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India’s Mental Healthcare Act 2017 provides a right to mental healthcare, revises admission and review procedures, effectively decriminalises suicide and has strong non-discrimination measures, among other provisions. The purpose of this paper is to examine Indian mental health professionals’ views of these changes as they relate to stigma and inclusion of the mentally ill.

The authors held nine focus groups in three Indian states, involving 61 mental health professionals including 56 psychiatrists.

Several themes relating to stigma and inclusion emerged: stigma is ubiquitous and results in social exclusion; stigma might be increased rather than remedied by certain regulations in the 2017 Act; stigma is not adequately dealt with in the legislation; stigma might discourage people from making “advance directives”; and there is a crucial relationship between stigma and education.

Implementation of India’s 2017 Act needs to be accompanied by adequate service resourcing and extensive education, including public education. This has commenced but needs substantial resources in order to fulfil the Act’s potential.

India’s mental health legislation governs the mental healthcare of 1.3bn people, one sixth of the planet’s population; seeking to use law to diminish stigma and enhance inclusion in such a large country sets a strong example for other nations.

This is the first study of stigma and inclusion since India’s 2017 Act was commenced and it highlights both the potential and the challenges of such ambitious rights-based legislation.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The purpose of this paper is to examine Philip B. Crosby’s 14 quality principles and analyze the interaction between them. Hitherto no research has been published on the implementation of total quality management (TQM) using Crosby’s 14 principles. To fill this gap, interpretive structural modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis have been designed to prioritize, sequence and categorize variables to find both the dependence and driving power of these variables.

At the initial stage experts from industry as well as from academia were contacted to provide an input for ISM methodology and examine interactions between identified variables. In this approach, interpretations of the interrelationships among variables have been discussed, whereas MICMAC analysis is used to discover dependence and driving power.

The results of the investigation revealed that “Management Commitment,” “Quality Improvement Team,” “Quality Awareness,” “Supervisor Training,” “Goal Setting” and “Cost of Quality Evaluation” are strategic requirements; “Corrective Action,” “Zero Defects Day” and “Error Cause Removal” are tactical requirements. “Recognition,” “Quality Measurement,” “Quality Councils” and “Do It Over Again” are operational requirements for TQM applications.

ISM is used as a part of this research to provide valuable insights into interrelationships among Crosby’s quality principles through a systematic framework. The research opens up a new focus area on the implementation of TQM for services as well as for the manufacturing industry.

The purpose of this paper is to study the 14 principles of Edwards Deming and create significant relationships between them. No research has been reported on the implementation of Total Quality Management (TQM) using Deming’s 14 principles. To fill this gap, Interpretive Structural Modeling (ISM) and MICMAC analysis have been developed to understand mutual interactions among variables and find both the dependence and driving power of these variables.

The research paper discusses a blend of practical applications and introduces a theoretical framework. An ISM-based methodology is used to study and examine interactions between identified variables, while MICMAC analysis is used to identify the dependence and driving power.

This research utilizes Deming’s 14 quality principles, with experts from academia and industry consulted to identify contextual relationships among variables. The result shows that the stated principles “take action to accomplish the transformation,” “institute training,” “encourage education to employees” and “institute leadership” are strategic requirements, while “drive out fear,” “break down barrier between staff areas” and “eliminate numerical quotas” are tactical requirements. “Adopt the new philosophy,” “create constancy in improvement of product and service” and “cease dependence on mass inspections” are operational requirements for TQM applications.

An ISM-based quality framework, dependence power and driving power of variables using MICMAC analysis have been recommended to the service and manufacturing industry as a new focus area in the implementation of TQM.

This study aims to examine whether customer trust is influenced by the congruence and incongruence between customers' perceptions of two types of omnichannel integration—perceived transactional integration (PTI) and perceived relational integration (PRI). The authors further considered the perceived effectiveness of e-commerce institutional mechanisms (PEEIM) as the boundary condition of omnichannel integration's effect.

Drawing upon the stereotype content model, this study hypothesizes the influences of PTI and PRI on customer trust wherein PEEIM moderates the relationships. The research model was empirically examined based on the responses surface analysis of survey data collected from 311 omnichannel customers.

Results showed that when PTI and PRI are congruent, customers are inclined to trust brands that have high levels of PTI and PRI rather than low levels of PTI and PRI. Moreover, the incongruence between PTI and PRI is positively related to customer trust. PEEIM was found to weaken the congruence effect while strengthening the incongruence effect. The authors also examined customer distrust as another relational outcome to provide a robust check.

This study uncovers customer cognition of omnichannel integration and examines the influences on customer trust, therefore contributing to our understanding of omnichannel integration's effect from the customer perspective. Findings from this research provide insights for brand managers on deploying channel integration strategies and institutional mechanisms to manage customer trust.

This paper aims to focus on compliance of workplace sexual harassment-related provisions under Indian companies and securities law, based on an empirical analysis of companies’ sexual harassment-related disclosures contained within their directors’ annual reports (ARs). Specifically, sections devoted to sexual harassment-related disclosures, inbuilt within directors’ ARs for the financial year 2019–2020 for a selected sample of companies listed under the National Stock Exchange, have been analysed.

To examine the nature of companies’ disclosures to demonstrate their compliance with statutory requirements under the POSH law, aligned with the Companies (Accounts) Rules, 2014 and Securities and Exchange Board of India’s regulations, an empirical-based, descriptive content analysis of ARs of 200 listed companies were used.

This study primarily finds that the majority of companies from the sample have disclosed to have prepared a corporate-level policy, as required under the POSH law. As also required under the POSH law, companies, reportedly, have constituted an Internal Complaints Committee to adjudicate and dispose of incidents related to sexual misconduct reported at their workplaces. However, companies lack in disclosing qualitative information, with sufficient detail, on many important aspects related to prevention and resolution of reported cases of workplace sexual harassment.

This paper adds to the broader narrative of the lacunae within the disclosure and reporting requirements on enhancing the liabilities of the companies to prevent and address sexual harassment under India’s corporate and securities regulations.

Drawing from boundary-spamming knowledge processes and knowledge-based theory, the purpose of this paper is to study enablers of the knowledge management (KM) process using robust multi-criteria decision-making (MCDM) tools like interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) method.

Drawing on the knowledge-based view and through the detailed literature review among different KM success, eight enablers were identified. By using the ISM-DEMATEL approach, a systematic framework was designed, and further cause–effect relationship diagram visualized a causal relationship among the enablers.

The combined approach of ISM-DEMATEL showcase that “knowledge creation” and “knowledge capture” are essential enablers. These two identified enablers have considered being pillars for KM implementation. On the other side, knowledge organization, knowledge application are dependent enablers.

From a practical viewpoint, the findings of this research work enable the industry consultants to identify the most prominent driving enablers for KM implementation. Additionally, it provides a clue for the effective implementation of KM in a systematic approach.

The integrated method depending on the hierarchical model and cause–effect relationship between enablers of the KM process is a novel approach that opens a new research area in this domain. Moreover, this is the first-ever attempt to combine ISM along with DEMATEL to identify enablers of the KM process.

Supplier Selection (SS) is one of the vital decisions frequently executed by numerous industries. In recent times, the number of suppliers has increased enormously depending on a wide range of criteria. A selection of suppliers is a sensitive process that may impact various supply chain activities. The purpose of this research is to explore an underutilized technique called PROMETHEE II method for SS.

Various tools and techniques are available under multi-criteria decision-making tools, which sometimes creates confusion in researchers' minds regarding reliability. PROMETHEE II was the most prominent method for ranking all available alternatives that ultimately avoid decision-making errors. To execute this equal and unequal weights approach has been used with three case studies.

In this research, three case studies have been used and soved with the help of the PROMETHEE II approach. The study also provides fundamental insights into the supplier's ranking on different criteria using sensitivity analysis. Further, criteria were divided as per benefits and non-beneficial to get a robust result. The pros and cons of PROMETHEE II approaches are also highlighted compared to other MCDM tools in this study.

Most of the SS research uses either AHP or TOPSIS as per existing literature. There are very few attempts highlighted in the literature that use PROMETHEE II for the SS problem with sensitivity analysis. The proposed method is probable to motivate decision-makers to consider using a more sophisticated method like PROMETHEE II in supplier evaluation processes. This study opens a new direction for the ranking of suppliers in the field of the supply chain. The study also bears significant practical as well as managerial implications.

Digital financial services (DFS) have substantial prospect to offer a number of reasonable, appropriate and secure banking services to the underprivileged in developing countries through pioneering technologies such as mobile phone based solutions, digital platforms and electronic money models. DFS allow unbanked people to obtain access to financial services through digital technologies. However, DFS face tough challenges of adoption. Realising this, the purpose of this paper is to identify such challenges and develop a framework.

The authors developed a framework of challenges by utilising interpretive structural modelling (ISM) and fuzzy MICMAC approach. The authors explored 18 such unique set of challenges culled from the literature and further gathered data from two sets of expert professionals. In the first phase, the authors gathered data from 29 professionals followed by 18 professionals in the second phase. All were pursuing Executive MBA programme from a metropolitan city in South India. The implementation of ISM and fuzzy MICMAC provided a precise set of driving, linkage and dependent variables that were used to derive a framework.

ISM model is split in eight different levels. The bottom level consists of a key driving challenge V11 (i.e. high cost and low return related problem), whereas the topmost level consists of two highly dependent challenges namely V1 (i.e. risk of using digital services) and V14 (i.e. lack of trust). The prescribed ISM model shows the involvement of “high cost and low return related problem (V11)”, which triggers further challenges of DFS.

None of the existing research has explored key challenges to DFS in detail nor formulated a framework for such challenges. To the best of the authors’ knowledge, this is the first paper on DFS that attempts to collate its challenges and incorporate them in a hierarchical model using ISM and further divide them into four categories of factors using fuzzy MICMAC analysis.