Dhiti Desai is an aspiring PhD candidate in Neuromarketing at GLS University, with a strong background in marketing, teaching, and content development. Currently pursuing a Master’s in Marketing at GLS University, Dhiti has also earned a Bachelor’s in Business Administration and a Master's in Business Management from NMIMS. With a passion for strategic marketing, she has worked as a Marketing Strategist at Tangerine Box, where she managed luxury brand campaigns and influencer marketing. She is the founder of Moodboard Inc., a marketing agency that specializes in social media and digital marketing.
In addition to her marketing expertise, Dhiti is deeply involved in academia, currently serving as a Visiting Faculty member at Knowledge Seekers Academy and a Teaching Associate at Ahmedabad University. She has contributed to multiple research conferences, earning accolades such as second rank at Welingkar Institute, and has published work in journals like Library Progress International and Journal of Informatics Education and Research. Dhiti’s diverse skill set includes communication, critical thinking, academic writing, and mentoring, which she combines to foster both academic and professional growth.
Dhiti is also an author and an advocate for creative expression, having conducted international poetry workshops in the UK, India, and the USA.
RESEARCH CONFERENCE - 2024 IIM BODHGAYA
RESEARCH CONFERENCE - 2023 SECOND RANK IN WELINGKAR INSTITUTE - MUMBAI
RESEARCH CONFERENCE - 2024 GLS UNIVESITY - ATRPM SIP COMPETITION - GTU - 2023 First Rank - research on impact of memetic marketing in conversion
RESEARCH CONFERENCE - 2020 GLS UNIVESITY - ATRPM
Speech for ATRPM 2025 Conference: Unlocking Potential through Seamless Human-Tech Collaboration
Abstract:Good morning, esteemed colleagues, scholars, and aspiring researchers. It is an absolute honor to stand before you today at the GLS University International Conference on Advances in Theory, Research & Practices in Management (ATRPM 2025). Let me begin with something we should all be proud of—India’s rise to the fourth position globally in research output. This is not just a statistic; it is a testament to our intellectual prowess and growing scientific capabilities. Our research output has surged by a staggering 54% between 2017 and 2022, a growth rate that outpaces the global average. If research were a cricket match, we would be leading the scoreboard in sheer number of runs. But, my friends, let’s not celebrate just yet. There’s a catch! While we are publishing more papers, our citation impact ranks ninth globally. This means that while we excel in producing research, the impact and credibility of our work need strengthening. Why is this happening? Is it the lack of novel ideas? Certainly not! Indian researchers are brimming with ingenuity. The issue often lies in how we use and interpret data. And this brings me to the often-overlooked hero—or villain—of research: secondary data. We live in an era where data is abundant, yet many researchers are still stuck in a cycle of relying solely on small, localized surveys. Think of it like trying to predict the monsoon by watching a single puddle. We have access to a wealth of robust secondary data—from government repositories to industry reports and platforms like Bloomberg—yet many hesitate to use it. Now, let’s consider a practical example. Imagine we want to study consumer behavior in Ahmedabad. We conduct a survey with 500 respondents. Sounds decent, right? But Ahmedabad has nearly 9 million residents! That’s like trying to capture the essence of an ocean with a teacup. For any findings to be statistically significant, the sample size must be far larger. Now, contrast this with authenticated secondary data sources—comprehensive, expert-curated, and continuously updated. Financial reports from Moneycontrol, behavioral insights from Nielsen, or national health data from ICMR—these datasets allow us to see the bigger picture. However, let me throw a curveball—how reliable is all secondary data? Can we blindly trust everything on Moneycontrol? Of course not! Just like not every YouTube video makes you a financial expert, not every dataset is credible. The key is discernment: Who collected the data? What methodologies were used? Can the source be independently verified? Data misinterpretation is a real issue. And in an age where artificial intelligence can fabricate realistic yet false narratives in seconds, researchers must be more vigilant than ever. This brings me to my next point—AI. For years, we’ve heard warnings that AI will replace jobs. Some even predict that AI will take over research itself! Imagine—an algorithm publishing papers faster than we can read them. Now, let’s be realistic. Has AI changed the research landscape? Absolutely. It can analyze vast datasets in minutes, detect patterns that humans might overlook, and even generate academic papers. But—and this is crucial—AI lacks human intuition, contextual awareness, and ethical reasoning. AI can tell us what is happening, but it cannot explain why. This is where the theme of our conference—'Unlocking Potential through Seamless Human-Tech Collaboration'—becomes so relevant. Instead of fearing AI, we must learn to leverage it. Think of AI as a supercharged research assistant. It can handle the heavy lifting—sorting through millions of data points—while we, the researchers, provide insight, context, and critical thought. Take, for instance, secondary data analysis. AI can help us clean and organize datasets, identify correlations, and even forecast trends. But without a human researcher to interpret these findings, AI’s conclusions are meaningless. AI should not replace us; it should enhance our capabilities. To fully grasp where we are today, we must look back at where we started. Research, like industry, has evolved through different eras of transformation. Industry 1.0 brought mechanization and steam power, leading to the first organized scientific explorations. Industry 2.0 introduced mass production and electricity, mirroring the rise of systematic research methodologies. Then came Industry 3.0—computers and automation. This was the first major disruption in how we processed information. Industry 4.0 pushed us further with cyber-physical systems and real-time data analytics, making research more dynamic and data-driven. And now, we stand in Industry 5.0—a time that is no longer just about integrating AI but about linking it to human behavior. Unlike Industry 4.0, which was focused on technological advancements and automation, Industry 5.0 is about harmonizing human intuition with AI’s capabilities. Today, consumers are just as empowered by technology as businesses. With AI tools at their fingertips, buyers scrutinize products deeply, analyze ingredients in seconds, and share insights through User-Generated Content (UGC) that can influence entire markets overnight. As marketers, researchers, and entrepreneurs, we can no longer rely solely on strategic marketing plans and advertising to push a product. We must acknowledge that the consumer today is highly analytical, informed, and reactive. The challenge now is not just leveraging AI but understanding how AI-driven insights impact human behavior. Industry 5.0 is about creating meaningful engagement by anticipating consumer reactions rather than simply pushing products into the market. So, where do we go from here? First, let us recognize that robust secondary data is the backbone of impactful research. It enables us to build on existing knowledge rather than reinventing the wheel. But let’s use it wisely—authenticate sources, question methodologies, and be skeptical of convenient but dubious statistics. Second, AI is not the enemy. It is a tool, and like any tool, its effectiveness depends on how we use it. If we embrace it strategically, it will elevate our research rather than replace us. Third, let’s embrace the era of Industry 5.0, where technology is a collaborator, not a competitor. The more we blend AI’s analytical power with human intuition, the more groundbreaking our research will become. We must acknowledge that technology is no longer just for sellers—it is in the hands of the consumers, who are dissecting, analyzing, and reacting to products in real-time. Understanding these shifts is critical to staying ahead in research and industry alike. And finally, let us, as researchers, lead the way in ensuring that technology serves us, not the other way around. We must be critical consumers of data, balancing AI’s efficiency with human insight and wisdom. The future of research is not about choosing between humans and AI; it is about harnessing the strengths of both. If we get this right, we won’t just be publishing more research—we will be shaping a smarter, more insightful, and profoundly impactful academic future. Thank you, and happy researching!