Tech Hawks Series: Episode #2

In the "Tech Hawks" podcast series, Feliciano School of Business faculty member Dr. David Eisenberg, Assistant Professor in the department of Information Management and Business Analytics, discusses key issues facing business, technology and society. In this episode, Dr. Eisenberg speaks with Dr. Jorge Fresneda, the Hurlburt Chair Professor and Associate Professor in Digital Marketing, Marketing Analytics and Sports Analytics at the Martin Tuchman School of Management at New Jersey Institute of Technology. Together, they discuss their research collaboration into the emerging technology of combining neural sensors with artificial intelligence for cutting-edge business applications at the Neural Analytics for Business Lab at NJIT. 

Dr. David Eisenberg is an Assistant Professor in the department of Information Management and Business Analyticshas completed two masters degrees, from Virginia Tech and Rutgers University, prior to completing his PhD at New Jersey Institute of Technology. David has published numerous peer-reviewed journal articles and conference proceedings, was named a Georgia Tech Research Institute Fellow, National Science Foundation I-Corps Fellow, George Mason University Mercatus Fellow, Junior Scholar of the American Marketing Association’s Public Policy Conference, Founder's Award recipient and Emerging Scholar of the Society of Business Ethics. He has served as 2023-2026 American Association of Information Systems Future of Work Track Chair, their 2023-2025 Cognitive Information Systems mini-track co-chair, and the 2025 Workplace Equality and Diversity Session Chair for the Society of Business Ethics.  

Jorge Fresneda is the Hurlburt Chair Professor and an Associate Professor in Digital Marketing, Marketing Analytics and Sports Analytics at the Martin Tuchman School of Management - NJIT. His research interest expands from the role of information in Online Consumer Reviews as influencing online consumers, to the economic impact derived from the lack of accessibility of e-commerce sites. Jorge has extensive experience as a higher education instructor in the areas of digital marketing and analytics at the undergraduate, master, and doctoral levels.

Chapters

• 0:00 Meet The Hosts And Lab Focus
• 0:41 Miniaturization Unlocks Real-World Research
• 3:55 Consumer Impact And Hyper Personalization
• 5:36 Inside The Neuroanalytics Lab Setup
• 8:28 AI, Sensors, And Real-Time Feedback
• 10:15 Emotionally Aware Robots And Service
• 12:24 Trust As The Missing Ingredient
• 17:13 Two Flagship Projects: Finance And Experience
• 22:03 Measuring Long Experiences In Real Time
• 24:18 Mass-Scale Emotion Signals From Devices
• 24:46 Emotion And Risk In Financial Decisions
• 26:05 Student Involvement And How To Join

Transcript

SPEAKER_00: 0:00

Hi, I'm David Eisenberg, Assistant Professor of Information Management and Business Analytics at the Feliciano School of Business, and I'm here proudly joined by Jorge Fresneta, who holds the Herbal Chair in the Department of Marketing at NJIT, New Jersey Institute of Technology, at the Martin Tubman School of Business. Hi Jorge, thanks for coming today.

SPEAKER_01: 0:22

Thank you very much for inviting me. Thanks.

SPEAKER_00: 0:25

Today I want us to talk a little bit about the business analytics, the Neural Analytics for Business Lab at NJIT, how that got started and what we're doing with it now. What are some of the things that you're most excited about that?

SPEAKER_01: 0:41

So the the um reality about uh neuroanalytics, uh marketing analytics uh in terms of uh neural sensors, is that it's something that uh neuromarketing 10-12 years ago uh require like crazy expensive devices, uh devices that only a few universities could afford, so big, so noisy, so and there is a uh growing uh uh mini-rization, making these devices smaller and smaller and smaller and making more available for everybody and more accessible for everybody, for marketing research, for business research. Um, and there is a little bit of a revolution in in this area with with these devices being more accessible, more portable, uh which allow researchers to capture and collect information in more like realistic settings, more like comfortable settings. You don't have to lay down within a crazy, noisy, claustrophobic machine to collect data anymore. Obviously, this comes at a price. I mean the accuracy of these portable devices, small devices are not um the accuracy of this monstrosity of machines, but it is reasonably good, reasonably high, and we can use it for many different applications, uh, such as finance research, marketing research, many different applications where we can actually identify emotions. Uh, we can identify and quantify um specific emotions and see how these emotions lead to certain patterns and behaviors, and and I think that is a very, very, very exciting field, uh, very accessible. Um data science, also, which is behind that interpretation of all these signals and interpreting identification of these emotions, these feelings, uh, is also contributing a lot to the field, uh, helping us identifying and quantifying these signals, interpreting these signals into emotions. So we are we are again kind of like a little bit within a revolution now of these devices, real life applications of these uh devices and and their application for business are endless.

SPEAKER_00: 3:26

So when um so one of the things that you know that that popped into my head when you were when you were talking is is the idea of the miniaturization of all of it. How do you think that the ability for these devices that can potentially um you know understand and sense people's um you know feelings potentially by becoming small and therefore and and also less expensive? Um how do you think that affects um you know consumers and the potential for businesses to create sort of entrepreneurial or new um inventions or devices to be able to for these things to be um you know both either amenable or um or available to the public at large?

SPEAKER_01: 4:15

So for consumers is is um there is a very exciting challenge in the sense that um can a device uh know you better? Can a device tell you what you really really really like in your inner yourself what you really really really want better of what you even like think about? So I think that there is um very exciting uh application of of this in again like finance, marketing, that are the fields that we are uh more interested right now of actually uh developing and and providing um products and services that are really really really what customers like individuals, what individuals really, really want. And in many cases they don't even know that that they they want it. Um so I think that that would be one of the I don't know if it's the best uh application of it, but I think that is the the one that is excites me the most. Being able to to truly, truly customize uh offerings in terms of products and and services for for consumers. I think that that would be my my number one. Sure.

SPEAKER_00: 5:36

And um so so tell us tell us a little bit about the lab that we've set up um at NJIT um that uh that focuses on neuroanalytics.

SPEAKER_01: 5:47

So we are basically working with two different devices. One is called EEG, basically it's an encephalogram, and capture like uh uh brain activity, brain waves, brain actually like electricity in your in your brain. And electro uh encephalogram is basically what is used in medicine to see that there's like mental activity in a in a brain. And we combine it with another set of devices that are called um GSR, that basically kind of like is in in plain English a lie detector. So we combine those devices. We have uh an specific space dedicated in in NGIT where we collect data from from individuals that volunteer to participate in our studies. They receive uh very briefly, very quickly, very easily, they receive some kind of like stimuli, and we use these two devices, EEG and GSR, to collect uh the body um response, uh uh physical and and mental response to these stimuli, and we collect all these data, we collect all these signals, and we use um uh machine learning uh algorithms to kind of like translate, clean and translate those signals and identify these um emotions in after the the stimuli that the volunteers so we have basically a a setup space with computers with these EAG EEG uh and GSR devices, and we have the the basically the space to uh uh get these stimuli um to our um volunteers and collect all this information, translate all this information and uh do research on that information. So this is basically the setting of our lab.

SPEAKER_00: 7:47

And uh yeah, so yeah, so I I was actually um you know, I helped set up the lab and Jorge and I, you know, run the lab together. Actually, um for those of you watching from Montclair State, there are Montclair State University students who have now participated in this lab, and we're you know, we're always looking for um you know additional interested students. Absolutely.

SPEAKER_01: 8:08

We need a lot of help. So please please volunteer.

SPEAKER_00: 8:13

So um and and we're actually in the process of um you know of uh putting together a grant application where we might be able to recruit even more students for that purpose. Um and uh fingers crossed. Um so um so in any case, uh the um one of the things that you know that you mentioned was machine learning, which is sometimes used as or considered a um you know a synonym or a um or a part of um the you know more overarching term of artificial intelligence or AI. Um how do you think that the ability for um for software or these kinds of devices, when when applied in in you know in a consumer way, could feed into the data that is that then is able to be interpreted and provide um you know feedback or dynamic interaction from artificial intelligence.

SPEAKER_01: 9:12

So artificial intelligence um in the end is going to be able to like provide like real-time recommendations for for consumers. That is the we are in the infancy of that, but these devices, these sensors are making and being more and more and more available, more and more portable. Some of them even like are already included in some like smart devices. Um so I think that through artificial intelligence, in the long run, I don't know how many years, but we are as a consumer, I'm going to be able to get these recommendations in in real time. Uh recommendations, again, that I may not be aware of something that I need, that I want, that I I'm striving to get. I think that uh artificial intelligence is going to be a key element. It is already and is going to be a key element in providing recommendations and suggestions in real time for consumers.

SPEAKER_00: 10:15

And so something that we haven't directly started addressing yet in our lab, but um but which I think is really interesting and something that we could potentially be eventually investigating is um is in the within the area of um of kind of AI and robotics, um the idea of um you know there's a whole field of kind of robotics and AI becoming emotionally aware. And and so the the notion of robots or AI being able to understand and and respond to our emotions, how does that change potentially the way that we interact with with software that functions as artificial intelligence?

SPEAKER_01: 10:58

I mean, in many, many, many ways. I mean, uh think about a very simple example, um customer service. Uh if uh consumers are going to be instead of like interacting with some kind of like call phone response system or something like that, to something that actually can respond to uh have a response to my my feelings, my emotions to that that is a game changer, at least in customer service, in in service marketing. Uh, and and I can think of like many other applications, finance as well. I think that that would be that would give a human touch of all these robotics and all that. I think that that from a again from a consumer. I'm a marketing professor, so I'm always like, from a consumer perspective, I think that is a game changer intensive least in service marketing.

SPEAKER_00: 11:51

Certainly a lot more um, you know, engaging or responsive than the than than when I call and it says press one for this and press two for that.

SPEAKER_01: 12:00

Robotic call boys.

SPEAKER_00: 12:02

Umdeed. Um and uh so um how do you think that um the ability for um for uh you know AI and for software to become emotionally aware of people's emotionally response and therefore on some level become empathic to people's um you know needs and and and feelings? Um how do you think that affects, say, and and now we're kind of crossing over from marketing a little bit into um you know into uh MIS or information systems and maybe to some extent HR, but how do you think that affects like the future of work, the ability for AI to kind of become in the role of people, um, you know, in the workplace or work with people?

SPEAKER_01: 12:51

So um you can uh think of many ways, uh, or there are many potential ways of applying that. Uh uh the first thing that comes to my mind in in this sense is that um different people are more effective in different different circumstances. So it would be very, very interesting to be able to kind of like assess, identify but under what like emotional circumstances people are more effective. Uh there are people that they work very well under pressure, which probably is my case. I need deadlines and I need like like to have something coming up to like I need pressure to be to be effective. Some other people don't. Uh they kind of like dilute under under pressure. So I think that uh being emotional, being able to uh recognize, identify, quantify uh these emotions, and somehow measuring how effective people are under certain like emotional states or under, I think that can be also a game changer in terms of making us like better performers, uh better uh workers, employees, by kind of like replicating that that or trying to find that the that emotional state that helps you being a better employee, a better manager, a better etc. etc. So I think that these can have also a very important application for the future of work and can help us eventually be better employees and performers in our job.

SPEAKER_00: 14:35

And in in in the space that I'm thinking of, which is which is kind of, I would say is sort of you know service work, which may even be considered the crossover between, you know, the area of you know consumer or marketing-oriented work and and you know and day-to-day work. Um you know, I'm I'm thinking of when I'm at the you know the supermarket or the Walmart, that there's this, that there's this tall, you know, uh robot that's like six feet tall and it has this creepy smile on its face, and and it comes up behind me and beeps really loudly, and then I freak out. And and so, you know, now now how would how is it different if something like that is able to be optimized or evolved to a way that it has that it's emotionally aware, that it's not just coming up behind me and freaking me out without realizing that it's freaking people out.

SPEAKER_01: 15:27

I mean the I think that the key word here is trust. I think that humans uh trust uh or distrust what is not like empathic, but it's not like emotional aware, what is not like uh reflecting or connecting to your to your emotion. I do think that the the key word here is trust and trusting devices that are emotionally aware of your of your feelings, of your emotions.

SPEAKER_00: 15:54

So that's interesting. Because I think yeah, I think you you can imagine across a lot of different devices that are currently trying to engage with people, that it becomes, you know, very fast for people to mistrust. I know that's I know the idea of trust is something that you were working on even before we got involved with um, you know, with with neural sensing and all that.

SPEAKER_01: 16:15

Yeah, that was the the even from the very beginning of e-commerce in in the 1990s, was a key element people tend to distrust the early beginnings of Amazon and e-commerce because you didn't know what those companies were going to do with with your credit card information and shipping. I don't want to provide my shipping information. So trust has been always uh a key element in everything that has to do with technology and technology um aspects of of our interaction with with uh humans and and technology. And trust is is something that I do I do think that something that is emotionally aware can can build that trust and let us like trust more these devices and these these services.

SPEAKER_00: 17:03

That makes sense. Um and so what are some of the what are some of the projects that you know that we're currently um you know working on in the neural analytics lab?

SPEAKER_01: 17:13

So we are working basically uh in two different areas. We are working on on finance and financial products, uh trying to come up with a system that can recommend and and can provide. You can first and foremost understand the role of emotions in making financial decisions and buying financial products, and eventually being able to provide a better recommendation. Once we understand uh the role of emotions and how people make uh emotionally loaded decisions uh in terms of finance, uh we want to apply that to to make like better recommendation systems for uh people to identify or get financial products. So this that is like like first major area of work. Uh we have a second major area of work that has to do with marketing and assessing uh products, uh assessing a specific type of product that is called experience uh products, experience products as a little bit the name says, are products that you really need to experience to kind of like assess, evaluate. You need to watch a movie, you have to be sitting in a uh watching a movie to evaluate the movie. You I can think of I want to watch this Star Wars movie, but I actually have to watch it, I have to experience to assess how good, how bad it is. So um in marketing evaluating experience products, um, a video game has been always uh a problem uh because there are not clear features of characteristics that you can assess. You don't have a processor with these characteristics, you don't have a laptop with this memory, you don't have specific features that you can use for to assess something, you have to actually be expo experienced the product. So we are embarking a project to use our web device, our devices to evaluate uh experience products and kind of like get a better idea of how people um evaluate, assess these experienced products, and again like use that to develop better products, better recommendations, etc. etc. So these are the two major areas that we are currently working on. We got IRB approval for for all of this. Um we have many other projects that we are uh developing, but these are the ones that we have uh currently like working on uh actively.

SPEAKER_00: 19:58

And when when we're talking about experience, um I think one of the key features of that is the ability is the fact that experience is something that is that occurs over time. And so it's not something like that I'm you know looking at a series of products on a table and I'm kind of saying that's the best one. Yeah, you know, and and so if I'm watching a Netflix movie or listening to music or something like that. That's easy to do two hours. Right. How how does it so how does it make a difference that the that something that is that's a neural sensing device, something that maybe um can be embedded, is currently being embedded in the iWatch in one of the upcoming um iterations of the of the new Apple Watch, um and you know, and or in other devices. I myself have a pair of headphones, which I got in a grant, which you've seen, um, where which allow me to um which which allow me to listen to stereo music, look just like regular stereo headphones, but um, but it's made by the brand that we use for the EEG and electroencephalography devices, and um, and therefore can be scanning my emotions and my brain while I'm listening to music. Um, how is the ability for that to happen in real time um over the course of time different from something which is which is just assessing an emotion um you know for a moment?

SPEAKER_01: 21:22

So that's that's very that's a key element of of what we are doing. Um a movie can be like two hours long, and there is uh there is a very long period of time that you need to collect and you have to assess all that um information and you have to make of uh an educated assessment and all these signals and all these emotions are leading to liking or disliking a movie, wanting to to watch it again, sharing the uh social media, this movie was so amazing. That is something that again has been like puzzling uh marketing marketers for a long, long time. Uh, we think we have uh a way to measure that that long uh experience exposure to a movie and an experienced product, something that is not like as easy uh of assessing as a pen that you use it and that's it. Uh so I think that we can uh hopefully provide a way of measuring, evaluating this product with our um portable devices, real time. We can actually collect data through like a whole movie. And again, it's not like a claustrophobic machine making a lot of noise or distracting, you know. You can't be like sitting in a cinema movie theater with the device on and going through the whole experience in a more realistic um setting. I I do think that uh it can contribute to to actually provide a good measurement or a good evaluation of these um uh experience products.

SPEAKER_00: 23:03

In some ways, one metaphor that comes to mind is is how during the presidential elections, if you're watching certain network television shows or something, that um you know that you'll see that there are certain people who have been hired to kind of sit at home with sort of these devices where they're pushing, you know, good or bad on, you know, over and over again. And um and so in in order to inward signal to the show, and they're quantifying that um to show to tell the show like how much people are enjoying listening to you know one one political candidate or the other. And and in this case, it's it's it's it almost baffles the mind to imagine how that could be transitioned into something where you know millions upon millions of um of uh you know of iPhone users or iWatch users um could be um could be essentially um you know automatically gauging even more instantly throughout the providing in real time and also in many cases not being aware, which is another important not being like aware of the device and collecting the data itself that gives you a more realistic measurement. Um and uh oh and in terms of the in terms of the finance aspect that you were talking about, um what do you think are um what would be an example of someone um you know making a decision of a financial decision, let's say, based on emotion, and how might that affect their their decision making?

SPEAKER_01: 24:39

Uh risk and risk assessment is uh probably the first thing that comes to my mind. Uh people uh tend to not have a very good idea of the level of risk that they are willing or they really think that they can take. Uh there are many, many instances of people buying um initially buying uh financial products thinking, oh, I want to take whatever risk it takes, I want to make a lot of money. And I think that the risk assessment from from uh financial products and buying financial products is one of the areas that I do think that uh people tend to do a lot of like emotionally loaded decisions in in finance. And and one area again I think that is risk. And I do think that we can contribute to really give you an idea of what is the real level of risk that you are willing to assume or take. I think that risk assessment is probably one of the areas, the initial area that uh we can help with with our devices and our methods.

unknown: 25:52

Cool.

SPEAKER_00: 25:53

Um all right. So um I guess um, you know, I guess with that, I think this is, you know, I mean, I'm biased, of course, but I think this is really exciting research that we're doing. Thank you, thank you very much.

SPEAKER_01: 26:05

It is, it it is.

SPEAKER_00: 26:07

And you know, hopefully, um, you know, hopefully uh, you know, more people will become interested, and um, you know, and if uh if any of if anyone listening is really interested in this kind of work, feel free to reach out to us um, you know, at um you know, either at NJIT or at Montclair State. And um thanks for you know thanks for being part of this.

SPEAKER_01: 26:25

Thank you for watching, thank you.