The​‍​‌‍​‍‌​‍​‌‍​‍‌ digital economy has changed the way business’s function, compete, and generate value. Companies that rely on stable processes and hierarchical structures are facing a serious challenge to their existence: either they adapt digitally or they will become obsolete. DT stands for digital transformation, which is a comprehensive change of an organization that not only digitizes but also renews the strategy and culture [1]. It is not just a matter of upgrading the IT systems, but rather a complete overhaul of the business model, customer touchpoints and internal processes with the aim of exploiting data-driven insights. The examples of Amazon, Siemens and Mahindra show how organizations can infuse their supply chains with digital capabilities and that technological investment alone is not sufficient to achieve agility, innovation and cultural readiness [2]. On the other hand, the case of Kodak as a legacy-heavy firm that succumbed to technological inertia is often cited as a warning. The relationship between digital capability and traditional business logic is still a matter of debate in emerging markets like India, where infrastructural bottlenecks and the shortage of skilled labor pose constraints [3]. This study not only describes the different facets of the digital transformation journey technology adoption, organizational agility, leadership support, and employee digital competence but also elaborates on the impact of these facets on business model and organizational performance in traditional organizations. We, by incorporating the Dynamic Capabilities Theory and Business Model Innovation (BMI) framework, aspire to:

1. Understand the impact of digital transformation aspects on business model innovation.
2. Investigate the role of organizational agility as a mediator.
3. Look into the firm performance moderation effect of legacy system rigidity.

The research makes a significant contribution to the existing body of knowledge by presenting real-world data from Indian companies and accentuating the double-edged character of DT as an opportunity and a challenge for traditional business ​‍​‌‍​‍‌​‍​‌‍​‍‌models.

Digital Transformation

Digital Transformation is an organizational change process involving multiple dimensions that, through the strategic integration of digital technologies, aims to redefine business models, improve operational efficiency, and create new sources of value [4]. The concept goes beyond technology adoption and represents a holistic change in processes, people, and culture. According to various scholars, DT is a continuous adaptation process aimed at responding to environmental volatility, customer expectations, and technological advancements. The conceptual framework of DT is based on three main pillars: Technology Adoption (TA), Organizational Agility (OA), and Cultural/Leadership Alignment [5]. Technology is the enabler; agility is the organization’s adaptive capability; and leadership and culture help maintain strategic coherence. As per the notes, digital transformation involves both the exploitation of existing resources and the exploration of new digital opportunities, hence it is in line with the dynamic capability’s theory principles [6]. Therefore, digital transformation should be considered as much a managerial and cultural challenge as it is a technological one.

Technology Adoption

Technology Adoption is at the core of digital transformation, signifying the level and extent at which digital tools such as AI, IoT, cloud computing, data analytics, and automation are ingrained in organizational routines and decision-making processes. According to [7], TA leads to operational efficiency, process innovation, and better strategic decision-making. Additionally, [8] state that companies that are digitally integrated to a greater extent have higher capacities for innovation and are more responsive to their customers. Studies on the matter reveal that TA alone is not enough for good business performance; employee skill development and top management support should accompany it. Nevertheless, the simple technological acquisition without the proper alignment with business processes often ends up in “digital inertia” [9] where the potential benefits remain unexploited. Therefore, the effectiveness of TA depends on how deeply it is embedded strategically and on the level of organizational ​‍​‌‍​‍‌​‍​‌‍​‍‌readiness.

Organizational Agility

OA refers to a company's dynamic capability to detect, take advantage of, and change as a result of the external environment changes [10]. Agility is considered a strategic requirement in the digital era, giving companies the ability to quickly turn to new market opportunities and reduce the impact of disruptions. The empirical study revealed that IT-enabled agility is a major source of firm competitiveness and adaptability. The concept of OA includes dimensions such as flexibility of processes, innovation responsiveness, and rapid learning cycles [11]. All of these lead to the acceleration of product development and customer-focused innovation. With the help of digital tools, organizations can become more agile in a data-driven manner since it allows for real-time analytics, collaborative platforms, and cloud-based integration [12]. However, structural rigidities, decision-making through hierarchies, and cultural resistance can battle against agility and thus, an adaptable digital culture is still necessary.

Leadership Support

LS is always pinpointed as a major factor that leads to the success of a digital transformation program. Transformational leaders communicate a relevant digital vision, change the people’s behavior by their example and support, and develop a culture which innovation easily thrives in [13]. The involvement of top management in leadership activities leads to the improvement of organizational alignment with digital strategy, makes resource allocation easier, and gets rid of resistance to change [14]. Leaders should be considered as the main stimulators since they bridge the gap between technology initiatives and strategic objectives and, at the same time, they ensure that the workforce is always learning and developing. In addition, digital leadership is about having knowledge of technological trends and understanding their impact on business value creation [15]. When there is no leadership alignment, it mostly leads to scattered digital initiatives which, in turn, make it difficult to have significant organizational ​‍​‌‍​‍‌​‍​‌‍​‍‌outcomes.

Employee Digital Competence

EDC is the component of digital transformation that is based on human capital. The term refers to the employees' ability to utilize, modify, and create with the help of digital tools efficiently. Brynjolfsson and [16] emphasize that the technological investments bring the desired results only when accompanied by the upskilling and reskilling of the workforce. EDC creates the environment for the sharing of knowledge, inter-departmental collaboration, and innovative problem-solving which in turn are the core aspects of sustaining digital innovation. Research [17] indicates that organizations that dedicate resources to digital literacy and continuous learning are far ahead of their competitors in technology utilization and innovation rates. On the other hand, insufficient competence causes the neglect of digital systems and employee resistance. Therefore, the development of digital skills is not merely a matter of training but a strategic decision.

Business Model Innovation

BMI refers to the transformation of the company’s value creation, delivery, and capture processes to be in line with the opportunities provided by digitalization [18] Digital technologies facilitate the coming of new business models like platform-based ecosystems, subscription models, and servitization. [19] state that BMI acts as a link between digital transformation activities and firm performance as it turns technological capabilities into real economic results. The empirical findings [20] indicate that firms practicing BMI become more profitable and flexible to changes in the market. Nevertheless, BMI usually implies the removal of heritage processes and reshaping of the old mindset which is a process that requires powerful leadership and organizational ​‍​‌‍​‍‌​‍​‌‍​‍‌agility.

Firm Performance

FP represents both financial aspects such as profitability, ROI, and revenue growth as well as non-financial ones like innovation capability, customer satisfaction, and market share [21]. Consequently, digital transformation has been evidenced to elevate FP by enabling data-driven decision-making, improving customer engagement, and simplifying operations [22]. The studies based on the Resource-Based View argue that DT-related capabilities as agility, innovation, and knowledge sharing are the core strategic resources that bolster long-term competitiveness [23]. Anyway, the increase in performance depends on how well technology, leadership, and human capital socio-strategic factors are aligned, thereby, pointing to the mutual dependency between DT elements.

Legacy Systems and Challenges

On its way to the realization of the full potential, DT encounters barricades of both kinds – organizational and technological. Legacy systems which are typical of rigid IT architectures, aged infrastructures, and bureaucratic hierarchies, limit agility and innovation [24]. Such deep-rooted frameworks not only stall data integration, but they also slow down decision-making processes [25]. Breaking with such resistance necessitates modular IT architectures, a learning-oriented culture as well as leadership-engaged change management [26]. Besides this, digital transformation is about the trade-off between the exploration of new technologies and the exploitation of existing technologies [27]. The ambidexterity issue that arises from this might cause the organization to have internal conflicts which in turn will increase the importance of change management, communication, and skill development as the factors that lead to sustainable transformation.

Theoretical Model

Based on the Dynamic Capabilities Theory, the current model sees digital transformation as a higher-order capability that allows firms to sense, seize, and reconfigure resources to cope with environmental turbulence [30]. In this context, components of digital transformation such as Technology Adoption, Organizational Agility, Leadership Support, and Employee Digital Competence are referred to as interdependent facilitators that lead Business Model Innovation, thereby, enabling Firm Performance enhancement [31]. Moreover, the model suggests that Organizational Agility is the mediator in the relationship between DT dimensions and BMI, while Legacy Rigidity is the moderator in terms of the strength of the BMI–FP connection [32]. Legacy rigidity being associated with obsolete systems, hierarchical inertia, and resistance to change is expected to dilute the positive impact of BMI on firm performance by limiting the exploitation of digital opportunities.

Hypotheses

• H1: Technology Adoption positively influences Business Model Innovation and the effect is statistically significant.
• H2: Leadership Support positively affects Business Model Innovation.
• H3: Employee Digital Competence positively impacts Business Model Innovation.
• H4: Organizational Agility is a mediator in the relationship that connects Digital Transformation dimensions with Business Model Innovation.
• H5: Business Model Innovation positively and significantly influences Firm Performance.
• H6: Legacy Rigidity is a negative moderator of the relationship between Business Model Innovation and Firm Performance in such a way that when legacy rigidity is high, the relationship is ​‍​‌‍​‍‌​‍​‌‍​‍‌weaker.

Figure 1: Conceptual Frame work

Sample and Design

The study involved a cross-sectional quantitative design. The sample included 312 managers of traditional firms (manufacturing, retail, and services) in India, who were engaged in digital initiatives. A structured questionnaire containing 5-point Likert scales (1 = Strongly Disagree; 5 = Strongly Agree) was distributed through online and in-person surveys.

Reliability and Validity

The study's measures were psychometrically robust, as evidenced by Cronbach's alpha, KMO, Bartlett's test, and CFA. SEM (AMOS 26) was used to examine the hypothesized relationships. The indirect effects were estimated using bootstrapping (5,000 ​‍​‌‍​‍‌​‍​‌‍​‍‌samples).

Statistical Analysis and Interpretation

Table 1. Descriptive Statistics (N = 312)

Means around 3.5–4.0 indicate moderate-to-strong presence of digital readiness. Variability is sufficient for analysis, suggesting diverse digital maturity levels across firms.

Table 2. Reliability

All α > 0.80 → excellent internal consistency. The constructs reliably measure intended variables.

Table 3. Correlation Matrix (Pearson)

Moderate correlations (0.35–0.60) confirm associations among constructs while avoiding multicollinearity. BMI strongly correlates with FP (r = .60), supporting H2.

Table 4. Confirmatory Factor Analysis

All loadings > 0.70 and AVE > 0.50, indicating convergent validity. Composite reliability > 0.85 confirms measurement stability.

Table 5. SEM Path Coefficients

All direct effects are significant. Technology adoption exerts the strongest influence on BMI (β = 0.36). Legacy rigidity weakens DT’s positive impact on FP, confirming the moderating hypothesis.

Table 6. Model Fit Indices

Model demonstrates excellent fit and substantial explanatory power. DT variables explain nearly half the variance in BMI and over half in FP.

Table 7. Mediation

Organizational agility partially mediates the DT–BMI link; agility translates technological and leadership inputs into tangible model innovation.

The conceptual model is supported by the empirical data. Among other things, the technology adoption turned out to be the most influential factor of BMI, which is a very clear indication of the importance of digital infrastructure. With the help of technological assets, organizational agility and employee digital competence open the door to the firms’ becoming innovative models. Leadership support, thus, not only provides the impetus for cultural alignment but also facilitates the allocation of resources. The fact that agility is the mediator here shows that agility is not a consequence of DT, rather it is a core strategic capability that directs tech initiatives towards business model transformation. Also, the existence of negative moderation due to legacy rigidity suggests that old IT systems and bureaucratic hierarchies are the primary factors that, quite substantially, lessen the benefits of DT a very important insight for traditional firms in emerging economies.

By far, the most significant implication of digital transformation is the radical redefinition of the traditional business model through digital innovation, new revenue architectures, and customer-centric approaches. Success in transformation, however, depends on leadership commitment, workforce readiness, and agile structures, rather than the mere technological investment. Companies which are able to sense digital trends, seize opportunities, and reconfigure their legacy systems, will be able to outperform others in line with the Dynamic Capabilities framework. The paper also clarifies that merely embracing technology will not provide a company with a competitive edge unless it is accompanied by organizational learning and cross-functional collaboration. The study has led to the conclusion that digital transformation should not be seen as a temporary endeavour which eventually comes to an end, but rather as a continuous strategic journey that entails ongoing capability and cultural renewal. In the end, those organizations that manage to integrate technological excellence with human-cantered agility will be the winners who enjoy innovation, resilience, and long-term value creation in the digital era.

Managerial Implications

The conclusions drawn from this research entail numerous management-related consequences for Digital Transformation (DT) bound organizations. It is of paramount importance for Executives to initially concentrate their efforts on strategic integration thus ensuring that digital objectives and vision become inseparable elements of the core company strategy. As a result, not only will more than one unit become digitally mature through common goals alignment and synergy gain, but also the entire corporation will be empowered to coordinate business lines into a single digital army. Next, potential of present workforce must be transformed into a burning issue of digital organizations’ faction by means of constant commitment to the cause. Practice of executing comprehensive digital literacy programs, upskilling initiatives, and cross-functional training for participants of the (organizational) challenge would ladder an adaptive workforce socially and technologically prepared for future tasks. Equally significant is the conversion to Agile firm designs; such design tends to decentralize decision-making, cuts down time necessary to test new ideas, and facilitates iterative cycles by which the firm can promptly respond to the change of surroundings. In addition, the change of legacy systems ought to be a slow yet intentional progression, where old architectures and tight hierarchies are swapped for modular, cloud-based, and interoperable platforms. At last, data governance should be reinforced so that businesses will be in a position to data analytics infrastructures and in turn be able to make timely decisions that are not only efficient but also lead to sustainable competitive advantage through insight-driven management.

Policy Implications

The findings of this study highlight digital readiness of the traditional sectors as a matter of systemic support from the policy side. The role of governments and regulatory bodies in facilitating digital transformation can hardly be overemphasized. Apart from that, subsidies in the form of tax reliefs can be put in place to encourage companies to allocate capital toward the following activities: acquiring state-of-the-art technologies, conducting a comprehensive infrastructure upgrade, and automating business processes. Public-private partnerships can be likened to vehicles that will further propel the digital transformation journey through the provision of resources, know-how, and easy recourse across the vertical’s implementation. In addition to this, a comprehensive reskilling program at the national level should top the agenda in order to close the digital skill gap. Employees who will be provided with the necessary skills and knowledge to successfully embrace and execute new technologies and business models will be the ones who will keep the ladder from collapsing at all organizational levels. These policy interventions, if effectively implemented, can not only create an enabling ecosystem that supports enterprise-level transformation but also contribute to a larger economic resilience and innovation-led growth.

Future Research Directions

In addition to the in-depth empirical insights of this paper, it also leaves open the question of future research in several important areas. Future research might consider using long-term research designs to probe the impact of digital transformation on company performance and environmental friendliness, thus, tracking changes in capability development and business model evolution over time. There could also be a comparative study of large and small businesses and different sectors, which may reveal digital maturity effects specific to the sector and provide a more detailed understanding of contextual differences in transformation results. The last point on the agenda of employing mixed-method approaches, which combine quantitative modeling with qualitative case studies, would provide a more comprehensive, multidimensional view of the interplay between organizational culture, leadership behavior, and the involvement of stakeholders in shaping the process of digital transformation. This research would not only enhance theoretical knowledge but also provide practical solutions that can help managers and policymakers steer through the complexities of digital ​‍​‌‍​‍‌​‍​‌‍​‍‌change.

1. Santos, T., Louca, J., & Coelho, H. (2019). The digital transformation of the public sphere. Systems Research and Behavioral Science, 36(6), 778–788. https://doi.org/10.1002/sres.2644
2. Cagatin, C. S. (2024). The societal transformation: The role of social networking platforms in shaping the digital age. Interdisciplinary Social Studies, 3(3), 103–107.*
3. George, G., & Schillebeeckx, S. J. D. (2022). Digital transformation, sustainability, and purpose in the multinational enterprise. Journal of World Business, 57(3), 101326. https://doi.org/10.1016/j.jwb.2022.101326
4. Vaniya, J. , Alizada, M. , Nagpal, P. , Kumar Dey, B. and Abbbasova, D. G. A. (2025). Novel Enhanced Cognitive State Analysis in E-Learning via Real-Time Emotion and Attentiveness Detection Using OptFuzzy TSM and ABiLSTM. Iranian Journal of Fuzzy Systems, 22(4), 57-75. doi: 10.22111/ijfs.2025.49950.8829
5. Schaarschmidt, M., & Bertram, M. (2020). Digital business intensity and constructive process deviance: A study of reactions to digitisation-focused process innovation. International Journal of Innovation Management, 24(7), Article 2050065. https://doi.org/10.1142/S136391962050065
6. Nagpal, K. V. Manju, K. A. Dongre, T. S. Talla, V. Rahul and S. Padma, "AI-Driven Predictive Models: Understanding Consumer Behaviour for Economic Forecasting and Policy Design," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 725-730, doi: 10.1109/InTech64186.2025.11198211.
7. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.
8. Pooja Nagpal, (2025). Leveraging artificial intelligence and machine learning for gaining competitive advantage in business development. AIP Conference Proceedings, 3327(1), 020002. AIP Publishing LLC. https://doi.org/10.1063/5.0289438
9. Nagpal, N. M.V.N, Y. V. N. S. S. Charan, G. Karthikeyan, S. Sudhin and S. Dhote, "Leveraging AI and Machine Learning for Advanced Predictive Analytics in Workforce Management," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 527-532, doi: 10.1109/InTech64186.2025.11198585.
10. Rahrovani, Y. (2020). Platform drifting: When work digitalization hijacks its spirit. Journal of Strategic Information Systems, 29(2), Article 101615. https://doi.org/10.1016/j.jsis.2020.101615
11. Rossi, M., Festa, G., Devalle, A., & Mueller, J. (2020). When corporations get disruptive, the disruptive get corporate: Financing disruptive technologies through corporate venture capital. Journal of Business Research, 118, 378–388. https://doi.org/10.1016/j.jbusres.2020.07.004
12. M. V. N., Y. V. N. S. S. Charan, D. S. Narayana, R. Patil, S. Sudhin and P. Nagpal, "AI-Powered Predictive Analytics: Enhancing Customer Experience Through Intelligent Solutions," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 533-537, doi: 10.1109/InTech64186.2025.11198566.
13. M. V. N., Y. V. N. S. S. Charan, D. S. Narayana, R. Patil, S. Sudhin and P. Nagpal, "AI-Powered Predictive Analytics: Enhancing Customer Experience Through Intelligent Solutions," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 533-537, doi: 10.1109/InTech64186.2025.11198566.
14. Priyono, A., Moin, A., & Putri, V. N. (2020). Identifying digital transformation paths in the business model of SMEs during the COVID-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 104. https://doi.org/10.3390/joitmc6040104
15. Berman, S. J. (2012). Digital transformation: Opportunities to create new business models. Strategic Leadership, 40(2), 16–24.
16. Inamdar, N. Inamdar, R. Paranjpye, P. Nagpal, N. K.B. and A. Adarsh, "Exploring the Transformative Role of Generative AI in Financial Forecasting and Advanced Fraud Detection Strategies," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 834-839, doi: 10.1109/InTech64186.2025.11198409.
17. Feyen, E., Frost, J., Gambacorta, L., Natarajan, H., & Saal, M. (2021). Fintech and the digital transformation of financial services: Implications for market structure and public policy. BIS Papers. Bank for International Settlements.
18. Nagpal, P., Aggarwal, S., Sharma, A., Datta, A., Kuzieva, N., & Gurusamy, M. (2025). Revolutionizing human resources for safer automotive work environments. In AI's role in enhanced automotive safety (501–514). https://doi.org/10.4018/979-8-3373-0442-7.ch032
19. Chesbrough, H. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43(2–3), 354–363. https://doi.org/10.1016/j.lrp.2009.07.010
20. Osterlie, T., & Monteiro, E. (2020). Digital sand: The becoming of digital representations. Information and Organization, 30(1), Article 100275. https://doi.org/10.1016/j.infoandorg.2019.100275
21. Nagpal, P. (2022). Organizational commitment as an outcome of employee engagement: A social exchange perspective using a SEM model. International Journal of Biology, Pharmacy and Allied Sciences, 11(1), 72–86.
22. Shrivastava, A., Suji Prasad, S. J., Yeruva, A. R., Mani, P., Nagpal, P., & Chaturvedi, A. (2025). IoT based RFID attendance monitoring system of students using Arduino ESP8266 & Adafruit.io on defined area. Cybernetics and Systems, 56(1), 21–32. https://doi.org/10.1080/01969722.2023.2166243.
23. Nagpal, A. Pawar and S. H. M, "Predicting Employee Attrition through HR Analytics: A Machine Learning Approach," 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), Noida, India, 2024, pp. 1-4, doi: 10.1109/ICIPTM59628.2024.10563285.
24. Foss, N. J., & Saebi, T. (2017). Fifteen years of research on business model innovation: How far have we come, and where should we go? Journal of Management, 43(1), 200–227. https://doi.org/10.1177/0149206316675927
25. BK Kumari, VM Sundari, C Praseeda, P Nagpal, J EP, S Awasthi (2023), Analytics-Based Performance Influential Factors Prediction for Sustainable Growth of Organization, Employee Psychological Engagement, Work Satisfaction, Training and Development. Journal for ReAttach Therapy and Developmental Diversities 6 (8s), 76-82.
26. Pooja Nagpal (2022) Online Business Issues and Strategies to overcome it- Indian Perspective. SJCC Management Research Review. Vol 12 (1) pp 1-10. June 2022, Print ISSN 2249-4359. DOI: 10.35737/sjccmrr/v12/il/2022/151
27. Rajput, N., Das, G., Kumar, C., & Nagpal, P. (2021). An inclusive systematic investigation of human resource management practice in harnessing human capital. Materials Today: Proceedings, 80(3), 3686–3690. https://doi.org/10.1016/j.matpr.2021.07.362
28. Pooja Nagpal., Kiran Kumar., A.C. & Ravindra., H. V. (2020). Does Training and Development Impacts – Employee Engagement? Test Engineering and Management, the Mattingley Publishing Co., Inc. 83. 19407 – 19411. ISSN: 0193-4120.
29. Nuccio, M., & Guerzoni, M. (2019). Big data: Hell or heaven? Digital platforms and market power in the data-driven economy. Competition & Change, 23(3), 312–328. https://doi.org/10.1177/1024529418816525
30. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
31. McGrath, R., & McManus, R. (2020). Digital transformation: Learning your way to a new business model—What’s your digital strategy? Harvard Business Review, 98(3), 125–133.
32. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
33. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
34. Pooja Nagpal & Senthil Kumar. (2017). A study on drivers and outcomes of employee engagement – A review of literature approach. Asia Pacific Journal of Research.4 (1) 56- 62. ISSN -2320-5504. Online E ISSN – 2347-4793.
35. Madhusudhan R. Urs & Pooja Nagpal (2019). A study on Determinants and Outcomes of Job Crafting in an Organization; Journal of Emerging Technologies and Innovative Research, 7, (15). 145-151. ISSN: 2349-5162
36. Pooja Nagpal., Senthil Kumar., & Ravindra. H V. (2019). The Road Ahead of HR-AI to boost Employee Engagement; Journal of Emerging Technologies and Innovative Research, 7,(15), 180-183. ISSN: 2349-5162
37. Nagpal, P., & Kumar, A. C. K. (2019). The effect of perceived high-performance work practices on employee engagement: An empirical study on IT firms in India. Think India Journal, 22(43), 272-278. ISSN: 0971-1260.
38. Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), Article 100017. https://doi.org/10.1016/j.jjimei.2021.100017
39. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
40. Nagpal, P., & RameshKumar, M. (2024). Navigating teachers' adoption of Artificial Intelligence in English foreign language: Uncovering inhibitors and drivers. SPAST Reports. https://www.spast.org/ojspath
41. Nagpal, K. V. Manju, K. A. Dongre, T. S. Talla, V. Rahul and S. Padma, "AI-Driven Predictive Models: Understanding Consumer Behaviour for Economic Forecasting and Policy Design," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 725-730, doi: 10.1109/InTech64186.2025.11198211.
42. M. V. N., Y. V. N. S. S. Charan, D. S. Narayana, R. Patil, S. Sudhin and P. Nagpal, "AI-Powered Predictive Analytics: Enhancing Customer Experience Through Intelligent Solutions," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 533-537, doi: 10.1109/InTech64186.2025.11198566.
43. Nagpal, N. M.V.N, Y. V. N. S. S. Charan, G. Karthikeyan, S. Sudhin and S. Dhote, "Leveraging AI and Machine Learning for Advanced Predictive Analytics in Workforce Management," 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan, 2025, pp. 527-532, doi: 10.1109/InTech64186.2025.11198585.
44. Nagpal, P. (2024). Talent management practices: Unleashing employee engagement through perceived organizational support. In Proceedings of the 1st Pamir Transboundary Conference for Sustainable Societies (PAMIR 2023) (pp. 499-505). https://doi.org/10.5220/0012492300003792
45. Kumar, S., & Nagpal, P. (2018). A study on drivers and outcomes of employee engagement: A review of literature approach. Asia Pacific Journal of Research, 2320, 5504.
46. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
47. Leih, S., Linden, G., & Teece, D. J. (2020). Business model innovation and organizational design: A dynamic capabilities perspective. In N. J. Foss & T. Saebi (Eds.), Business model innovation: The organizational dimension (1st ed., pp. 121–142). Oxford University Press.