Written by Dr David Tena Cucala, Lecturer in Computer Science, Royal Holloway University, 2026
In 2024, an Air Canada customer asked the airline’s chatbot a simple question: could he get a bereavement refund? The chatbot said yes and even explained exactly how to apply. So he bought the ticket, followed the instructions, and submitted the claim.
Air Canada refused. Their defence? The chatbot had made a mistake, as their policy did not allow bereavement refunds.
He took them to the British Columbia Civil Resolution Tribunal. He won. The ruling was beautifully blunt: if you deploy a chatbot to speak for your company, you own what it says.
Even if it hallucinates policies.
The End of the Rulebook
When I tell this story to people outside AI research, the conversation almost always goes the same way. “But someone programmed it, right? Someone wrote the rules?” they say.
And I say: “…not exactly.” Cue stunned silence.
For most of computing history, that assumption was correct. Most early AI systems were rule-based: enormous lists of human-written instructions, variables with sensible names, logic you could trace with your finger. A good engineer could read the code, understand it, and stake their reputation on it.
Then, in the mid-2010s, neural networks took over, and they work almost the opposite way. Instead of writing rules, engineers build systems that learn their own. These networks (loosely inspired by the brain) contain billions of adjustable connections between virtual “neurons.” You feed them examples, give feedback, and repeat the process billions of times. Eventually the system becomes uncannily good at whatever task you are applying it to; sometimes it even becomes superhuman, beating world champions at chess and Go, and making scientific breakthroughs like protein folding. Nobody tells it the rules. It figures them out on its own.
That has two big consequences. First, the system often discovers patterns no human explicitly taught it, including patterns nobody had even noticed before. That’s their genuine magic. But it also means nobody can be completely certain what the system has actually learned. Second, whatever it learned isn’t stored anywhere readable. The knowledge is smeared across millions (sometimes billions) of numerical weights. You can stare at the numbers, but they won’t mean anything to you. People sometimes call AI a “black box”, though the phrase is slightly misleading. The box isn’t opaque. We can look inside. We simply don’t understand what we’re seeing.
The Accountability Gap
That last point has real consequences. It’s why AI systems hallucinate, producing wrong answers with total, unblinking confidence. The Air Canada story is almost funny. But at the other end of the spectrum, you have systems influencing healthcare decisions, shaping legal outcomes, and moving financial markets.
To be fair: we already trust plenty of things we don’t personally understand, like aeroplanes, vaccines, and power grids. But in those cases, someone understands them. Engineers can explain why the plane flies. Scientists know how the vaccine works. There are people whose entire job is to understand these systems deeply, and to be held accountable when they fail. With modern AI, that accountability gap is real, and it’s growing.
A small, serious group of researchers is trying to close it, building something like a “neuroscience of AI”, reverse-engineering these models from first principles to figure out what’s actually happening inside. It’s slow, hard, important work. Meanwhile, the rest of the industry is moving in the opposite direction: build a bigger ship faster, patch the mistakes later.
So here we are, rapidly weaving a technology into education, medicine, finance, and the daily fabric of decision-making, even though nobody fully understands how it works. The question isn’t technical; it’s a choice. If AI is going to shape the infrastructure of society, we can either accept that it remains, at its core, a mystery, or we can demand that someone, somewhere, actually understands it.
But that choice is impossible to make if you don’t know it exists. And why would you? The reasonable assumption is that someone, somewhere, already checked. That there’s an engineer who can open the hood and explain exactly what went wrong.
It’s a fair assumption. It’s just not true.
Where do you stand? Should we slow down deployment until we can explain these systems, or are problems like Air Canada’s chatbot just a “growing pain” we can manage along the way? Drop your thoughts in the Comments.
“They’re basically attacking our entire digital existence – if we don’t like it, then we shouldn’t be posting it at all.” – Dr Daisy Dixon, Cardiff University [1] Written by Sophie Hawkes, PhD Researcher at the CDT in Cyber security for the Everyday, Royal Holloway University, 2026
Image-based sexual abuse (IBSA) includes the non-consensual creation and/or sharing of intimate images. This includes practices such as upskirting, hidden cameras, sextortion, cyber-flashing, semen images, and sexualised deepfakes. The Revenge Porn Helpline (RPH) – “a UK service supporting adults…experiencing intimate image abuse” [2] – saw a 400% increase in cases of non-consensual “synthetic” (AI-generated) intimate images (NSII), between 2017 and 2024. [3]
While the term “revenge porn” is perhaps more widely known, it can carry problematic implications of victim blame and obscure the reality that this is a form of abuse, often perpetrated by complete strangers. Such violations also introduce differential vulnerabilities; i.e., even when experiencing similar circumstances, different populations face different types and degrees of security, privacy, and safety risks with serious consequences to their lives.
Of the cases reported to the RPH, 72% were women. Among these, 44% reported that the perpetrator was “a known male”, while 53% reported the perpetrators were “completely unknown” [3]. Research consistently shows that women are overwhelmingly the targets of NSII. On the notorious deepfake video sharing site ‘Mr Deepfakes’, 95.3% of all targeted individuals were women, constituting 91% of all videos on the platform [4]. As early as 2019, a report by Deeptrace/Sensity AI found that, of the deepfakes they found, 96% were pornographic, and 100% of those depicted women. Indeed their case study of a computer app called ‘DeepNude’ further illustrates this imbalance and suggests a contributing factor: the model was trained only on images of women, so was unable to generate comparable nude images of men [5].
More recently, the Grok AI chatbot (built into the social media platform X) enabled the mass creation of non-consensual sexualised images. User requests soared after CEO Elon Musk posted a Grok AI-edited photo of himself in a bikini, showing how platform leadership and design decisions can permit the normalisation of image abuse. The New York Times [6] estimates that 41% (approximately 1.8 million) of images generated and posted by Grok in response to user requests over the nine days were sexualised images of women. The Center for Countering Digital Hate found that 65% of images in its random sample were sexualised, with 101 showing children – suggesting that over 23,000 total sexualised images of children may have been posted on X as a result of user requests to Grok AI.
With all this in mind, it is clear that the evidence demonstrates that sexualised deepfakes are a deeply gendered harm, and thus are understood as a type of technology-facilitated gender-based violence (TF-GBV). Yet a stark imbalance exists not only in who is targeted, but also in the perceived harms and response. Research shows that men are more likely to find the creation and sharing of synthetic intimate images acceptable [7], and are more likely to place less responsibility on perpetrators [8]. Men also tend to perceive sexualised deepfakes of themselves as more acceptable than non-male participants, with some participants (mostly men) responding that a partner creating sexual deepfakes of them would be “flattering” or “a compliment” [7]. Overall, men generally perceive less harm to victims of NSII [8].
These perception gaps may help to contextualise why “four times as many” victims report a negative police reporting experience than a positive experience [3], given the overrepresentation of men in the police force, e.g. women made up only 27.1% of the Metropolitan Police Service in 2021 [9].
By contrast, people from marginalised genders are more likely to find the creation and sharing of sexual deepfakes more unacceptable than non-sexual deepfakes [7], and women are perceived to experience greater harm from such abuse [8]. This dynamic is shaped by and reinforces wider societal structures that shame and suppress female sexuality. In this way, sexual deepfakes are wielded as a disciplinary force to silence women and marginalised genders who stand up against image abuse, such as happened to Dr Daisy Dixon (quoted above) [1]. The vicarious trauma of watching other women be targeted could act as a chilling mechanism, encouraging self-censorship, withdrawal from public platforms, and reduced trust and engagement with AI, reinforcing gendered patterns of digital exclusion and unequal technological participation [15].
A further dimension of this is reflected in racialised perception of harm. Evidence of ‘misogynoir’ (a term coined by Moya Bailey to describe gendered anti-Black racism) emerges in findings [8] that US participants uniquely (compared to UK and Australian participants) judged Black female victims as less harmed by the creation of sexual deepfakes than white or Asian women. This demonstrates enduring harmful stereotypes, like the “Strong Black Woman” [8], and highlights the need for intersectionality, as when harm to Black women is systematically minimised and their suffering normalised, adequate recognition, responses and support are less likely to follow.
Racialised patterns of deepfake sexual abuse were also clear in the creation and consumption of sexual deepfake content on ‘Mr Deepfakes’, where four out of the top ten video categories (by number of videos) were explicitly racial (Asian, Korean, Indian/Bollywood, and Interracial) [4], suggesting that race is an important factor shaping activity. Notably, the second most common nationality targeted (after American), both in the Sensity AI report [5] and on ‘Mr Deepfakes’ [4], was (South) Korean, with K-pop singers a core target. These trends may represent not only global popularity, but also a racialised sexualisation or exoticism within Western platform cultures.
It is also important to consider cultural differences, especially around the meaning of “intimate images”, since narrow legal and societal definitions of intimacy may fail to capture real-world harm, for example using AI to remove a woman’s hijab. The RPH reported that 1% of its cases were of “culturally sensitive content” and that 7.5% reported cultural sensitivity as an additional impact [3]. In certain contexts, the retaliatory threat of honour-based violence may pose a severe and immediate danger to victims. LGBTQ+ individuals may also face additional risks of exposure and retaliation from images outside of what may traditionally be considered “intimate”.
Another often overlooked group of victims are those whose intimate media is non-consensually used as the ‘body’ onto which another individual’s face is edited, primarily sex workers. A study [10] showed that participants attribute more victim blame to the ‘body victim’ than the ‘face victim’, and ‘face victims’ were considered to experience greater harms, especially when the ‘body victim’ was labelled as a sex worker. This reinforces hierarchies of respectability in which some bodies are treated as disposable inputs rather than victims of abuse.
Finally, recent work [11] has brought to light the longstanding ethical bad practice involving the non-consensual use and distribution of nude images in datasets for academic research, e.g. nudity detection. Out of 150 computer science papers using real nude images, none mentioned the consent or safety of the image subjects, or data deletion plans, and only two had received institutional review board (IRB) review and approval. Some nude datasets knowingly contained non-consensual images, for example upskirting and hidden camera images, and one scraped images from subreddits dedicated to sexual violence and borderline child sexual imagery. Serious concerns were flagged around the 813 example images published in the papers, of which 9 were completely uncensored and 28 were still identifiable.
On 6th February 2026, it will become illegal to create, or request the creation of, non-consensual sexual deepfakes, after legislation passed in the Data (Use and Access) Bill 2025 was finally signed on 15th January [12]. This follows years of tireless activism to end image abuse by many, from organisations like End Violence Against Women, to survivor-led campaigns like #NotYourPorn and Jodie Campaigns, to Glamour UK Magazine and academics like Clare McGlynn, Professor of Law at Durham University [13].
While Jodie said in a statement [14] “My hope is that this marks a genuine turning point”, she expressed frustration that swift action had only been taken in response to the public outcry against X’s Grok AI in recent weeks. “It should never have taken days of outrage and new victims being created for action to be taken, when this legislation has been sitting ready, with Royal Assent, for months. Survivors and campaigners warned, again and again, that delaying this law would cause real harm. We were right.”
[4] C. Han, A. Li, D. Kumar, and Z. Durumeric, “Characterizing the MrDeepFakes Sexual Deepfake Marketplace,” in Proceedings of the 34th USENIX Security Symposium, Seattle, WA, USA, Aug. 2025, pp. 5169–5188. Available: https://www.usenix.org/conference/usenixsecurity25/presentation/han.
[5] H. Ajder, G. Patrini, F. Cavalli, and L. Cullen, “The State of Deepfakes: Landscape, Threats, and Impact,” Deeptrace Labs, Amsterdam, Netherlands, Sep. 2019. [Online]. Available: https://regmedia.co.uk/2019/10/08/deepfake_report.pdf.
[7] N. G. Brigham, M. Wei, T. Kohno, and E. M. Redmiles, “Violation of my body: Perceptions of AI-generated non-consensual (intimate) imagery,” in Proceedings of the Twentieth Symposium on Usable Privacy and Security, Philadelphia, PA, USA, 2024, pp. 373–392. Available: https://www.usenix.org/conference/soups2024/presentation/brigham.
[8] A. A. Eaton, A. J. Scott, A. Flynn, and A. Powell, “Perceptions of sexualized deepfake abuse across three nations: An exploration of how victim gender and race shape attitudes towards deepfake abuse in the United States, the United Kingdom, and Australia,” Computers in Human Behavior, vol. 177, p. 108899, 2026. Available: https://doi.org/10.1016/j.chb.2025.108899.
[11] P. Cintaqia, A. Arya, E. M. Redmiles, D. Kumar, A. McDonald, and L. Qin “Stop the Nonconsensual Use of Nude Images in Research,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1), 628-629. Available: https://doi.org/10.1609/aies.v8i1.36576.
[15] K. P. L. Coopamootoo, M. Mehrnezhad, E. Toreini, “I feel invaded, annoyed, anxious and I may protect myself”: Individuals’ Feelings about Online Tracking and their Protective Behaviour across Gender and Country, Proceedings of the 31st USENIX Security Symposium, Boston, MA, USA, Aug. 2022, pp. 287–304. Available: https://www.usenix.org/conference/usenixsecurity22/presentation/coopamootoo
Dr Beenish Ayaz (PhD, SMIEEE, CEng, FHEA) Department of Electronic Engineering Royal Holloway University of London
Artificial intelligence is often perceived as intangible — algorithms in the cloud and intelligence embedded in software. In practice, AI demands an immense physical backbone of so-called “AI warehouses”: data centres that consume growing amounts of electricity and water. In the UK, data centre demand is rising sharply alongside ambitions in AI, digital resilience, and high-performance computing. This creates a significant engineering and societal challenge: how can digital progress be aligned with environmental sustainability and social responsibility?
One emerging approach is the deployment of underwater data centres, cooled by the ocean’s naturally low and stable temperatures. Projects such as Microsoft’s Project Natick demonstrated that subsea cooling can significantly reduce the overhead energy associated with thermal management. However, cooling efficiency does not reduce the energy required for computation itself. True progress toward net-zero therefore depends not only on innovative cooling, but also on integration with renewable energy systems, lifecycle-aware design, and responsible operational practices.
Although Microsoft concluded its experimental programme in 2024, the concept has evolved from research prototype to strategic infrastructure. Several countries are now exploring commercial-scale deployments. China, for example, has established some of the earliest large-scale operational subsea data centres near Hainan and Shanghai, reporting substantial reductions in cooling energy demand. These developments illustrate technical feasibility, but they also highlight the need for shared standards for environmental protection and governance.
Relocating digital infrastructure offshore does not eliminate impact—it redistributes it. Subsea deployments interact with fragile marine ecosystems and with coastal communities that may have limited voice in how digital infrastructure is governed. Potential thermal plumes and broader ecological change raise the risk of an “out of sight, out of justice” scenario. At the same time, strategic competition around AI introduces questions of digital sovereignty and control over the physical foundations of the cloud. For the UK, with its extensive coastline and marine research capability, this represents both an opportunity and a responsibility. Here, technology can support trust-building, and intelligent underwater wireless sensor networks (UWSNs) become vital. Acting as a “nervous system”, they can monitor temperature, acoustics, and water chemistry in real-time. Yet technical capability alone is insufficient. Underwater networks face constraints such as limited bandwidth, power scarcity, biofouling, and harsh propagation environments. More fundamentally, they raise governance questions concerning data ownership, interpretation, and distribution of benefits. When monitoring data are confined within proprietary systems, transparency is diminished and public trust is undermined.
(Image created using Google Gemini)
A collaborative, solution-oriented approach aligned with responsible research and innovation is therefore essential. Open standards, interoperable platforms, and shared data frameworks can allow regulators, scientists, industry, and citizens to jointly assess environmental impact. In this way, underwater sensing becomes not only a performance tool, but a mechanism for accountability, inclusion, and societal benefit. Evidence from real-time marine sensing supports informed debate, policy engagement, and conservation-centred design, transforming technology from a closed system into a more inclusive public resource.
Ultimately, underwater data centres are not a simple route to net-zero computing. They offer a powerful test case for whether we can build AI infrastructure that is not only efficient, but also transparent, equitable, and environmentally responsible. The future of digital infrastructure is not merely computational. It is socioenvironmental and governance-driven. As engineers and researchers in the UK and beyond, we must design for bandwidth and biology, latency and justice, working collaboratively from the seafloor up.
Royal Holloway, University of London has published a major new report setting out how the UK can ensure artificial intelligence (AI) serves the public good. Produced by the University’s Digital Organisation and Society (DOS) Research Centre, the report draws on insights from a high-level roundtable held at the House of Lords in April 2025 and brings forward a set of evidence-based recommendations to guide ethical, inclusive and sustainable AI adoption.
Hosted by Baroness Manzila Pola Uddin, the roundtable convened 20 Royal Holloway academics and 14 senior experts from organisations including Roche, NHS, Oracle, Shaw Trust, the Institute of Directors, Ofcom, Surrey AI Centre, Bloomberg, Toyota and Vertis Media. Together, they examined how AI can be governed and deployed in ways that uphold democratic values, protect communities and drive inclusive innovation.
Aligned with the University’s RH2030 strategy, the report reinforces Royal Holloway’s leadership in shaping national policy on responsible AI and strengthening cross-sector collaboration.
A roadmap for responsible and socially purposeful AI
The report sets out what is required to ensure AI works for society—rather than the other way around. At its core is a clear message: fairness, transparency and accountability must underpin every stage of AI development and adoption. These principles, it argues, are essential for earning public trust and safeguarding democratic integrity as technologies evolve.
Addressing skills gaps is identified as a national priority. The report calls for investment in AI literacy at every level—from school classrooms and vocational pathways to higher education and workplace training—ensuring people are equipped to navigate an AI-driven future. It also highlights the risk of biased or incomplete datasets, urging improved data governance to prevent discrimination in sectors such as recruitment, healthcare and finance.
The recommendations extend beyond human systems to environmental ones. With large-scale AI models consuming significant energy, the report calls for greener innovation, energy-efficient infrastructure and sustainable development practices to reduce the carbon footprint of AI. Together, these actions form a practical roadmap for achieving technological ambition while embedding social purpose and environmental responsibility.
Leadership perspectives
Professor Julie Sanders, Vice-Chancellor and Principal at Royal Holloway, said: “The pace and extent of developments in AI are astonishing. With such far-reaching implications, this report is hugely important in highlighting the opportunities in tandem with our shared responsibilities here at Royal Holloway. This report offers critical insights into enabling equitable progress and driving public benefits. I encourage all those working in this dynamic field to read it and reflect on its recommendations.”
Professor Christos Tsinopoulos, Dean of the Faculty of Business and Law, added: “The insights captured in this report reflect a shared commitment to responsible innovation and collaborative progress. As a Business School, within a University of social purpose, we remain committed to using these insights to inform our research and courses. We look forward to continued dialogue that ensures AI benefits society as a whole.”
Professor Mark Lycett, Vice Dean, Research and Knowledge Exchange, said: “This report is a clear example of us leading in bringing policymakers, practitioners and the community together to address the social challenges of Artificial Intelligence.”
Dr Nisreen Ameen, Associate Professor in Digital Marketing and Director of DOS, commented: “This report reflects Royal Holloway’s commitment to shaping the future of AI in ways that serve society. By bringing together experts from different sectors, we are helping lead the national conversation on how technology can protect democratic values and drive innovation that benefits everyone.”
*This is the sixth and final post in a series of blog entries published as part of the author’s ongoing research.
Cevdet Bulut | PhD Candidate | School of Business Management, Royal Holloway, University of London
Situated in the scenic hills of Muggia, a charming Italian town near the Slovenian border, the Lenardon family farm has been dedicated to traditional wine and olive oil production for over a century. During my recent field trip, I had the pleasure of meeting Bruno Lenardon, one of the customers of Primo Principio (See my previous blog on the company), who kindly accepted to take part in my multiple case study research, exploring how the diffusion of IoT innovations unfolds within diverse geographical and socio-economic contexts. I was accompanied by Nicole Salvatori from Primo Principio who assisted with interpretation.
The Lenardon farm‘s origins date back to the early 1900s, when Bruno’s grandfather first cultivated the land. Today, Bruno continues this legacy on two hectares of vineyards and one hectare of olive groves. Despite losing a portion of their land to Yugoslavia during border changes in 1954, the Lenardon family has remained dedicated to their craft.
Sign on the wall of Bruno’s house marking the dividing line between Yugoslavia and Italy.
“My father had much more land, but after the border moved, we lost a few hectares,” Bruno recounted. His father was a pioneer, bottling wine at a time when few others did, selling to local restaurants and shops. This innovative spirit has clearly been passed down to Bruno, who now runs the operation almost single-handedly, with occasional help from his sons and seasonal workers.
Running a farm is not without its challenges. Bruno shared that the business is no longer as profitable as it once was, particularly compared to his father’s time. Nevertheless, his passion remains undiminished. Today, he produces around 8,000-9,000 bottles of wine and 1,000-1,300 bottles of olive oil each year, mostly for local restaurants, shops, and a high-end Italian food retailer, Eataly.
Despite the demanding nature of his work, Bruno does not shy away from the labour-intensive tasks that keep his farm thriving. From early morning starts to long days in the fields, he is deeply involved in every aspect of production. “Most of the time, I’m in the farm,” he said, noting that while the olive trees require less attention, the farm demand constant care.
While Bruno honours the traditional methods of winemaking, he is also open to integrating modern technology to improve efficiency and product quality. As environmental factors became more unpredictable, Bruno knew he needed to adapt—but he also knew he couldn’t do it alone.
“I was sceptical at first,” Bruno admitted, referring to WiForAgri by Primo Principio introduced first by Federico Longobardi, an engineer and co-founder of the company who recognized the farm’s potential for innovation. Despite initial doubts, Bruno now relies on this technology to make informed decisions, particularly during the rainy season when disease risk is high. “The technology has done a lot of improvements, so why not use them?”
Interviewing Bruno with Nicole Salvatori from Primo Principio as an interpreter.
Founded ten years ago, Primo Principio is a pioneer in applying IoT technology to farming in Italy. The company was established in Alghero, Sardinia, and later expanded to Trieste, near the Slovenian border. The core of their technology is providing decision support systems for farmers, using IoT to gather data from the fields and transform it into actionable insights. The company designs and oversees the production of specialised weather stations equipped with sensors, which are then installed and maintained on-site. These devices monitor everything from soil moisture to weather conditions, feeding data into complex analytics models. However, as Andrea Galante, co-founder and Head of Business Development at Primo Principio, points out, the real challenge lies not in the technology itself but in making it reliable and understandable for farmers.
The WiForAgri mobile interface displays the number of primary infections and ascospores over time, with the incubation period shown as well.
Bruno’s reliance on the weather station has grown over the years. He first piloted the product with support from a local agricultural association, which later reimbursed him some of the costs. Impressed by its reliability, Bruno has now committed to a five-year contract. He uses the weather station to monitor conditions critical to crop’s health, such as rainfall, humidity, temperature, and potential In a region where many have abandoned traditional farming for industrial jobs, Bruno remains one of the few dedicated farmers. His commitment to both preserving and modernizing his family’s legacy is evident in his approach. While some of his neighbours continue to make wine using centuries-old methods, Bruno believes in the importance of understanding and controlling the winemaking process scientifically.
“The technology has allowed us to understand processes that we didn’t know about even 25 years” Bruno’s commitment to innovation is not shared by all his neighbours. Many local winemakers still adhere to traditional methods, avoiding modern technology in favour of practices that have been passed down for centuries. This hesitation reflects findings from my earlier review paper on barriers to technology adoption in agriculture. Bruno believes that this reluctance stems from a deep respect for tradition and a belief that winemaking should remain unchanged. However, he is convinced that embracing scientific advancements can enhance the quality of the wine without compromising its authenticity.
When asked how to convince others to adopt new technologies, Bruno suggests direct communication and demonstrations. He acknowledges that discussing the benefits with other farmers could seem self-serving, but he emphasizes the importance of understanding the science behind winemaking. For Bruno, making wine is a complex process that requires careful monitoring and control, something that modern technology can significantly aid.
My visit to Bruno Lenardon’s farm was more than just a field trip; it was an exploration of how tradition and innovation coexist. Bruno’s approach highlights the resilience of small-scale farmers who nurture the land with passion and expertise, blending respect for history with a forward-looking mindset.
About
This is a series of blogs curated by PhD candidate Cevdet Bulut, who is investigating the adoption of IoT in agriculture. In this series, the author shares a limited version of his field notes and highlights from multiple case studies in a news story format as part of his current research study. The scope of the case studies is to uncover what factors (socio-economic, cultural, technical, etc.) affect the adoption of IoT and to gain experience from the field that can guide the design of viable IoT-based business models for the sector. A new blog will be published on each case study with the participant’s permission.
Dr Lucy Gill-Simmen, Vice-Dean for Education & Student Experience, Royal Holloway University of London. She can be contacted by email Lucy.Gill-Simmen@rhul.ac.uk
In a creative ‘studio’ space at our university (Royal Holloway, University of London), something fascinating is happening. Digital Marketing Masters students are crafting brand stories using AI tools, but not in the way you might expect. Rather than replacing human creativity, artificial intelligence is acting as what Vygotsky would call a “mediating tool” – a scaffold that supports students in reaching new creative heights.
The Creative Partnership We Didn’t Expect
Remember when we thought AI might be the end of human creativity? The reality is proving far more interesting. As one of our students recently shared: ‘Working on the Digital Brand Storytelling module has been a great learning experience. Building a brand from scratch with the help of AI allowed us to explore new ways of brand storytelling and think about branding in fresh ways.’
This student’s experience perfectly illustrates what creativity theorist Teresa Amabile describes in her componential theory of creativity. The AI tools aren’t doing the creative work; they’re enhancing what Amabile calls the three critical components of creativity: domain-relevant skills, creativity-relevant processes, and task motivation.
Breaking Down Creative Barriers: More Than Just Digital Tools
Here’s where it gets really interesting. When Mihaly Csikszentmihalyi developed his systems model of creativity, he couldn’t have anticipated AI, but his theory helps explain why our approach is working. The AI tools are effectively expanding what he calls the ‘domain’ of creative possibilities while simultaneously making that domain more accessible.
An industry partner who observed our students’ final presentations noted: ‘I was impressed by the creativity of the students during the workshop, particularly with regard to their innovative business concepts. Presenting is not always natural for people, but it is an essential skill in preparing students for the job market.’
The Science Behind the Success
What’s particularly fascinating is how this aligns with what we know about learning and development. Vygotsky’s Zone of Proximal Development (ZPD) – the gap between what learners can do independently and what they can achieve with support – takes on new meaning in the AI era. The technology creates what we might call a ‘dynamic ZPD,’ constantly adjusting to each student’s developing capabilities.
As another student reflected: ‘The dedication, innovation, and collaboration that went into crafting our brand vision with AI at its core has been truly inspiring. Immense gratitude to my teammates for their remarkable creativity and hard work.’
Why This Matters More Than Ever
In today’s rapidly evolving digital landscape, the ability to collaborate with AI while maintaining human creativity isn’t just nice to have – it’s essential. Drawing on Howard Gardner’s multiple intelligences theory, we’re seeing how AI tools can support different learning styles and creative approaches simultaneously.
Industry professionals are noticing. As one industry partner observed: ‘The presentations and students showcased were impressive. Congratulations on the course! It was practical, forward-thinking, and I imagine it will be very useful in the workforce.’
Five Key Insights from the experience
Enhanced Critical Thinking Students develop more sophisticated evaluation skills when working with AI tools.
Emotional Intelligence Growth Students develop deeper understanding of human emotions and motivations through AI-assisted storytelling.
Collaborative Innovation The studio environment created a community of practice where AI is just another tool in the creative process.
Cultural Awareness AI tools are helping students consider multiple cultural perspectives in their storytelling.
Strategic Thinking Students are developing sophisticated strategies for when and how to use AI effectively.
Looking Forward: The Future of Creative Education
What we’re witnessing isn’t just an interesting teaching experiment – it’s a glimpse into the future of creative education. As Rogers’ diffusion of innovations theory suggests, what seems revolutionary today will become standard practice tomorrow.
The integration of AI in creative education isn’t about replacing human creativity – it’s about expanding what’s possible. By grounding our approach in established learning and creativity theories while embracing new technologies, we’re preparing students not just for the current job market but for a future where human creativity and artificial intelligence work in harmony. The enthusiasm of both students and industry partners suggests we’re on the right track. As we continue to refine this approach, one thing becomes clear: the future of creative education lies not in resistance to AI, but in thoughtful integration that enhances rather than replaces human creativity.
And isn’t that exactly what higher education should be about? Creating spaces where technology and human creativity come together to unlock new possibilities, all while building the skills and confidence students need for their future careers.
Bibliography
Amabile, T. M. (1996). Creativity in Context: Update to the Social Psychology of Creativity. Boulder, CO: Westview Press.
Csikszentmihalyi, M. (1996). Creativity: Flow and the Psychology of Discovery and Invention. HarperCollins.
Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences. New York: Basic Books.
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.
With thanks to Joe Williams from Joe Wills and Daniel Watts from Bark.London for supporting the project and for their part in this blog post.
*This is the fifth in a series of blog posts published as part of the author’s ongoing research.
Cevdet Bulut | PhD Candidate | School of Business Management, Royal Holloway, University of London
A remote corner of Sardinia, far from the typical tech hubs, lies an unexpected setting for a tech company. Surrounded by quiet farms and coastal breezes, Primo Principio was founded not to chase markets, but to fulfil a vision.
Federico Longobardi, co-founder and CTO of Primo Principio, reflects on their beginnings with a sense of nostalgia and humility. “We didn’t choose this place because of its strategic location,” he admits. “We chose it because it was quiet, far from the hustle, surrounded by farmers who mirrored our simple but ambitious ideals.” Federico, originally from Torino, was part of a group of engineers and friends who first came together under the banner of “Engineering Without Borders”.
Their journey began over a decade ago, under the name Secondo Principio, a cooperative that later evolved into Primo Principio. Initially, they weren’t an IoT company—they weren’t even a typical IT company. They were a group of researchers and dreamers, eager to develop solutions for agriculture. “At that time, we had no idea about DSS, prediction models, or AI,” Federico recalls. “We were just focused on our engineering competencies in telecommunications, trying to create something meaningful.”
Co-founders: Andrea Galante and Federico Longobardi
Despite the odds, Primo Principio has achieved its mission, creating jobs in the agri-tech sector and allowing its team to grow and pursue their passions. Today, the company remains small, with a turnover of less than one million euros and a team of just eight people. They cover the entire product development process in-house, from hardware production to software development.
Based in Alghero, Sardinia, with additional offices in Trieste and Torino, the company offers IoT-based solutions predominantly tailored to small and medium-sized farms, providing services ranging from irrigation management to disease and pest detection.
The company’s true strength, however, lies in its in-house development of highly specialised models designed for specific crops, with a particular focus on combating diseases such as powdery mildew and downy mildew. This capability is the result of extensive, multi-year research projects that have enabled the company to refine its models with exceptional precision. Their commitment to continuous research and development underscores their dedication to staying at the forefront of model innovation, making their models not just a product, but a core component of their business and a key differentiator in the market.
One of the key challenges Primo Principio faces is the slow adoption rate of IoT technologies among farmers. According to Andrea Galante, co-founder and Head of Business Development at Primo Principio, many farmers are still reluctant to fully trust these technologies, partly due to the complexity of agriculture and the variability of conditions such as soil type, microclimates, and plant genetics. Moreover, the added value of IoT is not always immediately evident to farmers, especially when it requires them to change long-standing practices.
The company’s business model is tailored to the average Italian farmer, who, as Federico describes, “does not have a degree, and typically owns about three hectares of land. You have to imagine an older person with an old tractor, not using any kind of technology.” This underlines the unique challenges of Italian agriculture, where farms are often small, fragmented, and managed by ageing farmers.
Their go-to-market strategy is clever. Primo Principio relies heavily on a network of agronomists across Italy, whom they train to be completely independent in using their technology. These agronomists then recommend the solutions to their clients, leveraging the trust they’ve already built. “Farmers trust consortia, their neighbours, and the agronomist. The idea is to reach the agronomist,” says Nicole Salvatori, Head of R&D at Primo Principio, describing their primary marketing channel. “I believe the chain of trust is more important, because without that you cannot build a chain of value. It is foundational,” says Nicole, emphasising that the chain of trust is crucial for the successful adoption of IoT in agriculture. Without trust, the chain of value cannot be established. Building relationships with farmers, addressing their concerns, and providing reliable data are essential for fostering this trust.
Interviewing Nicole Salvatori, Head of R&D
The company’s growth, while steady, has been slower than that of larger competitors, partly because they choose to focus on close customer relationships rather than rapid scaling. This strategy is reflected in their diverse customer base, which includes individual farmers, agronomists, and even universities, all benefiting from the partnerships with Primo Principio.
Despite these challenges, Primo Principio is optimistic about the future. The company is entering a new phase of growth, driven by increasing market awareness and supportive government policies. The European Union has been a significant catalyst for the adoption of IoT technologies, offering financial incentives that make it easier for farmers to invest in these systems. Federico notes, “Without these incentives, the market for Smart Agriculture in Italy would be much smaller.” However, he criticizes the current approach as “completely random,” highlighting the need for more coordinated efforts.
When discussing the potential of complementary technologies such as blockchain and satellite technologies, Federico is sceptical. He dismisses much of the hype around these technologies as “rumours,” arguing that many of these solutions are either not yet practical or are unsustainable in their current form. He also casts doubt on the effectiveness of farm-to-fork initiatives, labelling them as mere “greenwashing” efforts. Federico believes that genuine transparency in the food supply chain is hampered by deep-rooted issues, such as unethical labour practices, that technology alone cannot solve. “It’s not the technology solution that is missing for the transparency,” he says.
The future of IoT in agriculture is promising, but Nicole concludes with a cautionary note: there is a potential risk of widening the digital divide and fostering more monopolies, risks that could be accelerated by the use of IoT in agriculture. She points to broader societal challenges that must be addressed alongside technological advancements.
About
This is a series of blogs curated by PhD candidate Cevdet Bulut, who is investigating the adoption of IoT in agriculture. In this series, the author shares a limited version of his field notes and highlights from multiple case studies in a news story format as part of his current research study. The scope of the case studies is to uncover what factors (socio-economic, cultural, technical, etc.) affect the adoption of IoT and to gain experience from the field that can guide the design of viable IoT-based business models for the sector. A new blog will be published on each case study with the participant’s permission.
“Farmers cannot be the same as they were 50 years ago; they must keep pace with technology,” says Athanasia, a research engineer and project manager at Future Intelligence. She emphasises that climate change is reshaping agriculture, making it imperative for farmers to adapt. “The changes in climate and microclimates present significant challenges, but they also offer us a unique opportunity to develop more optimised solutions,” adds Nikos Triantafillidis, who oversees the company’s IoT operations.
Future Intelligence’s flagship product, Quhoma, is central to these efforts. Quhoma, a user-friendly application designed for farmers, integrates data collected from weather monitoring stations deployed in fields. “Imagine you’re a farmer with a large field that you can’t monitor closely,” Athanasia explains. “Quhoma allows you to track environmental data like moisture levels and weather conditions in real-time. It also provides tools like farm diaries for recording key activities such as irrigation and harvest days.”
Athanasia Karagianni (Research Engineer and Project Manager), Nikos Triantafillidis (IoT Operations), Panagiotis Katses (Head of Sales)
“Our customers are not just buying a gadget; they’re investing in a technology that genuinely improves their farming practices. For instance, one farmer discovered through our sensors that his fields drained differently—a detail he hadn’t noticed in 20 years. This kind of insight is invaluable,” says Harris, a business development manager at Future Intelligence.
Quhoma’s simplicity is one of its most praised features, especially among farmers who may not be technologically savvy. “The interface is plain, simple to use, and offers full functionality,” says Panagiotis, head of the sales department at Future Intelligence. “Farmers appreciate its straightforwardness, as it makes technology accessible to those who may not have a deep understanding of it.”
The success of Quhoma wasn’t just about the technology itself, but also about how it was integrated into the farmers’ daily routines. Farmers can compare metrics between different fields, receive alerts, and track the traceability of their crops. Panagiotis notes, “By comparing moisture levels between two fields, farmers can make informed decisions about irrigation, saving both water and money. In one case, farmers in Sparta saved 1,000 euros in just the first month of using Quhoma, thanks to a reduction in water usage. Similarly, a farmer in Agrinio saved 1,800 euros on fertilizer costs.”
Originally founded in 2009 as a telecommunications company, Future Intelligence pivoted to IoT solutions as the technology became more widely recognised and integral to various industries. Headquartered in Athens with additional entities in the UK and Cyprus, the company employs between 24 and 30 people, with a dedicated team of six working specifically in the agricultural domain. “When we started back in 2009, we were focused on two main areas: short-range and large-scale communication. But by 2015, the rise of the Internet of Things (IoT) led us naturally into the smart farming sector,” says Harris, underlining the agriculture as a key sector, crucial not only to the Greek economy but also carrying significant social implications.
Starting from scratch is never easy, and this was especially true for Future Intelligence. When Panagiotis first joined the company, there were some existing clients and limited marketing material to work with. “We had to start from the ground up,” he explained. The team took the risk on even performing door-to-door visits to connect with potential customers, a non business-as-usual process for a smart agriculture provider. Despite these challenges, their persistence paid off, and today, the company serves a wide range of clients, including those growing tomatoes, olives, vineyards, kiwis, and even medical cannabis.
Interviewing Harris Moysiadis, Business Development Manager
Future Intelligence is not just a software company; it also develops its own hardware, a strategic choice that Harris believes gives them a competitive advantage. Although hardware development comes with challenges, it also allows the company to be more flexible and innovative, particularly in their R&D efforts.
When asked about the company’s business model, Harris candidly shared the challenges and strategies they employ. “Our approach has been a mix of B2B and B2C, but we’ve had to adapt to the market’s needs. While we initially focused on B2B, we realised that to build a sustainable business, we needed to offer end-to-end solutions directly to farmers.”
The IoT ecosystem in Greece is still maturing, with several small companies competing in a market that, while growing, remains relatively small. Harris pointed out that while there is no clear leader in the ecosystem, various stakeholders, including technical consultants, farmers, and the government, are working towards the digital transformation of agriculture. The government, in particular, plays a role by promoting digital adoption through funding and tenders, although these initiatives have met with mixed success.
The discussion around government subsidies in Greece reveals a unique challenge in the market. Unlike other countries studied, where subsidies might accelerate sales, Harris points out that in Greece, these subsidies can actually have the opposite effect. “Farmers are keen on our solutions, but when they know a government program might fund their purchase, they delay their decision. This makes the sales cycle much longer. Despite this, we’ve managed to build a customer base of real paying clients from day one, especially among larger agricultural businesses like wineries and olive oil producers.” This reliance on subsidies creates a barrier to market penetration, despite the clear cost savings and efficiency gains their products offer.
Other significant challenges the company faces is convincing traditional farmers to adopt new technology. Panagiotis shared that initial reactions were often sceptical, particularly among older farmers. “They’ve always relied on their own experience, so trusting a new technology was difficult at first,” he said. However, over time, as farmers began to see the tangible benefits of using Quhoma, their trust in the technology grew.
Nikos also recognised the challenges that come with growth. “As the market for smart farming solutions expands, so does the number of low-quality, do-it-yourself products that undermine the industry. We’ve had to work hard to differentiate ourselves from these lower-end solutions. It’s frustrating when a farmer tries a cheap gadget that doesn’t work and then hesitates to invest in our more robust system. But we’re committed to providing serious, reliable solutions that deliver real value.”
Looking ahead, Future Intelligence is keen to expand Quhoma’s capabilities. The company plans to develop new features from constructive feedback of its customers along with technology-driven innovative functionalities. In addition, they aim to scale their operations by partnering with larger agricultural entities that can leverage their established market channels to distribute these solutions more broadly. This strategy could help overcome some of the distribution and market penetration challenges they currently face. Moreover, their commitment to using open-source technology and focusing solely on the technology aspect of the value chain, rather than venturing into agronomy, positions them as a specialised player in the market.
About
This is a series of blogs curated by PhD candidate Cevdet Bulut, who is investigating the adoption of IoT in agriculture. In this series, the author shares a limited version of his field notes and highlights from multiple case studies in a news story format as part of his current research study. The scope of the case studies is to uncover what factors (socio-economic, cultural, technical, etc.) affect the adoption of IoT and to gain experience from the field that can guide the design of viable IoT-based business models for the sector. A new blog will be published on each case study with the participant’s permission.
*This is the third in a series of blog posts published as part of the author’s ongoing research.
Cevdet Bulut | PhD Candidate | School of Business Management, Royal Holloway, University of London
Returning to Türkiye, I embarked on a journey to explore the evolving landscape of agriculture, an industry integral to the livelihoods of many including my own parents for many years, comprising 17% of the country’s employment as of 2021. My research trip starts in Bursa, where I grew up, revealed a stark transformation from agricultural lands to burgeoning urbanisation in my local neighbourhood, leaving farmers to adapt to new roles as builders and traders while their children sought opportunities elsewhere.
At Uludağ University, I found myself amidst a passionate discussion during a group interview hosted by Professor Kemal Sulhi Gündoğdu, Professor of Agricultural and Biosystems Engineering. “If it wasn’t for the [covid] pandemic, they wouldn’t think of agriculture… Government support has just started. They are just waking up…,” says Celil Serhan Tezcan, the visionary founder and CEO of Tarsens and Yieldestimator, highlighting the uphill battle, citing years of neglect and ignorance regarding agricultural policies.
The renewed focus on digital transformation in agriculture stems from escalating food prices, prompting the government’s involvement. However, Prof. Gündoğdu stressed the need for private sector leadership in smart agriculture initiatives. He asserted that initiatives driven by government-owned entities often suffer from a “civil servant mentality” lacking the agility required for success on the ground. Nevertheless, it may not be easy for the private sector either, particularly within an innovation ecosystem that is still in its early stages. “Since the ecosystem has not been formed, everything is new,” says Prof. Gündoğdu. “The fact that Türkiye does not have an agricultural policy. All these are down to the individual efforts,” adds Mustafa Cem Aldağ, co-founder of Yieldestimator.
At Uludağ University campus with participants
Tarsens, founded by Tezcan during his PhD under the supervision of Prof. Gündoğdu who later became a collaborator building the product by working on the sense-making of data coming from sensor network. The flagship product is called Yieldestimator (a spin-off from Tarsens today), a groundbreaking product for pre-harvest yield estimation in vineyards. Despite success abroad, Tezcan noted the struggle for acceptance in the Turkish market, attributing it to a lack of understanding and skills among local farmers.
“Unfortunately, farmers differ from other customer groups. They comprehend through touch, firsthand experience, and a deep understanding of their surroundings. The farmer’s perception is intertwined with their visual observations. Unlike relying solely on the intellect, the farmer processes information through their own eyes. It is imperative to provide evidence, and they must witness it firsthand.”
Probably, that’s why “farmer needs to see a demonstration somewhere,” Prof. Gündoğdu adds, indicating that farmers are risk-averse by nature.
“Soil fertility analysis was conducted on a farm in Mustafakemalpaşa [District of Bursa]. After identifying the fertilization requirements for the field, we proposed an appropriate fertilization plan. The farmer responded, saying, ‘This is the amount of fertilizer I will apply. Please ensure it is not less than that. Apply it on top, but not below. I cannot take the risk.’”
According to Dikbaş, the problem is more than that and has a deeper socio-economic aspect to it.
“The primary obstacle here is the prevalence of numerous small family farmers. In essence, the number of agricultural plots per farmer is exceedingly small in terms of units. The major contributing factor to this situation lies in an inheritance law established many years ago. According to the inheritance law in our country, lands can be subdivided without restriction. Presently, this has now been blocked. The land cannot be divided further, but unfortunately, it is too late to rectify the situation.”
Under the current circumstances, “cheap hybrid solutions” are only “realistic” option for a wider adoption, according to Dikbaş, proposing “datactor” – a fusion of tractor and IoT, considering the high tractor ownership in the country.
The final leg of my journey led me to Doktar Technologies, situated in Istanbul Technical University’s science park. This is one of many science parks spread across the country which have been at the forefront of tech innovation ecosystem, providing numerous advantages to startups, including financial incentives and proximity to a talent pool.
Doktar’s Tanzer Bilgen is the first founder with no family background in agriculture that I am interviewing. He was introduced to agriculture while working as a management consultant in the industry, for a client that produces French fries and frozen food. “Their profitability was low. Events in the field were reported with an accuracy of 30%, and only after a delay of 2 weeks. There was no comprehensive overview,” says Bilgen, recognising the gap and the value of capturing data in real-time. This would later serve as the foundation of company’s business model that he calls “Connected Farm” for automating data collection from the field.
The key differentiator to competitor offerings is Doktar’s portfolio management use case. “We add another layer on top of data collection. For instance, we can group thousands of fields. So, if I am a contractual agricultural company with 10 agricultural engineers and 500 farmers, the question arises: which farmers should I visit? With sensors deployed in the fields, we monitor them via satellite, providing data on various factors, such as the number of insects. This enables us to gain a deeper insight into the field, allowing for more informed decision-making and enabling prioritisation,” Bilgen elaborates.
Doktar primarily targets large food producers engaged in contract farming, seeing them as “early adopters”. Bilgen identified small farmers as “slow adapters” projecting it would take around eight years for widespread IoT adoption in this segment.
“We do not engage with individual farmers because there is currently no viable business case for such outreach. It is not sustainable, and it would result in financial losses. Companies that attempted similar approaches in America and elsewhere ultimately went bankrupt, squandering millions of dollars. Therefore, our preferred go-to-market strategy is to approach one company at a time and offer solutions in bulk, such as selling 50 or 100 sensors or securing a single contract valued at 300k Euros.”
With aspirations for further growth, Doktar faces the challenge of a market where IoT penetration remains below 1% in Türkiye and below 3% globally. Bilgen identified the scarcity of qualified human resources as the primary bottleneck and emphasized the need for agriculture to become a valuable global product to attract new talent and drive sector-wide digitalisation.
About
This is a series of blogs curated by PhD candidate Cevdet Bulut, who is investigating the adoption of IoT in agriculture. In this series, the author shares a limited version of his field notes and highlights from multiple case studies in a news story format as part of his current research study. The scope of the case studies is to uncover what factors (socio-economic, cultural, technical, etc.) affect the adoption of IoT and to gain experience from the field that can guide the design of viable IoT-based business models for the sector. A new blog will be published on each case study with the participant’s permission.
Dr Ling Xiao is a Senior Lecturer in Finance at Royal Holloway University of London. She can be contacted by email ling.xiao@rhul.ac.uk
Dr Lucy Gill-Simmen, Vice-Dean for Education & Student Experience, Royal Holloway University of London. She can be contacted by email Lucy.Gill-Simmen@rhul.ac.uk
Small and medium enterprises (SMEs) play a vital role in the UK economy, comprising over 99% of private businesses and contributing to over 50% of GDP (FSB, 2022). However, many SMEs face barriers to achieving net zero emissions by 2050, including lack of knowledge, funding, and measuring return on investment (BritishChambers, 2022).
Premium snacks company Made for Drink aims to become an SME sustainability leader. They’ve implemented eco-friendly packaging despite 5X production costs and invested £90K into an Exmoor carbon offset program capturing 300 tonnes of CO2 annually. But scaling business growth 10X while maintaining net zero requires massive investments concerning shareholders.
To showcase sustainability efforts transparently, we created an immersive storytelling experience using extended reality technology. This emotionally engages consumers while avoiding allegations of greenwashing plaguing major brands like Google and Amazon.
Our project collected digital assets from Made for Drink’s Exmoor forest site using LiDAR scanning for a WebXR experience with VR capability. It documents founder Dan Featherstone’s sustainability journey through interactive spaces and audio narration.
Exposing consumers to this digital storytelling and surveying their reactions helps identify its effectiveness for spreading awareness and building brand loyalty. Storytelling influences consumer behaviour by stimulating brand identification, allowing emotional value experience, and supporting engagement (Junior et al., 2023).
Immersive technologies strengthen brand-building by communicating sustainability initiatives authentically (Van Laer et al., 2019; Rivera-Pesquera et al., 2021). As SMEs scale, they must showcase net zero efforts clearly without exaggeration. Creative digital storytelling bridges the gap between business growth and environmental commitments for stakeholders.
Our research impacts UK national brands directly and internationally by highlighting novel communication tactics. It enables SMEs to lead business sustainability using transparency. Different audiences have varying needs, so nuanced messaging told simply yet engagingly is key.
SMEs must explain their net zero transitions to investors, employees and customers. Digital storytelling is an exemplary method for creatively addressing the cost versus return challenge in pursuing green business practices. This project offers an avenue for small brands to spearhead the sustainability race.
Visit the recorded WebXR experience here. The WebXR is VR compatibility. You can access the APK which can be sideloaded onto a Quest 2 here. Following exposure to the digital immersive storytelling, a survey conducted with consumers helps us identify whether digital immersive storytelling effectively narratively transports and persuades customers of the brand’s net-zero initiatives. We will appreciate if you could complete the survey after you had a go with the WebXR.
Rivera-Pesquera, M., Cacho-Elizondo, S. and Duran-Dergal, R., 2021. Forget-me-not? Using Immersive Technologies in Brand-building Communication Processes: An Exploratory Study in the Mexican Context. Journal of Creative Communications, 16(3), pp.303-313. https://doi.org/10.1177/09732586211036768
Júnior, J. R. d. O., Limongi, R., Lim, W. M., Eastman, J. K., & Kumar, S. (2023). A story to sell: The influence of storytelling on consumers’ purchasing behavior. Psychology & Marketing, 40, 239–261. https://doi.org/10.1002/mar.21758
Van Laer, T., Feiereisen, S. and Visconti, L.M., 2019. Storytelling in the digital era: A meta-analysis of relevant moderators of the narrative transportation effect. Journal of Business Research, 96, pp.135-146. https://doi.org/10.1016/j.jbusres.2018.10.053
