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Editorial
(Chitkara University Publications, 2026-07-29) Yam B Limbu
We are pleased to share Volume 16, Issue 2 (2025) of the Journal of Technology Management for Growing Economies, which contains ten articles capturing the rapid changes happening in the field of technology management, artificial intelligence, financial innovation, marketing, sustainability, and organizational development. These papers cover a variety of empirical, conceptual, bibliometric, and systematic review studies demonstrating the increasingly interdisciplinary approach to management research happening today in well-developed countries and emerging markets.
The opening paper written by Minh Nguyen Hoang, Thota Sai Karthikeya, and Thota Sree Mallikharjuna Rao deals with what can be viewed as the most crucial problem of financial technology—how to reach the balance between the predictive ability of a model and its transparency in the area of lending to small businesses. By using a champion LightGBM model together with an Explainable Boosting Machine, the authors proved their point that it is possible to have very accurate credit-risk prediction without decreasing the interpretability at the same time.
The authors of the second article, Sukhmani Bhatia, Archana Goel, and Jayalakshmi Ramachandran, cover a bibliometric study devoted to the research on global economic policy uncertainty (GEPU) conducted over the last decade. The authors show the main topics of research, leading countries, and the new directions, while also presenting ideas for future research on firm-level implications of uncertainty in policy.
Rajesh Gupta and Karnika Gupta provide the analysis of whether demographic characteristics affect the perception of the usefulness of financial technology in India. According to their findings, age, education, income, occupation, and living in urban areas are the key features affecting the perceived usefulness of financial technology, while gender and education do not play a significant role in this case. The research provides useful insights for the advancement of financial inclusion and the development of effective fintech promotion strategies.
In their paper, Falak and Pooja Malhotra discuss the application of artificial intelligence in financial markets by presenting a systematic analysis of the introduction of AI technologies to credit rating methods. The study reflects on the departure from classic credit assessment, which used only the opinions of analysts, to the adoption of machine learning technologies, providing a higher degree of accuracy and efficiency.
Business ethics remain an important question for management during the times of institutional shifts. Shallu Dhiman and Sultan Singh examine the impact of mergers and acquisitions on employee perceptions about ethical issues, commitment to their organization, and effectiveness of their work in state-owned banks of India. To the authors’ surprise, the results show that employees in the banks analyzed had similar concerns, which proves that consistent practices in ethics can lead to easier organizational integration in the times of radical transitions.
Technology-driven consumer behavior is studied by Paramjit Singh and Pavleen Soni, who investigate how age affects the showrooming behavior of Indian consumers of cosmetics. The findings reveal that Generation Y is more price-conscious than Generation Z, who cares more about tactile experience and social interaction. However, the two generations display similar attitudes towards technology-driven shopping.
Smriti Goudhaman investigates the role of artificial intelligence in organizational learning by conducting a longitudinal assessment of AI-enabled training microbots interacting with humans in the decision-making process. The research shows how, despite lower engagement rates once training sessions are no longer small-scale initiatives, the quality of learning keeps getting better through time, demonstrating the significance of organizational systems that merge together technological knowledge with human oversight.
The authors Sanjeewani Sehgal and Divya Mehta focus on ecological sustainability within digital finance. The authors rely on deep learning and LSTM forecasting methods to explore the carbon emissions related to crypto mining within different energy scenarios, coming to the conclusion that the use of renewable energy sources can narrow the damage inflicted on the environment, especially hydroelectric energy.
Authors Pallavi Kumari and Anjan Niyogi study marketing strategy and review a newer body of literature on purpose-driven marketing. Their review suggests that people tend to accept organizations when they believe their purpose is real and is consistently incorporated into their activities. At the same time, any declaration of a purpose made just for a show will only trigger disbelief amongst consumers.
Finally, the last article has the authors Shivani Pandey and Umme Ara discussing the use of AI in green human resource management. The authors argue that the implementation of AI makes workforce planning, recruiting, and diversity management more effective, along with enhancing organizational performance and achieving ESG objectives. However, the authors pay attention to the importance of responsible management based on strong ethical governance.
In conclusion, this issue proves that nowadays systems for addressing management challenges in many instances include not only new technologies but also responsible leadership, ethics, and a focus on people and sustainability as well. Artificial intelligence and digital innovations are two crucial concepts that demonstrate the current state of development of management techniques.
We express our appreciation to all authors, reviewers, and the editorial board for their contributions.
Sustainable yet Ethical: Balancing Workforce Practices in AI-Integrated HRM Systems
(Chitkara University Publications, 2026-05-31) Shivani Pandey; Umme Ara
Purpose: This study examines how artificial intelligence can be strategically integrated into human resource management to support sustainable organizational practices aligned with environmental, social, and governance objectives. It focuses on the role of AI-enabled HRM in facilitating sustainable workforce planning, promoting fairness in talent acquisition, enabling energy-efficient work arrangements, and enhancing employee well-being, while addressing ethical concerns associated with AI adoption in HR functions.
Methods: The study adopts a conceptual and analytical research design grounded in an extensive review of academic literature and established theoretical frameworks related to AI, sustainable HRM, and ESG principles. Through a systematic synthesis of prior research and current organisational practices, the study develops an integrated perspective on AI-driven HRM and identifies key dimensions influencing its ethical and sustainable implementation.
Findings: The findings suggest that AI has strong potential to act as a catalyst for sustainable HRM practices. AI-driven workforce analytics improve planning efficiency and resource utilisation, while algorithm-based recruitment tools can reduce bias and support diversity and inclusion. However, the study also identifies critical challenges associated with AI adoption in HR functions.
Implications: The study highlights the need for ethically grounded AI adoption in HRM, emphasising fairness, accountability, transparency, and employee well-being. It offers practical insights for HR professionals, organisational leaders, and policymakers on aligning AI-enabled HR practices with ESG objectives to achieve long-term sustainability.
Originality: This study contributes by positioning AI as a strategic enabler of sustainable and ethical HRM. It proposes a novel conceptual framework based on green talent analytics, ethical AI governance, and sustainable workforce management, providing a holistic roadmap for responsible AI adoption.
Purpose-Driven Marketing and Its Behavior Impact on Modern Consumers
(Chitkara University Publications, 2026-05-15) Pallavi Kumari; Anjan Niyogi
Purpose: Marketing discourse has gradually shifted from a narrow emphasis on product features toward broader narratives centered on organizational purpose, ethics, and social contribution. In digitally connected markets, consumers increasingly judge brands based on perceived values, authenticity, and responsible conduct rather than functional superiority alone. The purpose of this review paper is to explore how purpose-driven marketing influences consumer behavior, particularly in terms of attitudes, trust formation, emotional engagement, and behavioral intentions.
Methods: The study adopts a structured narrative review approach, examining peer-reviewed academic literature published between 2015 and 2025. Relevant studies were sourced from established academic databases using keywords related to brand purpose, ethical branding, consumer-brand relationships, and value-based consumption. Both conceptual frameworks and empirical findings were analyzed through thematic synthesis to identify recurring patterns, contrasts, and research gaps.
Findings: The review reveals that purpose-driven marketing tends to generate positive consumer responses when organizational purpose is perceived as genuine and consistently embedded in business practices. Authentic purpose communication is found to strengthen trust, enhance emotional attachment, and support favourable behavioural outcomes. Conversely, symbolic or opportunistic adoption of purpose narratives often leads to consumer skepticism, erosion of credibility, and adverse behavioral reactions.
Implications: The review reveals that purpose-driven marketing tends to generate positive consumer responses when organisational purpose is perceived as genuine and consistently embedded in business practices. Authentic purpose communication is found to strengthen trust, enhance emotional attachment, and support favourable behavioural outcomes. Conversely, symbolic or opportunistic adoption of purpose narratives often leads to consumer scepticism, erosion of credibility, and adverse behavioural reactions.
Originality: This paper contributes originality by synthesising recent and dispersed scholarship on purpose-driven marketing into a coherent analytical framework. By highlighting both enabling factors and limitations, the study advances a balanced understanding of how brand purpose operates within contemporary consumer markets.
Can Cryptocurrencies Be Sustainable? AI-Based CO₂ Emission Forecasting
(Chitkara University Publications, 2026-04-20) Sanjeewani Sehgal; Divya Mehta
Purpose: This study evaluates the energy consumption and environmental impact of blockchain supported cryptocurrency systems, focusing on CO₂ emissions generated by Bitcoin mining. It investigates the relationship between cryptocurrency economics, energy usage, and ecological sustainability, and assesses the future viability of cryptocurrencies as a global financial system in alignment with the Sustainable Development Goals (SDGs).
Methods: A quantitative time series forecasting approach was adopted using 5030 days of historical CO₂ emission data obtained from the Cambridge Blockchain Network Sustainability Index (CBECI). Three energy scenarios, hydroelectric power, coal based power, and mixed energy sources, were analyzed. A Long Short-Term Memory (LSTM) neural network model was implemented to predict future emission trends and evaluate sustainability outcomes under each scenario.
Findings: The results indicate that hydroelectric energy produces the lowest CO₂ emissions as the most sustainable option, while coal-based mining results in the highest emissions, representing the worst-case scenario. The mixed energy model provides a feasible compromise, significantly reducing emissions compared to coal while maintaining mining efficiency. These findings identify certain environmental risks associated with energy intensive mining practices and emphasize the urgent need for sustainable operational strategies.
Implications: This research contributes to blockchain sustainability literature by providing a predictive framework for environmental assessment. Policymakers can leverage these insights to design regulations promoting renewable energy integration, while industry practitioners can adopt energy efficient mining models to reduce carbon footprints and achieve long term sustainability.
Originality: The study uniquely integrates deep learning-based forecasting with large scale longitudinal emission data to conduct scenario-based sustainability analysis, offering dynamic insights beyond conventional static evaluations.
Strategic Human Oversight Frameworks for AI-Enabled Training Microagents: Evidence from a Longitudinal Adoption Study
(Chitkara University Publications, 2026-04-15) Smrite Goudhaman
Purpose: The purpose of this study is to examine how AI-enabled training outcomes evolve as an intervention transition from a supervised doctoral pilot to a scaled, longitudinal organizational deployment. The study focuses on learning adoption and learning efficiency in a frontline hospitality context, while explicitly examining the role of AI micro-agents operating within a human-in-the-loop governance framework.
Methods: The study adopts a longitudinal cohort extension design, building on a doctoral pilot conducted with 100 frontline employees during 2024 and extending into a scaled operational deployment during 2025. Objective learning-platform trace data from an AI-enabled training system were analyzed across two deployment phases. Learning adoption was measured using exposure-adjusted completion rates, while learning efficiency was assessed using assessment performance and time-on-task metrics. The analysis controls for workforce churn characteristic of frontline service environments.
Findings: Results show that completion rates normalized from 100% in the pilot phase to 86.82% under real-world scale, reflecting operational normalization rather than reduced effectiveness. Importantly, learning quality and efficiency improved over time: mean assessment scores increased, while average time-on-task declined significantly. These findings indicate faster mastery and deeper learning as AI-enabled training matured, rather than superficial compliance.
Implications: The findings demonstrate that AI-enabled training systems can sustain adoption and improve learning efficiency at scale when designed with constrained agency and supported by human oversight. For organizations in high-churn frontline environments, the results emphasize the importance of evaluating training effectiveness beyond pilot completion metrics and focusing on longitudinal learning quality, efficiency, and governance structures.
Originality: This study provides rare longitudinal, post-dissertation evidence on AI-enabled training effectiveness, directly linking a doctoral pilot to scaled organizational deployment. It advances technology management and digital learning research by introducing a churn-aware evaluation framework and empirically demonstrating how AI micro-agents, operating within human-in-the-loop governance, shape sustainable learning outcomes beyond pilot conditions.