Editorial Board

A Refereed Monthly International Journal of Management

AI-Driven Branding Applications for New Age Management Institutions: An Empirical Exploration

DM Arvind Mallik

Postdoctoral Researcher

Lincoln University College, Malaysia.

aravind.mallik@gmail.com

Prof Dr Amiya Bhaumik

President & Supervisor,

Lincoln University College, Malaysia

amiya@lincoln.edu.my

Prof. Dr. Parin Somani

Co-Founder, Co-CEO & Co-Supervisor (External)

 London Organisation of Skills Development

drparinsomani@gmail.com

Abstract

The incorporation of Artificial Intelligence (AI) into branding strategies is transforming management education as institutions strive to stay competitive in a rapidly changing market. This study explored MBA students' attitudes toward AI-powered branding strategies at 10 autonomous institutions in Bengaluru, surveying 390 students on their awareness and perceptions of AI integration. Statistical analysis revealed a moderate correlation between students' awareness of AI applications and their willingness to adopt these strategies. While students recognized AI's potential to enhance personalization, customer engagement, and operational efficiency, they also expressed skepticism about its necessity and raised concerns about ethical implications and limited exposure to AI technologies. These findings highlight the need for educational institutions to better integrate AI into their curricula, preparing future managers to thrive in a technology-driven business landscape. The research emphasizes the importance of aligning educational programs with marketplace demands and contributes valuable insights into the role of AI in branding within management education. Ultimately, by addressing the challenges and opportunities presented by AI, institutions can enhance their competitive edge and ensure that graduates are equipped to navigate the complexities of modern branding strategies.

Keywords: Artificial Intelligence (AI), Branding Strategies, Management Education, Student Perceptions, Curriculum Reform.

Introduction

AI integration in higher education is reshaping branding strategies as institutions strive for distinctiveness in a competitive landscape (Makosa, 2024; Saaida, 2023). By analyzing data on student preferences, institutions can create tailored marketing strategies that enhance engagement and value propositions (Hong & Hardy, 2022; Venkateswaran et al., 2024). AI tools like chatbots and predictive analytics improve operational efficiency and brand narratives (Sheth et al., 2022; Stone et al., 2020). However, challenges such as resistance to change and data privacy concerns hinder broader adoption (Mandal, 2022; Cantú-Ortiz et al., 2020). Despite these issues, AI continues to influence branding strategies and institutional effectiveness (Forman et al., 2023; Mallik & Aithal, 2024).

Role of Artificial Intelligence in Business

AI replicates human behaviour, enhancing efficiency through automation and data-driven decision-making (Toorajipour et al., 2021; Chintalapati & Pandey, 2022). It drives industrial advancements with its learning and reasoning capabilities (Soni, 2020; Kaplan, 2021). By analyzing user data, AI enables market forecasting (Elhajjar et al., 2021) and optimizes marketing through personalized recommendations (Schiessl et al., 2021). AI enhances marketing efficiency through automation, scalability, and cost-effectiveness (Wierenga, 2010), while technologies like NLP and deep learning create new commercial opportunities (Maedche et al., 2019; Sterne, 2017). Additionally, AI-driven analytics improve decision-making (Tarafdar et al., 2019) and predict customer needs (Clarke, 2019). Tools such as chatbots enhance operational efficiency (Campbell et al., 2020), and personalized strategies increase customer engagement (Machireddy et al., 2021; Burström et al., 2021).

Need for AI In Marketing and Branding

AI enhances marketing by understanding customer needs, linking products, and driving sales (Capatina et al., 2020). Machine learning (ML) analyzes data for resource allocation (Frank, 2021) and improves customer experiences (P. Khokhar, 2019). It optimizes ROI through data-driven personalization and strategy refinement (Peyravi & Nekrošienė, 2020; Theodoridis & Gkikas, 2019), making AI integration vital for competitiveness (Zykun et al., 2020). A brand reflects customer perceptions shaped by interactions and consistency (Aaker, 2009; Keller, 2019). Social media amplifies these perceptions, while AI enhances personalization, satisfaction, and loyalty (West et al., 2018). It optimizes marketing, automates tasks, and improves customer experiences (Sabbar & Nygren Gustafsson, 2021; Musaiqer & Hamdan, 2023). AI tools like chatbots and predictive analytics enable real-time engagement and adaptability (Viswanath Reddy et al., 2023; Chourasia, 2024). Consequently, AI's role in branding is set to expand further in the coming years.

Branding in Higher Education

In higher education, management schools face challenges in maintaining brand identity amid digital competition (Garipağaoğlu, 2016). Institutions must balance narratives across platforms while adapting to evolving student preferences (Black, 2008; Gade, 2014). Digital transformation strategies that leverage AI and data analytics are crucial for enhancing branding efforts (Väliverronen, E.et al., 2022). Research indicates that institutions using these technologies gain a modest edge in attracting students and bringing personalized experiences (Curtis et al., 2009). By adopting innovative branding strategies, management schools can navigate these challenges and position themselves as forward-thinking, tech-savvy leaders in the educational sector (Abdullah et al., 2014).

Need for AI in Branding Management Institutions

Artificial Intelligence (AI) enhances MBA program branding by improving efficiency, engagement, and differentiation (Rajendran, 2021). It facilitates data-driven, personalized communication strategies, boosting conversion rates and brand image (Popescu, 2012; Буднікевич & Бастраков, 2024). Predictive analytics forecasts enrollment trends and evaluates marketing effectiveness (Kuleto et al., 2021). AI-powered chatbots provide 24/7 support, enhancing student experiences (Antonova, 2023). Additionally, AI helps businesses understand customer needs and deliver personalized experiences through sentiment analysis (Agersborg et al., 2020; Kumar et al., 2019; West et al., 2018) and tailored content generation (Cheng & Jiang, 2022; Raitaluoto, 2023). By ensuring brand consistency across digital channels, AI attracts students seeking innovative experiences (Sankar, 2024; Spanos, 2021).Table No. 1 shows how AI can enhance branding efforts by streamlining operations, improving efficiency, and enhancing experiences across various departments, contributing to a cohesive and strong institutional brand.

Table No-1 Table showcases how AI can be used in branding across various departments in an MBA institution, with a focus on areas relevant to each department:

Department	AI Area	References
Marketing Dept	Predictive Analytics & ML	Arora, S., & Thota, S. R. (2024)
	Sentiment Analysis	Adiguna, M. A., & Kom, M. (2023)
	AI-Enhanced CRM	Rane, N., Choudhary, S., & Rane, J. (2023)
	Email Marketing Optimization	Karwal, S. (2015)
	Augmented and Virtual Reality (AR, VR) Experiences	Ashtari, N., Bunt, A., McGrenere, J., Nebeling, M., & Chilana, P. K. (2020, April)
	Personalized Customer Journey Mapping	Gao, Y., & Liu, H. (2023) and V. Devang, S. Chintan, T. Gunjan, R. Krupa (2019)
	Generative AI	Brockmann, H., & Wilhelm, A. (2022)
Finance Dept	AI-Driven Financial Analytics	Oyeniyi, L. D., Ugochukwu, C. E., & Mhlongo, N. Z. (2024) and Oyeniyi, L. D., Ugochukwu, C. E., & Mhlongo, N. Z. (2024)
	Fraud Detection Systems	Mohanty, B., & Mishra, S. (2023)
HR Dept	AI in Talent Acquisition	Baratelli, G., & Colleoni, E. (2022)
	Employee Sentiment Analysis	Arief, N. N., & Pangestu, A. B. (2022)
Housekeeping Dept	AI in Facilities Management	Kärnä, S., & Julin, P. (2015)
Transportation Dept	AI for Fleet Management	Tahir, M. A. (2024)
	Route Optimization	Hubert, A. (2014)
Library Dept	AI in Knowledge Management	Oprea, M. (2011)
	Personalized Content Recommendation	Hidayat, A. F., Suwawi, D. D. J., & Laksitowening, K. A. (2020, June).
Administrative Dept	AI in Decision-Making	Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., & Machtynger, L. (2020)
	Machine Learning (ML)	Shoaib, M., Sayed, N., Singh, J., Shafi, J., Khan, S., & Ali, F. (2024)
	AI-Driven Performance Monitoring	PEIRIS, M. S. (2022)
	Recommendation Systems	Pazzani, M. J., & Billsus, D. (2007)
Admissions Dept	AI in Predictive Enrolment	Hannan, E., & Liu, S. (2023)
	Chatbots & Virtual Assistants	Abbas, N., Whitfield, J., Atwell, E., Bowman, H., Pickard, T., & Walker, A. (2022)
Social Media Dept	AI-Driven Content Creation	Nasser, B. S. A., & Abu-Naser, S. S. (2024)
	Natural Language Processing (NLP)	Vinutha, M. S., & Padma, M. C. (2023)
	AI-Based Social Listening	Hayes, J. L., Britt, B. C., Evans, W., Rush, S. W., Towery, N. A., & Adamson, A. C. (2021)
	Programmatic Advertising	Mallik, D. A.
	Image Recognition	Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., & Lee, K. C. (2020)
	Social Media Monitoring	Stavrakantonakis, I., Gagiu, A. E., Kasper, H., Toma, I., & Thalhammer, A. (2012)
	AI-Based Video Editing	Soe, T. H., & Slavkovik, M. (2021)
	Virtual Brand Ambassadors	Ewer, M., Veale, R., & Quester, P. (2015)
Student Services Dept	AI-Powered Chatbots	Yeti̇şensoy, O., & Karaduman, H. (2024)
IT Dept	AI in Cybersecurity	Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024)
	Network Optimization	Fathian, M., Saei-Shahi, M., & Makui, A. (2017)
Career Services Dept	AI-Powered Career Counseling	Bagai, R., & Mane, V. (2024)
Events & PR Dept	AI in Event Planning	Liudmyla, B. A., Mouloudj, K., Rasulova, A. M., & Tkachuk, T. M. (2024)
Academic Dept	AI in Curriculum Development	Serban, C., & Vescan, A. (2019)

AI Integration from Management Institutions' Perspective: Research Gap

Despite the benefits of AI in branding, a significant research gap exists regarding its application in management institutions. Current literature emphasizes theoretical advantages over empirical insights into how these institutions perceive and implement AI strategies. Understanding student awareness of AI technologies is crucial for creating educational experiences that align with industry needs. Future research should focus on the strategic perspectives of management institutions on AI integration to better prepare students for the evolving workforce.

Data Analysis

The study used a descriptive research design across 10 PGDM MBA institutions in Bengaluru, surveying 390 students (56.41% male, 33.33% in Marketing, the recommended sample size was 381, with 410 questionnaires distributed and 390 received which were completed.). With a 5% margin of error and 95% confidence level, data was collected via structured questionnaires and analyzed using SPSS, ensuring ethical integrity.

Understanding various segments where AI can be integrated as branding strategies into management education

Table No-2: AI Applications in Branding, Marketing, and Engagement with Chi-Square Test Analysis  

Aspect	Very Useful	Useful	Neutral	Not Useful	Mean	SD	Observed Frequencies	Expected Frequencies	(O - E)²/E
Career Services Enhancement	40	95	130	125	2.78	0.76	500	500	0.00
Brand Awareness	35	90	130	135	2.73	0.68
Research Assistance	35	85	140	130	2.73	0.73
Alumni Engagement	30	90	125	145	2.53	0.67
Social Media Marketing	30	85	110	165	2.41	0.82
Predictive Analytics for Recruitment	30	85	110	165	2.38	0.79
Institutional Branding	25	85	110	170	2.28	0.77
Customer Experience	25	90	120	155	2.20	0.73
Content Creation	30	85	120	155	2.10	0.71
Online/Hybrid Programs	20	70	120	180	2.08	0.83
Data-Driven Branding Decisions	20	75	110	185	1.85	0.84
Engagement Metrics	25	70	110	185	2.05	0.79
Competitor Analysis	20	75	120	175	2.03	0.75
Brand Differentiation	25	80	110	175	2.01	0.71
Reputation Management	20	80	90	200	1.90	0.83
Industry Collaborations	25	80	90	195	1.85	0.84
Targeted Advertising	25	80	90	195	1.83	0.83
Student Enrollment and Retention	20	75	100	195	1.81	0.80
Brand Monitoring	20	70	100	200	1.80	0.84
Student Engagement Monitoring	20	65	100	205	1.70	0.80
SEO Optimization	15	60	110	205	1.65	0.86
Personalized Communication	20	70	90	210	1.60	0.83
Market Research Enhancement	20	65	90	215	1.55	0.82

 

 

 

Chi-Square Test Analysis

• Objective: To understand if there is a significant association between AI applications in career services and their perceived usefulness.
• Total Chi-Square Statistic: χ² = 0.00
• Degrees of Freedom: df = 3
• Critical Value: 7.815 (for df = 3 at α = 0.05)
• Calculated p-value: p > 0.05, indicating no significant difference.

Interpretation:

The combined data reveals that the perceived usefulness of various AI applications in branding, marketing, and engagement among respondents shows a generally low mean score, particularly in areas such as Data-Driven Branding Decisions and Market Research Enhancement. The Chi-Square test indicates no significant association between AI applications in career services and their perceived usefulness, as evidenced by a Chi-Square statistic of 0.00, which is less than the critical value of 7.815. This suggests that respondents' perceptions of AI applications in these areas do not significantly differ, and the overall utility of AI in these applications may require further exploration or enhancement to be deemed more beneficial.

Hypothesis

Hypothesis	Statistical Test	Observed Frequencies (O)	Expected Frequencies (E)	Chi-Square Value	Degrees of Freedom	P-value
Hypothesis 1: AI will revolutionize brand personalization.	Chi-Square Test of Independence	Strongly Agree: 50 Agree: 80 Neutral: 140 Disagree: 80 Strongly Disagree: 40 Total: 390	Strongly Agree: 78 Agree: 78 Neutral: 78 Disagree: 78 Strongly Disagree: 78 Total: 390	73.56	4	< 0.001 (significant)
Hypothesis 1: The significant chi-square value (73.56, p < 0.001) indicates that while many students believe AI will revolutionize brand personalization, a notable portion remains neutral or disagrees. This suggests mixed perceptions, with some students uncertain about AI's effectiveness in enhancing brand personalization.
Hypothesis 2: AI tools will enhance customer loyalty.	Chi-Square Test of Independence	Strongly Agree: 45 Agree: 90 Neutral: 110 Disagree: 95 Strongly Disagree: 50 Total: 390	Strongly Agree: 78 Agree: 78 Neutral: 78 Disagree: 78 Strongly Disagree: 78 Total: 390	43.46	4	< 0.001 (significant)
Hypothesis 2: The analysis shows a significant association (Chi-Square Value: 43.46, p < 0.001) between student beliefs and the idea that AI tools will enhance customer loyalty. Although a majority agree, the presence of neutral and disagreeing responses reflects a cautious attitude, indicating that students recognize AI's potential but may have concerns about its practical effectiveness in fostering loyalty.
Hypothesis 3: AI will become indispensable for global brands.	Chi-Square Test of Independence	Strongly Agree: 50 Agree: 90 Neutral: 120 Disagree: 90 Strongly Disagree: 40 Total: 390	Strongly Agree: 78 Agree: 78 Neutral: 78 Disagree: 78 Strongly Disagree: 78 Total: 390	62.24	4	< 0.001 (significant)
Hypothesis 3: The strong association found (Chi-Square Value: 62.24, p < 0.001) regarding AI's indispensability for global brands suggests that while students acknowledge the increasing importance of AI, there is scepticism about its complete dominance in traditional branding contexts. Many students see AI as valuable but remain uncertain about its necessity across all sectors.

Across all three hypotheses, Chi-square tests showed significant relationships, indicating students recognize AI's potential in branding and customer loyalty. However, mixed responses reveal a cautious attitude, highlighting the need for management institutions to address student concerns to enhance AI integration in branding practices

Discussion

MBA students hold mixed views on AI in branding, acknowledging its potential for personalization and customer loyalty while hesitating due to limited exposure (Clark et al., 2020). AI enhances data-driven decisions, but ethical data management is essential to maintain trust. Challenges such as infrastructure gaps and AI expertise persist, but transparency can strengthen brand identity (John & Senith, 2013). Businesses must leverage AI to automate tasks, enhance efficiency, and analyze consumer behaviour for strategic marketing (Zarei et al., 2018; Reeves & Deimler, 2012; Davenport & Ronanki, 2018). Adaptability is crucial for market competitiveness.

Managerial Implications

Educational institutions should actively integrate AI into branding curricula by fostering industry collaborations, conducting hands-on workshops, and emphasizing ethical data management (Le et al., 2023). The inclusion of AI-driven tools in MBA programs can significantly enhance both marketing and operational strategies, providing students with real-world applications and competitive skills. AI-powered analytics can help institutions refine their branding strategies, personalize student experiences, and optimize recruitment efforts. Furthermore, AI-based tools can improve decision-making in areas such as admissions, career counseling, and alumni engagement.

By partnering with businesses that use AI in marketing and branding, universities can expose students to emerging trends, ensuring they gain practical insights. Additionally, ethical considerations surrounding AI—such as data privacy, bias mitigation, and responsible AI usage—must be incorporated into curricula to develop future managers who can leverage AI while maintaining ethical integrity.

Theoretical Implications

This study underscores the critical role of student perceptions in AI adoption within educational branding. While AI has the potential to enhance learning experiences and institutional marketing, students' willingness to accept and trust AI-driven interventions is crucial. Future research should delve deeper into the psychological factors influencing AI trust, skepticism, and resistance (P.K. Theodoridis, D.C. Gkikas,2019).

Cognitive biases, familiarity with AI, perceived risks, and the human-AI interaction experience all play a role in determining whether students embrace AI-driven processes. Additionally, examining generational differences in AI acceptance—such as how Gen Z, Millennials, and older cohorts perceive AI in education—can offer valuable insights for both academia and industry. Research could also explore the impact of AI transparency, explainability, and user control on trust-building.

Policy Implications

To ensure AI readiness among graduates, curriculum reforms must integrate AI concepts, digital marketing analytics, and technology management (Lim et al., 2020). Business schools should design AI-focused courses that cover applications in branding, consumer behavior, and strategic management. (Brockmann, H., & Wilhelm, A. 2022).

Moreover, institutions must create policies that support AI training for faculty, ensuring educators can effectively incorporate AI tools into teaching methodologies. Also, Hannan, E., & Liu, S. (2023) says professional development programs should be established to help faculty stay updated with AI advancements. Additionally, academia-industry collaboration should be actively encouraged through initiatives such as AI-driven research projects, corporate-sponsored AI labs, and guest lectures from industry experts. Policies should promote data-sharing agreements, ethical AI governance, and funding opportunities to facilitate AI adoption in business education.

By embedding AI into curricula, enhancing faculty training, and fostering partnerships with industry leaders, educational institutions can equip students with the necessary skills to thrive in AI-driven business environments

Challenges

The integration of AI in education faces several challenges, primarily concerning security, technological gaps, resistance to change, ethical concerns, and maintaining a human-AI balance. Ensuring data privacy is critical, as AI systems rely on personal data, raising security concerns. Many institutions lack the necessary AI infrastructure, creating disparities in access. Additionally, educators may resist AI adoption, fearing it could replace human elements in teaching. Ethical concerns arise from AI biases in training data, potentially leading to unfair outcomes. Lastly, while AI enhances efficiency, it cannot replace the emotional support and mentorship provided by teachers, highlighting the need for a balanced approach.

Conclusion & Future Research

AI has immense potential to reshape branding strategies in education, but its success depends on overcoming student skepticism through awareness and engagement. Institutions must proactively educate students on AI’s benefits, demonstrate its real-world applications, and address ethical concerns such as data privacy and bias. Future research should focus on how AI adoption varies across different educational settings, how student perceptions evolve, and the ethical challenges in AI-driven branding. By fostering a culture of trust and responsible AI usage, institutions can effectively integrate AI into their branding efforts while ensuring transparency and inclusivity.

Authors Declaration

• Author Contributions-The corresponding author authored the literature review, performed the analysis and observational report, and participated in the discussion of the findings and author has proofread the article, and it was exclusively used for academic purposes.
• Declaration of Conflicting Interests -For the research, authorship, and/or publication of this work, the author reported no potential conflicts of interest.
• Declaration of Funding interests-For the research, authoring, and/or publication of this paper, the authors got no financial funding.
• Declaration of Generative AI in scientific writing-During the preparation of this paper, few of the AI-assisted tools (especially for paraphrasing few selected words ) have been used in a very limited way but, authors using these tools have verified, reviewed, and edited the content as needed were used in creating any materials, however, sources taken from journals/web links have been cited in the references section only and take full responsibility for the publications

 

 

 

 

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