Meenakshi Verma

@siu.edu.in

Assistant Professor, Symbiosis Centre For Management Studies, Nagpur
Symbiosis International University

Meenakshi Verma


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https://researchid.co/meenakshi.verma
2.5 Years as an HR in the Corporate, 14.5 years in Academics, 4 Papers in Scopus, 1 Paper in Web of Science, 1 Book Chapter in Scopus

EDUCATION

BBA, MBA (HRM and Tourism), Ph.D. (OB & HR)

RESEARCH INTERESTS

Organizational Behaviour, Stress Management, HRM
17

Scopus Publications

Scopus Publications

  • The impact of augmented reality on retail dynamics
    Anuj Verma, Debarun Chakraborty, Meenakshi Verma, Paul W. Ballantine, Ravi Kumar Jain
    International Journal of Retail and Distribution Management, 2026
    Purpose Augmented reality (AR) is a technology that boosts or augments real-time experiences by incorporating digital information into live objects. Retailers’ application of AR has taken the entire world by storm, enriching the relationship between consumers and brands. This study explores the multifaceted usage of AR in the retail sector. Design/methodology/approach Two studies were conducted across two different time frames to understand the continued use of AR in retail dynamics. 394 respondents took part in Study One, while 368 respondents participated in Study Two. The research presented probes the moderating effect of e-word of mouth (e-WOM). The researchers also further explore the impact of AR on consumer behaviour by considering factors such as convenience, experience, curiosity, fantasy, and entertainment. Findings Based on Study One’s findings, the adoption of AR for online shopping is influenced more by fantasy and curiosity rather than entertainment. The findings of Study Two indicate that experience, convenience and social influence are key factors in driving the adoption of AR for online shopping. By exploring the relationship between these variables, we provide a comprehensive understanding of how AR impacts consumer perceptions. Originality/value This study extends gratification theory by analysing AR adoption in retail through a two-study approach, identifying key motivations and the moderating role of e-WOM. It differentiates fantasy from entertainment in AR adoption, emphasising curiosity and social influence. Our findings offer valuable insights for retailers and technologists to enhance AR-driven shopping experiences and consumer engagement.
  • Exploring the drivers of beneficiaries’ WOM towards CSR initiatives and its alignment with SDG
    Meenakshi Verma, Anuj Verma, Jaiprakash Paliwal, Rajani Pillai
    Discover Sustainability, 2025
    Purpose The present research aims to measure the impact of corporate social responsibility initiatives on the attitude of beneficiaries and the generation of positive WOM. It attempts to fill the gap in existing literature by exploring the role of education, clean drinking water, health care and vocational training on beneficiaries and aligning them with SDG (Sustainable Development Goals) goals, with attitude acting as a mediating variable. Method The researchers adopted a quantitative method in which a structured questionnaire was shared with 408 respondents, and after data cleaning, 334 responses were analyzed. SPSS and AMOS were used to analyze the data and perform SEM (structural equation modelling) to test the relationship between constructs and to examine the mediating role of attitude. Findings The study revealed that beneficiaries’ attitudes and WOM were highly influenced by safe drinking water and vocational skill development, Education and healthcare also significantly impacted the beneficiaries but were influenced by accessibility and perceived quality. Attitude played a significant role as a mediating variable. Author contributions This research highlights the factors that shape the beneficiaries’ attitude towards CSR, thus offering a deep insight into the behavioral aspects of the community towards CSR initiatives. It examines the role of attitude as a mediating variable between CSR engagement and beneficiaries opinion. It offers a practical exposure to organizations and government agencies, which would help them to design the CSR programs in more effective manner and align it with SDGs.
  • Exploring the Adoption of Wearable Technologies by Employees of Ed Tech Sector and Its Impact on Productivity: A Perspective Through the Theory of Gratification
    Meenakshi Verma, Anuj Verma, Anuradha Goswami
    IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
    The researchers have utilized the Theory of Gratification as a framework to gauge the adoption intention and hence the impact on productivity by embracing wearable technologies by the employees of Ed Tech sector. The proposed theoretical model has highlighted the impact of Workplace safety (WPS), Curiosity (CUR), Convenience (CON), Social Influence (SOI) and Trialability (TRB) on Adoption Intention (ADI) and productivity of employees. Moderating impact of Trust on the above-mentioned factors have also been validated by using SPSS and AMOS. Theory of Gratification has corroborated the primary responses collected from the employees of Ed Tech sector pertaining in the Bangalore region (n=377). The results arrived at by using Structural Equation Model (SEM) analysis suggest that there exists a significant positive relationship between ADI & WPS, ADI & CUR, ADI & CON, ADI & SOI, ADI & TRI. The moderation results signify that trust moderates the relationship between WPS & ADI, Con & ADI. This study will facilitate the Education sector to arrive at informed decision-making and embrace new technology for the wellbeing of its employees.
  • Modeling Recalls for Automobiles
    Anuradha Goswami, Anuj Verma, Meenakshi Verma
    IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
    Managing the product recalls in automotive industry is a crucial and relevant problem which impacts human lives and ensures secure operation of automobiles. Recalls of products deals with withdrawing vulnerable products from the marketplace, thus optimizing the corporate's liability for their negligence. The existing techniques and technologies available for product recall management has a deficit of transparency showing recall data patterns depicting the recall urgency derived from its potential risks. This study provides a structured approach which reveals patterns inherent in recall data and classifies the recalls based on its potential risks. The outcome offers relevant insights for manufacturers and regulatory bodies, facilitating in improving the recall management processes and strengthen public safety by treating critical vehicle defects within proper timeline.
  • Factors Influencing the Customer Satisfaction and WOM (Word of Mouth) of Smart Watches: A Perspective of Technology Acceptance Model
    Anuj Verma, Meenakshi Verma, Anuradha Goswami
    IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
    The researchers have adopted Technology Acceptance Model (TAM3) to gauze the customer satisfaction level and WOM for smartwatches. The paper intends to examine the factors which influence the adoption intention, customer satisfaction and WOM of smartwatches. TAM3 model was used to validate the primary data comprising of 383 respondents which was further examined by SEM. The findings of the result revealed that motivation and innovation influence the perceived ease of use (PEU). Compatibility and social influence significantly impacted PUF (perceived usefulness). Both PEU and PUF significantly impacted the adoption intention of smartwatches. The adoption intention was positively connected with customer satisfaction which in turn led to word of mouth (WOM) . The study will help the smartwatch manufacturers gain deep insight understanding the factors influencing customer satisfaction and generation of word-of-mouth publicity
  • Evaluating the inhibitors in the growth of high-speed railway in India: A multi-stakeholder perspective
    Arindam Debroy, Krishna Kumar Dadsena, Pushparenu Bhattacharjee, Anuj Verma, Meenakshi Verma
    Transport Policy, 2024
  • Adoption of AI in CRM in the Retail Sector. A perspective from Technology Acceptance Model
    Anuj Verma, Meenakshi Verma, Anuradha Goswami
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
    The researchers have used the Technology Acceptance Model (TAM3) to measure the adoption intention of AI in the retail sector. The paper aims to capture the factors that influence the adoption intention of AI in customer relation management in the retail sector. The primary data comprising of 487 respondents was validated by TAM3 which was further analyzed by SEM. The findings of the result revealed that motivation does not impact the perceived ease of use (PEU). Automation and self-efficacy significantly influenced PEU while accuracy, social influence, and customer experience positively impacted PUF (perceived usefulness). Both PEU and PUF significantly impacted the adoption intention of AI in the retail sector. The study will help retail service providers and CRM software developers gain better insight into the adoption of AI
  • Exploring the Adoption of Human Resource Analytics by Human Resource Professionals of the EdTech Sector
    Meenakshi Verma, Anuj Verma, Anuradha Goswami
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
    The researchers have developed a technology adoption model to gauge the adoption intention of Human Resource Analytics (HRA) by the HR professionals belonging to the EdTech sector to carry out various HR functions. The proposed theoretical model has captured the impact of Performance Expectancy (PET), Effort Expectancy (EET), Social Influence (SIF), Facilitating Conditions (FCT), Trialability (TRB) on Behavorial Intention (BHI) of HRA Adoption. Moderating impact of Gender and Experience on the above-mentioned factors have also been validated by using SPSS and AMOS.Unified Theory of Acceptance and Use of Technology extension model (UTAUT 2) has validated the primary responses collected from employees of EdTech sector operating in Bangalore region (n=223). The results arrived at by using Structural Equation Model (SEM) analysis suggest that there exists a significant positive relationship between EET and BHI, SIF and BHI, PET and BHI. The moderation results denote that gender moderates the relationship between SIF and BHI and that experience moderates the relationship between EET and BHI & SIF and BHI. This study will facilitate the EdTech sector to arrive at informed decisions pertaining to their human resource and prepare customized strategies for implementing HRA.
  • Analyzing the efficacy of Deep Learning and Transformer models in classifying Human and LLM-Generated Text
    Anuradha Goswami, Guneet Kaur, Shivam Tayal, Anuj Verma, Meenakshi Verma
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
    Large Language Models (LLM) are widely accepted by the humans for their powerful ability to understand, follow and create human-like text. The text generation capabilities of LLMs have reached such a height that it is now comparable to text generated by human.LLMs are a powerful subset of Artificial Intelligence (AI)models, trained on huge amount of human-written text to create indistinguishable content. AI models have taken an important step ahead in constructing hyper-realistic content which can be in any modality such as text, image, and video. The output content generated by these models can be hard to distinguish from human-written content as these models are iteratively trained on texts written by human. Further this has got serious implications across various domains, including news reporting, information fraud, academic plagiarism, data manipulation, financial fraud, theft and deep fakes. The abilities of AI-generated text pose a significant challenge, blurring the lines of differentiation between human and machine-created content. Therefore, to effectively navigate the evolving landscape of AI-generated text, we need a robust and reliable AI text detection model. This study leverages a substantial dataset of 100,000 entries. empowering our model with adaptability across various domains. By employing diverse word embedding techniques along with machine and deep learning models, we explore optimal configurations for exceptional performance. Notably, we implement pre-trained models like BERT and DeBERTa on this extensive corpus, potentially representing the first such application. The resulting models demonstrate remarkable accuracy exceeding 95% across all configurations, signifying a robust advancement in differentiating AI generated text from human written one.
  • Enhancing Student Outcomes with LSTM-CNN and Data Analytics in Higher Education
    S.K.B. Sangeetha, S.. Maheswari, Sukumar Rajendran, Sandeep Kumar Mathivanan, Balamurugan Balusamy, Meenakshi Verma
    International Conference on Intelligent and Innovative Practices in Engineering and Management 2024 Iipem 2024, 2024
    With a focus on the use of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) approaches to predict students' academic performance, the study provides an extensive review of the literature on the application of artificial neural networks (ANNs) and machine learning (ML) techniques. The study highlights the possible advantages of implementing cutting-edge technology innovations like analytics and data mining in learning environments. The analysis reveals that the reviewed literature primarily focuses on higher education. The results show that LSTM-CNN and data mining methodologies are consistently integrated, suggesting a trend in using these combined approaches to evaluate academic progress. No observable patterns were found in spite of the variability in the input variables, which was dictated by the study's context and the availability of data. It is acknowledged, nevertheless, that there is a dearth of hard data regarding the effective application of these strategies for raising student achievement and outcomes. The study emphasizes how critical it is to close the theoretical and practical divides regarding the limited applicability of LSTM-only CNN models in practical educational contexts. It highlights the need for more research to address this discrepancy and suggests putting more effort into creating and putting into practice strategies that directly improve student performance and help schools achieve their goals.
  • Unveiling Millennials’ Motivations to Purchase Smartwatches
    Mohd Salman Shamsi, Anuj Verma, Meenakshi Verma
    Indian Journal of Marketing, 2023
  • Barriers of food delivery applications: A perspective from innovation resistance theory using mixed method
    Anuj Verma, Debarun Chakraborty, Meenakshi Verma
    Journal of Retailing and Consumer Services, 2023
  • A study of sleep disorders, mental distress, and depression among students during COVID pandemic
    Meenakshi Verma, Anuj Verma, Gangu Naidu Mandala
    Cogent Education, 2023
  • Can new education policy 2020 serve as a paradigm shift to the employability gap in India? A viewpoint
    Shakti Chaturvedi, Sonal Purohit, Meenakshi Verma
    Public Affairs Education and Training in the 21st Century, 2021
  • Effective Teaching Practices for Success During COVID 19 Pandemic: Towards Phygital Learning
    Shakti Chaturvedi, Sonal Purohit, Meenakshi Verma
    Frontiers in Education, 2021
  • The influence of agricultural farmers' entrepreneurial behavior on the business performance of dairy farmers in Andhra Pradesh
    Gangu Naidu Mandala, Meenakshi Verma, Anuj Verma, Suresh Sirisetti, Venkata Ramakrishna Rao Gandreti
    Universal Journal of Agricultural Research, 2021
  • Service quality of CRM: With reference to public and private banks in Nagpur City
    Gangu Naidu Mandala, Meenakshi Verma, Anuj Verma, Pushpanatham Arumugam
    Universal Journal of Accounting and Finance, 2021