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Article
Publication date: 24 July 2024

Biswajit Paul, Raktim Ghosh, Ashish Kumar Sana, Bhaskar Bagchi, Priyajit Kumar Ghosh and Swarup Saha

This study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis…

Abstract

Purpose

This study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.

Design/methodology/approach

To address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.

Findings

Financial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.

Originality/value

The study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

Details

Journal of Economic and Administrative Sciences, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2054-6238

Keywords

Content available
Book part
Publication date: 30 January 2023

Raktim Ghosh and Bhaskar Bagchi

Abstract

Details

Economic Policy Uncertainty and the Indian Economy
Type: Book
ISBN: 978-1-80455-937-6

Article
Publication date: 7 February 2025

Anshul Jain, Biswajit Behera and Khyati Kochhar

Green banking has evolved in the financial sector to reduce the negative impact of economic progress. However, customers’ behavior remains indifferent towards bank’s green…

Abstract

Purpose

Green banking has evolved in the financial sector to reduce the negative impact of economic progress. However, customers’ behavior remains indifferent towards bank’s green initiatives across nations. Therefore, this study examines the various factors affecting the behavior of Indian banking customers towards green banking by extending the theory of planned behavior (TPB).

Design/methodology/approach

The study employed a quantitative research approach and distributed a self-administered questionnaire. Data from the 293 green banking service users in India’s Delhi-National Capital Region (NCR) have been collected using purposive and snowball sampling techniques. SPSS 26 and SmartPLS 3 were used to analyze data.

Findings

The findings explained that environmental concern substantially predicts customer attitude, subjective norms and perceived behavior control towards green banking. Moreover, all the constructs within the TPB model were found to substantially impact customers’ inclination to adopt green banking, thereby leading to actual behavior.

Research limitations/implications

Academically, the findings have broadened the TPB model’s application by adding a new construct in the context of green banking while confirming its applicability. Practically, it advises financial regulators and banking personnel to prioritize establishing a conducive environment for customers, characterized by the accessibility of green banking services under favorable conditions and at affordable rates.

Originality/value

The study enhanced the understanding towards green banking by correlating different variables and extending the TPB model using SmartPLS 3 in the domain of green banking.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 3 November 2020

Rofin TM and Biswajit Mahanty

The purpose of this paper is to investigate the impact of wholesale price discrimination by a manufacturer in a retailer–e-tailer dual-channel supply chain for different product…

Abstract

Purpose

The purpose of this paper is to investigate the impact of wholesale price discrimination by a manufacturer in a retailer–e-tailer dual-channel supply chain for different product categories based on their online channel preference.

Design/methodology/approach

This paper considers a dual-channel supply chain comprising of a retailer and an e-tailer engaged in competition. Game-theoretic models are developed to model the competition between the retailer and e-tailer and to derive their optimal price, optimal order quantity and optimal profit under (1) equal wholesale price strategy and (2) discriminatory wholesale price strategy. Further, a numerical example was employed to quantify the results and to capture the variation with respect to online channel preference of the product.

Findings

It is beneficial for the manufacturer to adopt a discriminatory wholesale price strategy for products having both high online channel preference and low online channel preference. However, equal wholesale price strategy is beneficial for the e-tailer and the retailer in the case of products having high online channel preference and in the case of products having low online channel preference, respectively.

Practical implications

The study helps the manufacturers to maximize their profit by adopting the right wholesale price strategy considering the online channel preference of the product when the manufacturers are supplying to heterogeneous retailers.

Originality/value

There is scant literature on the wholesale price strategy of the manufacturer considering the heterogeneous downstream retailers. This paper contributes the literature by bridging this gap. In addition, the study establishes a link between the wholesale price strategy and online channel preference of the product.

Details

Management Decision, vol. 60 no. 2
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 25 October 2024

Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra and Manashi Chakraborty

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO…

Abstract

Purpose

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

Design/methodology/approach

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

Findings

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R2 of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R2 of 0.887 and a slight overestimation bias of +0.01.

Research limitations/implications

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Practical implications

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Social implications

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

Originality/value

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 14 March 2024

Zhihui Yang and Dongbin Hu

Digital technology plays a vital role in empowering omnichannel integration. Research on digital technology has recently attracted attention and rapidly developed. However, a…

Abstract

Purpose

Digital technology plays a vital role in empowering omnichannel integration. Research on digital technology has recently attracted attention and rapidly developed. However, a comprehensive assessment of the research status and potential gaps is yet to be conducted. Thus, this study investigated the current research status of digital technology-empowered omnichannel integration, and future research directions are proposed.

Design/methodology/approach

A three-stage bibliometric analysis was conducted on 764 articles published from 2000 to 2023, cited in the Web of Science database. Furthermore, performance and thematic analyses were performed.

Findings

The most productive contributors and influential articles in this field were identified, and four themes of focus were discovered: service quality, o2o commerce, omnichannel retailing, and digital transformation.

Originality/value

To the best of our knowledge, this work is the first attempt to enable researchers to understand the vast body of published scholarship on digital technology-empowered omnichannel integration.

Details

International Journal of Retail & Distribution Management, vol. 52 no. 4
Type: Research Article
ISSN: 0959-0552

Keywords

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