How firms engage with and serve their customers has been totally revolutionized in the past 20 years by the confluence of digital technologies and artificial intelligence (AI). Formerly restricted to websites, email marketing, and banner advertising, digital marketing has rapidly evolved into a very personalized, data-focused environment that maximizes customer interactions through the application of natural language processing (NLP), machine learning (ML), and predictive algorithms (Huang & Rust, 2018; Chintalapati & Pandey, 2022). This revolutionary context has seen artificial intelligence (AI) transform from a helper tool to a primary facilitator of marketing effectiveness, altering the customer experience as well as strategic choice.

One particularly fascinating context for exploring that transformation is the Indian real estate market. One of the largest drivers of the Indian economy, the industry is expected to become USD 1 trillion by 2030 and account for approximately 7–8% of GDP (KPMG, 2023).  Yet, the industry is characterized by long purchase cycles, intense competition between companies, and intricate consumer decision-making. The diverse and information-dense requirements of Indian real estate consumers, who more and more depend upon online platforms for the search, comparison, and evaluation of properties, have often not been fully addressed by conventional promotion strategies.  Under such circumstances, AI-driven digital marketing offers a critical tool for surmounting information asymmetry, enhancing lead generation, enhancing customer satisfaction, and establishing long-term brand capital.

Artificial Intelligence in Marketing: A Theoretical Lens

There are a number of theoretical positions available through which the application of AI in marketing can be explained.  Businesses' use of digital marketing through AI is determined by perceived utility, user-friendly nature, and conditions that enable, as follows from Technology Acceptance Models (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2012). In the same vein, the Resource-Based View (RBV) posits that predictive algorithms and AI-based data analytics are scarce, valuable, and hard to imitate resources that provide firms with sustainable competitive advantage (Barney, 1991).  From the user's point of view, brand equity theory (Keller, 1993) provides a lens through which to analyze how AI-led engagement and personalisation strategies enhance perceived quality, brand loyalty, brand awareness, and brand associations. AI applications like chatbots that offer round-the-clock support, predictive content recommendations, and automated customer profiling have a direct impact on consumers' perceptions of a brand's responsiveness and dependability in the real estate industry, where credibility and trust are crucial.  These theoretical pillars highlight the importance of examining AI as a strategic enabler of brand equity and business performance, in addition to its potential as a technological advancement.

The Indian Real Estate Context

One of South Asia's most dynamic and competitive property markets is the National Capital Region (NCR) of India, comprising Delhi, Noida, Gurugram, Ghaziabad, and Faridabad.  The market is highly fragmented and yet opportunity-laden, with a diverse range of customer groups, from middle-class end-users to high-net-worth investors. Developers and companies in the NCR region have to deal with challenges such as changing consumer tastes towards online content, fluctuating patterns of demand, and regulatory changes like RERA.  Websites, smartphone applications, and social media are some of the online channels that Indian property firms have increasingly employed during recent years in order to interact and win new customers. Compared to industries such as banking or e-commerce, the sector's move towards AI-based digital marketing is a fledgling effort.  Top players have begun to test the waters using AI instruments in customer profiling, lead scoring, and predictive pricing, but no systematic knowledge exists about how these instruments impact financial performance and brand equity (Simion & Popescu, 2023).

Although global studies have already demonstrated how AI is transforming marketing, there are not many sector-based research studies in India, particularly in the real estate sector, which is both economically and culturally significant. The available literature usually concerns customer-facing industries such as retail and hospitality (Lee et al., 2019) or the overall use of AI in marketing (Verma et al., 2021; Ziakis & Vlachopoulou, 2023). Conversely, this research bridges a huge research gap by considering only real estate firms in the NCR.  Most prior studies are conceptual or qualitative in nature.  Few have employed data-driven, empirical techniques such as structural equation modelling (SEM) to measure the impacts of AI-powered digital marketing on firm performance outcomes and brand equity factors. By plugging this research gap, the present study is both theoretically (by relating AI adoption with real estate brand equity) and practically relevant (by consulting companies on strategic AI adoption).  This is the way the paper proceeds.

Research gap and rationale are identified in the next section, which is followed by clearly formulated research goals and hypotheses. As it focuses on SEM-based mediation analysis, the methodology outlines the sample design, measurement scales, and analytic methods.  The results and analysis, such as model fit indices, mediation results, and descriptive statistics, are given in the subsequent sections. The managerial and theoretical implications, limitations, and avenues of future research are ultimately emphasized in the conclusion and discussion.  By doing so, the research presents an overall framework for understanding how AI-powered digital marketing strategies enhance firm performance and brand value within India's real estate sector, presenting valuable information for both practitioners and academics.

As one of the prime facilitators of marketing today, artificial intelligence (AI) allows companies to foresee future actions, streamline campaigns, and tailor customer interactions (Verma et al., 2021; Chintalapati & Pandey, 2022).

AI-facilitated solutions such as chatbots, recommendation systems, and predictive analytics have been shown to be effective in enhancing customer interactions and business performance across various industries (Ziakis & Vlachopoulou, 2023). Despite that, there are still several key gaps in the literature.  On the first point, despite booming marketing AI research, there are limited sector-specific findings, especially in industries such as real estate that have long decision horizons and high information asymmetry.  Most existing studies focus on financial services, e-commerce, or consumer goods (Haleem et al., 2022; Montoya et al., 2024). Though possessing unique complexities—high-value transactions, trust-building necessity, and segmented customers—it has not drawn much attention for studies in the Indian real estate sector, particularly in the NCR region.  Secondly, existing work often stresses potential benefits without empirical validation and is qualitative or conceptual in nature. Only a few have quantitatively measured the impact of digital marketing supported by AI on business performance and brand equity. More comprehensive empirical insights are essential for scholars as well as practitioners.

Thirdly, the impact of mediation in this context has not been extensively researched. While scholars concur that AI influences brand performance, its mechanism by which AI-powered digital marketing translates consumer interaction into brand equity and subsequently into firm performance is unknown. Conceptional frameworks of technology adoption and brand value can be significantly advanced through the discovery of AI as a mediating variable between customer tastes and company performance.  Finally, studies on the Indian organisational setting continue to remain under-represented in global scholarship.  India's fast urbanisation and digital uptake in the real estate sector create a wealth of learning ground for understanding how emerging economy firms utilize AI to achieve a competitive advantage. Being a hub of real estate activity, the NCR region provides the ideal backdrop for the generation of context-relevant evidence that contributes to both local practice and global literature.  The present research, in its attempt to empirically examine the role of AI in digital marketing, more specifically enhancing brand equity and firm performance in Indian real estate companies, is warranted by these gaps combined.

Research Objectives

Based on the identified gaps and theoretical foundations, the study sets the following objectives:

1. To examine the factors driving the adoption of AI-enabled digital marketing strategies in the Indian real estate sector.
2. To analyze the role of AI-enabled digital marketing in enhancing brand equity dimensions (brand awareness, brand associations, perceived quality, and brand loyalty).
3. To investigate the impact of AI-driven digital marketing on firm performance, including customer acquisition, revenue generation, and competitive positioning.
4. To test the mediating role of AI-enabled digital marketing between customer engagement preferences and firm outcomes.

Hypotheses Development

Drawing from the Resource-Based View (RBV) and Brand Equity Theory (Keller, 1993), the study develops the following hypotheses:

H1: Demographic factors of customers (age, income, education) significantly influence the adoption of AI-enabled digital marketing in real estate firms.

H2: Customer preference for digital engagement positively influences the adoption of AI-enabled digital marketing strategies.

H3: AI-enabled digital marketing has a significant positive impact on brand equity in real estate firms.

H4: AI-enabled personalized marketing campaigns positively influence brand awareness and customer trust.

H5: Among AI-enabled digital marketing modes, predictive analytics and personalized content recommendation are the most preferred by real estate firms.

H6: The adoption of AI-enabled digital marketing strategies has a significant positive impact on the performance of real estate firms.

H7 (Mediation Hypothesis): AI-enabled digital marketing mediates the relationship between customer engagement preferences and firm performance outcomes.

Conceptual Framework

Conceptual framework of this research amalgamates customer engagement preferences, AI-powered digital marketing, brand equity, and firm performance. Developing on RBV and Brand Equity theory, it supposes that AI adoption fosters one-of-a-kind capabilities that increase brand-related results and eventually convert into better firm performance.

Model Components:

1. Independent Variables: Customer demographics and customer engagement preferences.
2. Mediator: AI-powered digital marketing adoption.
3. Dependent Variables: Brand equity (composite construct: awareness, associations, perceived quality, loyalty) and firm performance (revenue growth, lead generation, market competitiveness).
4. Mediation Path: Customer engagement → AI-enabled digital marketing → Firm performance.

Figure 1: Conceptual Framework

This framework posits that AI-enabled digital marketing not only has direct effects on brand equity and performance but also mediates the relationship between customer preferences and firm success.

Research Design

In an attempt to empirically examine the anticipated relationships between customer engagement, digital marketing using AI, brand equity, and firm performance, the current research applies a quantitative, explanatory research design. The central analytical tool employed to test for mediation effects is structural equation modelling (SEM), which is commonly utilized within marketing and management studies to test complex models and causal directions (Hair et al., 2019).

Population and Sampling

The National Capital Region (NCR) of India, i.e., Delhi, Noida, Gurugram, Ghaziabad, and Faridabad, is the key area of study. Customers of these firms' online platforms and workers/decision-makers are the population. Based on SEM guidelines, which require a minimum of 10–15 respondents per indicator variable, a sample size of 500 respondents was the target (Kline, 2016). To ensure representation for different firm sizes (large developers, medium-sized firms, and startups) and customer segments, stratified random sampling was used.

Data Collection

Both offline (field surveys in real estate offices and expos) and online (via Google Forms, LinkedIn, and firm mailing lists) standardized questionnaires were employed to collect the data. With a response rate of 71%, 355 valid customer and 145 valid employee responses were received, making up a total of N = 500.

Four sections were there on the questionnaire:

1. Demographics: size, experience, and role (for employees); age, gender, education, and income (for customers).
2. Use of AI-facilitated Digital Marketing: leveraging technologies such as automated curation of content, recommendation systems, chatbots, and predictive analytics.
3. Brand equity is a function of perceived quality, loyalty, associations, and brand awareness (adapted from Keller, 1993; Yoo & Donthu, 2001).
4. Firm Performance: Competitiveness, lead generation, revenue growth, and customer acquisition (adapted from Venkatraman & Ramanujam, 1986).

Measurement and Scales

Five-point Likert scales (range 1-strongly disagree to 5-strongly agree) were utilized to measure all constructs. Sample items included:

1. AI-powered digital marketing:

1.1. "In order to respond to customer inquiries, our company employs chatbots powered by AI."

1.2. "To identify potential leads, predictive analytics are employed."

2. Brand Equity: 2.1. Based on Brand Awareness, "I can easily recognize this real estate company among its competitors."

2.2. From Brand Associations, "I associate this firm with trust and credibility."

2.3. "This firm's services meet my expectations in terms of quality" (Perceived Quality).

2.4. "When making decisions about real estate, I would choose this firm over others" (Loyalty).

3. Company Performance:

3.1. "Our lead conversion rate has increased thanks to AI-enabled marketing."

3.2. "As a result of AI-based marketing campaigns, revenue has increased."

Analytical Approach

The study utilized a two-stage approach (Anderson & Gerbing, 1988):

1. Measurement model: Confirmatory Factor Analysis (CFA) was employed to check discriminant validity, convergent validity, and reliability.
2. Structural model: Hypotheses were tested using SEM, and bootstrapping (5,000 samples) was utilized to examine mediation for indirect effects.

Descriptive Statistics

Table 1 presents demographic characteristics of customer respondents.

Table 1. Demographic Profile of Customers (N = 355)

The majority of respondents were aged between 31–40 years, with postgraduate education and mid-to-high income levels—consistent with the profile of urban property buyers in NCR.

Reliability and Validity

Reliability

Cronbach’s alpha and Composite Reliability (CR) were calculated to test internal consistency.

Table 2. Reliability Results

All alpha and CR values exceeded the recommended threshold of 0.70 (Hair et al., 2019), indicating strong internal consistency.

Convergent Validity

Average Variance Extracted (AVE) was computed.

Table 3. Convergent Validity Results

All AVE values exceeded the threshold of 0.50, confirming convergent validity.

Discriminant Validity

The Fornell–Larcker criterion was applied. The square roots of AVEs (diagonal values) were greater than inter-construct correlations.

Table 4. Discriminant Validity (Fornell–Larcker Criterion)

Discriminant validity was established, as each construct was empirically distinct.

The research design, sampling strategy, and analytical techniques align with rigorous empirical standards. Measurement assessments confirm reliability, convergent validity, and discriminant validity, establishing a robust foundation for structural model testing in the next section.

Structural Model Analysis

After validating the measurement model, the structural equation model (SEM) was tested to examine hypothesized relationships. SEM was chosen as it allows simultaneous estimation of multiple relationships, including direct, indirect, and mediation effects (Hair et al., 2019).

Model Fit Indices

The model fit was assessed using established indices.

Table 5. Model Fit Indices

All indices indicated a good model fit, supporting the adequacy of the structural model.

Hypotheses Testing

Standardized path coefficients were examined for each hypothesis.

Table 6. Structural Path Results

All hypothesized relationships were statistically significant, with AI-enabled digital marketing serving as a key mediator between customer engagement preferences and firm performance.

Mediation Analysis

Mediation was tested using bootstrapping (5,000 resamples).

Table 7. Mediation Results (Bootstrapping)

The mediation test confirms that AI-enabled digital marketing partially mediates the relationship between customer engagement preferences and firm performance. This suggests that customer preferences directly enhance performance but are significantly amplified when channeled through AI-enabled strategies.

(Note: *** indicates p < 0.001; diagram shows mediation + direct effects)

Figure 2. Structural Equation Model (SEM) with Path Coefficients

Figure 3. Mediation Model

1. Customer demographics significantly influence the adoption of AI-enabled digital marketing, with younger and more digitally literate consumers driving adoption.
2. Customer engagement preferences strongly predict firms’ investment in AI-driven marketing strategies.
3. AI-enabled digital marketing significantly enhances brand equity dimensions, especially brand awareness and trust, critical in real estate decisions.
4. Predictive analytics and personalized content recommendations are the most preferred AI-enabled tools for firms, confirming their role in strategic marketing.
5. AI-enabled digital marketing has a positive and significant effect on firm performance, measured by revenue growth, lead generation, and customer acquisition.
6. Mediation analysis shows that AI-enabled digital marketing partially mediates the relationship between customer engagement and firm performance, highlighting its central role as a strategic enabler.

The findings underscore the transformative role of AI in digital marketing within the Indian real estate sector. By adopting AI-enabled tools, firms are able to reduce information asymmetry, build trust, and engage customers more effectively, thereby enhancing both brand equity and performance.

Theoretical Implications

1. The results confirm that AI-enabled digital marketing constitutes a strategic resource that provides competitive advantage when deployed effectively (Barney, 1991).
2. AI tools enhance all four dimensions of brand equity—awareness, associations, perceived quality, and loyalty—providing empirical validation of Keller’s (1993) model in a real estate context.
3. This study enriches technology adoption literature by demonstrating that AI acts as a mediating construct, bridging customer preferences with firm performance outcomes.

Practical Implications

1. Real estate firms should prioritize predictive analytics and recommendation systems as they directly enhance customer trust and brand value.
2. Firms must design AI-driven campaigns that cater to digitally savvy younger consumers, who are more receptive to automated, data-driven interactions.
3. AI-enabled digital marketing is not only about efficiency but also revenue growth, as confirmed by its significant path to firm performance.
4. Given challenges around adoption, firms should invest in employee training, ethical AI use, and customer education to maximize effectiveness.

This research investigated the contribution of artificial intelligence (AI) in digital marketing in India's real estate industry, and its relationship with brand equity and firm performance. Based on survey responses from 500 people located in the NCR area and structural equation modeling (SEM) analysis, the research verified that AI-based digital marketing has a considerable impact on customer interaction, brand awareness, and firm performance. The results illustrate that marketing technologies like predictive analytics, chatbots, and customized recommendation systems serve as strategic enablers, building greater customer trust and loyalty. Secondly, mediation analysis also identified that AI-powered marketing partially mediates the connection between customer tastes and firm performance—emphasizing how it serves as a link between customer interactions and business performance. From a theoretical perspective, the research extends the Resource-Based View (RBV) by illustrating that AI-powered digital marketing is a rare and valuable resource for companies. It also empirically tests brand equity theory in real estate, illustrating that AI-powered strategies enhance all four aspects: awareness, associations, perceived quality, and loyalty. Practically, the research provides useful recommendations for executives: AI implementation in digital marketing cannot be treated as a voluntary innovation but as a strategic imperative to establish trust, amplify brand value, and gain competitive edge in the volatile real estate sector.

Recommendations

1. Real estate firms should invest in AI-based predictive models and personalized recommendation systems, as these tools significantly influence both brand equity and firm performance.
2. Firms should deploy AI-driven chatbots capable of handling multi-language support, instant responses, and context-sensitive engagement to improve customer trust and reduce decision-making anxiety.
3. As AI depends heavily on customer data, firms must ensure ethical use of data, compliance with privacy regulations, and transparent communication with customers to build long-term trust.
4. Training programs for marketing professionals should focus on developing AI literacy, enabling them to effectively integrate AI tools into marketing strategies.
5. Firms should align AI adoption with customer demographics, as younger, digitally literate customers are more receptive to AI-driven engagement.

Limitations

Despite its contributions, this study has certain limitations:

1. The study is limited to the NCR region of India; findings may not fully generalize to other regions or countries.
2. Data were collected at a single point in time, limiting the ability to capture evolving AI adoption trends.
3. Reliance on survey responses may involve social desirability bias or subjective perceptions.
4. While key AI tools were studied, the rapidly evolving nature of AI means newer applications (e.g., generative AI, immersive AR/VR marketing) were not included.

Future Research Directions

Building on these limitations, future research could:

1. Compare AI adoption and outcomes across different Indian regions or cross-country contexts in emerging markets.
2. Examine how AI-enabled digital marketing influences firm performance over time.
3. Conduct experiments to measure customer responses to AI-driven campaigns more objectively.
4. Investigate the role of generative AI, virtual reality (VR), and augmented reality (AR) in reshaping customer experiences in real estate marketing.
5. Include perspectives from customers, employees, and policymakers to provide a more holistic understanding of AI adoption.

1. Adha, W. M., Mas’ud, A. A., & Erwin. (2024). Digital marketing development strategy of cocoa products. Revista de Gestão Social e Ambiental, 18(6). https://doi.org/10.24857/RGSA.V18N6-022
2. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
3. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
4. Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428
5. Garg, A., Tripathi, S., Agarwal, S., Tomar, D., & Kumar, V. (2023). Consumer attitude towards counterfeit products: An extension of theory of planned behaviour. Empirical Economics Letters, 22(2), 76–91.
6. Garg, A., Jain, R., Verma, D., Jha, D., Singhal, R., & Singhal, R. (2024). Examining the effect of FDI on economic growth of developing nations: A comprehensive analysis of Indian food processing industry through ARDL method. Educational Administration: Theory and Practice, 30(4), 242–248.
7. Varshney, A. K., Garg, A., Pandey, T., Singhal, R. K., Singhal, R., & Sharma, H. (2024). The development of manufacturing industry revolutions from 1.0 to 5.0. Journal of Informatics Education and Research, 4(1), 12–30.
8. Garg, A., Pandey, A., Sharma, N., Kumar, A., Jha, P. K., & Singhal, R. K. (2023). An in-depth analysis of the constantly changing world of cyber threats and defences: Locating the most recent developments. 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), 181–186. IEEE.
9. Singhal, R. K., Garg, A., Verma, N., Saxena, N., Sharma, H., & Singh, A. K. (2023). Unlocking diverse possibilities: The versatile applications of blockchain technology. 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), 187–191. IEEE.
10. Garg, A., Pandey, T. R., Singhal, R. K., Sharma, H., & Singh, A. K. (2024). Exploring enlarged perceptions of value: The utilization of virtual reality in Indian tourism. In Service Innovations in Tourism: Metaverse, Immersive Technologies, and Digital Twin (pp. 215–253). IGI Global Scientific Publishing.
11. Garg, A., Sharma, H., Singh, A. K., Sharma, N., & Aneja, S. (2024). Understanding the unpredictable: Technological revolutions' transformative impact on tourism management and marketing. In Service Innovations in Tourism: Metaverse, Immersive Technologies, and Digital Twin (pp. 19–38). IGI Global Scientific Publishing.
12. Varshney, A. K., Garg, A., Pandita, S., Gaur, M. P., Singhal, R. K., & Sharma, H. (2024). Exploring the impact and factors to consider in higher education practice: A study in reference to generative artificial intelligence. European Economic Letters, 14(1), 9–15.
13. Sharma, H., Sahu, N., Singhal, R. K., Garg, A., Singhal, R., & Tripathi, S. (2024, January 6). Data-driven forecasting and inventory optimization using machine learning models and methods. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
14. Swaroop, T. S., Kumar, S., Garg, A., Tripathi, V., Singh, S., Singhal, H., & Kumar, B. (2024). Behavioral consequences of customer inspiration: The role of social media inspirational content and cultural orientation. Journal of Informatics Education and Research, 4(3).
15. Singhal, H., Singhal, R. K., Garg, A., Singhal, R., Sharma, H., & Jaiswal, G. (2024, January 5). Analyzing bibliometric systematic reviews on blockchain's role in international e-commerce supply chain management. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
16. Garg, A., Sharma, S., Singh, R., Agarwal, S., Kumar, K. S., Tyagi, A., & Singhal, H. (2024). Exploring the fairness implications of A.I replacing human decision-makers in HR management: A case study on resume screening. Journal of Informatics Education and Research, 4(3), 280–291. https://jier.org/index.php/journal/article/view/1315/1108
17. Sharma, H., Garg, A., Singhal, R. K., Gaur, M. P., Sharma, H., & Sharma, N. (2024, January 6). Utilizing deep learning and advanced machine learning methods in economic data analysis. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
18. Pandita, S., Garg, A., Kumar, S., Das, S., & Gaur, M. P. (2024). The impact of HR technologies on digital library staff efficiency and engagement. Library of Progress–Library Science, Information Technology & Computer, 44(3).
19. Garg, A., Agarwal, S., Singhal, H., Sharma, H., & Sharma, N. (2025). Towards a sustainable circular economy: A comprehensive study on awareness, critical success factors, and implementation models. In Implementing ESG Frameworks Through Capacity Building and Skill Development (pp. 329–348). IGI Global Scientific Publishing.
20. Rathi, C., Varshney, A. K., Chaudhary, S., Prakash, V., & Garg, A. (2024). A paradigm shift in fintech and rural inclusion challenges in India. International Journal of Science and Research Archive, 12(1), 299–311.
21. Singh, S., Garg, A., & Walsh, J. C. (2025). AIvertising: Mapping the transformation of marketing in the experience economy. In Leveraging AI-Powered Marketing in the Experience-Driven Economy (pp. 71–104). IGI Global Scientific Publishing.
22. Garg, A., Jaiswal, G., & Singhal, R. K. (2025). A study of vital aspects of advertisements in the information swamp world for creating an appealing advertisement. International Journal of Internet Marketing and Advertising, 23(2), 190–207.
23. Garg, A., Verma, D., Das, S., Gaur, M. P., Srivastava, J., & Dwivedi, P. K. (2025). Emerging trends and technologies in contemporary marketing: Strategic approaches for enhanced consumer engagement. Advances in Consumer Research, 2(3).
24. Verma, N., Varshney, A. K., Singhal, R. K., Gaur, M. P., Garg, A., & Das, S. (2025). Explainable artificial intelligence (XAI) in insurance. 2025 International Conference on Pervasive Computational Technologies (ICPCT), 305–310. IEEE.
25. Garg, A., Verma, D., Pandey, L., Kumar, K. S., Singh, R., & Sharma, A. (2025). Enhancing tailored travel by integrating generative AI with insights driven by personality. 2025 International Conference on Intelligent Control, Computing and Communications (IC3), 404–409. IEEE.
26. Verma, D., Raj, P., Tripathi, S., Agarwal, S., Garg, A., & Singhal, H. (2024). An analysis of the systematic landscape and potential future directions in studies on neuro-tourism. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 1133–1138. IEEE.
27. Dixit, A., Sahu, S., Gupta, A. K., Sawant, A., Garg, A., & Verma, N. (2024). Smart vision: A unified system for enhanced navigation and accessibility for visually impaired individuals. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 284–288. IEEE.
28. Garg, A., Saxena, N., Verma, N., Singhal, R. K., Singhal, R., & Kaggallu, N. (2024, January 5). Examining the smart tourism landscape: A bibliometric study and analytical synopsis of IoT research. 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE.
29. Varshney, A. K., Mishra, A. K., Agarwal, S., Garg, A., Das, S., & Tripathi, S. (2024). Changing aspects: Examining financial market forecasting via textual representation—A critical evaluation. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 1127–1132. IEEE.
30. Singhal, R., Srivastava, A., Thakkar, U. P., Garg, A., Agarwal, S., & Singhal, R. K. (2024, January 3). Composition of board of directors and its effect on the performance of banking firms: Analysis through OLS method. 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE.
31. Singhal, R. K., Garg, A., Agarwal, P., & Jaiswal, G. (2025). Investigating technology adoption and consumer behaviour in digital age. In Demystifying Emerging Trends in Green Technology (pp. 120–131). Bentham Science Publishers.
32. Verma, D., Garg, A., Das, S., Singhal, R., Sharma, H., & Gaur, M. P. (2025). Increasing the competitiveness and branding of travel destinations: A case study on stakeholder integration and perspectives from Indian regional development. Journal of Marketing & Social Research, 2, 86–91.
33. Pandey, A., Singh, R., Sharma, A., Agarwal, P., & Garg, A. (2025). A review of applications combining blockchain technology with artificial intelligence. In Demystifying Emerging Trends in Green Technology (pp. 96–106). Bentham Science Publishers.
34. Sharma, A., Pandey, A., Singh, R., & Garg, A. (2025). Examining the viability of integrating blockchain technology into IoT devices for the supply chain. In Demystifying Emerging Trends in Green Technology (pp. 75–83). Bentham Science Publishers.
35. Agarwal, P., & Garg, A. (2023). Human values and environment studies: e-Book for BA 3rd Semester for all UP State Universities as per common syllabus of NEP-2020. Thakur Publication Private Limited.
36. Garg, A., Sharma, R. B., Tripathi, S., Kumar, K. S., Varshney, A. K., & Sharma, A. (2025). AI-powered virtual reality: Transforming education beyond conventional approaches. 2025 International Conference on Intelligent Control, Computing and Communications (IC3), 398–403. IEEE.
37. Garg, A., Nayyar, R., Singh, S., Sharma, H., & Singhal, R. (2026). Green logistics 5.0: An examination of innovation driven by sustainability using foundation models in logistics. In Emerging Trends in Smart Logistics Technologies (pp. 367–396). IGI Global Scientific Publishing.
38. Garg, A. (2016). Impact of office ergonomics on business performance: In special reference to Noida region. International Journal of Advanced and Innovative Research, 5(4), 888.
39. Garg, A. (2015). Study of online shopping in Ghaziabad and Noida region: A customer perspective. International Journal of Advanced and Innovative Research, 4(5), 48.
40. Garg, A. (2019). A study of different aspects of consumer behavior for online buying in Delhi NCR for FMCD products. PRANJANA: The Journal of Management Awareness, 22(2), 76.
41. Garg, A. (2021, January 17). The relevance of Engel-Blackwell-Miniard model of consumer behavior during COVID-19: A contemporary consumer behavior survey on FMCG products in urban demography in Uttar Pradesh West. ANVESAK, 51(2), [XVIII].
42. Taparia, R., Chatterjee, A., Garg, A., & Pandey, T. R. (2022, January 14). A study of the impact of risk avoidance and financial welfare on the intent to invest in the equity market. Manager: The British Journal of Administrative Management, 58(9).
43. Garg, A., Das, S., Gaur, M., & Singhal, R. K. (2022). Marketing intelligence recent research trends: Systematic literature review approach. NeuroQuantology, 20(7), 1464–1479.
44. Singhal, R. K., Garg, A., Das, S., & Pandey, T. R. (2022). Health care management using blockchain technology: A conceptual framework. NeuroQuantology, 20(7), 1452–1463.
45. Garg, A., & Kumar, S. (2022, January 11). Consumer panic buying and consumer behaviour during pandemic years for innovative sanitization goods: A study of buying behaviour to sanitization durable goods. Manager: The British Journal of Administrative Management, 58(154). https://tbjam.org/
46. Poonia, S., Sharma, R., Baranwal, A., Jain, I., & Tiwari, V. K. (2022). An examination of the impact of prevailing nationalist sentiments on Indian consumer behaviour for selected products and services: Is it contributing to or limiting a self-sufficient economy? AAYAM: AKGIM Journal of Management, 12(2), 36–45.
47. Garg, A., Singhal, R., & Singh, A. (2022). Artificial intelligence: An emergence for electrical automation control. AAYAM: AKGIM Journal of Management, 12(2), 150–152.
48. Singhal, R., & Garg, A. (2015). Study of online shopping in Ghaziabad and Noida region: A customer perspective. Unpublished/Journal unspecified.
49. Garg, A., Singh, S. K., & Singhal, R. K. (2022). Consumer behavior towards FMCG products: A contemporary survey of consumer behaviour using EKB model. AAYAM: AKGIM Journal of Management, 12(2), 186–191.
50. Garg, A., & Sharma, J. (2022). Copper complexes as potential catalytic, electrochemical and biochemical agents. Materials Today: Proceedings, 62, 1632–1635. https://doi.org/10.1016/j.matpr.2022.02.490
51. Vishnoi, S. K., Virmani, N., Pant, D., & Garg, A. (2022). Big data in healthcare: Technological implications and challenges. In Designing Intelligent Healthcare Systems, Products, and Services Using Disruptive Technologies and Health Informatics (pp. 211–227). CRC Press.
52. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE.
53. Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
54. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
55. Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101
56. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
57. Montoya, R. T., Devece, C., Llopis-Albert, C., & García-Agreda, S. (2024). Effectiveness of digital marketing and its value in new ventures. International Entrepreneurship and Management Journal. https://doi.org/10.1007/s11365-024-00959-5
58. Simion, P. C., & Popescu, M. A. M. (2023). Assessing the use of artificial intelligence in digital marketing: Evidence from Romanian companies. Proceedings of the International Conference on Business Excellence, 17(1), 1128–1138. https://doi.org/10.2478/picbe-2023-0101
59. Venkatraman, N., & Ramanujam, V. (1986). Measurement of business performance in strategy research: A comparison of approaches. Academy of Management Review, 11(4), 801–814. https://doi.org/10.5465/amr.1986.4283976
60. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
61. Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 642. https://doi.org/10.3390/info14120664