Human Resource Management / Organizational Behavior
14
Scopus Publications
267
Scholar Citations
8
Scholar h-index
7
Scholar i10-index
Scopus Publications
Evaluation of energy consumption data for business consumers Anchal Pathak, A. Deivasree Anbu, Azlin Binti Abd Jamil, Sunil Kumar Vohra, Shashi Kant Gupta, Ashish Kumar Pandey, Getnet Worke Abate Environment Development and Sustainability, 2025
Introduction to 5G new radio Shaifali Garg, Shashi Kant Gupta, A. Deivasree Anbu, Anchal Pathak Machine Learning for Mobile Communications, 2024 Wireless technologies have been growing rapidly in all corners of the world. Fifth-generation (5G) wireless technology has emerged as one of wireless research’s most exciting and challenging topics. As a multiservice network, 5G is expected to accommodate a broad variety of industries, each with its own set of performance and service needs. To meet the increased demand for mobile connections, 5G delivers new radio (NR) technology, and we review the essentials of future mobile communications regarding the introduction of 5G NR; we explain multiple-input/multiple-output antennas, radio access networks, and frequency bands. We also review the convergence of wireless communication, the supporting technological drivers, and spectra regarding the 5G NR environment to examine NR applications in 5G networks. We outline some of the most essential research areas in 5G NR in this chapter.
Reciprocated Bayesian-Rnn classifier-based mode switching and mobility management in mobile networks Shashi Kant Gupta, Anchal Pathak, Sultanuddin SJ, Nupur Soni Machine Learning for Mobile Communications, 2024 The proliferation of linked mobile communication devices worldwide has inspired researchers to devise a creative mode switching paradigm. Reciprocated Bayesian-recurrent neural network classifier (RB-RNN-C) is used in this work to develop a unique mode-switching model. Quality characteristics such as link usage, bandwidth, latency, energy usage, and signal strength are used to switch modes. Whenever the network moves from cellular mode to user mode, the quality characteristics must be maintained. To enhance the capacity of network mobility management, a methodology for calculating user mobility has been developed. The presented method is critical to enhancing user mobility while communicating. In terms of latency, energy usage, link usage ratio, and throughput, RB-RNN-C mode switching is excellent. These factors are depicted as findings in the graphical formation using the Origin tool.
Analysing the role of Industry 4.0 in sustainable lean manufacturing Naveen Anand Daniel, Anchal Pathak, Praveen Ravichandran, S.V. Adith, Nirbhay Kumar International Journal of Services and Operations Management, 2024 Globalisation has led industries all over the country to follow sustainable lean practices to minimise the environmental impact and to maintain a high-quality for all products and business services. The implementation of sustainable lean practices has a varied number of challenges. Efforts are made in this paper to analyse the most important challenge responsible for the implementation of sustainable lean manufacturing (SLM) along with Industry 4.0. The challenges were identified after a thorough literature review and with the help of industry experts. The key challenges were then processed and verified using two methodologies, best worst methodology and analytical hierarchy process (AHP). The data collected from BWM was then verified using AHP process with the help of industry experts. Finally in the end 'working culture' was found to be the most important criteria responsible for the implementation on Industry 4.0 in SLM.
Exploring the psychometric properties of personal optimism and self-efficacy optimism-extended (POSO-E) scale among Indian teachers Nidhi Sharma, Anchal Pathak, B. Latha Lavanya, Naval Garg, Kusum Lata Benchmarking, 2023 PurposeThe present study aimed to evaluate the psychometric properties of the short form of personal optimism and self-efficacy optimism-extended (POSO-E) among Indian teachers.Design/methodology/approachTwo studies were conducted to adjudge the reliability and validity of the scale. In the first study, the sample of 510 respondents was randomly divided into subsamples. The first subsample was subjected to the Exploratory Factor Analysis which yielded a two-factor solution explaining 71.02% of the variance. This model was subjected to the Confirmatory Factor Analysis using a second subsample. Acceptable model fit indices suggested factorial validity of the two-dimensional POSO-E among Indian teachers. In the second study, acceptable Cronbach's alpha and composite reliability estimates (greater than 0.70) indicated the scale's reliability. Also, as expected, personal optimism, self-efficacy optimism and overall optimism reported a positive correlation with spiritual well-being and a negative association with distress. It confirmed the criterion validity of the POSO-E among Indian teachers.FindingsThe results showed appreciable psychometric properties of the POSO-E in the context of Indian teachers. The study offered a valid and reliable scale to measure teachers' optimism levels. It is poised to generate renewed interest among scholars to emphasize teachers' positive and optimist thinking. The findings also reported a positive association between teachers' optimism and spiritual well-being. It suggests that spiritual practices and interventions could be used to develop an optimistic academic workforce.Originality/valueThe study is one of the pioneer studies that evaluated the reliability and validity of the POSO-E among Indian teachers.
Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19 Manish Mohan Baral, Subhodeep Mukherjee, Ramji Nagariya, Bharat Singh Patel, Anchal Pathak, Venkataiah Chittipaka Benchmarking, 2023 PurposeThe micro, small and medium scale enterprises (MSMEs) faced various challenges in the ongoing COVID-19 pandemic, making it challenging to remain competitive and survive in the market. This research develops a model for MSMEs to cope with the current pandemic's operational and supply chain disruptions and similar circumstances.Design/methodology/approachThe exhaustive literature review helped in identifying the constructs, their items and five hypotheses are developed. The responses were collected from the experts working in MSMEs. Total 311 valid responses were received, and the structural equation modeling (SEM) approach was used for testing and validating the proposed model.FindingsCritical constructs identified for the study are-flexibility (FLE), collaboration (COL), risk management culture (RMC) and digitalization (DIG). The statistical analysis indicated that the four latent variables, flexibility, digitalization, risk management culture and collaboration, contribute significantly to the firm performance of MSMEs. Organizational resilience (ORS) mediates the effects of all the four latent variables on firm performance (FP) of MSMEs.Practical implicationsThe current study's findings will be fruitful for the manufacturing MSMEs and other firms in developing countries. It will enable them to identify the practices that significantly help in achieving the firm performance.Originality/valueThe previous researches have not considered the effect of “organizational resilience” on the “firm performance” of MSMEs. This study attempts to fill this gap.
AI-Based Competency Model and Design in the Workforce Development System Parin Somani, Shashi Kant Gupta, Chandra Kumar Dixit, Anchal Pathak Designing Workforce Management Systems for Industry 4 0 Data Centric and AI Enabled Approaches, 2023 Competence always gives a business a competitive advantage. Competence-based recruiting, development, and performance evaluation are well-known phenomena that have been studied in the literature but are not very frequent in actual practice. The complexity of the topic and the lack of a universal framework that can be implemented with little to no adjustments may be the reason these are not frequently used, despite their relevance. There have been attempts to build competency frameworks, although they are often small-scale and occupation-specific. There is a need for a general framework that can be simply copied, developed using a disciplined and scientific process and professional skills, is clearly understood, and can be used for as many different projects as needed. Though little academic study has been done on the subject, artificial intelligence (AI) has been suggested as a potent tool in human capital management systems. This chapter proposes a novel AI-based competency framework for the management of human capital. The data related to human capital is collected and preprocessed using normalization. We design the competency model using the discriminant regressive artificial neural network (DR-ANN), which assists in the effective hiring of human capital by accurately identifying their competencies. The proposed system is also statistically analyzed using analysis of variance (ANOVA). We compare the proposed competency model with traditional models to prove the efficacy of the suggested system.
Data-Centric Predictive Modeling of Turnover Rate and New Hire in Workforce Management System Chandra Kumar Dixit, Parin Somani, Shashi Kant Gupta, Anchal Pathak Designing Workforce Management Systems for Industry 4 0 Data Centric and AI Enabled Approaches, 2023 Employee turnover (ET) is a significant problem that businesses in all industries must deal with. In order to avoid paying for hiring and training, businesses are constantly seeking ways to retain professional employees. Being able to foresee an employee's resignation will let the business take preventative action. Using historical employee records, artificial intelligence (AI) and machine learning (ML) prediction models can assist in categorizing the chance of people quitting their jobs voluntarily. However, the lack of transparency and interpretability in the output responses produced by these AI-based ML models makes it challenging for human resource (HR) managers to comprehend the reasoning behind the AI forecasts. Managers will not be able to enhance data-driven decision-making and provide value to the businesses if they do not comprehend how and why responses are generated by AI models based on the input datasets. Utilizing the evolutionary gradient boosted random forest algorithm (EGB-RFA), we provide a novel data-centric predictive model to address these disadvantages. To calculate employee turnover, the dataset from the company's HR department was used. We normalized the data as part of the preprocessing step to eliminate duplicates. Based on the attributes, the dataset indicates whether the employee is leaving or staying. Now, we use 80% of the preprocessed data for training and 20% for testing to build a prediction model. The essential features are extracted using principal component analysis (PCA). The suggested framework is assessed on the chosen dataset with various feature settings and dataset sizes. Results show that the final model created by our approach performs better than expected.
Prediction of Employees’ Performance Using Machine Learning (ML) Techniques Anchal Pathak, Chandra Kumar Dixit, Parin Somani, Shashi Kant Gupta Designing Workforce Management Systems for Industry 4 0 Data Centric and AI Enabled Approaches, 2023 Developing a dynamic and real-time awareness of company behaviors enables a new degree of organizing and regulating the whole value chain within an industrial sector. The COVID-19 epidemic has resulted in major changes to how service firms operate, altering the daily tasks and activities of employees. The emergence of digitalization at the same time brought about new technology that may assist such operations and lessen COVID-19's effects. By allowing data collection and analysis across machines, Industry 4.0 will enable quicker, more adaptable, and more efficient production processes that will result in higher-quality products being produced at lower prices. Prediction and control of the employees' performance are crucial in competitive growth. Unlike physical systems, employee performance cannot be quantified using a mathematical formula. Therefore, the best tools for attaining this aim are machine learning (ML) methodologies. This chapter proposes a novel ML-based algorithm for predicting the performance of employees. Initially, we collect the employee dataset and preprocess the data using normalization. For feature extraction, we use principal component analysis (PCA), and feature selection is done using a random forest (RF) algorithm. We propose a weighted multilayer support vector machine (WM-SVM) classifier that predicts the performance using the selected features accurately. To validate the efficiency of the proposed model, we compare it with traditional approaches.
Evaluation of energy consumption data for business consumers A Pathak, AD Anbu, ABA Jamil, SK Vohra, SK Gupta, AK Pandey, ... Environment, Development and Sustainability, 1-24 , 2025 2025 Citations: 9
Introduction to 5G new radio S Garg, SK Gupta, AD Anbu, A Pathak Machine Learning for Mobile Communications, 1-14 , 2024 2024 Citations: 5
Reciprocated Bayesian–Rnn Classifier-Based Mode Switching and Mobility Management in Mobile Networks SK Gupta, A Pathak, N Soni Machine Learning for Mobile Communications, 116-132 , 2024 2024 Citations: 6
Research on Organizational Behavior and Human Resource Management's Policy Recommendations SK Vohra, A Pathak Advancements in Business for Integrating Diversity, and Sustainability, 198-203 , 2024 2024
The Evolution of Human Resource Management through Digitalization SK Vohra, A Pathak Advancements in Business for Integrating Diversity, and Sustainability, 204-209 , 2024 2024
Adoption of Artificial Intelligence Technology for Effective Human Resource Management A Pathak, P Tyagi, B Sharma, R Natarajan Advancements in Business for Integrating Diversity, and Sustainability, 149-153 , 2024 2024
Human Resource Development as a Component of Long-term HR Management Emphasizing Production Engineers A Khang, A Pathak Advancements in Business for Integrating Diversity, and Sustainability, 191-197 , 2024 2024 Citations: 1
Analysing the role of Industry 4.0 in sustainable lean manufacturing NA Daniel, A Pathak, P Ravichandran, SV Adith, N Kumar International Journal of Services and Operations Management 48 (1), 62-79 , 2024 2024 Citations: 6
4 AI-Based Competency Model and Design P Somani, SK Gupta, CK Dixit, A Pathak Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI … , 2023 2023
11 Prediction of Employees’ Performance Using Machine Learning (ML) Techniques A Pathak, CK Dixit, P Somani, SK Gupta Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI … , 2023 2023
AI-based Competency Model and Design in the Workforce Development System P Somani, SK Gupta, CK Dixit, A Pathak Designing Workforce Management Systems for Industry 4.0, 47-64 , 2023 2023 Citations: 10
Data-centric predictive modeling of turnover rate and new hire in workforce management system CK Dixit, P Somani, SK Gupta, A Pathak Designing Workforce Management Systems for Industry 4.0, 121-138 , 2023 2023 Citations: 13
Prediction of Employees' Performance using Machine Learning (ML) Techniques A Pathak, CK Dixit, P Somani, SK Gupta Designing Workforce Management Systems for Industry 4.0, 177-196 , 2023 2023 Citations: 21
Data mining processes and decision-making models in the personnel management system SK Gupta, A Khang, P Somani, CK Dixit, A Pathak Designing Workforce Management Systems for Industry 4.0, 85-104 , 2023 2023 Citations: 57
Exploring the psychometric properties of personal optimism and self-efficacy optimism-extended (POSO-E) scale among Indian teachers N Sharma, A Pathak, BL Lavanya, N Garg, K Lata Benchmarking: An International Journal 30 (7), 2234-2247 , 2023 2023 Citations: 12
Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19 MM Baral, S Mukherjee, R Nagariya, B Singh Patel, A Pathak, ... Benchmarking: An International Journal 30 (6), 1942-1965 , 2023 2023 Citations: 68
Government implications of infrastructural development and CSR in industry 4.0 T Saharan, A Pathak Industry 4.0 and the Digital Transformation of International Business, 251-271 , 2023 2023 Citations: 4
AI-based Competency Model and Design in the Workforce Development System , CRC Press P Somani, SK Gupta, CK Dixit, A Pathak 2023 Citations: 2
Data Mining Processes and Decision-Making Models in Personnel Management System, CRC Press SK Gupta, A Khang, P Somani, CK Dixit, A Pathak 2023
Impact of COVID-19 Vaccination: A Machine Learning Approach E Gupta, M Goyal, A Srivastava, A Pathak Proceedings of the Third International Conference on Information Management … , 2022 2022 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19 MM Baral, S Mukherjee, R Nagariya, B Singh Patel, A Pathak, ... Benchmarking: An International Journal 30 (6), 1942-1965 , 2023 2023 Citations: 68
Data mining processes and decision-making models in the personnel management system SK Gupta, A Khang, P Somani, CK Dixit, A Pathak Designing Workforce Management Systems for Industry 4.0, 85-104 , 2023 2023 Citations: 57
Understanding perceived risk: A case study of green electronic consumer products VK Pathak, A Pathak Management Insight 13 (1), 33-37 , 2017 2017 Citations: 35
Prediction of Employees' Performance using Machine Learning (ML) Techniques A Pathak, CK Dixit, P Somani, SK Gupta Designing Workforce Management Systems for Industry 4.0, 177-196 , 2023 2023 Citations: 21
Data-centric predictive modeling of turnover rate and new hire in workforce management system CK Dixit, P Somani, SK Gupta, A Pathak Designing Workforce Management Systems for Industry 4.0, 121-138 , 2023 2023 Citations: 13
Exploring the psychometric properties of personal optimism and self-efficacy optimism-extended (POSO-E) scale among Indian teachers N Sharma, A Pathak, BL Lavanya, N Garg, K Lata Benchmarking: An International Journal 30 (7), 2234-2247 , 2023 2023 Citations: 12
AI-based Competency Model and Design in the Workforce Development System P Somani, SK Gupta, CK Dixit, A Pathak Designing Workforce Management Systems for Industry 4.0, 47-64 , 2023 2023 Citations: 10
Evaluation of energy consumption data for business consumers A Pathak, AD Anbu, ABA Jamil, SK Vohra, SK Gupta, AK Pandey, ... Environment, Development and Sustainability, 1-24 , 2025 2025 Citations: 9
Transforming Human Resource Functions with Automation A Pathak, S Rana IGI Global , 2020 2020 Citations: 7
Reciprocated Bayesian–Rnn Classifier-Based Mode Switching and Mobility Management in Mobile Networks SK Gupta, A Pathak, N Soni Machine Learning for Mobile Communications, 116-132 , 2024 2024 Citations: 6
Analysing the role of Industry 4.0 in sustainable lean manufacturing NA Daniel, A Pathak, P Ravichandran, SV Adith, N Kumar International Journal of Services and Operations Management 48 (1), 62-79 , 2024 2024 Citations: 6
Introduction to 5G new radio S Garg, SK Gupta, AD Anbu, A Pathak Machine Learning for Mobile Communications, 1-14 , 2024 2024 Citations: 5
Coping With Deviant Workplace Behavior Through Employee Participation: An Exploratory Study S Rana, A Pathak Analyzing Workplace Deviance in Modern Organizations, 270-283 , 2020 2020 Citations: 5
Government implications of infrastructural development and CSR in industry 4.0 T Saharan, A Pathak Industry 4.0 and the Digital Transformation of International Business, 251-271 , 2023 2023 Citations: 4
Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19. Benchmarking MM Baral, S Mukherjee, R Nagariya, B Singh Patel, A Pathak, ... An International Journal , 2022 2022 Citations: 4
AI-based Competency Model and Design in the Workforce Development System , CRC Press P Somani, SK Gupta, CK Dixit, A Pathak 2023 Citations: 2
Human Resource Development as a Component of Long-term HR Management Emphasizing Production Engineers A Khang, A Pathak Advancements in Business for Integrating Diversity, and Sustainability, 191-197 , 2024 2024 Citations: 1
Impact of COVID-19 Vaccination: A Machine Learning Approach E Gupta, M Goyal, A Srivastava, A Pathak Proceedings of the Third International Conference on Information Management … , 2022 2022 Citations: 1
Addiction to Social Media Platforms and Perceived Stress Level Amongst Youth of India: A Machine Learning Approach DE Gupta, DA Srivastava, DM Goyal, DA Pathak Proceedings of the International Conference on Advances in Management … , 2021 2021 Citations: 1
Research on Organizational Behavior and Human Resource Management's Policy Recommendations SK Vohra, A Pathak Advancements in Business for Integrating Diversity, and Sustainability, 198-203 , 2024 2024
