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The psychological empowerment theory of intrinsic motivation is the primary theoretical basis for the case. Other theories discussed include personality theories and transformational leadership theory.

This is a field-researched case, based on face to face interviews with V.G. Jayakrishnan. The author also visited Jayakrishnan’s institution and attended an event there. The author also relied upon archival data in the form of newspaper reports: both print and video. The case is based on events before July 31, 2017.

This case examines how entrepreneur V.G. Jayakrishnan, between 1995–2017, set up two successful, yet distinct businesses, namely, ICD (Institute for Career Development), a leading banking test prepping center in Kerala, India and JK Farms, a state-of-the-art dairy farm. Jayakrishnan’s vision and ability to think far ahead of his competition led to ICD becoming one of the most successful banking test prep centers in Kerala, India. Similarly, Jayakrishnan’s vision of scientific dairy farming allowed him to set up the state of the art JK Farms. The case allows students to examine the concepts of intrinsic motivation and transformational leadership.

The case can be used both at the graduate and undergraduate levels. At the graduate level, it can be used at the beginning of any leadership class to foster discussion about intrinsic motivation and transformational leadership. At the undergraduate level, it would be better positioned at the middle of the organizational behavior course after the students have discussed the chapters on motivation, leadership and personality in principal textbooks (Colquitt, LePine and Wesson, McGraw Hill, 2018).

The purpose of this paper is to minimize environmental problems related to raw poultry manure application by stabilizing its nutrient and organic matter (OM) content. This can be achieved by prior digestion before its application to agricultural soils.

In the present study, changes in physicochemical and microbial properties of poultry litter were studied in order to evaluate the suitability of using the digested (stabilized) product for soil amendment. The poultry litter was digested in autothermal thermophilic aerobic digestor (ATAD) where organic solids were degraded and the heat released during the microbial degradation was used to maintain the thermophilic temperatures ( < 50ºC) at a hydraulic retention time of about 3 d.

Results of this study showed that the poultry litter undergoes physicochemical and microbial changes similar to other digestion systems; these changes include self-heating, relative increase in Cu, Zn, Fe, Mn, P, K and NO₃-N and decrease in microbial population numbers, C, OM and NH₄-N contents.

ATAD is an effective method for the conversion of poultry litter into organic fertilizer, which can be readily applied to the agriculture land. ATAD is an eco-friendly, cost effective method which also reduces the length of stabilization.

In this chapter, the emerging education workforce management approach known as talent-centered education leadership (TCEL) is reviewed. The approach takes inspiration from progressive and cutting-edge talent management thinking and practices that emphasizes employers' intentional focus on humanizing and authentically engaging with their workforce. Pertinent to the theme of the book, the discussion then segues to the importance of diversity and inclusion as a precursor for these efforts and demonstrates how equity and organizational excellence are mutually compatible in the workplace. Relatedly, consideration is given to how traditional perceptions of “professionalism” can exacerbate inequity in the workplace. The chapter concludes by highlighting the seven core principles of TCEL to prepare school employers to embrace the future of education work.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.

The purpose of this paper is to propose and develop a distinct perspective from the consumer culture theory in the context of Muslim consumers, marketing and the feminist theory.

This paper is based on a critical review of the literature for insights into the consumer culture theory in the context of Muslim consumers, Islamic marketing paradigm and the feminist theory.

The study suggests that scholars in the area of marketing may consider drawing on the theory of Islamic consumer culture, film and feminist theory. This theory can be used as a platform to understand the Muslim mind and the related cultural traits to create greater engagement and interest in Malaysian horror genres among local and international audience. The Malaysian local horror genres currently have an interesting blend of Islam, local culture and gender biases addressing the universal concept of good against the evil forces, and this has the potential of offering new experiences to especially international audiences.

This study is purely theory-based and is aimed at knowledge development in this field of Islamic consumer culture. It also invites academics to engage in scholarly activities toward theory building in this area.

The study provides directions for areas of possible future research in Islamic marketing, consumer culture and film studies.

This study intends to broaden the research efforts in Islamic consumer culture marketing in terms of innovative ways to serve this growing Muslim market.

This study contributes to the discipline by providing new perspectives in Islamic consumer culture inquiry in the context of film studies.

The aim of this paper is to examine the effect of talent management (TM) practices on employee performance (EP) among academic staff of Malaysian private universities (MPU) through employee engagement (EE).

This paper used both descriptive and quantitative approaches, while the research unit of analysis consists of MPU academic staff. A simple random and stratified sampling approach was utilized in this study while, the research sample consists of 314 MPU academic staff. A questionnaire was used to collect data from the target population, while partial least squares–structural equation modeling was used to evaluate the study hypotheses through a bootstrapping approach.

This paper results demonstrated that TM practices (succession planning practice, promotion practice and performance appraisal practice) have a significant effect on EP, while EE mediates the relationship between TM practices and EP in MPU.

This paper encourages university management to adopt and invest in TM practices for effective EE to achieve and sustain EP.

This paper has made a significant contribution to knowledge and to the operationalization of EE, EP and TM practices literature, which could help to develop theory, model, practice and research in areas of work performance.

This research lies in the domain of Vedic mathematics, and it explores the application of the related Vedic sutras in different branches of mathematics, science, education, and engineering across Asia and Europe.

The study embraced a qualitative research design followed by a systematic literature review (SLR) approach which describes the significance of Vedic mathematics. The study made use of purposive sampling through which the data were collected from 102 articles by using inclusion and exclusion criteria. It includes publication years, the types of research methods, and uses of Vedic mathematics sutras in different branches of knowledge stated by the researchers. Its goal is to offer a more thorough explanation and an evaluation of how the inquiry affected the conclusions. The articles examined in this review included all the journal articles and doctoral theses from the databases of Google Scholar, Science Direct, Scopus, and Sodhganga which were published during the period 2010–2022.

The research found that the application of the sutras of Vedic mathematics has been increasing immensely in India. The researchers in this area are fond of qualitative research methods. This research has shown that sutras of Vedic mathematics especially “Urdhvatiryakbhyam” and “Nikhilam Navatascaramana Dasastah” have been frequently used in mathematics and engineering in technical higher education. The impact of other sutras has been quite useful, which augments that in many disciplines where the applications of Vedic mathematics are prevalent, it can be functional. The study concludes by reprising the result, its limitations, and the use of Vedic mathematics as a sustainable source of knowledge.

Vedic mathematics is an area where a lot of potential applications are created in science, mathematics, engineering, and education. Even with the latest technological advancements like learning analytics, artificial intelligence has its connection with this branch of learning, which is the greatest treasure of the Indian knowledge system. The research in this area is not reported in any databases or any standard format so researchers find it difficult to locate and study this broad conceptual domain.

It will help the reader and other academic stakeholders to widen their view on the new and innovative techniques of Vedic mathematics. It is advised that additional studies would look at and evaluate papers published after this time so that readers may get a wider view of the concept of Vedic mathematics.

It will help society to know the essence of Vedic mathematics that how useful it is. Vedic mathematics helps learners to learn in a very factual and accurate manner especially while dealing with mathematical calculations. It will enhance the problem-solving skills among learners. It will be beneficial for all types of learners which will help them to become better individuals for a nation.

The paper enriches understanding of the potential applications of different sutras from Vedic literature in different fields of knowledge. The outcome of the research encourages educationists and policymakers to include Vedic mathematics in the curriculum to foster quantitative reasoning and problem-solving at varied levels of learning.

The reality with many developing countries is that the countries have failed to create enough jobs for the poor and vulnerable. Under such circumstances, vulnerable employment plays a critical role in providing earning opportunities to people who are unemployed and determining the economic and social progress of such economies. The study aims to examine the possible non-linear relationship between vulnerable employment and growth in light of this background.

The study employed five-yearly averaged data of 73 developing countries for the period 2000–2019. The empirical analysis is performed using the dynamic panel data analysis and the two-step system generalised method of moments (GMM) approach. The estimations are run separately for male, female and total vulnerable employment. The threshold levels are obtained using Sasabuchi (1980) and Lind and Mehlum (2010) (SLM) test. Several sensitivity checks are performed to validate the results.

The findings of the study suggest a non-linear U-shaped relationship between vulnerable employment and growth. Thus, a positive association between vulnerable employment and growth is witnessed at higher levels of vulnerable employment. At lower levels, the relationship is negative. Threshold levels for male, female and total vulnerable employment are 46.80%, 49.29 and 50.94%, respectively. Therefore, vulnerable employment beyond the threshold levels is found to be positively associated with growth.

Countries below the threshold level of vulnerable employment should understand why these workers are not able to contribute to the growth despite working so hard. If any socio-economic barriers hinder their contribution towards growth, such barriers require greater policy attention. Countries with vulnerable employment levels above the threshold level should recognise the contributions of these workers towards the growth and actively support them in increasing their economic contribution. In either case, given the precarious circumstances under which these workers work and the pittance earnings, policy interventions aimed at ensuring decent working conditions and better earnings for these workers are encouraged.

The current study is the first one to examine the relationship between vulnerable employment and growth to the best of the author's knowledge. As such, it makes novel contributions to the literature on development policy.

This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique.

This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error.

The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress.

This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.