Arbind Samal, Sabyasachi Patra and Devjani Chatterjee
The purpose of this paper is to examine the influence of culture on organizational readiness to change (ORC) within the context of merger and acquisition (M&A) in the banking…
Abstract
Purpose
The purpose of this paper is to examine the influence of culture on organizational readiness to change (ORC) within the context of merger and acquisition (M&A) in the banking sector in India.
Design/methodology/approach
A multisource approach is used to collect data from a public-sector bank in India for testing our hypothesis. A hierarchical approach based on higher-order modelling has been deployed for confirming the path model. The foundation of the study is based on power distance (PD) and uncertainty avoidance (UA) cultural dimensions of Hofstede (1984).
Findings
Employees in organizations with large PD and high UA index exhibit low readiness to change. Findings support a negative relationship of culture (large PD and high UA) with organizational readiness to change at the individual level.
Research limitations/implications
The study has three major implications. First, measures and importance of change readiness at the individual level during corporate events such as M&A is elucidated in the study. Second, a paradigm for assessing higher-order models grounded in theoretical and methodological rigour for testing our hypothesis is presented in the paper. Last, the role of culture in M&A processes is highlighted vis-à-vis factors related to PD and UA on ORC.
Practical implications
The findings of the research answer to the call for a study on factors that help in creating a synergy for successful M&A across all sectors especially in the banking sector. People representing high UA and large PD often look forward to direction and guidelines for guiding employee actions. Leaders therefore need to set clear agenda and effectively communicate the appropriateness of change to their employees for developing positive behaviour towards desirable organizational outcomes. This study touches upon this important perspective for its practical utilization.
Originality/value
The study adds to the limited literature on change which addresses the need for studying socio-cultural factors in the M&A process, especially in an emerging economies context.
Details
Keywords
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.