Shikha Pandey, Yogesh Iyer Murthy and Sumit Gandhi
This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian…
Abstract
Purpose
This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.
Design/methodology/approach
A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.
Findings
The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.
Originality/value
This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.
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Shikha Pandey, Yogesh Iyer Murthy and Sumit Gandhi
This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using…
Abstract
Purpose
This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis to provide structural health monitoring prognostic tools.
Design/methodology/approach
This study evaluated model performance using standard measures including root mean square error (RMSE), mean square error (MSE), R-squared (R2) and mean absolute error (MAE). Interpretability was evaluated using SHAP and LIME. The X and Y distances, concrete age, relative humidity and temperature were input parameters, whereas half-cell potential (HCP) values were considered output. The experimental data set consisted of observations taken for 270 days.
Findings
Gaussian process regression (GPR) models with rational quadratic, square exponential and matern 5/2 kernels outperformed others, with RMSE values around 16.35, MSE of roughly 267.50 and R2 values near 0.964. Bagged and boosted ensemble models performed well, with RMSE around 17.20 and R2 values over 0.95. Linear approaches, such as efficient linear least squares and linear SVM, resulted in much higher RMSE values (approximately 40.17 and 40.02) and lower R2 values (approximately 0.79), indicating decreased prediction accuracy.
Practical implications
The findings highlight the effectiveness of GPR models in forecasting corrosion in concrete buildings. The use of both SHAP and LIME for model interpretability improves the transparency of predictive maintenance models, making them more reliable for practical applications.
Social implications
Safe infrastructure is crucial to public health. Predicting corrosion and other structural problems improves the safety of buildings, bridges and other community-dependent structures. Public safety, infrastructure durability and transportation and utility interruptions are improved by reducing catastrophic breakdowns.
Originality/value
This study reduces the gap between model accuracy and interpretability in predicting concrete corrosion by proposing a data-driven method for structural health monitoring. The combination of GPR models and ensemble approaches provides a solid foundation for future research and practical applications in predictive maintenance. This comprehensive approach underscores the potential of data-driven methods for predictive maintenance in concrete structures, with implications for broader applications in various industries.
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Shikha Pandey, Sumit Gandhi and Yogesh Iyer Murthy
The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to…
Abstract
Purpose
The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.
Design/methodology/approach
In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.
Findings
In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.
Originality/value
Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.
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Shikha Sharma, Divya Pandey and Madhoolika Agrawal
Varanasi, an ancient city has witnessed the conversion of forest into agricultural lands. The high urbanization rate along with affluent lifestyle is adding another category of…
Abstract
Purpose
Varanasi, an ancient city has witnessed the conversion of forest into agricultural lands. The high urbanization rate along with affluent lifestyle is adding another category of land use, i.e. landfill. Such land use changes significantly affect the fluxes of greenhouse gases (GHGs) from soil thus contributing to global warming. The purpose of this paper is to quantify the global warming potential (GWP) of the three land uses in Varanasi city taking into consideration CH4 and CO2.The paper also highlights the land use pattern of Varanasi.
Design/methodology/approach
Sites representing land uses under forest, agriculture and landfill were identified in and around the city and measurements of GHG fluxes were conducted periodically using closed static chambers. The GWP from each land use was calculated using the standard formula of IPCC (2007).
Findings
Landfill was found to be the land use with the highest GWP followed by agriculture. GWP from forest was negative. The study indicated that conversion of natural ecosystems into man made ecosystems contributed significantly to GHGs emissions.
Research limitations/implications
The present research is a seasonal study with inherent uncertainties. To reduce the uncertainties long-term monitoring covering wider spatial area is required.
Practical implications
The sustainable use of land along with the increment of forest cover will not only reduce the contribution in GHGs emission, but will also increase the carbon sequestrations thus limiting the implication of climate change.
Originality/value
This study is the first of its kind comparing the soil borne emissions from three different land uses in a rapidly urbanizing ancient city, suggesting if there is rapid conversion of forested land into other two land uses there will be considerable increase in global warming. No similar studies could be found in the literature.
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Sunaina Kanojia and Shasta Gupta
This study aims to analyse the outcomes of Indian insolvency proceedings for their ex-post economic efficiency. Ideally, insolvent yet viable companies should witness resolution…
Abstract
Purpose
This study aims to analyse the outcomes of Indian insolvency proceedings for their ex-post economic efficiency. Ideally, insolvent yet viable companies should witness resolution, whereas insolvent-unviable companies should be liquidated. This study aims to ascertain the key forces that ensure or prevent the application of the first part of this maxim in practice.
Design/methodology/approach
The study uses logistic regression on a sample of 320 corporate insolvencies (out of 942 insolvencies) reported under the Insolvency and Bankruptcy Code (IBC), 2016. Two-stage least squares regression is used to check endogeneity issues.
Findings
The results claim high levels of rationality from the financial creditors and acceptable levels of viability from the plan proposers for precluding liquidation of insolvent yet viable companies. The findings reveal that an excess of value from resolution over that from liquidation, controls the outcomes of insolvency proceedings. Further examinations indicate that financial creditors’ focus on upfront recovery prevents them from judging the plans on other viability-related factors. Based on the findings, this study recommends that IBC must focus on the importance of both long-term recovery rates and resolution.
Originality/value
To the best of the authors’ knowledge, this is one of the first studies to empirically analyse Type 2 efficiency-related errors prevalent in the Indian insolvency proceedings since the enactment of its new code. The empirical explorations offered in this research can prove to be unique for policy-making.
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Shikha Bhatia and Sanjay Dhamija
After working through the case and assignment questions, students will be able to recognize essential considerations for the initial public offerings (IPO) decision, compare…
Abstract
Learning outcomes
After working through the case and assignment questions, students will be able to recognize essential considerations for the initial public offerings (IPO) decision, compare different types of fundraising options for startups, evaluate the free pricing regime for IPO pricing, examine the pricing process of IPOs, explore the issue of valuation of IPOs and assess the decision choices of the founder regarding IPO given the trade-offs and market conditions.
Case overview/synopsis
The case study explores the dilemma of Ghazal Alagh, the co-founder and chief innovation officer of Mamaearth, a direct-to-consumer babycare and skincare unicorn, regarding its IPO decision. Mamaearth had filed the draft offer document with SEBI in December 2022, and Ghazal was busy engaging with the investment bankers for the upcoming IPO. However, the weak market sentiments and shelving of IPO plans by many startups were forcing her to think about facing the possibility of postponing the IPO or continuing the IPO process but at lower valuations. The case study provides an opportunity to explore a startup’s financing choices. It allows for discussion of various IPO challenges from the perspectives of founders, venture investors, regulators, investment bankers and new IPO investors.
Complexity academic level
This case study is best suited for senior undergraduate- and graduate-level business school students in courses focusing on entrepreneurship, corporate finance, financial management, strategic management and investment banking.
Subject code
CSS1: Accounting and finance.
Supplementary materials
Teaching notes are available for educators only.
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Manju Dahiya, Shikha Sharma and Simon Grima
Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of…
Abstract
Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.
Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.
Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.
Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.
Priyanka, Shikha N. Khera and Pradeep Kumar Suri
This study aims towards developing a conceptual framework by systematically reviewing the available literature with reference to job crafting under the lens of an emerging economy…
Abstract
Purpose
This study aims towards developing a conceptual framework by systematically reviewing the available literature with reference to job crafting under the lens of an emerging economy from South Asia, i.e. India, which is the largest country and the largest economy in the South Asian region.
Design/methodology/approach
The study employs a hybrid methodology of a systematic literature review (SLR) and bibliometric analysis using VOSviewer and Biblioshiny. Bibliometric analysis provides glimpses into the current state of knowledge like-trend of publication, influential authors, collaboration with foreign authors, the major themes and studied topics on job crafting in India etc. Further, a detailed SLR of the selected articles led to the development of the conceptual framework consisting of the enablers and outcomes of job crafting.
Findings
It discusses implications for academia, business and society at large, and also provides valuable insights to policymakers and practitioners paving the way for better adoption, customization and implementation of job crafting initiatives.
Originality/value
Owing to its own unique social, cultural, and economic characteristics, the dynamics of job crafting in India may vary from other countries and regions which can also be reflective of how job crafting operates in South Asia in general. As job crafting was conceptualized and later evolved mostly in the western context, our study assumes greater significance as it is the first study which attempts to systematically review the job crafting literature to understand how job crafting manifests in the Indian context and presents a conceptual framework for the same.
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Due to government policies, accreditation demands, competition, digital India reforms and National Education Policy (NEP) 2020, the need for electronic human resource management…
Abstract
Purpose
Due to government policies, accreditation demands, competition, digital India reforms and National Education Policy (NEP) 2020, the need for electronic human resource management (e-HRM) has increased considerably in the Indian higher education (HE) sector, but the literature has revealed that the adoption of e-HRM practices in Indian HE institutions (HEIs) is still in its embryonic stage; therefore, the purpose of the current qualitative study is to explore the challenges and facilitators of e-HRM adoption in the Indian HE sector through interpretative phenomenological analysis (IPA).
Design/methodology/approach
The present study incorporates IPA, to capture the personal lived experiences of the HR executives employed in the Indian HEIs. Using purposive sampling, semi-structured interviews were conducted with the HR executives employed in Indian universities and institutions to know the perspectives on the adoption of e-HRM practices in Indian HEIs.
Findings
The study identified two superordinate themes, namely, challenges and facilitators of e-HRM adoption in the Indian HE sector. The superordinate theme “challenges” comprises eight sub-themes. Further, the theme “facilitators” consists of six subthemes.
Practical implications
The study has implications for the stakeholders of the HE sector, i.e. HR practitioners, top executives of the HE sector, government and HE regulators and other stakeholders of the HE sector.
Originality/value
This study has given deep insights into the challenges and facilitators in the adoption of e-HRM practices in the Indian HE sector, and to the best of the authors’ knowledge, no study till date has filled this knowledge gap through qualitative exploration using IPA.
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To augment the Insolvency and Bankruptcy Code, the Indian Government introduced the pre-packaged insolvency process exclusively for small and medium firms. This paper aims to…
Abstract
Purpose
To augment the Insolvency and Bankruptcy Code, the Indian Government introduced the pre-packaged insolvency process exclusively for small and medium firms. This paper aims to critically review some of the key features of the process and also identifies potential glitches imminent in the initial years of implementation.
Design/methodology/approach
This study is descriptive and based on secondary data. The provisions of the pre-pack scheme, Insolvency and Bankruptcy Code and reports on the progress of insolvency resolution in India are used to substantiate the observations.
Findings
This study shows that pre-packs would certainly help enhance the small and medium enterprise insolvency resolution process in India. However, the ambitious time frame can be adhered to only if the institutional framework for bankruptcy is strengthened.
Research limitations/implications
This paper is based on the initial regulatory provisions of the pre-pack process. Subsequent changes in regulations may affect the findings.
Practical implications
Some of the concerns in the process and the changes required to facilitate a smooth, speedy and efficient resolution process have been highlighted in this study.
Originality/value
Pre-packs are a very recent introduction to the insolvency regime in India. This paper makes a fervent attempt to explain the pre-pack process and the outcomes that can be expected in the early years after its rollout.