS macro in SPSS 21.

The results revealed that workplace toxicity could explain 45.8% variations in employees' turnover intentions. It also reported significant negative regression coefficients between all four dimensions of workplace toxicity and turnover intentions. It suggested that toxic health organizations may promote turnover intentions among healthcare employees. Also, findings recommended a significant moderating effect of gratitude amid the relationships of four dimensions of workplace toxicity and turnover intentions.

Hospital administrators must ensure that health professionals have the necessary support to remain effective in the field by providing a conducive working environment emerging from sound human resource practices that promote respect, collegial relationships, teamwork and collaboration. The present research demonstrates gratitude as one such factor that could act as a catalyst within the workplace. Practitioners could achieve a healthy work environment by developing complementary relief measures that build organizational capacities and improve its culture while sponsoring programs for individual employees that instill positivity through awareness of gratitude in everyday life.

This study offered a comprehensive understanding of workplace toxicity by investigating its four dimensions. Also, it is one of the pioneer studies that evaluate the role of gratitude in restricting workplace toxicity-induced turnover intentions.

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

Information communication technologies can serve as a crucial missing link toward tacking wicked problems of social welfare policy implementation through collaborative governance. Using a mixed methods approach, a pre- and postanalyses were used to investigate whether and how cell phones can increase awareness of pregnant women about different cash and service benefits of maternal health benefit policies of 82 pregnant women in a remote tribal community in Melghat forest of Maharashtra, India. Pregnant women received customized prerecorded bilingual audio calls on their mobile phones about maternal health benefit policies. The author then traced whether those audio messages increased the claiming of policy benefits and public engagement. The key contribution of this research is that contrary to the optimism about digital governance, findings suggest that cell phones are not a “silver bullet” for increasing receipt of maternal health benefits. This book chapter concludes with the prescription that the impact of mobile phones and other information technologies will be marginal as long as there are administrative deficiencies in policy implementation and a misalignment in state and federal policy designs.

Benchmarking is a very important tool to know the gap in your performance and best performance. It is possible to apply benchmarking in a wide variety of area. This paper uses benchmarking for assessing women entrepreneurship. Women entrepreneurship is a symbol of the balanced growth of the society. Financial inclusion schemes offered by the government are aimed to encourage a large number of women of the country to entrepreneurship. The purpose of this paper is to examine the effect of financial inclusion and others factor like family circumstances, benchmarking, entrepreneurial motivation and entrepreneurial intention of women on their decision to take up entrepreneurship as a career choice.

This research study is undertaken to understand the relation of financial inclusion schemes on women entrepreneurship and also to develop a cause–effect relationship. Here, financial inclusion effort is an independent variable, whereas women entrepreneurship is a dependent variable. A sample size of 250 women entrepreneur was taken. The sample was selected on the basis of convenience. Out of the total sample, 125 women belonged to the self-help group and 125 women were registered under the RSETI program of lead banks of Haridwar and Dehradun districts of the Uttarakhand state of India.

The results of the present study indicate that there is a statistically significant impact of financial inclusion on women entrepreneurship. It provides the platform to the women that help them in establishing a new business.

Data for present study were collected from two districts of Uttarakhand. In future, data can be collected from different geographical areas of India for generalizing the findings of the study.

The results of present study indicate that there is a statistically significant impact of financial inclusion on women entrepreneurship. It provides the platform to the women that help them in establishing a new business. For promoting women toward entrepreneurship, the government has launched many schemes.

After the study, the author found that the society will accept the fact that women entrepreneurship is emerging as a dire need for the country.

The author followed all the guidelines that were concerned about the originality of the paper. This paper is not under review of any journal.

This research investigates the barriers hindering women from pursuing entrepreneurship as a career choice, particularly in the Indian context.

The study employs rigorous data analysis techniques, including Confirmatory Factor Analysis and Multiple Regression Analysis, on a sample of 590 MBA students, comprising both male and female participants.

The findings reveal that social and psychological factors significantly influence women’s decisions regarding entrepreneurship. Social factors such as social stigma and cultural norms, along with psychological factors like societal expectations, emerge as major barriers.

The findings have implications for policymakers, practitioners, and academics in designing interventions to address social and psychological barriers effectively. Recommendations include promoting cultural sensitivity and fostering confidence among women entrepreneurs.

This study contributes to the existing literature by quantifying the specific barriers faced by women entrepreneurs in India. It offers insights into advancing gender equity and inclusive economic development through targeted policies and programs.