Yet, the relationship between scarcity and conflict is far less straightforward than these headlines might suggest. Empirical evidence on the causal link between scarcity and conflict is surprisingly mixed: Sometimes scarcity increases conflict, but in other cases it appears to reduce it. Why does scarcity sometimes ignite tensions, while at other times it seems to encourage restraint and cooperation?

The Spark: Asymmetry

Many high-profile conflicts over scarce open-access resources share a common feature: the actors involved are seldom equal. Consider international negotiations on climate change or fisheries. Disputes in these negotiations rarely arise between countries with comparable histories of resource use. Instead, they typically involve actors with very different past levels of extraction – those who have already taken a large share of the resource and those who are now seeking access. Not surprisingly, tensions emerge when the former try to close the cookie jar just as others are reaching for it.

From the perspective of those who have historically used less of the resource, restrictions may appear deeply unfair. Why should they limit their use when others have already benefited for decades? At the same time, those who have long relied on the resource may feel entitled to their existing share and see it as legitimate or even necessary. When scarcity forces these competing claims into the open, conflict can easily follow.

The Test: A Laboratory Experiment

We set out to test whether asymmetry was the missing link between scarcity and conflict. While contemplating a trip to COP29 in Baku, we remembered how messy these international negotiations can become. We needed a more controlled environment to study the underlying dynamics. An environment that would allow us to causally test whether even a small asymmetry in past resource use could spark conflict among otherwise similar individuals.

We turned to our university laboratory. There, we created a simple game in which student participants jointly managed a renewable resource in the form of a fish pond. In each round, participants could catch fish from the shared resource. They also had the option to destroy part of another participant’s catch, a behavior commonly used in experimental economics as a proxy for conflict and hostility. The more conflict emerged around the use of the shared resource, the more likely participants were to engage in such destructive actions. We then systematically varied two conditions: the scarcity of the resource and the inequality in past resource use.

We induced inequality in past resource use through a simple visual nudge. Participants initially observed only half of the pond, with the other half blackened out. Some saw one half of the pond with plenty of fish, while others saw only a few. The small difference shaped behavior: those who saw more fish tended to take larger initial extractions, while those who saw fewer were more restrained. In this sense, the experimental setup mirrors real-world dynamics. A history of resource-intensive economic growth has led some countries to perceive nature as plentiful and readily exploitable, while others have historically contributed far less to resource exploitation and emissions.

After the first round in the experiment, the veil of ignorance was lifted, and all participants could see the full pond. From that point on, everyone had complete information about the resource, just as we today have common knowledge of the planetary limits. This allowed us to observe how the initial asymmetry influenced subsequent behavior. In many ways, it reflects the current global situation, where the limits of natural resources are increasingly visible to all, even if past patterns of use remain deeply unequal.

The Result: When Scarcity Meets Asymmetry

What we found fit remarkably well with the puzzle that set us on this journey. Scarcity alone did not increase conflict. Instead, it even had a mitigating effect on conflict. Participants engaged in fewer destructive actions against one another when the fish became scarce due to exogenously reduced reproduction. Scarcity seemed to activate a social norm, encouraging greater cooperation in the face of a shared crisis. Faced with the prospect of a shrinking resource, participants appeared less willing to harm others, even as competition over the remaining fish intensified.

Inequality in past resource use alone also did not entail more conflict. Instead, participants tended to coordinate their behavior to reduce the initial imbalance. Those who had taken more initially were more restrained in later rounds, while others increased their extraction. In effect, participants willingly shared the cookie jar to ensure that, by the end of the experiment, everyone received a fair share. This informal coordination helped keep destructive conflict at low levels.

Only when both factors, scarcity and inequality in past resource use, were present simultaneously did the dynamics change. The cooperative social norm appeared to break down, and conflict began to rise. Sustaining such norms of cooperation seemingly becomes more difficult when actors are in markedly different positions due to past resource use.

Figure: Effects of scarcity and asymmetry in past resource use on conflict behavior. Conflict is measured as the destruction of others’ catch. Scarcity and asymmetry individually each reduce destructive behavior, reflecting cooperation and coordination on equitable outcomes. However, when both factors are present simultaneously, conflict increases, indicating a breakdown of cooperative norms.

The Lesson: The Dangers of Asymmetry

Our laboratory experiment carries both hope and caution. On the hopeful side, human behavior appears to include cooperative tendencies that can help societies cope with scarcity. This finding echoes a broader strand of research showing that environmental stress can foster cooperation across many forms of life, from plants and animals to human societies. When facing shared adversity, collective restraint and coordination can emerge even without strong external enforcement.

Yet our findings also highlight how fragile these cooperative tendencies can be. Scarcity itself may not be the greatest danger. Instead, tensions arise when scarcity interacts with unequal histories of resource use. When some actors feel that others have already taken more than their fair share, the willingness to cooperate can quickly erode.

For international negotiations over global commons such as the climate or the oceans, this insight carries an important implication. Managing scarcity alone may not be enough. Policymakers may also need to explicitly address historical asymmetries in resource use. Emphasizing the egalitarian principles of the international system, acknowledging differences in past emissions or catches, and highlighting the shared nature of global commons may help sustain the cooperative norms that scarcity alone can sometimes produce. Framing negotiations in this way may give hope a chance to prevail over the dangers posed by the divide between those who have long had their hands in the cookie jar and those now seeking access.

Reference

This opinion piece is based on the following article:

• Geschwind, S., Lambsdorff, J. G., & Werner, K. (2026). When scarcity intensifies conflict: On the role of asymmetry in common-pool resource games. Ecological Economics, 244, 108963.

Stephan Geschwind Stephan Geschwind is a doctoral researcher at the Chair of Economic Theory at the University of Passau. He holds a Master’s degree in International Economics and Business from the University of Passau and a Bachelor’s degree in International Relations from TU Dresden. His research focuses on behavioral economics, with a particular interest in conflict, sustainability, and collective action.

Prof. Dr. Johann Graf Lambsdorff Prof. Dr. Johann Graf Lambsdorff, distinguished professor of economic theory at the University of Passau since 2003, is recognized worldwide for his work in measuring corruption, experimental economic research on conflict and ethical behavior, institutional economics and the application of AI for teaching. A graduate of the Universities of Frankfurt/Main and Göttingen, he developed the Corruption Perceptions Index for Transparency International in 1995, a tool that has become indispensable for global governance and policy-making in the fight against corruption. His innovation extends to the classEx platform, which allows for interactive experiments and integration of AI and is used by nearly 800 institutions worldwide across various disciplines. He is chairman of the German Association for Experimental Economic Research (GfeW). Graf Lambsdorff’s insights, shared through key publications and as a sought-after keynote speaker, continue to influence economic theory and practice worldwide.

Imagine sending part of your wages home to a family in rural Niger. The trip from the city is long. The informal agent charges a fee of anywhere between 1% and 30% of whatever you send. And even when the money arrives, it may not arrive on time – or at all. Now imagine doing this every month, for years, while living on a low wage in the first place. This is not a hypothetical scenario. It is the everyday reality for millions of migrant workers across Sub-Saharan Africa, caught in what researchers have called a “double whammy”: poverty drives migration, and punishingly high transfer costs erode the wages earned abroad before they ever reach the people who need them most.

Mobile money, such as M-Pesa in Kenya, promises to change this. The evidence that it can do so is compelling: studies from Kenya, Mozambique, Tanzania and beyond show that mobile money can dramatically reduce transaction costs, smooth consumption in times of crisis, and meaningfully reduce poverty. Yet despite mobile money services existing in Niger since 2009, only around 6% of adults had a mobile money account as recently as 2025. Why is a technology with such clear benefits so underused? And what would it actually take to get people to try it?

A randomized controlled trial (RCT) conducted across rural villages in Niger set out to answer these questions – and the results are striking. A modest two-sided financial incentive worth just about USD 4.50 in total led to a more than 200% increase in mobile money account ownership, with effects that persisted even nine months after the intervention ended.

The Problem: Knowing Is Not Enough

It is tempting to assume that low adoption of mobile money reflects a lack of information. If people just knew how it worked, the thinking goes, they would use it. But the Niger study challenges this view directly. The experiment tested two distinct interventions in a village-level randomized design across 978 households (337 in a control group, 641 in treatment groups).

The first was a pure information treatment: enumerators visited households, explained how mobile money worked, and provided a printed flyer. The second added a two-sided financial incentive on top of the information: households received 2,000 CFA francs (roughly USD 3) if they received a mobile money transfer within a set window, and the sender received 1,000 CFA francs (roughly USD 1.50), making the total incentive around USD 4.50.

The information-only treatment increased knowledge about mobile money. But it had no measurable effect on actual adoption. People learned about the technology, but did nothing differently. This is a familiar finding in behavioral science: information alone rarely drives behavior change, particularly when adoption involves unfamiliar technology, upfront effort, and uncertain payoffs. Knowing that something is beneficial is not the same as experiencing it.

The Intervention: Making the First Step Cheap and Social

The incentive treatment worked through two reinforcing mechanisms. First, it made trying mobile money cheap and salient. The small cash reward effectively subsidized the first transaction, making “learning by doing” essentially free. People did not need to trust a system they had never used; they were being paid to find out. This is a classic application of behavioral insights: reducing the cost of a first action to overcome inertia and status-quo bias.

Second, the two-sided design, i.e. rewarding both sender and receiver, deliberately targeted network effects. Mobile money is only valuable if the people you want to send or receive money from are also using it. By incentivizing both sides of a transaction simultaneously, the experiment seeded adoption across social networks, not just within individual households. The result was a self-reinforcing dynamic: as more people in a village started using mobile money, it became easier and more natural for others to follow.

Notably, the information treatment also generated meaningful knowledge spillovers within villages: untreated households in information villages showed increased awareness compared to pure control villages. This suggests that information does travel through social networks, even if it does not, on its own, translate into behavior change.

The Results: A Boost to Adoption and Usage That Lasted

The numbers from the incentive treatment are remarkable. After the intervention, 30% of households in treatment villages held a mobile money account – compared to just 9% in the control group. That is an effect size of over 230%. 20% of households used mobile money and saved via mobile money, an increase of 280% as compared with the control (5%). And crucially, these effects persisted nine months after the intervention had ended, i.e. well beyond the period in which the incentive was active.

On welfare outcomes, the picture is more nuanced. In the short run, no significant consumption-smoothing effects were detected. But over a longer horizon, treated households were more likely to have received a remittance transfer (a 24% increase), and reported higher subjective wellbeing relative to their neighbors. The direction of effects on the number and value of transfers was positive, though not statistically significant, possibly reflecting the short follow-up window relative to the slow-moving dynamics of remittance behavior.

The persistence of adoption is perhaps the most policy-relevant finding. Often, incentivized behavior reverts once the incentive is removed. Here, it did not, suggesting that the intervention succeeded not just in getting people to try mobile money once, but in integrating it into how households manage money.

Effects of information and incentive treatments (relative to control group)
Bars show the share of households having a mobile money account and having used mobile money recently

Notes: *** Effect statistically significant at the 1% level.

Is It Worth It? The Cost-Effectiveness Case

The intervention has a good chance to be cost-effective, both for participants and for policymakers. The total incentive cost per household is modest (roughly USD 4.50), and the downstream benefits – reduced transfer fees, access to formal financial infrastructure, improved consumption-smoothing over time – accumulate. The results imply that the cost of an additional adopter was about 47 USD, this is about 35% less of what comparable interventions have achieved. For mobile network operators and financial inclusion programs, a one-time, low-cost nudge that generates durable adoption should be a compelling investment.

This matters not only for Niger, but for the broader challenge of financial inclusion across Sub-Saharan Africa, where cross-border remittance fees remain among the highest in the world, and where over half the population remains outside the formal financial system despite rapid growth in mobile money infrastructure.

What Policymakers and Practitioners Can Take Away

3. Information is necessary but not sufficient. Awareness campaigns can raise knowledge, but they do not reliably change behavior. Policymakers and financial inclusion programs should not mistake information provision for meaningful intervention.
4. Small, well-designed incentives can have outsized and lasting effects. A subsidy of around USD 4.50 per household, structured to reward both sender and receiver, produced effects that persisted nearly a year after the program ended. The design matters: two-sided incentives that seed network adoption are likely more effective than one-sided individual rewards.
5. Behavioral frictions, not just financial ones, are holding back adoption. The barriers to mobile money use are not primarily about cost or access. They are about the unfamiliarity of a new technology, the absence of a trusted social reference point, and the coordination problem of needing your network to also adopt. Interventions that address these frictions directly, by making first use easy, salient, and social, are more likely to succeed.
6. Low-cost behavioral interventions can be a powerful complement to infrastructure investment. Building the infrastructure for mobile money is necessary. But infrastructure alone is not sufficient if behavioral barriers prevent take-up. Pairing infrastructure rollout with targeted, evidence-based behavioral interventions could substantially accelerate financial inclusion outcomes.

References

This opinion piece is based on the following working paper:

• Aker, J., J. Awonon, M. Grimm, C. Petrik & O. Wirth (2026), If you build it, will they come? Addressing the Barriers to the Adoption of Digital Financial Services. Cornell University.

Further readings:

• Aker, J.C., R. Boumnijel, A. McClelland & N. Tierney (2016). Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger. Economic Development and Cultural Change, 65 (1): 1-3. https://doi.org/10.1086/687578.
• Batista, C. & P.C. Vicente (2025). Is Mobile Money Changing Rural Africa? Evidence from a Field Experiment. Review of Economics and Statistics, 107(3): 835–844. doi: 10.1162/rest_a_01333.
• Klapper, L., D. Singer, L. Starita & A. Norris (2025). The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC. doi: 10.1596/978-1-4648-2204-9.
• Riley, E., A.S. Shonchoy & R. D.Osei (2025). Incentives and Endorsement for Technology Adoption: Evidence from Mobile Banking in Ghana. Journal of Development Economics, 176: 103511. doi: 10.1016/j.jdeveco.2025.103511.
• Suri, T. & W. Jack (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317) : 1288–1292. doi: 10.1126/science.aah5309.

Prof. Dr. Michael Grimm

Michael Grimm is a Professor of Development Economics at the University of Passau. He is also affiliated with  IZA@LISER, the DIW, the RWI Research Network and the Abdul Latif Jameel Poverty Action Lab (J-PAL). He studied in Frankfurt and Paris and earned a PhD at Sciences Po Paris. Before joining the University of Passau, he had positions at the University of Göttingen and the Erasmus University Rotterdam. He was a guest professor at the Paris School of Economics and the University of Bordeaux.  He has been a consultant for the World Bank, for various UN agencies, for Dutch, French, and German donor agencies, and for PARIS21 at the OECD.

In boardrooms across the region and beyond, leaders are told that algorithms can understand their customers better than traditional research ever could. The promise is simple: more data equals more insight by AI!

Yet, that equation does not hold. Data reveals what people do; understanding, on the other hand, reveals why they do it. The difference is not academic – it is strategic. It determines whether organizations make informed decisions or misinterpret the very markets they aim to serve. AI can illuminate patterns at unprecedented scale, but meaning still sits on the human side of the equation.

1. The promise – and illusion – of AI-driven insight

Across the Arab region, businesses and governments are adopting AI at remarkable speed. Predictive engines recommend products before consumers ask. Sentiment tools scan live social feeds to flag emerging trends. Real-time analytics, smart dashboards, and automated segmentation have created the sense of a market that is more knowable than ever.

These capabilities are unquestionably powerful:
AI can tailor messaging to individual behavior.
AI can forecast demand by reading subtle shifts in attention.
AI can deliver personalized support at scale.

AI can adjust pricing dynamically.

But these systems excel at correlation, not interpretation or causation. In fact, they map behavior without understanding the cultural, emotional, or social forces that shape it. For example, a surge in online engagement might signal enthusiasm. However, it could just as easily reflect sarcasm, social pressure, disappointment, or coordinated advocacy. Treating patterns as motives collapses the distinction between signal and meaning, creating the illusion of deep insight where none exists.

In fact, true understanding requires context, and context is precisely where AI struggles most…

2. When AI ‘misreads’ the human world: some documented cases

When the AI misinterprets content, the problem often isn’t a ‘technical error’ – the model did exactly what it was trained to do. The failure comes from the fact that human meaning is not reducible to statistical patterns. The machine or AI only sees statistical patterns, not human meaning or implications. These limitations are not theoretical. They have been documented across academic research, industry reports, and real-world deployments. Here are concrete instances where AI fell short:

• Sentiment and sarcasm misclassification in Arabic social media: A recent study analyzing tens of thousands of Arabic-language tweets about generative AI found a measurable amount of sarcasm – a communication form rich in implicit context⁽¹⁾. Even state-of-the-art natural language processing models continue to struggle with sarcasm and irony, especially when cultural or dialectal nuance is involved⁽²⁾. As a result, a post that is sarcastic criticism could be interpreted as praise; a serious risk for brands using sentiment analysis to gauge public opinion.
• Bias and discrimination in automated classification systems: Concerns about algorithmic bias are no longer speculative. Research in AI from multiple sources has highlighted a tension: while algorithms can, in theory, be designed to outperform humans in unbiased perception, real-world systems often inherit and amplify structural biases embedded in data, objectives, and deployment contexts⁽³⁾. The problem is not that machines or AI are inherently biased, but that bias becomes encoded, scaled, and difficult to contest once automated. Moreover, marketing and targeting algorithms can sometimes over-represent some consumer groups and other times render others invisible⁽⁴⁾. These distortions directly affect segmentation, demand estimation, and strategic decision-making which could be questionable to developing ethical and equitable AI systems.​
• Over-personalization and the erosion of trust: Industry and marketing analyses have documented multiple cases where AI-driven personalization crossed the line into intrusion. One widely cited example, discussed in marketing and tech commentary, involved retail recommendation systems inferring sensitive life events (such as pregnancy) before consumers had disclosed them in their families⁽⁵⁾. Rather than signaling relevance, these predictions triggered discomfort and backlash. The failure was not technical accuracy, but a lack of consent and social judgment: prediction without permission.
• Over-reliance on AI in high-stakes decision systems: domains leading to failure: Outside marketing, the promise of AI has sometimes led to disastrous overconfidence. As a high-profile example, an AI-based medical-recommendation system, once heralded as a game-changer, produced unsafe and inaccurate treatment recommendations ⁽⁶⁾. This demonstrates that AI’s “insight” may appear authoritative but can be dangerously misguided when applied without human oversight and contextual judgment.

Taken together, these cases point to a consistent conclusion: AI’s strengths – scale, speed, pattern recognition – do not compensate for its fundamental inability to interpret human meaning and nuance, context, culture, or ethics. Without interpretation, automation merely accelerates misinterpretation.

3. A balanced path: pairing machine scale with human intelligence

Success will come not from replacing humans with machines, but from combining their strengths:

• Hybrid human-AI models: Use AI to flag emerging patterns or weak signals, but keep humans responsible for interpretation, strategy, and final decisions. AI can surface “what changed”, humans decide what it means.
• Reskilling and repositioning human talent: As AI automates data collection and segmentation, human roles should evolve toward strategic interpretation, scenario planning, cultural reading, and ethical judgment; areas where machines remain blind.
• Explainability, transparency, and consent: Systems should be designed so that consumers and researchers understand how data is used, what is inferred, and why. Opt-outs, clear data-use policies, or “human-in-loop” checks should limit misuse or overreach.
• Ethical governance and data protection: Organizations should adopt frameworks to monitor bias, privacy, discrimination, and fairness – not as afterthoughts but as integral parts of insight systems. Regulation analogous to global standards (e.g. GDPR) becomes essential, especially in culturally diverse contexts.

4. In conclusion: Meaning remains human

AI will continue to transform how we gather data and detect signals, but unless we acknowledge its limitations, we risk building insights on shaky foundations. It is true that machines can cluster behavior, forecast probabilities, and highlight patterns. However, they cannot interpret intention, emotion, cultural meaning, or ethical weight. Those remain human domains.

For marketers, researchers, and policymakers, the challenge is clear: Build insight infrastructures where AI accelerates discovery and humans provide anchoring interpretation. Data may fuel the engine, but meaning must come from human thinking, beliefs, and judgement.

AI may shape the future of consumer understanding, but human reasoning remains what guides it responsibly.

References

1. Al-Khalifa, S., Alhumaidhi, F., Alotaibi, H., & Al-Khalifa, H. S. (2023). ChatGPT across Arabic Twitter: A study of topics, sentiments, and sarcasm. Data, 8(11), 171. https://doi.org/10.3390/data8110171
2. Bhargava, N., Radaideh, M. I., Kwon, O. H., Verma, A., & Radaideh, M. I. (2025, April 8). On the impact of language nuances on sentiment analysis with large language models: Paraphrasing, sarcasm, and emojis. arXiv. https://arxiv.org/abs/2504.05603
3. Van Eyghen, Hans (2025). “AI Algorithms as (Un)virtuous Knowers”. Discover Artificial Intelligence. 5(2) 2. doi:1007/s44163-024-00219-z
4. Marabelli, Marco (2024). AI, Ethics, and Discrimination in Business. Palgrave Studies in Equity, Diversity, Inclusion, and Indigenization in Business. Springer. doi:1007/978-3-031-53919-0
5. (2025). Worst ad campaigns: AI’s marketing failures. reelmind Blog. https://reelmind.ai/blog/worst-ad-campaigns-ai-s-marketing-failures
6. Özerdem, H. (2025, October 7). Lessons to be learned from 30 AI fiascos. Medium. https://hakanozerdem.medium.com/lessons-to-be-learned-from-30-ai-fiascos-acc2a61a03ab

Dr. Maha Baz

Dr. Maha Baz is a Senior Researcher / Senior Consultant at Behavia and a Strategic Communication Advisor for Excellence. She has 20 years of experience in strategic marketing, communication, and consumer behavior. She holds a PhD in Marketing from the University of Leicester on the subject of ‘Sharing Economy in the Arab world’ and has written a book chapter about it in the “Handbook on the Sharing Economy”. She advises and consults public and private sector clients on marketing with behavioral insights.

The paper offers a structured approach to test and scale behavioral interventions for policymakers and provides an overview of the most common types of experiments used for policy evaluation. Several best-practice case studies from the Kingdom and key developments are discussed, such as the establishment of dedicated BI units and networks. Additionally, the authors discuss seven strategic topics and challenges that are expected to shape the future of behavioral public policy in Saudi Arabia.

Arabic version: https://www.kfcris.com/ar/view/post/440

English version: https://www.kfcris.com/en/view/post/440

Or click below to download the paper directly in English or Arabic.

The post Behavioral Insights for Vision 2030 in Arabic first appeared on Behavia.]]> https://behavia.de/reflections-on-the-future-of-behavioral-public-policy/ Thu, 11 Sep 2025 13:41:10 +0000 https://behavia.de/?p=4995 Over the last couple of months, we attended several conferences in Behavioral Public Policy including the IAREP in Tartu, BX2025 in Abu Dhabi, and this week the IBPPC in London. Each of these events was inspiring and we enjoyed learning from our peers and colleagues about the latest advances in our field. At the same […]

Understanding these norms isn’t just a cultural curiosity – it is a strategic imperative for marketers who want to thrive in a society where community values, identity signaling, and social conformity matters.

1. The science behind social norms in consumer behavior

Social norms shape consumer choices in two main ways:

• Descriptive norms: what people observe others are doing and assume is normal.
• Injunctive norms: what people believe is socially approved or expected.

A great example of descriptive norms in action is the rapid adoption of Tamara, a Buy Now, Pay Later (BNPL) platform in Saudi Arabia. What started as a fintech solution quickly evolved into a social signal. When young Saudis saw friends using Tamara to split payments on Farfetch orders or food delivery apps, the platform gained credibility. It wasn’t just convenient; it became cool.

According to SAMA’s 2022 Fintech report, Tamara’s user base skyrocketed from 76,000 in 2020 to over 10 million by 2022. That kind of growth isn’t just about tech adoption; it’s about momentum rooted in visible behavior. The more it was used, the more “normal” it became. What started as a smart way to manage money became an unspoken lifestyle statement.

The same logic applies to STC Pay, which rapidly overtook traditional banking among young Saudis and professionals. Their sponsorships of events like Gamers8 and integration with online retailers sent a message: digital wallets aren’t just functional – they’re part of a modern Saudi identity.

By contrast, injunctive norms shape behavior by signaling what is socially approved. These norms for example increasingly influence eco-conscious consumption. For example, in Jeddah, Al-Esayi Beverage Corporation partnered with SIG in August 2024 to introduce sustainable Bag-in-Box (BIB) packaging, minimizing environmental impact and tapping into a growing eco-conscious norm.

Another example is offered by Unilever’s Love Beauty and Planet. It launched shampoo bottles made entirely of post-consumer recycled (PCR) plastic. Customers feel they’re doing the “right thing” by choosing brands visibly committed to sustainable packaging.

These examples highlight how brands can leverage injunctive norms authentically, promoting socially responsible behavior and aligning marketing messages with values that resonate deeply in the Kingdom.

2. The Bandwagon Effect: following the crowd, Riyadh style

Where people adopt a trend simply because others are doing so, has always been a pattern in Saudi Arabia’s social media-driven economy.

During Riyadh Season, Nike’s custom sneaker booth in Boulevard City became a viral phenomenon. Not (just) because the shoes were stylish, but because they became social proof of being part of something bigger – a cultural moment. Local celebrities, influencers, and even mid-tier content creators flaunted their pairs on Instagram and TikTok, turning their sneakers into more than a product. They were a statement of inclusion.

Even food isn’t immune. The surge in popularity of local burger chains like Section-B or Shawarma House wasn’t just about taste – it was fueled by Instagrammable interiors and long queues that created the illusion (and reality) of must-try status.

3. Social Proof: trust in the age of influence

Saudi consumers place high value on trust and community endorsement; traits deeply rooted in tribal culture, religious values, and the collective spirit of Gulf societies.

Digital word-of-mouth is the new majlis. For example, platforms like TikTok and X amplify peer validation. Brands that appear often on “What I bought this week” videos by Saudi creators get an automatic leg-up. Another example comes from Dove Arabia: they tapped into this sentiment by featuring Saudi women and influencers in their “Real Beauty” campaign. This wasn’t just inclusive marketing. It was social proof that Dove understood local realities. Or how about Shein? Their viral success in the Kingdom can be traced directly to Saudi beauty and fashion influencers (like Hessa Al-Awaji and Lama Alakeel) who showcased budget-friendly looks, reassuring their followers that affordability and trendiness weren’t mutually exclusive.

In Saudi culture, “Who else is using this?” isn’t a superficial question. It is an evaluation of legitimacy and alignment with local values.

4. The Scarcity Heuristic: limited means desirable

In Saudi Arabia’s status-conscious consumer market, scarcity is a powerful cue for value. If it’s hard to get, it must be good.

Starbucks Arabia mastered this with seasonal exclusives like the Saffron or Pistachio Latte. These limited releases became social media fodder, with Saudis posting first-sip selfies before they vanish from the menu. Abdul Samad Al Qurashi, one of the oldest perfume houses in the Kingdom, regularly releases limited-edition ouds and attars available only during Hajj season or National Day. Owning one signals sophistication, exclusivity, and cultural grounding. Apple product launches regularly see queues outside major malls like Riyadh Park and Cenomi Mall of Arabia, not because of a lack of online ordering, but because lining up is part of the hype; a ritual signaling dedication and elite access.

Scarcity creates urgency, but in a high-context culture like Saudi Arabia, it also grants status.

Ethical Marketing: navigating norms without exploitation

While social norms can supercharge brand growth, marketers have a responsibility not to exploit them unethically. Luxury brands like Rolex or Chanel subtly perpetuate class distinctions, leveraging social pressure to drive aspiration. So the question remains: are young Saudis buying for personal meaning, or to signal status to peers?

It is imperative to know why consumers may adopt certain brands for ethical reasons not for commercial reasons alone. More importantly, responsible Saudi brands should consider the financial well-being of young consumers. The rise of Buy Now, Pay Later services, while convenient, can also encourage overspending and financial instability especially among Saudi youth. Instead of reinforcing the pressure to buy luxury goods or keep up with trends, brands should promote mindful consumption – positioning high-end products as an occasional treat for oneself rather than a necessity to impress others.

This approach not only aligns with ethical marketing principles but also fosters long-term brand loyalty by demonstrating that the brand genuinely cares about its customers’ financial well-being. For example, a premium Saudi chocolate brand like Bateel could frame its luxury boxes not as Eid must-haves for social display, but as rare indulgences tied to heritage, craftsmanship, and self-care.

Beyond Marketing: challenging social norms with purpose

Social norms in Saudi Arabia are powerful forces shaping consumer behavior. However, they are not immutable. While ethical marketing is one path forward, combatting the more harmful effects of conformity-driven consumption requires a multi-layered approach that includes education, regulation, and community leadership.

Educational institutions and public campaigns can play a crucial role in fostering media literacy, financial awareness, and critical thinking. When young Saudis understand how influence works – how trends are constructed, and social proof is amplified – they are better equipped to make independent, values-driven decisions rather than simply following the crowd.

Influencers and public figures also hold significant sway. When creators normalize alternative behaviors (such as reusing fashion, celebrating local craftsmanship over global luxury, or prioritizing personal wellness over social performance) they quietly rewrite what is aspirational. A Saudi TikToker highlighting thrift shopping, or a local chef promoting sustainable food practices and smaller portions, may have a more lasting impact on consumer culture than any ad campaign.

Policymakers and platforms have a role, too. Regulation around misleading promotions, installment-based overspending, or greenwashing practices can shift the incentives for brands and influencers alike. Platforms like TikTok or Instagram should explore features that encourage content transparency, e.g., labeling sponsored content more clearly or nudging users toward mindful consumption.

Finally, brands themselves can go beyond ethical messaging by building community-led initiatives like co-creating products with local artisans, supporting youth entrepreneurship, or funding consumer education efforts that show a long-term commitment to social progress, not just profit.

In a society as dynamic and youth-driven as Saudi Arabia, reimagining social norms in marketing isn’t about rejecting tradition; it’s about updating the collective story of what success, status, and self-worth really mean. By embracing thoughtful interventions across culture, commerce, and community, Saudi Arabia can shift from reactive consumption to reflective choice – building a marketplace that mirrors its broader ambitions: modern, intentional, and deeply rooted in its values.

Dr. Maha Baz

Dr. Maha Baz is a Senior Researcher / Senior Consultant at Behavia and a Strategic Communication and Media Advisor for Excellence. She has 20 years of experience in strategic marketing, communication, and consumer behavior. She holds a PhD in Marketing from the University of Leicester on the subject of ‘Sharing Economy in the Arab world’ and has written a book chapter about it in the “Handbook on the Sharing Economy”. She advises and consults public and private sector clients on marketing with behavioral insights.

The paper offers a structured approach to test and scale behavioral interventions for policymakers and provides an overview of the most common types of experiments used for policy evaluation. Several best-practice case studies from the Kingdom and key developments are discussed, such as the establishment of dedicated BI units and networks. Additionally, the authors discuss seven strategic topics and challenges that are expected to shape the future of behavioral public policy in Saudi Arabia.

Read the full paper in English published in KFCRIS’ Dirasat Series: https://www.kfcris.com/en/view/post/440

Arabic version: https://www.kfcris.com/ar/view/post/440

Or click below to download the paper directly.

This thought has stayed with me for some time, shaped largely by my experience as an advisor on the board of Behavia, a consulting firm that specializes in behavioral science and public policy. I’ve watched them build something different that goes beyond frameworks & deliverables. Their approach doesn’t just solve problems; it equips others to solve them.

One thing that stands out is how Behavia treats knowledge transfer. They don’t relegate it to a training module at the end of a project or frame it as a separate activity. Instead, they embed it in every task, every discussion, every deliverable. If a policy is being designed, the client learns how to design one. If a behavioral intervention is being crafted, the client becomes fluent in the rationale behind every design choice. It’s not “let us do this for you”, it’s “let us do this with you, so next time, you won’t need us.”

And perhaps the most radical part? They’re fine with that.

This philosophy of building clients who will eventually outgrow you isn’t just admirable. It’s strategic. Because when a client sees that your priority is their independence, not your invoice, the relationship shifts. Trust is no longer transactional; it becomes intrinsic. It isn’t earned through presentations or polished reports; it’s earned through honesty, through the act of handing over the wheel even before being asked.

Of course, not every firm is willing to adopt this model. Many operate on the premise of dependency: offering just enough to be useful, but never enough to be replaceable. That’s not consulting, it’s gatekeeping.

True consulting is the opposite. It’s about giving clients the tools, language, and confidence to carry on without you. It’s about knowing that your value doesn’t vanish when the project ends; it echoes in your clients’ decisions long after you’ve stepped away.

But translating that philosophy into practice takes discipline & intention. It’s not just about mindset. It’s about how we show up every day, how we design our engagements, and how we define success.

If you’re a consultant seeking to embed knowledge transfer in your work in a meaningful, lasting way, here are some reflections drawn from experience:

1. Start with the end in mind; your own absence: Design every project as if it will be your last with that client. What would you build if you knew they had to manage everything on their own the next day?
2. Teach by doing, not by hosting: While formal training is essential, everyday modeling of how to think, decide, and reflect is also more impactful. Let clients sit in the decision-making seat early and often.
3. Share the “why,” not just the “what”: Processes and tools can be copied, but judgment comes from understanding. When you explain the reasoning behind your choices, you’re transferring wisdom, not just methods.
4. Build champions, not dependencies: Identify those in the client’s team who are curious, invested, and willing to take ownership. Empower them to become internal ambassadors of the work.
5. Document wisely & engage more: Knowledge transfer doesn’t live in manuals; it lives in dialogue, co-design, and iteration. Don’t bury what matters in files no one reads.
6. Make yourself available after you’re gone: Trust is reinforced when support doesn’t vanish with the final invoice. A quick call, a check-in, a shared article, all can keep the spirit of partnership alive without overstaying your role.
7. Celebrate outgrowing: If your client no longer needs you, that’s not failure, it’s fulfillment. Let it be known that their independence is the success you were aiming for all along.

Of course, one might wonder: If clients become independent, how does a consultancy make a living?

It’s a fair question & one worth addressing.

The answer lies in trust & evolution. A firm that empowers its clients doesn’t lose them, it earns their respect. And respected partners are invited back, not to repeat the past, but to build what’s next. Because when the relationship is built on growth, not dependence, there’s always a new frontier to explore together.

There’s something deeply human about this approach. It respects the client’s potential. It assumes they are capable of learning, adapting & leading in their own right. It also turns the act of consulting into something closer to mentorship, even stewardship.

That’s the kind of impact worth aiming for.

That’s the kind of legacy worth leaving behind.

Mozah Al-Otaibi

Mozah is a seasoned innovation and strategy leader with over 15 years of cross-sector experience spanning logistics, public service, insurance and social development. As an advisor to the board at Behavia, Mozah brings her expertise in process architecture, service design, and innovation management to support impactful, human-centered policies and programs. Her work is defined by a deep commitment to systems thinking, cultural sensitivity, and building capabilities that last beyond the intervention.

In remote workplaces, we are often overwhelmed by shared information and struggle to empathize with distant colleagues’ views and arguments. To manage this, we unconsciously rely on heuristics, i.e., mental shortcuts that simplify complex decisions. While helpful, these shortcuts can increase misunderstandings within teams and lead to suboptimal group decisions. Below are four common biases and strategies to mitigate their impact on remote-working teams.

1. Availability bias

When forming expectations about likelihoods, we often over-rely on easily accessible information – things we vividly remember, events that occurred recently, or emotionally impactful experiences. In remote settings, where shared experiences and routines are limited, this bias can loom larger. As a result, teams may place too much emphasis on readily available scenarios while systematically underestimating less vivid ones.

What can we do?

• Broaden your perspective: Consider what probabilities or scenarios someone with a different background might envision. This helps refine spontaneous assumptions.
• Separate likelihood from impact: Evaluate the probability of a risk separately from the harm it could cause. You may find that the actual likelihood is smaller than it initially seemed.

2. Fundamental attribution error

We tend to over-attribute others’ actions to their personality while underestimating the role of external factors on their decisions. In remote work, the lack of informal interactions and casual conversations makes it harder to understand the external influences on colleagues’ decisions or anticipate their emotional states. This can lead to frictions and misunderstandings within teams

What can we do?

• Change your perspective: Before jumping to conclusions, put yourself in your colleague’s shoes. Given the same circumstances, would you have acted differently at a particular moment of decision making?
• Practice empathy: Take critical feedback as an opportunity to do better next time rather than taking it as personal criticism. Plan proactively for personal responsibilities, such as family time, social or medical obligations, and align off desk time with your remote co-workers to avoid misunderstanding.

3. Affinity bias

Also known as the similarity bias, this bias refers to our inclination to gravitate toward people like ourselves in appearance, beliefs, interests and backgrounds. We might avoid or even dislike people who are different from us. The affinity bias is known to negatively affect recruitment decisions. In remote environments, this bias can lead to the formation of cliques and exacerbate cultural differences between teammates.

What can we do?

• Find common ground: When meeting new teammates, invest time in discovering shared experiences or interests. For example, arrange short, recurrent coffee touchpoints with colleagues from “the other culture” just to discuss each other’s news.
• Institutionalize cultural exchange: Organize intercultural onboarding sessions, retreats, and team-building activities to foster mutual understanding and empathy.
• Promote diversity: As a manager, embrace the global recruitment opportunities remote work provides. Don’t limit your and your team’s performance by surrounding you with people who have the same background and experiences either in shared projects or in the process of recruiting new colleagues.

4. Present bias

We are often inconsistent in evaluating returns on investment. When thinking about the near future, we settle for smaller returns than when thinking about the far future. In remote work, this can manifest as procrastination, with attention diverted to more enjoyable activities – such as watching TV or online shopping – at the expense of timely submissions of high-quality outputs.

What can we do?

• Set self-imposed deadlines: Commit to self-imposed deadlines by informing your managers or co-workers about submission dates (ahead of the end deadline).
• Break tasks into smaller steps: Completing smaller milestones allows your progress to be acknowledged, providing immediate rewards that keep you motivated.
• Maintain focus on long-term goals: To ensure that such rewards do not undermine long-term objectives you can display a desired end result in an online canvas visible at all times by everyone.

In today’s interconnected and fast-paced world, recognizing and addressing biases is critical to fostering inclusive and productive remote work environments.

However, it is important to bear in mind that the four behavioral biases do not capture all the root causes of work-related misalignments but rather serve as a starting point for understanding the challenges of remote collaboration.

Sources and further reading material

• Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219–224.Kahneman, D. (2015). Thinking, fast and slow. Farrar, Straus and Giroux.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
• Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality and Social Psychology, 16(2), 329–337.
• Staw, B. M. (1976). Knee-deep in the big muddy: A study of escalating commitment to a chosen course of action. Organizational Behavior and Human Performance, 16(1), 27–44.
• Zschirnt, E., & Ruedin, D. (2016). Ethnic discrimination in hiring decisions: A meta-analysis of correspondence tests 1990–2015. Journal of Ethnic and Migration Studies, 42(7), 1115–1134.

Stefanos Stasinopoulos

Stefanos Stasinopoulos is a Senior Consultant for Behavioral Public Policy at Behavia. He holds a MSc in Behavioral Science from the Erasmus University of Rotterdam, Netherlands, and has more than 8 years of experience in Human Resources Management and experimentation in the private sector and the United Nations.

He specializes on empowering HR practices, online experimental design, hypotheses development, qualitative analysis, and research scoping. Besides his work at Behavia, Stefanos is an active member of the Nudge Unit Greece initiative.

It’s about understanding their needs, desires, and decision-making processes. Behavioral science reveals the subtle yet powerful drivers behind consumer behavior, such as the cognitive biases that shape purchasing decisions and the emotional triggers that influence brand loyalty.

In today’s saturated market landscape, where every brand is vying for consumers’ attention, leveraging these behavioral insights can make the difference between a campaign that resonates and one that fades into the noise. A decade ago, I might not have fully grasped the significance of psychological nuances, but experience has taught me that they are the cornerstone of effective advertising. Understanding what truly matters to your audience isn’t just a strategy, it’s the key to building meaningful, lasting connections.

Imagine you have produced a natural energizing juice that could tap into the growing health and wellness market. In this scenario, you need to find a way to break through the clutter and tap into the target market wants and needs. Traditional marketing and communication methods often rely on what customers say they want. However, what if I told you there’s a more accurate way to uncover their true preferences?

The Challenge: Listening vs. Observing

Take a look at the above two illustrative images for an energy drinks ad; which ad do you think would be more appealing to consumers? A quick survey would have probably shown that participants preferred ad B – an ad highlighting the health benefits of the new energy juice. On the surface, this makes sense – the health and wellness trend is booming, and consumers claim to prioritize natural healthy products. However, relying solely on responses can be misleading, as we often find in our personal life, a gap may exist between what we say we want and what we actually end up doing. Behavioral Insights (BI) offer a deeper understanding by observing what people actually do, not just what they say.

What could advertisers miss?

While the natural ingredients and health benefits are important, highlighting those facts may not be enough. Context is key. Energy drinks aren’t just for physical performance—they’re about focus, productivity, and mental stamina. An image of someone drinking energy juice while working (ad A), would speak directly to professionals who need energy to power through their day, making the product relatable to larger audience. Our wants and desires are multi-faceted and intertwined, and so consumers consciously and subconsciously consider a multitude of elements beyond the apparent benefits that could create a stronger emotional connection that motivates them to consume that healthy product. These might include:

• Excitement and ‘Focused’ Energy: While health benefits are appealing, energy drinks are traditionally linked to excitement, vitality, and performance (whether physical or mental). An ad focusing solely on wellness risks missing the dynamic, energizing appeal customers expect. By connecting the drink to both mental focus and high-energy outcomes, advertisers can bridge excitement with productivity to drive real impact.
• Social Proof: Seeing others, especially influential figures or peers, using and endorsing the product can significantly impact purchasing decisions. Social proof creates a sense of trust and community around the product.
• Brand Identity and Storytelling: Customers are much more likely to connect with brands that have a compelling story or a strong identity. Ads that incorporate storytelling and a unique brand persona can create a deeper, more emotional connection.
• Immediate Gratification: Customers are often driven by immediate gratification. Highlighting instant energy boosts and performance enhancements can be more compelling than long-term health benefits.
• Visual Appeal: Aesthetics matter. Ads that are visually striking and engaging can capture attention more effectively than those that are purely informational.
• Convenience and Lifestyle Fit: Showing how the product fits seamlessly into the consumer’s lifestyle, whether through portability, taste, or ease of use, can make the product more appealing.

Harnessing BI for Effective Advertising: Lessons Learned

Reflecting on my past campaigns, I see how Behavioural Insights (BI) could have taken them further—not by replacing traditional approaches but by building on and complementing them. Traditional marketing tools like sales data and customer feedback are still invaluable for understanding brand awareness and initial preferences. BI expands on these methods, providing a deeper dive into the behavioural drivers and barriers to bridges the gap between what customers say they want and what truly drives their decisions. By putting as much focus on measurable behaviors as on awareness measures, BI helps refine advertising strategies and tactics, ensuring they resonate not just with customer ideals but with their real-world choices.

1. Digital/Online Experiments

Digital experiments, which are often conducted through A/B testing, involve testing different versions of an advertisement or a marketing message online to see which performs better. This can be done for example using Google Ads where you can run two versions of an ad—one highlighting natural ingredients and another showcasing high-energy activities. You can also use an email campaign to send different email versions to segmented audiences, one focusing on health benefits, another on immediate energy boosts. As another option, you can conduct an A/B test on a website where you display different homepage banners, one emphasizing health, the other excitement. And lastly, you can use social media posts where you post variations of content to see which garners more engagement.

How it works:

1. Create Variants: Develop multiple versions of digital content. Change only one element and leave the rest the same (for example, the headline).
2. Randomize Audience: Use the tools available for A/B tests to ensure each variant reaches a distinct group and that no overlapping occurs, i.e., each distinct group receives only one variant.
3. Track Engagement: Monitor metrics such as clicks, shares, and conversions.
4. Evaluate Effectiveness: Analyze which variant performs best across the different metrics.

By running A/B tests simultaneously and analyzing the engagement, click-through rates, and conversions, you will be able to see which variant resonates more with our audience. This real-time data provides a more accurate understanding of customer preferences and behaviors that allows you to test tailored variants to different segments within your audience.

2. Field Experiments

Field experiments involve testing interventions in real-world settings. For example, you could launch the energy drink in selected stores with different promotional strategies. One store could display an ad that emphasizes health benefits, while another an ad that focuses on excitement or social proof. By observing sales data and customer feedback between the different supermarkets, you could determine which approach resonates better.

How it works:

1. Determine the Test Environment: Choose different regions or clusters of stores for different ad strategies.
2. Implement Interventions: Roll out the targeted ads in these regions/stores.
3. Collect Data: Gather sales figures, customer feedback, and engagement metrics.
4. Analyze Results: Compare performance across regions to identify the most effective strategy.

3. Survey Experiments

Survey experiments combine traditional surveys with experimental manipulations. By embedding experimental conditions within surveys, we can test different scenarios and measure responses. Create survey versions that show participants either a health-focused or excitement-focused ad before asking questions about their purchase intentions and perceptions.

How it works:

1. Design the Survey Experiment: Develop multiple versions with varying content. For instance, develop one version featuring the health benefits of the energy drink, another focusing on the excitement and immediate energy boost, and a third one using social proof by including testimonials or endorsements. Test visual elements like images or videos of the ads.
2. Randomize Participants: Ensure respondents are randomly assigned to different versions.
3. Collect Responses: Use survey tools to gather data that measure purchase intentions, brand perceptions, and emotional responses, etc.
4. Analyze Data: Compare responses to understand which elements drive positive perceptions and intentions.

Complementing Traditional Marketing with BI

Incorporating BI doesn’t mean abandoning traditional marketing methods. Surveys and focus groups are valuable for initial insights, but BI tools like field experiments, digital experiments, and survey experiments offer straightforward ways to gather actionable insights and real-time analytics. This combination ensures we target customers more effectively, reducing guesswork and maximizing impact.

By leveraging BI and marketing, we can understand what truly drives customer behavior and make informed decisions that enhance marketing effectiveness with more accurate results.

Dr. Maha Baz

Dr. Maha Baz is a Senior Researcher / Senior Consultant at Behavia and a Strategic Communication and Media Advisor for Excellence. She has 20 years of experience in strategic marketing, communication, and consumer behavior. She holds a PhD in Marketing from the University of Leicester on the subject of ‘Sharing Economy in the Arab world’ and has written a book chapter about it in the “Handbook on the Sharing Economy”. She advises and consults public and private sector clients on marketing with behavioral insights.