National Power Limited (NPL) was a public sector undertaking of the Government of India under the Ministry of Power, having a total generating capacity of 4,860 megawatts of coal-based thermal power. NPL had seven power plants in operation across three states in the northern part of the country, with 8,212 employees. The case describes an appointment of a non-executive post at a power-generating plant of NPL, which is referred to as the Chandanpur unit. This appointment was made to honour an unprincipled demand by the Minister of Forestry and Environment of the concerned state government. The matter got exposed through a complaint received by the Vigilance Department of NPL. The manager of the coal handling department of the Chandanpur unit had lodged the complaint, mentioning the possibility of anomalies in the appointment. On verification, it was found out that the executive director of the Chandanpur unit had appointed a person in the highest supervisory post of that unit by flouting all rules and guidelines of recruitment in the organization. Anomalies included not taking approval for upgradation of the offer made initially, interviewing directly without holding any written test and changing the minimum eligibility criterion for the post. All this was a major breach of Article 14 of the Constitution of India, which confers the right to equality to a citizen of India as a fundamental right. Based upon findings of the investigation report, the culpability established in the irregularity and gross violation of the established rules of NPL, major disciplinary proceedings were initiated against all the involved officials and the appointee. Since it was a criminal case, it was also referred to the Federal Investigating Agency for further investigation and prosecution of the offenders. All NPL executives involved, and the appointee was implicated, though no culpability could be established against the Minister or his confidential assistant. © 2022 Lahore University of Management Sciences.

Business operations became challenging when the COVID-19 pandemic struck, and governments applied significant lockdown measures to stop the spread. Start-ups and small-medium businesses started facing hardships due to the changes in the business environment and the resulting constricted cash flow. This case describes challenges faced by a high-tech product start-up company and the way they tackled these hardships. The company had adopted an agile programming methodology for product development which faced a major challenge because of the sudden introduction of “work from remote” (WFR). WFR had disrupted collaboration between clients, sales teams, and development teams. The success of agile methodology was dissipating as teams could not do on-premise huddles. The company witnessed an overrun in the sprint schedule, increase of budgeted cost, and sliding customer satisfaction. Senior management could not manage process parameters properly, and therefore predictability of the processes waned. The start-up owners, through the application of the “dynamic capabilities framework,” focused on reconfiguring the agile working processes and improving the remote working capability of employees. This case shows the start-up’s agile development process transformation journey following the dynamic capabilities framework to overcome the challenges posed by the pandemic. The teaching case focuses on imparting knowledge on agile development and dynamic capabilities to Information Systems students. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Industrial Internet of Things emerged as one of the major technologies enabling Industry 4.0 for industries. Multiple start-ups started working in the Industrial Internet of Things field to support this new industrial revolution. Distronix, one such Industrial Internet of Things start-up of India, started operations in 2014, when companies were not even aware of Industrial Internet of Things. Distronix started executing fixed-fee projects for implementation of Industrial Internet of Things. They also started manufacturing sensors to support large customers end-to-end in their Industry 4.0 journey. With the advent of public cloud, companies started demanding pay-per-use model for the solution Distronix provided. This posed a major challenge to Distronix as they had developed technology skills focusing fixed-fee customized project delivery for their clients. The situation demanded that they change their business model from individual project delivery to creation of product sand-box with pre-registered sensors and pre-defined visualization layer to support use cases for Industrial Internet of Things implementation in multiple industry sectors. It forced Rohit Sarkar, the 26 years old entrepreneur and owner of Distronix, to upgrade capabilities of his employees and transform the business model to support pay-per-use economy popularized by public cloud providers. The case discusses the challenges Rohit faced to revamp their business model in such an emerging technology field, like, to develop new skills of the technical people to support such novel initiative, reorienting sales people towards pay as use model, developing new concept of plug and play modular product, devising innovative pricing, better alliance strategy and finding out a super early adopter. © Association for Information Technology Trust 2021.

Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer's credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings. © 2022 Elsevier B.V.

The increasing carbon dioxide level in the earth's atmosphere and continuously changing climate creates a significant challenge to sustainability in the world. It is not easy to control pollution due to carbon dioxide emissions from coal-fired power plants into the atmosphere. However, carbon capture technology provides an advantage for capturing carbon from power plants. Various researchers suggested the non-linear optimization model with post-combustion carbon capture technology in coal-fired power plants to reduce carbon emission. However, in their research articles, most researchers did not include loss of power due to retrofitting carbon capture technology in power plants and carbon emission from the compensatory power plant. This paper proposes a linear optimization model that minimizes the emission release from the power plant and its compensatory plant by appropriate selection of carbon capture technology. Our proposed model incorporates loss of power due to adopting carbon capture technology and emission release from the power plant and compensatory power plant in the problem formulation. We have also generated the Pareto curve that determines the trade-off solutions between emission release and the overall electricity cost. The applicability of our model is illustrated through power sector data from two Indian states. The net reduction of emissions in the two states are 27.17 % and 26.29 %, achieved by a mixed integer linear programming approach in coal-fired power plants. The model developed is generic and provides a sustainable environment for the generation of electricity. © 2021 Elsevier Ltd

The consciousness of safety risk factors and the emergence of WHO guidelines for the preparedness of health care workers have pushed the health care systems to take proactive decisions to maintain a safe and productive working environment during the COVID-19 outbreak. In order to provide this working environment, detailed identification, and analysis of safety risk factors are required. In this context, we proposed a hybrid fuzzy-based decision-making framework to rank the Indian hospitals based on the prevalence of safety risk factors among the health care workers. First, fifteen relevant safety risk factors are identified with the help of the Fuzzy Delphi Method (FDM). Second, the weights of categories and their respective factors are computed and are ranked based on their criticality by the Fuzzy Analytic Hierarchy Process (FAHP). Finally, Indian Hospitals are ranked based on these factors using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). © 2020 Informa UK Limited, trading as Taylor & Francis Group.

In recent times, the increasing healthcare spending due to the rising health awareness signifies the importance of identifying the appropriate factors that influence patient satisfaction, weight assignment to these factors, and measurement of patient satisfaction becomes important. However, devising a robust objective weighting method for weight assignment of the factors and an analytical method for determining patient satisfaction scores has not been paid enough attention. Motivated by these issues, this work focuses on devising a robust objective weighting method for weight assignment of the factors that influence patient satisfaction, an analytical method for determining patient satisfaction, and real-life implementation. We first propose a joint weighting methodology to allocate the weights to the factors by integrating the weights determined by different objective weighting methods, namely, mean weight, SD, entropy, criteria importance through intercriteria correlation, and preference selection index-based approaches. Next, using the weights of these factors, we design a modified weighted aggregated sum product assessment method to determine a single patient satisfaction score by integrating the scores obtained from the weighted sum model and the weighted product model. The proposed methodology is applied to a real-world dataset provided by a large healthcare provider and diagnostic clinic in Kolkata, India, to exhibit the real-life implementation. The theoretical insights obtained through non-parametric tests highlight the significant difference between joint weighting-based and single weighting-based methods. The context-specific insights signify that the patients suffering from arthritis and hyperlipidaemia exhibit lower satisfaction. Also, the patients suffering from dengue express lower satisfaction than the patients suffering from malaria. Additionally, the healthcare provider should focus on improving the service quality of the departments such as ophthalmology, ENT, and dietician. © 2021 John Wiley & Sons Ltd.

Feature Selection (FS) is an important topic in the domain of machine learning. Support Vector Machine (SVM) is one of the most popular ML models for classification tasks. Efficient feature selection may ensure enhanced classification accuracy. Although there are several feature selection algorithms in practice, they are either separately used, combinedly used with linear SVM or, used with kernel-based SVM. Additionally, there exists another problem in classification methods, which is the selection of correct kernel function. There seems to be as such no general rule for selecting a kernel that maximizes the model’s performance. To handle the issues together, we propose a model called non-linear kernel-free twin quadratic surface SVM with optimized feature selection (LTQSSVM-OFS) which can eventually tackle both feature selection and classification tasks efficiently. We are doing feature selection in a kernel-free way by applying optimization method on a Laplacian Twin Quadratic Surface SVM classifier. We have validated our proposed model using (i) min-max approach-based SVM without FS, (ii) linear SVM, and (iii) radial basis function-based SVM models applied on four datasets, namely ‘Star3642balanced’, ‘Diabetes’, ‘Health care: Heart attack possibility’, and ‘Blood Transfusion’. Experimental results reveal that the proposed model outperforms the other models. Additional worth noting benefit of our model is that it yields better results in case of working with fewer features. © 2022, Springer Nature Switzerland AG.

Green innovation could become a valuable firm resource for establishing competitive advantage while simultaneously contributing towards sustainable development; in other words, green innovation has the potential to address the dilemma between consuming available resources and preserving them for the future. However, there is a dearth of studies systematically examining the present structure and future scope of research on green innovation as a firm resource. Seeking to explain the sustainable development dilemma of green innovations through the theoretical perspective of the resource-based view of the firm, we address this gap with a comprehensive bibliometric analysis of 951 relevant articles. The key contributors to the extant literature are recognised with bibliographic coupling, citation analysis and co-authorship analysis. A co-citation analysis identifies four major thematic areas of research: green supply chain management, green product design, corporate environmental responsibilities and social sustainability. Further, a dynamic co-citation analysis tracks the progression of these thematic areas. Content analysis of the thematic areas provides insights into the status of the research domain. This study also contributes to the extant literature by identifying prestigious articles on green innovation as a firm resource, analysing the co-occurrence of keywords and suggesting future research agendas. © 2021 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd.

The main aim of this chapter is to build a smart security device to cater to the security problems that have arisen in the recent times, in the healthcare facilities, with the onset of the covid19 pandemic. With more and more people occupied with providing emergency services, the special restricted mode in our designed smart security system will help in flexible surveillance of hospital wards as per requirement. The demand within the global market for healthcare security systems has been rising on account of the need to ensure optimal security and productivity across all hospitals and healthcare facilities. A smart security system with IoT and cloud connectivity, along with multiple smart modes to cater to the needs of the user is the need of the hour. With the onset of the pandemic situation, things have become more intense, with minimal efforts given towards surveillance. This is where our smart security system finds its application along with other restricted zones. With its seamless connection with AWS for 24 × 7 connectivity with the user, one can expect constant updates as per requirement. With the integration of raspberry pi, other security features are also achieved like a fire alarm, motion detection, gas sensing, temperature detection, footfall tracking and noise detection with stable conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Purpose: The paper aims to investigate the cardiology department’s operational system for improving flexibility by minimizing the patient waiting time and simultaneously maximizing the utilization of service capacity in an uncertain environment. This article also proposes a policy framework that suggests a pool of additional resources and inter-firm collaboration can boost healthcare service delivery excellence. Design/methodology/approach: A discrete event simulation (DES) approach is followed for modeling patient flow and determining the service capacity to respond to demand variability and uncertainty. The model's outputs are used to minimize patient waiting time, maximize the utilization of the resources and match the service capacity with the patient demand. Findings: This research has tested two hypotheses and proved that an increase in waiting time decimates the throughput rate, and additional resources deployment in bottleneck activity positively impacts the throughput rate. The simulated scenarios prescribe an enhanced service capacity with quality care and further contribute to operational performance in reduced waiting time and cost. The results indicate that flexibility reduces the patient waiting time and maximizes the throughput rate. Practical implications: The study guides the healthcare policymakers to develop flexible competence and facilitate service mechanisms that are adaptive and robust while operating under a volatile environment. The article contributes to the healthcare literature that conjoins flexibility through simulation and resource utilization. Originality/value: This research is based on real-life primary data collected from healthcare providers. This study adds value to the healthcare systems to adopt strategic decisions to build flexibility through resource allocation, sharing and coordinated care. © 2022, Emerald Publishing Limited.

Information communication technology has changed people’s behavior towards gathering and assimilating information resulting in their dependence on electronic word-of-mouth to fulfill their information needs. With the help of the theory of planned behavior as the conceptual base, the present study aimed at examining the role of antecedent factors - to inform other travelers, show concern, express their enjoyment, subjective norms, and perceived behavioral control on travelers’ attitude. Further, the impact of attitude on their intention to write reviews was also studied. The results suggested a significant impact of informing others, concern for others, expressing enjoyment, and subjective norms on travelers’ attitude towards writing reviews. This attitude triggered travelers’ intent to provide reviews. The original contribution of the study is in identifying the antecedent factors influencing travelers’ attitude and intention towards writing online reviews in the context of an emerging market (India). © 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved.

A platform is a business model that allows business-to-business (B2B) participants to connect, interacts, create and exchange value. B2B exploits social media for brand building and branding is vulnerable to attacks, which leads to a brand crisis. B2B should characterise such crisis and respond proportionally to avert damage to social listening (SL). To diminish damages, the solution is to measure customer experience (CX), especially in a crisis situation. The study proposes an analytics-enabled customer experience (AeCX) platform for emotion detection in social media and measures CX after recovering from such crisis, by exploring recovery time objective (RTO), recovery point objective (RPO), techno-business features (TBF), SL and perceived risk (PR). A quantitative research methodology is used on primary data collected from 302 B2B participants. The study reveals improvement in CX and the results provide evidence that social media channels and the TBF of AeCX have become important. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The purpose of this study is to determine the impact of adopting a artificial intelligence-embedded customer relationship management (CRM) system for business-to-business relationship management. After reviewing the literature and considering the theory, a conceptual model was developed. The model was validated using the PLS-SEM technique with 312 responses from 14 firms in the B2B context. The study finds that an AI-embedded CRM system has a significant positive impact towards B2B relationship satisfaction and firm performance. Also, the study highlights that there is a negative impact of the moderator ‘technology turbulence’ on the relations of ‘automated decision making’ and ‘operational efficiency’ with ‘B2B relationship satisfaction’, whereas there is a positive impact of moderator ‘leadership support’ on ‘B2B relationship satisfaction’ and ‘firm performance’. There is presently no study on the impact of AI-CRM in the B2B context. Also, the study contributes to the existing literature by incorporating the moderator impact of ‘technology turbulence’ and ‘leadership support’ in the context of AI-embedded CRM systems for B2B relationship management. © 2022 Elsevier Inc.

To prevent the occurrences of accidents at workplaces, accident data should be analyzed properly. However, handling such data of higher dimension is often a difficult task for analysis to achieve efficient decision making due to the slow convergence and local minima problem. To address these issues, the present study proposes a new clustering algorithm called growing self-organizing map (GSOM)-based genetic K-means (GSGKM) for classifying accident data into an optimal number of clusters. Tolerance rough set approach (TRSA) is later used on each cluster to extract useful accident patterns, which enables helps in accident analysis and prevention. To validate the effectiveness of our proposed methodology, accident data obtained from an integrated steel plant are used as a case study. Besides, a total of four benchmark datasets collected from the University of California, Irvine (UCI) machine learning repository are also used for comparative study to prove its (i.e., GSGKM) superiority over some other state-of-the-arts. Experimental results reveal that the proposed methodology provides the highest clustering accuracy. A total of four clusters are obtained from the analysis. A set of 16 accident crisp patterns or rules are extracted from clusters using TRSA. Company employees are found to be more exposed to accidents than contractors. Additionally, behavioral issues are identified as the most determinant factor behind the injuries at work. The proposed methodology can be effectively used in decision making for different industries, including construction, manufacturing, and aviation. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Purpose: The purpose of this study is to determine the antecedents of obesity among the younger generation of Indians (Generation Y) from a psychological and lifestyle consumer perspective. The study also investigates the moderating role of demography on the body mass index (BMI) of Indian youths. Design/methodology/approach: The study initially develops a conceptual model, stemming from an extensive theoretical research, and subsequently validates this using structural equation modeling (SEM) technique with a sample size of 1,242 Indian youths. Findings: The study concludes that consumers' food habits (FH) and physical activity (PA) positively impact consumers' physical health (PH), which influences their BMI levels (BLs). Anxiety (AX), depression (DE), stress (ST), peer pressure (PP) and work pressure (WP) impact individuals' mental health (MH), which also influences their BLs. Finally, there is a significant moderating impact of demographic factors, such as age (AG), gender (GE) and income levels (ILs) on the relationship between individuals' physical and MH and individuals' BLs. Research limitations/implications: This study proposes a new model which highlights the issue of youth consumer obesity from the psychological and lifestyle perspectives. The model is effective as it has a high explanative power of 73%. The study investigates consumer obesity from emerging market like India perspective, but the study does not examine consumer food consumption behavior and obesity from developed market perspective. Practical implications: Youth obesity could be considered a global pandemic, and obesity rates among the Indian youth are also increasing. This study provides valuable inputs and understanding of consumer markets to policy makers, consumer protection institutions, organizations related to the food and beverage industry, healthcare workers and consumers themselves regarding the antecedents of youth obesity (BL) in developing and emerging markets. Originality/value: The study adds value to the body of literature related to consumer obesity, FH, consumer psychology and lifestyle through findings that are new in terms of findings' specificity, contextual focus and explication. Moreover, the study extends the cognitive theory of DE and the theory of planned behavior (TPB). The research effectively offers significant theoretical and practicable market knowledge to both scholars and marketing practitioners, as well as policy makers and institutions dealing with youth obesity, particularly in emerging markets. © 2022, Emerald Publishing Limited.

Purpose: This study aimed to determine the antecedents of privacy concerns and their impact on consumers' online information disclosure. It also investigated the moderating role of government regulation on the relationship between privacy concerns and online information disclosure. Design/methodology/approach: With the help of literature review and theories, a theoretical model was developed and then validated using the partial least squares structural equation modeling technique to analyze data from 309 respondents. Findings: The study found that online users' privacy awareness, privacy experience, personality and cultural differences significantly and positively impact their privacy concerns, which in turn positively and significantly influence their online information disclosure. The study also found that government regulation has a significant impact on online information disclosure. Research limitations/implications: The study is cross-sectional in nature and cannot be generalized, and therefore, a longitudinal study could be conducted. Also, the study identified four antecedents of online users' privacy concerns. More antecedents and more sample data with other boundary conditions could have increased the predictive power of the model. Practical implications: This study will help practitioners to better understand the privacy concerns of online users, which could help them to develop better products and enhance service quality. Policymakers can develop regulations as per the online users' requirements to increase their confidence in disclosing personal information online and other online activities. Originality/value: Few studies have dealt with online users' information disclosure and their privacy concerns or the moderating role of government regulations on online information disclosure. The study is unique as its proposed model is the first that accounts for both online users' privacy concerns and government regulation and their online information disclosure. © 2022, Emerald Publishing Limited.

Human-wildlife conflict (HWC) is a major concern for protected area management. Managing HWC around protected areas requires structured and replicable processes to reduce subjectivity and promote adherence to good governance principles. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely-used process model for structured decision-making. This study demonstrates the novel application of CRISP-DM to HWC related decision-making. We apply CRISP-DM and conduct hotspot and temporal (monthly) analysis of HWC data from Ramnagar Forest Division, India. Based on the patterns of crop loss, livestock loss, and human loss, we propose conflict-type and species-specific preventive strategies. A qualitative assessment of the initial outcomes of the ongoing implementation finds the preventive strategies to be effective. We suggest a participatory approach, localization of strategy, and need for data management as opportunities for improvement. © 2022 Newcastle University.

Using monthly fund-level portfolio holdings data on Indian equity mutual funds, we explore the predictable patterns in the trading biases of fund managers induced by changing attention allocation patterns. In an emerging market setting, we investigate the tendency of fund managers to sell winners and increase their exposure to losing stocks. Their biases are examined during two states of economic cycles. We adjust for random choice probability while selecting funds for reasons unrelated to the disposition effect. Our research contributes to the relatively nascent psychology-based asset pricing literature by examining fund managers’ psychology concerning financial decision-making. © 2022

Nowadays, telemedicine services based on Artificial Intelligence are not confined to research labs rather they have become a part of human efforts to improve Healthcare services. To coordinate distant medical operations in clinical centers, telemedicine used digital information and broadcast inter-communicative approaches. The overall management of medical norms and patient well-being framework is disrupted by machine intelligence in telemedicine by providing advanced methods of coordination. This scenario can be seen in regions of telehealth applications where Artificial Intelligence use cases are utilized to influence or build new rare medical approaches. This study discusses the use of AI in telehealth. Some vital applications are discussed here. A brief literature survey highlighting some contributions of AI in telehealth is presented. Major challenges and solutions are also highlighted. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.