Dr.Punamkumar Hinge

@jspmuni.ac.in

Assistant Professor, School of business management
JSPM University Pune

Dr.Punamkumar Hinge


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https://researchid.co/punamhinge
10

Scopus Publications

132

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Designing Low Emission Last Mile Networks: A Multi Objective Optimization Approach for Indian Urban E Commerce Distribution
    Journal of Distribution Science, 2026
  • Decision Support System Using ChatGPT in Human Resource Management
    P. P. Halkarnikar, M. Guru Vimal Kumar, Simmi Madaan, Sundarapandiyan Natarajan, Melanie Lourens, Punamkumar Hinge
    Recent Trends in Engineering and Science for Resource Optimization and Sustainable Development, 2025
    An organisation can run successfully with human resource management which is considered to be an significant aspect. Efficient and effective HR Management helps the organisation for optimal performance and to achieve their goals by involving every team member of that organisation. Even though HR Management faces huge number of challenges such as technological environment changes, business changes expectations and diverse need of the members as well as large team management. The main objective of the study to use ChatGPT as decision support system in human resource management. The research study involves methods for interpreting and analysing ChatGPT as the effective decision support tool for its great potentiality transparency effectiveness and efficiency of HR processes. ChatGPT is an artificial intelligence model that provide virtual assistance in the form of text responses employee support process performance management employee development and providing assistance in recruitment process. ChatGPT has more insights and valuable data about the work environment as well as employees to provide informed editions to the HR Managers. The accuracy of the ChatGPT as decision support system in human resource management can be analysed with KNN and CNN algorithm.
  • Bias in AI-Driven Performance Management Systems: Ethical and Practical Considerations
    Ankita Saxena, Punamkumar Hinge, Suchitra Yadav, Sujay Mugaloremutt Jayadeva, Melanie Lourens, Sunil Manoharrao Ikharkar
    2025 World Skills Conference on Universal Data Analytics and Sciences Worldsuas 2025, 2025
  • Measuring employee attrition intention in an auto-component manufacturing organisation
    Punamkumar Hinge, Harshal A. Salunkhe, Mohit Boralkar, Sanjay Bang, Abhijeet Thakur, Vikas R. Adhegaonkar
    SA Journal of Human Resource Management, 2025
    Orientation: The auto-component manufacturing sector, a critical contributor to industrial growth, faces persistent challenges related to employee attrition, affecting operational efficiency and workforce stability. This study examines the influence of job satisfaction, work-life balance, and job stress on attrition intention among employees in Indian auto-component manufacturing organisations. Research purpose: To identify the key factors contributing to employee turnover and evaluate their relative impact on attrition intention. Motivation for the study: Amid rising concerns over attrition in the manufacturing industry, this research aims to explore how work-life balance and job stress influence employees’ intentions to leave their organisations. Research approach/design and method: Data were collected from 192 employees across 10 auto-component manufacturing companies in Pune, Maharashtra, India, using a structured questionnaire. The responses were analysed through structural equation modelling (SEM) using SPSS and AMOS. Main findings: The study reveals that work-life balance and job stress significantly impact attrition intention. Employees with poor work-life balance and high job stress are more likely to consider leaving. However, job satisfaction does not have a direct effect on attrition intention. Practical/managerial implications: Organisations should prioritise improving work-life balance and managing job stress by implementing flexible work policies, wellness programmes, and realistic workload distribution. Contribution/value-add: This study underscores the importance of addressing work-life balance and job stress in retention strategies, offering actionable insights for HR managers to mitigate attrition in the auto-component manufacturing sector.
  • Impact of human resource practice on work engagement and turnover intention in information technology companies
    Harshal A. Salunkhe, Deshana Jain, Punamkumar Hinge, Mohit Boralkar
    SA Journal of Human Resource Management, 2024
    Orientation: The information technology (IT) sector, a global economic driver, faces high employee turnover because of low work engagement. This study examines the relationship between human resource management (HRM) practices and their impact on work engagement and turnover intention (TI) in IT companies.Research purpose: The primary purpose of this research article is to investigate how HRM practices influence employee work engagement and TI in the IT sector.Motivation for the study: This study is motivated by the need to address this critical issue by exploring the role of HRM practices in shaping employee engagement and TI.Research approach/design and method: The research data came from 10 IT organisations in Pune IT parks. Non-probability convenience sampling was used to collect data. Data were analysed using Structural Equation Modelling (SEM), Statistical Package for Social Science (SPSS) and Moment Structure Analysis to evaluate the hypotheses.Main findings: The study found that HRM practices such as effective communication (EC), training satisfaction (TS), performance appraisal satisfaction (PAS), pay satisfaction (PS) and opportunities for development (OFD) positively influence work engagement among IT employees. Addressing these HRM practices can enhance employee retention and engagement in the IT sector.Practical/managerial implications: Implementing these strategies can lead to a more committed and productive workforce, improving overall organisational performance and retention.Contribution/value-add: This research offers actionable recommendations for IT companies to improve employee retention and engagement, filling a gap in existing literature by focussing exclusively on the unique challenges and dynamics of the IT industry.
  • Integrating Artificial Intelligence and Machine Learning for Competitive Advantage in Developing Business Ecosystems
    Srinivas D, C. Vijai, Krishan Bansal, Shaunak Pal, Sumeet Gupta, Punamkumar Hinge
    Tqcebt 2024 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024, 2024
    The previous few years have witnessed significant shifts in people's aspirations and lives as a result of technology improvements. When discussing the advancements in technology over the past twenty years, we must first discuss the pre-Internet era before moving on to the Internet era.After that, systems that are service-oriented replaced those of the Internet age. After then, we entered the era of social applications and left behind service-oriented systems. The social application era was eventually superseded by the age of artificial intelligence and the Internet of Things.The theory, subsequent development, and integration of intelligent behavior akin to that of humans in machines is known as artificial intelligence, or AI. A branch of AI called machine learning has important uses in many different fields of technology.In recent years, there has been a substantial advancement in the business model framework for artificial intelligence and machine learning. Nonetheless, there is one area that needs more investigation and understanding even though it is crucial for academics, policymakers, and aspiring managers to know: how companies are evolving their AI business model frameworks to achieve ongoing value creation.Research and experience on AI business model frameworks have consequences for machine learning (ML) in this investigation. In order to create and adapt their company model and maintain value generation, executives can use this tool to reproduce and categorize significant difficulties. We want to investigate the theoretical underpinnings of the Business Model Framework in this study, which uses Consumer Business Analytics to transform the business market ecology.
  • Application of Machine Learning Techniques for Decision Making Process in Human Resource Management
    C.Balarama Krishna, Meeta Joshi, K. Sathesh Kumar, Nalla Bala Kalyan, Shivani Bhardwaj, Punamkumar Hinge
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2023, 2023
    The strategic alignment between organisational objectives and human resource management is stronger in contemporary organizations. As deep learning methods and machine learning solutions play a larger role in managing human resource management operations, organizations are focusing on more applicable sets of solutions. Models based on machine learning are now making progress in a variety of HRM-related fields. Machine learning is being used in human resource management to anticipate who will remain and who will depart the company, as well as to gauge workers' interest in their specific organisation.Data scraping methods are used to extract the data, which is then saved in CSV format. With the aid of ML algorithms, the many characteristics in the data acquired using this method may be used to make predictions. The management may develop a strategy to keep a deserving person in the organization by using the analysis to draw conclusions about who will remain or depart the company.We used a variety of methods in our investigation, including feature scaling and SMOTE. The recommended techniques, such as random forest and XG boost classifier, are supported by the findings. We'll arrive to a judgment based on the accuracy rate (%) numbers for the results generated by the offered approaches.
  • Design and Empirical Analysis of a Artificial Intelligence-Based Human Resource Management Processing Systems for Detecting Personal Stress
    Geetha Manoharan, Vinay Kumar Sharma, Melanie Lourens, Akshay Kumar, Bijaya Bijeta Nayak, Punamkumar Hinge
    Proceedings of International Conference on Contemporary Computing and Informatics Ic3i 2023, 2023
    Artificial intelligence (AI), deep learning (DL), and automated processes have been quickly advancing, considerably boosting the significance of information technology (IT) within corporate procedures. Rising AI-based responses in human resource management (HRM) have been rapidly being used to handle time-consuming and difficult activities within HRM capabilities.Workers in most businesses are currently experiencing high work stress, which has an adverse impact on efficiency, security, and wellness. To cope with personal stress, it is critical for the HR sector to handle stress efficiently, connecting the barrier between administration and stressed personal. This research creates 2 stress prediction frameworks and also 2 neural network designs. This research use data from personal to train these 2 stress prediction systems. Investigations on 2 real-world databases, indicate that the suggested DL-driven method can accurately predict personal’ stress condition with 71.2 percent accuracy in the classification method model and 11.1 prediction decline in the regression framework. The HRM of businesses can be enhanced by precisely forecasting personal’ stress levels using this approach.
  • Machine Learning Methods for Online Education Case
    Manikandan Rajagopal, BaigMuntajeeb Ali, S.Sharon Priya, W.Aisha Banu, Madhavi G. M, Punamkumar
    Proceedings of 8th IEEE International Conference on Science Technology Engineering and Mathematics Iconstem 2023, 2023
    Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decision-making process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles.
  • Artificial Intelligence & Data Warehouse Regional Human Resource Management Decision Support System
    Manikandan Rajagopal, Punamkumar Hinge, Kolachina Srinivas, Manesh R. Palav, P. Balaji, Iskandar Muda
    Proceedings of 5th International Conference on Contemporary Computing and Informatics Ic3i 2022, 2022
    High-quality data is utilized to make informed decisions that effectively help to successfully safeguard our environment. When there is an abundance of information that is both heterogeneous in nature (coming from a wide variety of fields or sources) and of unknown quality, various problems may occur. Furthermore, the problem’s dynamic nature also imposes some other complications. In order to deal with such complications, the central role played by supercomputers in the modern environment is to promote protection initiatives like monitoring, data analysis, communication, and information storage and retrieval. In current days, the higher dependency on the data management process forced the developers to integrate and enhance all these initiatives with Artificial Intelligence knowledge-based techniques so that smart systems can be utilized by a vast number of people. In this context, this study has illustrated how Artificial Intelligence methods have changed the nature of Environmental Decision Support Systems (EDSS) over the course of the last two decades. The strengths that an EDSS should exhibit have been emphasized in this review. In the final section, we look at some of the more innovative solutions used for various environmental issues.

RECENT SCHOLAR PUBLICATIONS

  • Designing Low Emission Last Mile Networks: A Multi Objective Optimization Approach for Indian Urban E Commerce Distribution
    S SINGH, P HINGE, DEB Pinak, S SHRIVASTAVA, R PALSAPKAR
    Journal of Distribution Science 24 (2), 31-47 , 2026
    2026
  • Analysis and Prediction of Employee Turnover with Effective Feature Selection based on Lee–Carter and ANN Model
    L Vijayakumar, B Kannadasan, P Hinge, LKK Reddy, RVS Praveen, ...
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-6 , 2025
    2025
    Citations: 2
  • Bias in AI-Driven Performance Management Systems: Ethical and Practical Considerations
    A Saxena, P Hinge, S Yadav, SM Jayadeva, M Lourens, SM Ikharkar
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
  • Measuring employee attrition intention in an auto-component manufacturing organisation
    P Hinge, HA Salunkhe, M Boralkar, S Bang, A Thakur, VR Adhegaonkar
    SA Journal of Human Resource Management 23, 3003 , 2025
    2025
    Citations: 1
  • Decision Support System Using ChatGPT in Human Resource Management
    PP Halkarnikar, MGV Kumar, S Madaan, S Natarajan, M Lourens, ...
    Recent Trends In Engineering and Science for Resource Optimization and … , 2025
    2025
  • Supply Chain Management
    DPHDPSP Dr. Subas Ch Mahapatra
    2025
  • Impact of human resource practice on work engagement and turnover intention in information technology companies
    HA Salunkhe, D Jain, P Hinge, M Boralkar
    SA Journal of Human Resource Management 22, 2723 , 2024
    2024
    Citations: 20
  • Integrating Artificial Intelligence and Machine Learning for Competitive Advantage in Developing Business Ecosystems
    D Srinivas, C Vijai, K Bansal, S Pal, S Gupta, P Hinge
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024
  • Design and empirical analysis of a artificial intelligence-based human resource management processing systems for detecting personal stress
    B Bijeta Nayak, G Manoharan, VK Sharma, M Lourens, A Kumar, P Hinge
    6th International Conference on Contemporary Computing and Informatics (IC3I … , 2024
    2024
    Citations: 6
  • Application of Machine Learning Techniques for Decision Making Process in Human Resource Management
    CB Krishna, M Joshi, KS Kumar, NB Kalyan, S Bhardwaj, P Hinge
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023
    2023
    Citations: 3
  • Design and Empirical Analysis of a Artificial Intelligence-Based Human Resource Management Processing Systems for Detecting Personal Stress
    G Manoharan, VK Sharma, M Lourens, A Kumar, BB Nayak, P Hinge
    2023 6th International Conference on Contemporary Computing and Informatics … , 2023
    2023
    Citations: 9
  • Use of Artificial Intelligence (AI) in recruitment and selection
    A Thakur, P Hinge, V Adhegaonkar
    Proceedings of the Internatioal Conference on Applications of Machine … , 2023
    2023
    Citations: 32
  • Analysis of human resources attrition: A thematic and sentiment analysis approach
    P Hinge, A Thakur, H Salunkhe
    International Conference on Applications of Machine Intelligence and Data … , 2023
    2023
    Citations: 6
  • Artificial intelligence (ai) in hrm (human resources management): A sentiment analysis approach
    P Hinge, H Salunkhe, M Boralkar
    International Conference on Applications of Machine Intelligence and Data … , 2023
    2023
    Citations: 25
  • Machine Learning Methods for Online Education Case
    M Rajagopal, BM Ali, SS Priya, WA Banu
    2023 Eighth International Conference on Science Technology Engineering and … , 2023
    2023
    Citations: 7
  • Impact COVID-19 on Investor’s Perception towards Systematic Investment Plan in Mutual Funds
    MBPH Harshal Anil Salunkhe
    Empirical Economics Letters 22 (2), 151-160 , 2023
    2023
  • A STUDY ON RECRUITMENT FUNCTION IN INFORMATION TECHNOLOGY; EMPHASIS ON THE ROLE OF IT SECTOR
    RPK Patnaik, P Hinge, H Shamina, G Santhakumar, MM Ishaq, ...
    Journal of Clinical Otorhinolaryngology, Head, and Neck Surgery 27 (1), 967-980 , 2023
    2023
  • Growing Trend Of Mobile Marketing In The Sustainable Development Of Global Business
    P Mishra, D Muniswamy, RR Prasadh, K Sharma, P Hinge, IAK Shaikh
    resmilitaris , 2023
    2023
    Citations: 1
  • Artificial intelligence & data warehouse regional human resource management decision support system
    M Rajagopal, P Hinge, K Srinivas, MR Palav, P Balaji, I Muda
    2022 5th International Conference on Contemporary Computing and Informatics … , 2022
    2022
    Citations: 5
  • REVIEW ON GIG ECONOMY IN INDIA AND ITS RISE
    P Hinge, S Saxena, HD Patil, M Boralkar, H Salunkhe, A Thakur
    KOREA REVIEW OF INTERNATIONAL STUDIES 15 (39), 198-212 , 2022
    2022
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Use of Artificial Intelligence (AI) in recruitment and selection
    A Thakur, P Hinge, V Adhegaonkar
    Proceedings of the Internatioal Conference on Applications of Machine … , 2023
    2023
    Citations: 32
  • Artificial intelligence (ai) in hrm (human resources management): A sentiment analysis approach
    P Hinge, H Salunkhe, M Boralkar
    International Conference on Applications of Machine Intelligence and Data … , 2023
    2023
    Citations: 25
  • Impact of human resource practice on work engagement and turnover intention in information technology companies
    HA Salunkhe, D Jain, P Hinge, M Boralkar
    SA Journal of Human Resource Management 22, 2723 , 2024
    2024
    Citations: 20
  • Digital Payment System with Reference to Financial Transactions in India: An Empirical Analysis
    DPH Dr. Harshal Anil Salunkhe , Dr. Pankaj Nandurkar
    ADALYA JOURNAL 8 (7), 70-76 , 2019
    2019
    Citations: 11
  • Design and Empirical Analysis of a Artificial Intelligence-Based Human Resource Management Processing Systems for Detecting Personal Stress
    G Manoharan, VK Sharma, M Lourens, A Kumar, BB Nayak, P Hinge
    2023 6th International Conference on Contemporary Computing and Informatics … , 2023
    2023
    Citations: 9
  • Machine Learning Methods for Online Education Case
    M Rajagopal, BM Ali, SS Priya, WA Banu
    2023 Eighth International Conference on Science Technology Engineering and … , 2023
    2023
    Citations: 7
  • Design and empirical analysis of a artificial intelligence-based human resource management processing systems for detecting personal stress
    B Bijeta Nayak, G Manoharan, VK Sharma, M Lourens, A Kumar, P Hinge
    6th International Conference on Contemporary Computing and Informatics (IC3I … , 2024
    2024
    Citations: 6
  • Analysis of human resources attrition: A thematic and sentiment analysis approach
    P Hinge, A Thakur, H Salunkhe
    International Conference on Applications of Machine Intelligence and Data … , 2023
    2023
    Citations: 6
  • Artificial intelligence & data warehouse regional human resource management decision support system
    M Rajagopal, P Hinge, K Srinivas, MR Palav, P Balaji, I Muda
    2022 5th International Conference on Contemporary Computing and Informatics … , 2022
    2022
    Citations: 5
  • Application of Machine Learning Techniques for Decision Making Process in Human Resource Management
    CB Krishna, M Joshi, KS Kumar, NB Kalyan, S Bhardwaj, P Hinge
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical … , 2023
    2023
    Citations: 3
  • REVIEW ON GIG ECONOMY IN INDIA AND ITS RISE
    P Hinge, S Saxena, HD Patil, M Boralkar, H Salunkhe, A Thakur
    KOREA REVIEW OF INTERNATIONAL STUDIES 15 (39), 198-212 , 2022
    2022
    Citations: 3
  • Analysis and Prediction of Employee Turnover with Effective Feature Selection based on Lee–Carter and ANN Model
    L Vijayakumar, B Kannadasan, P Hinge, LKK Reddy, RVS Praveen, ...
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-6 , 2025
    2025
    Citations: 2
  • Measuring employee attrition intention in an auto-component manufacturing organisation
    P Hinge, HA Salunkhe, M Boralkar, S Bang, A Thakur, VR Adhegaonkar
    SA Journal of Human Resource Management 23, 3003 , 2025
    2025
    Citations: 1
  • Growing Trend Of Mobile Marketing In The Sustainable Development Of Global Business
    P Mishra, D Muniswamy, RR Prasadh, K Sharma, P Hinge, IAK Shaikh
    resmilitaris , 2023
    2023
    Citations: 1
  • A STUDY OF TALENT MANAGEMENT AND ITS IMPACT ON PERFORMANCE OF ORGANIZATIONS
    T Singh, S Tatiparti, SC Gaikwad, NV Jagannath, P Hinge, A Pandey
    KOREA REVIEW OF INTERNATIONAL STUDIES 15 (38), 88-98 , 2022
    2022
    Citations: 1
  • Designing Low Emission Last Mile Networks: A Multi Objective Optimization Approach for Indian Urban E Commerce Distribution
    S SINGH, P HINGE, DEB Pinak, S SHRIVASTAVA, R PALSAPKAR
    Journal of Distribution Science 24 (2), 31-47 , 2026
    2026
  • Bias in AI-Driven Performance Management Systems: Ethical and Practical Considerations
    A Saxena, P Hinge, S Yadav, SM Jayadeva, M Lourens, SM Ikharkar
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
  • Decision Support System Using ChatGPT in Human Resource Management
    PP Halkarnikar, MGV Kumar, S Madaan, S Natarajan, M Lourens, ...
    Recent Trends In Engineering and Science for Resource Optimization and … , 2025
    2025
  • Supply Chain Management
    DPHDPSP Dr. Subas Ch Mahapatra
    2025
  • Integrating Artificial Intelligence and Machine Learning for Competitive Advantage in Developing Business Ecosystems
    D Srinivas, C Vijai, K Bansal, S Pal, S Gupta, P Hinge
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024