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1 – 6 of 6Biswajit 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.
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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.
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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.
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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.
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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.
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