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The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent.

The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms.

The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium.

The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Ostensibly the case is about an employee resigning from the organization due to lack of support, job clarity, and information about reporting structure. It addresses issues of socialization process, performance appraisal, and communication issue between colleagues in a consulting organization. The case serves as medium for diagnosis and action planning around integration of new employees into the organization, effective performance appraisal, and resolving communication barriers.

Matter is an important topic of science as a discipline since its inception. Nevertheless, along with the evolution of semantic web, matter has got equal importance among the ontology developers. The current work describes an ontology of matter that the authors developed in the lab. The purpose of this paper is to come up with an exhaustive list of concepts and relations to cover matter domain under one umbrella, after identifying the gaps in the present ontologies.

Ontology was developed following faceted analytico-synthetic approach of knowledge organization. The authors followed hybrid developmental approach which includes top-down as well as bottom-up development strategy, for creating classes and subclasses. The authors modelled matter domain comprehensively considering different aspects of matter. The theories behind the modelling approach helps to maintain the consistency of further extensions.

Final ontology has around 280 concepts and as many as 60 properties which include both object property and datatype property.

There exists very vague definition of concepts in different subject areas, as matter is a domain of study in physics, chemistry, material science, metallurgy, etc. Same material has been adopted differently depending upon purpose of its study/use in that field. For example aspirin is simply a chemical compound in chemistry, whereas in medicine it is also an agent.

Present work claims to influence the ontology engineers to develop more extension to this core ontology of matter (COMAT). Also this will find its use in information retrieval, semantic annotations and in several other semantic knowledge-based systems.

COMAT is the most recent work of the domain. Originality lies in the way matter domain has been looked up, from a very wide perspective, as well as in the approach of modelling the domain.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Innovation, information technology, e-marketing and sales, strategy and entrepreneurship.

The case is intended for use in advanced graduate and executive education level management and technical programs of study. The case is high inter-disciplinary appropriate for all courses dealing in use of innovation, information technology, entrepreneurship, marketing and sales and strategy.

Practo Technologies Pvt Ltd is a company established in 2008 by entrepreneurs Shashank, N.D. along with Abhinav Lal. At Practo Technologies engineers practice to create technology that supports healthcare industry. In India, there is not enough assistance given to the patients in terms of searching for appropriate doctors and fixing an appointment with them easily. The patient's data lie scattered in reports and the patient's medical history goes unrecorded. Practo provides a robust platform for doctors and patients to collaborate together under one umbrella. It provides patients with open basket of doctor profiles and special search based on doctor's specialty. The case showcases the journey of transformation healthcare sector in India has gone through. The change was resisted, it never came that easy! The case explores issues in implementing information technology for clinics and hospitals in India. This case illustrates interesting facts Indian healthcare and the preparation they need to equip with to manage global technology tide.

The case can be used for teaching service innovation. Managing innovative practices and low-cost online business models is illustrated. In particular, it deals with how the early start up employs innovation to set up new working ideas. The case is designed to stimulate discussions of broad array of issues regarding deployment of IT services specifically in the hospitals and clinics across India. Evaluate the advantages and liabilities of expanding globally at a very early start up stage. The case focuses on Practo early development with only concept in hand and transforming it into a profit generating business. Evaluate the advantages and challenges of innovation, technology development, deployment and training of manpower to use it. The case offers students opportunity to understand and take view of strategically building early start up and management of concerns with no prior experience. To identify business opportunities and explore various possibilities of expansion into IT healthcare business. The students are given base to explore sales and marketing strategies for online model. The students get overview of cutting edge business offerings and surviving the dynamic competition in the era of globalization.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

The purpose of this study is to thoroughly review studies that have used blockchain technology in financial services. This study will help provide a holistic framework that would highlight the current state and challenges of the blockchain in the financial services sector.

The objective of this study is to systematically examine and organize the current body of research literature that either quantitatively or qualitatively explored the use of blockchain technology in financial services. The study uses PRISMA-guided systematic review along with bibliometric analysis to achieve the purpose.

This study contributes to the existing literature by exploring and analyzing systematic studies available on blockchain with special reference to financial services sector. With blockchain based on five principles, namely, computational logic, peer-to-peer transmission, irreversibility of records, distributed database and transparency with pseudonym has immense potential to unleash and transform the financial service industry. With increasing blockchain-based operations of decentralized banking, insurance, trade finance, financial markets and cryptocurrency market, the subject is rapidly growing and seeking considerable contribution from scholars from around the world.

This study uses systematic literature review approach, which has its own demerits. Like other studies based on Systematic Literature Review, this study also suffers from a certain bias such as sample selection bias, publication bias, data interpretation and the combination of quantitative and qualitative studies in the population. Further, the adoption and resultant benefits of blockchain have not been empirically tested.

This study can help policymakers and institutions in determining their future course of action, as it highlights the state of research in the area of blockchain technology and financial services.

Very few studies have done a comprehensive review of literature on blockchain in financial services.