Good day, Geeta Raut. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Hi Arjun, I am Gita, umm, I'm happy to connect here. Umm, well, OK, talking about fitting with the VIT Business School, I've already been teaching at School of Business in Calgotias University for more than a year now. So I can say I've had some experience here. Uh, I have taught umm, marketing, especially digital marketing here, digital and social media marketing, social media, influencer marketing. I've also taught research methodology here. Umm, vit in itself stands for.

Umm, core values, uh, innovative research, high teaching standards. And I think I can imbibe all of them. I've been teaching with active learning methodology in my classes, which includes, umm, role-play-based learning, which includes simulations, which include umm, life projects, which includes life quizzes, umm, active tools such as woo clap, woo flash to teach students in an active manner. Uh, I don't act like a teacher, I act like a facilitator and.

I encourage students to participate in the learning process.

Thanks for sharing that, Professor.

Professor Raut, let's proceed with evaluating your skills relevant to the position of Marketing Professor. To start, could you explain your experience with Marketing Analytics? Specifically, describe a project where you have applied specific tools such as SmartPLS, Excel, SPSS, VOSviewer, or NVivo to derive meaningful insights. How did you use these tools, and what were the outcomes?

OK, So let's start with marketing 1st. And my research, my research background is more on digital technology. So it is digital marketing, how we can use digital tools to connect better with the users, how we can humanize AI, in fact, something like humanized AI HR that you're using right now, so. User interaction with anthropomorphic AI. That has been my core research.

My research topic is E tail users intentions to adopt anthropomorphic recommendation agents on 2. So I'm talking about ethail platforms and in that I've used for my analysis, I've used PLSM to to structure my my data and to analyze my data I've used I've done some bibliometric studies. Qualitative studies, so for that I've used was fear for thematic analysis. Is um, I've used SPSS for.

What do you call exploratory factor analysis? I've used that. Other than that, I have taught digital marketing in that we've talked about digital analytics. What are different tools, techniques, for example, customer acquisition cost, ROAS, which is return on ad spends. We're talking about other metrics such as?

Social media metrics, impressions, clicks, we're talking about website metrics, bounce rates, dwell time, etcetera. So this is how I'm integrating this.

Understood, Professor Raut. You’ve highlighted a robust integration of your research background with teaching in Marketing Analytics as well as Digital Marketing. Building on this, could you elaborate on how you connect these analytics tools—such as SmartPLS or VOSviewer—to practical teaching examples? Specifically, how do you simplify highly technical analyses like exploratory factor analysis for students in a classroom or laboratory environment?

Around 10 students on their master thesis and some students, I would say bachelor undergrad students on their summer internship programs. So during that I've taught them how to how they can integrate systems such as what's weird for thematic analysis when they want to synthesize the existing knowledge on a particular topic. I think that is how I integrate this PLSM goes a little more higher order. I I use that for students who are serious about getting into research.

OK so umm, talking about PLSM and Boss viewer because I've also mentored a few students.

So then collection of data and then how we can analyze when we have a substantial amount of data, how we can analyze and make predictions from that data so that then we use PLSL in that?

It’s clear that you have a well-structured, research-oriented approach to teaching analytics tools, tailoring to both research-aspirant students and foundational learners. Thank you for clarifying. Shifting focus slightly, could you now provide an example from your experience involving Services Operations Management? How have you imparted theoretical concepts alongside practical applications in this area?

OK. So as I have shared previously, I've taught digital and social media marketing. I've also taught research methodology, but more focus on digital marketing in that my classes are very active, learning oriented and continuous evaluation oriented as per the NEP policies. So talking about those, there is a continuous evaluation. So in those internal assessments, I make students work in groups.

On some case studies and then I, I've taught them, uh, building, uh, websites on WordPress and Google sites and then how they can use content management techniques and digital marketing techniques to, to post content onto their websites that they have built. So that has been a part of their internal assessment, wherein there is a continuous assessment and experiential learning essentially, umm, and, and then, uh, using certain metrics to measure the performance of the website.

Clicks, views, impressions. In my social media class, I've I've asked students to use Meta Business Suite to make a social media page run.

On programs or or run their own projects brands. So this is how my teaching goes into the classroom.

It’s commendable that you adopt an experiential and active learning approach, engaging students through practical applications like content management and social media experiments. Your incorporation of metrics adds a solid data-driven perspective. With your focus on interactive teaching, have you encountered challenges in ensuring all students grasp both the fundamental theoretical concepts and real-world applications, especially when managing diverse student capabilities? How have you addressed these?

My approach towards teaching is always outcome based and I I like to act not like a knowledge provider, but I'm more of a scaffolder. So usually I'm dividing every class and dividing them into groups of five or six students and then. As per whatever the the concept or topic of that day could be, I would divide certain topics to each group. For example, if we're teaching digital marketing.

Correct. So my classes are always.

Social media and there is, uh, we're talking about suppose customer journey and so there is a customer journey and I'll give each Group A different set of problem of a customer journey and they will have to use the Aida model. Each student will act like, uh, somebody will act like, umm, the content creator. Somebody will act like, uh, the FTC or a idea they will comply the, the.

Content of it as per the FTC rules or or.

The rules that are, they are in India and the guidelines, somebody will be the regulator, somebody will be the content creator, somebody would be the business head looking at the metrics, return on investment, what is the spending we're giving, what is the customer acquisition cost. So each student by dividing, giving them different tasks almost like in every class and each student's participation is involved. That is how I ensure that every class is meaningful to all.

Your outcome-based and role-driven participatory approach demonstrates a strong commitment to active learning and meaningful engagement. It’s impressive how you manage to cater to diverse student abilities by breaking down concepts into practical, smaller roles—a creative method of scaffolding. Let’s now turn our attention to your experience teaching theory and laboratory courses as they relate to marketing. Can you share a specific example of how you address both theoretical underpinnings and practical skills in these courses? Additionally, how do you balance time between theory and practice to ensure comprehensive learning?

OK. So like I told you in digital marketing, when it came to website building, when it came to using Meta Business Suite or Meta Edge. I give life projects to students and use that as my internal assessment to to evaluate their understandings better because in theory they can understand. OK, this is how I have to post an ad. This is how a social media campaign goal goes. There are different goals and then you select your.

You, you make your ad copy and then this is our target. But when they have to actually use Meta Business Suite, they, they learn that, OK, Meta has six different types of goals. What is our goal is our goal lead generation is our goal sales conversion is our goal brand awareness. And then they see that and then they select the next step, which is using the target audience, what kind of target they want to use scheduling, budgeting. So when they do that hands on activity in.

Because I think that really bridges their theoretical learning with their practical learning.

It’s evident you place significant emphasis on aligning theoretical knowledge with hands-on practice. Your choice of live projects and tools like Meta Business Suite provides students a platform to directly apply what they learn, making the concepts more tangible and impactful. How do you assess the outcomes of these projects? Specifically, what metrics, feedback mechanisms, or evaluation criteria do you use to measure students’ progress effectively in these blended theory-practical settings?

OK, so I would generally assign certain rubrics to assess them. I would see how how much they were able to analyze, how much they were able to create. Evaluate usually because I use Bloom's taxonomy to to evaluate their outcomes. So and and their assignments would be usually to, to create, to decide, to develop and so then I would judge them based on, for example, in the. Website development assessment.

Umm, my rubrics were whether they used, umm, SEO techniques, whether they used, umm, keyword research, whether there was a usage of, uh, content strategy models such as A702010 models, umm, and then, umm, and then also I would see whether they were creative with it, how involved each member of the group would be. This would be my rubrics.

I see. Your use of structured rubrics grounded in Bloom’s taxonomy reflects a meticulous and comprehensive evaluation strategy. Assessing elements like SEO implementation, content strategy, and individual group contributions ensures both technical and collaborative skills are captured. Let’s shift to student evaluation and exam duties. Could you discuss your approach to designing exams and assessments for marketing courses? Specifically, how do you ensure these assessments are aligned with learning objectives and industry relevance?

Correct. Umm, so.

I have been the course coordinator of Social Media and Influencer Marketing this semester. So with that comes the responsibility of not only creating the whole course package module for it but also creating a question bank for end term exam, semester end exams or midterm exams. My approach towards making these question banks are always that the questions should align with the knowledge level.

And so if it is a higher knowledge level, I'm usually. Testing them based on case studies and scenarios. So for example, if I am teaching or there is a knowledge level of say 4 or the the question is of 14 marks, I would give them a full case scenario wherein I will give them a company's example and what they're going through, what is going right with them, what is going wrong with them. And then I will ask them to analyze where is the sales funnel breaking down? Where is the customer journey breaking down? What are the strategies you.

Can use to, uh, rebuild those, uh, those breaking points. How can we umm, better fight with online reputation management? So my umm, questioning or assessment would be based more on scenario based answers, strategies that they can develop.

It’s clear you prioritize scenario-based and strategy-oriented questions to challenge students at a higher cognitive level, aligning assessment rigor with industry relevance. Your approach to adapting exams for different knowledge levels ensures inclusivity while maintaining academic standards. Building on this, let’s explore student mentorship. Can you share how you guide student projects and research, particularly in areas related to your expertise, such as digital marketing or anthropomorphic AI? How do you tailor your guidance to suit individual student interests and research goals?

Uh, as I shared earlier, I've mentored umm, some, some 10 plus students, uh, master on their master thesis. I've mentored some tentless students on their summer.

Excuse me, internship projects. So, uh, whilst guiding, guiding them, I have to understand and constantly be in touch with them on what kind of industry they're in, what kind of work they're doing. Uh, one of my students was, uh, working in.

At Test, what is uh, how they can use descriptive statistics to describe their, umm, the the population from which they've collected their data? So this is how my mentorship usually goes.

Uh, digital marketing itself. So, uh, I mentored them to collect certain data for it and then how to analyze it and taught them how to use, um.

Your mentorship clearly reflects a personalized and hands-on approach. Guiding students to collect data and analyze it using tools like descriptive statistics ensures they gain both technical skills and a deep understanding of their subject matter. Let’s now discuss your communication and structured teaching approach. How do you ensure clarity and engagement in both lectures and course materials? Additionally, what strategies do you employ to foster participation, especially in challenging topics like advanced statistics or digital technologies?

This is usually open with the iterative of the topic that we're going to study from. For that class, I start with the learning objectives of that class so students have a clear understanding of what they will learn or what they will, what skills they will acquire by the end of that session. Then I begin with a session plan or session breakdown of that class. And then I usually open with a certain question or a certain case or a.

Correct. Uh, my class.

There's certain video. A clip, something that will stimulate their minds and get them curious about the topic they're going to study. And once I have their attention, their curiosity is building up, then I'll start breaking down what the concept of the class we're studying. What is that concept? How is it breaking down? There will be interaction amongst the class during the whole lecture and then once they've understood or grasped or I've explained. Somewhat the concept of it. I will start with a certain case exam.

So they can understand how theory applies to. The practical world and once that has happened, I'll start with either a case for them to crack down and break down and understand the concept in groups or you can have. End of class assessment wherein I employ live quizzes such as hooklap, wooflash. So we will have a live quiz in class. There will be on competition modes. Every student is fully in the competition mode and.

Actively participating and there will be a show of who won in that quiz. So there is active participation from the class. Usually that is how it ends.

Your structured, engaging approach integrates clear learning objectives, interactive case discussions, and competitive live quizzes effectively. This demonstrates your ability to foster both clarity and enthusiasm among students, even in complex topics. Finally, let me turn to your qualifications. You’ve pursued a Ph.D. in Management at Guru Gobind Singh Indraprastha University, scheduled for completion in 2025. Could you provide an overview of any significant research you've published during this time, specifically in reputed journals, and how this research ties into your teaching?

OK, so my research was on e-mail users intentions to adopt anthropomorphic recommendation agents. For this I had developed a model after extensive literature review that that model extended the technology acceptance model and included constructs like social presence, trust and how those constructs were they had their own antecedents such as anthropomorphism data.

See transparency. So including all of these constructs led to a more exhaustive and a robust model that could study anthropomorphic recommendation agents and how users perceive them and whether they would adopt them or not. And this using this whole model I gathered data of about 150 plus respondents which I had analyzed using PLSM and that whole study. Was published in Personal and Ubiquitous Computing, which is an ABDC, a category journal.

So I can say that my whole thesis concept, a snapshot of it, was actually published in an A category journal.

Publishing your research in an ABDC A-category journal such as *Personal and Ubiquitous Computing* is a remarkable achievement. Extending the Technology Acceptance Model with constructs like social presence and trust, backed by empirical data, illustrates both academic rigor and practical relevance. It’s commendable how your research aligns with emerging areas like anthropomorphic AI. Now, could you share if you’ve had any experience working on industry projects or consultancy in marketing or digital technologies? How have these experiences influenced your academic approach or enriched your teaching methodologies?

So far, I've not yet worked on a life project that involves any of these areas of my research. Since I'm a budding researcher, I'm in in my in my early career, I'm still working on developing my research force, so I'm working in in. In multi collaboration studies, one of the studies that I've worked on is a study on the adoption of AI technology in in different contexts of UK versus India. So East versus West context. What are the privacy issues? What are adoption issues that leads to the adoption of AI tech in individualistic country like UK which is more privacy-driven versus a more collectivistic?

Country like India which is more emotion driven so if humanized AI can work better here. So my research is more developing right now. So not not no live projects yet. But yeah, maybe in the future soon.

Understood, Professor Raut. While you’re currently focused on advancing your research trajectory, your study on AI adoption in East versus West contexts showcases your effort to address globally relevant, nuanced issues. It’s evident that this depth of insight significantly informs your academic approach. Let me take a moment to summarize here. You’ve demonstrated expertise in marketing analytics, a structured and interactive teaching approach, and commendable research output. I’ll now finalize this review. Please hold for just a moment.

Professor, to begin, could you share which type of teaching environment you find most effective and enjoyable—whether it’s a traditional lecture-style delivery, a more interactive, discussion-based seminar, or perhaps something entirely different?

I do really enjoy being a mentor instead of a lecturer to students. I really enjoy having an interactive class where students are actively participating. They're working in in pairs or groups to understand or practically apply the knowledge onto a campaign or analytics or or marketing tools and how they can integrate everything about marketing into.

Certain class, uh, roles into role play simulations. I think those kinds of classes I really enjoy. I do not particularly, I'm a fan of uh, just delivering lectures and saying goodbye to students. I prefer having interactions and competitions and healthy competitions in the class and students being active members of the of the learning community.

Professor Raut, I see that you have a Ph.D. in Management and that one of your achievements is receiving the "Best paper in Track" recognition at ICBM 2025. Can you briefly outline the research focus of that particular paper and why you believe it stood out within your field?

Sure. So that research was about the demographic differences or the cultural conundrums when it comes to AI adoption. It talked about what are the certain cultural factors that may lead to adoption differences in in the whole, if you talk about the whole all the countries because I believe that AI is not one model fits all.

Different. I'd say demographics, different ethnographic communities.

Pervasive now humanizing AI. So we need to study what are these factors that lead to certain differences and why they're acting this way. So the study was more about the cultural conundrums and demographic differences between different groups when it comes to AI humanized AI tech adoption.

These different countries, different groups will react differently to AI, especially humanized AI. And because this is so.

I see. A fascinating topic, certainly of increasing relevance. What were the primary methodologies you employed to examine these cultural and demographic variables effectively within your research?

Correct. So for my theory I had extended again tamp with.

With constructs such as data privacy, trust, transparency, also with the anthropomorphization or humanization factors such as social presence, but also moderated by.

Tech savviness of the respondent also moderated by their ethical standing of how how ethical do are they and how ethical they perceive the AI should be. So these were the factors by study was.

Umm, uh, the, the sample was uh, collected using, uh, non probability sampling. So it was used uh, my respondents were users of AI, users of online recommendation tools. So only those were approached, umm, the collected responses. And then for that I had used PLSM to analyze.

To understand the differences between two demographies of UK versus Indian respondents.

The data I had used MGA multigroup analysis.

Understood. Very comprehensive. How would you approach teaching undergraduate students about such a complex and nuanced topic? Specifically, how would you simplify these constructs and analysis methods to ensure clear understanding?

This generation, ever since they're born, they're born into the era of AI. So they're very in touch with technology, very interactive with AI everyday lives. They're using AI as a search tool. So Google AI is replacing Google as a search tool. So they understand that the pervasive tests of it and I, I just have to explain to them.

Their understanding of how how trustworthy are they with with giving out their data?

How is it how the companies are using AI as a forefront or a face to collect their information and whether they're OK with it? I will introduce data protection laws to them, privacy laws to them, how that is coming into operation and affecting how third party data is collected or in fact, how cookies are dying now, what is the opt in versus opt out method? And when they're interacting with AI, the sense of emotion they feel, the sense of connectedness they feel.

Two AI.

Lead to their increasing usability of that AI or that tech or that ease of use to use that tech and how that is leading for a better adoption of AI. So I think breaking down terms in into simpler concepts, into simpler everyday interactions, they can understand this.

All these factors.

Interesting approach, linking everyday AI interactions to theoretical constructs for better student understanding. With such a practical and engaging method, how would you balance the theoretical rigor required in undergraduate courses against the ease of comprehension you aim to achieve?

Umm, so theory is only built upon phenomena which is understood by some constructs and concepts.

As long as they have a clear, easy to understand, easy to apply understanding of concepts, of phenomena, of what is leading to what. Is there a cause, effect, relationship? Or am I just describing something? Or am I just quantifying something?

When they start to develop that kind of analysis and evaluation.

And they start moving up the Blooms Taxonomy. They can understand complex models, they can understand how theory is built. They can understand. They can teach them that about.

Analysis as a tool of validating data as a tool of validating theory. What is generalizability? What is reliability validity? If they understand the concept of it in an easy to understand manner, they can forever take it along with them and apply to different segments.

Thank you, Professor Raut. Let us proceed.