Innovations in digital advertising and their associated business models have emerged largely through technological advancement, raising essential questions about user trust and data privacy (Zuboff, 2019; Richards & Hartzog, 2020). While organizations and their data science teams continue developing sophisticated solutions for marketing optimization, operational efficiency, and service enhancement, research suggests that sustainable digital trust requires a customer-centric approach beyond technical capabilities (Kaptein et al., 2015).

Understanding digital consumer behaviour and decision-making processes is crucial for building genuine trust relationships. Recent findings in neuroeconomics and behavioural economics have revolutionized our understanding of how individuals make decisions under uncertainty – the fundamental context in which trust develops (Riedl & Javor, 2012). Research using functional magnetic resonance imaging (fMRI) has revealed that trust decisions activate specific neural networks, particularly in the striatum and prefrontal cortex. This suggests that trust formation involves both emotional and cognitive processes.

The widespread sharing of personal data online, often without clear compensation or understanding of future implications, appears to contradict traditional economic theories of rational decision-making (the Homo Economicus model). However, contemporary behavioural economics research has demonstrated that human decision-making is inherently bounded in its rationality (Thaler, 2016; Simon, 1982). Individuals frequently rely on cognitive shortcuts (heuristics) and are influenced by contextual factors, emotional states, and social cues when making privacy-related decisions (Acquisti et al., 2015). This growing body of research provides crucial insights into the neurological and psychological mechanisms underlying decision-making under uncertainty. Understanding these cognitive limitations and biases is essential for developing digital environments that promote genuine trust rather than exploit psychological vulnerabilities. Recent studies have shown that transparency in data practices and user control over personal information significantly influence trust formation in digital contexts (Waldman, 2018; Martin et al., 2023).

Enabled by digital technologies and social platforms, contemporary consumers have become increasingly sophisticated and challenging to understand through traditional marketing approaches (Lamberton & Stephen, 2016; Kozinets, 2019). To maintain competitive advantage, organizations must expand beyond conventional customer analytics to comprehend consumers within their complex social network ecosystems and digital environments (Kuma & Reinartz, 2016). While information and communication technologies have achieved widespread adoption and increasing sophistication, businesses must fundamentally reimagine their marketing strategies to address emerging consumer characteristics and behaviours. Research demonstrates that technological advancement has facilitated the rise of an empowered global consumer characterized by unprecedented access to information, peer recommendations, and market alternatives (Denegri-Knott & Zwick, 2012; Belk, 2013):

Establishing an authentic, bidirectional dialogue and sustained customer partnerships has become critical for organizational success in the digital economy (Morgan & Hunt, 1994; Kumar & Reinartz, 2016). The evolution of marketing theory and practice reflects a fundamental shift from traditional transaction-based approaches to relationship-centric models that address the needs of increasingly empowered consumers (Vargo & Lusch, 2004). Studies indicate that organizations must fundamentally reimagine their marketing strategies to align with the emerging paradigm of consumer empowerment and digital engagement (De Keyser et al., 2015).

Contemporary marketing research emphasizes that successful customer relationships in digital environments require transparency, authentic engagement, and value co-creation, moving beyond traditional customer relationship management approaches (Payne & Frow, 2005).

Traditional mass markets, characterized by monopolistic structures, relied heavily on Push-Pull Marketing approaches. In Push strategies, companies provide monetary and non-monetary incentives to channel partners to include products in their assortment. This approach enables markets to increase product visibility, and, as Szeliga (1996) argued, enhanced differentiation from competitors’ products serves as a crucial stimulus for consumer purchase decisions. Research has shown that such strategic differentiation through channel partner activation remains effective even in today’s digital marketplace (Gielens & Steenkamp, 2019).

In contrast, Pull strategies focus on direct-to-consumer advertising to generate brand awareness and demand. Recent studies indicate that when consumers actively seek out products, retailers are motivated to stock these items to meet customer expectations (Kumar & Pansari, 2016). However, implementing effective Pull strategies typically requires significant organizational scale and financial resources to sustain consumer communication campaigns and brand-building initiatives. This resource requirement often limits pure Pull strategies to larger, well-established organizations (Wilson et al., 2017).

The emergence of digital technologies, particularly the internet, enabled genuine customer dialogue and unprecedented access to consumer behavioural data. This technological evolution facilitated the rise of Customer Relationship Marketing (CRM), which places individual customer data and interactions at the centre of communication strategies. Unlike traditional Push-Pull Marketing, which focuses on short-term awareness and sales, CRM emphasizes building sustained customer engagement and loyalty through personalized interactions.

Successful implementation of this paradigm shift requires sophisticated, integrated IT systems capable of capturing and leveraging customer knowledge across all touchpoints (Kumar & Reinartz, 2016). Research indicates that organizations implementing such CRM infrastructures achieve significantly higher customer lifetime value and brand equity than those maintaining traditional transaction-focused approaches (Morgan & Hunt, 1994).

Research demonstrates that Customer Advocacy focuses on developing deeper customer relationships through three key elements: mutual transparency, sustained dialogue, and collaborative partnerships. This approach extends beyond conventional market orientation to address contemporary drivers of consumer choice, involvement, and knowledge in digital environments. Gartner presents a set of case studies for customer advocacy here.

Customer advocacy builds on the mindset and tools of the CRM approach, with total quality and customer satisfaction as a foundation. Companies that follow this strategy act as honest, unselfish advocates for customers. This includes providing the customer with the best offer even if it comes from a competitor (Urban 2005b). Following this approach in trust-based marketing is essential to engender online trust effectively.

The discussed advances in information and communications technology have given rise to a new, more empowered consumer. The increasing importance and reliance on data not only changed the way companies produce and provide services and products, but also the way customers make decisions about their consumption and behaviours. Traditional theories in social sciences are no longer able to explain customer behavior in a digital world.

To understand the minds of digital customers (and eventually how digital trust works), marketing strategists need to draw on new findings from neuroscience, experimental and behavioral economics, and cognitive and social psychology. The rest of this chapter is dedicated to this promising, interdisciplinary field of science: Neuroeconomics.

Consumer neuroscience research applies tools and theories from neuroscience to better understand decision-making and related processes. Drawing on new methods and technology (such as functional magnetic resonance imaging, fMRI), this scientific discipline can make important contributions to consumer behavior and marketing (Plassmann et al. 2015). Neuroimaging can help turn the black box of the consumer’s mind into an aquarium. It can help validate, refine, and extend marketing theories by providing insights into the underlying mechanisms. The next sections provide an easy-to-understand introduction to neuroscience and the core concepts of human decision-making.

A. Two systems of the brain

An understanding of the basic structure and mechanisms of the human brain is key to understand the digital customers mind. The most fundamental insight in this context is the fact that our thinking relies on two distinct systems – dual-process accounts of reasoning. The first system operates automatically and quickly, with little or no effort and no sense of voluntary control. The second operates more slowly and allocates attention to the effortful mental activities that demand it. Popular science refers to these systems as cool and hot thinking (Mischel, 2014) or simply system 1 and system 2 (Kahneman, 2011).

Research has come a long way in analyzing and differentiating these two systems. Whereas the neurobiological approach divides the roles of information processing along anatomical lines (into the limbic system and the prefrontal cortex), the neuropsychological approach uses a dual-mode of thinking and is reluctant to assign information-processing functions to a specific organ within the brain. When thinking about the brain, the critical part is to understand the research on both sides of the issue and to create a mental model that helps explain how individuals process information.

The two systems of the brain can be easily explained by studying the famous Stanford marshmallow experiment conducted in the late 1960s by psychologist Walter Mischel. In these studies, Mischel tested children’s ability for self-control. Children were offered a choice between one small reward provided immediately and two small rewards if they waited for a short period. These experiments showed that children with high self-control were more successful in life than those with low self-control. In addition, Mischel showed that this ability can be easily trained and that effective strategies can increase one’s self-control. However, we will focus on the brain systems that are active when we are exposed to a stimulus and must make a decision. As shown in the picture below, there are two types of processing – hot and cool – involving distinct interacting systems (Metcalfe/Mischel, 1999):

The hot system is specialized for quick, emotional processing. It responds based on unconditional or conditional trigger features. Therefore, this system reacts fast and to a certain extent mechanically to emotions, instincts, or drives such as lust, pain, or fear. From an anatomical perspective, we are talking about the limbic system. The area of the brain arose in an early phase of evolution. For our early ancestors, the limbic system was key to effective flight behavior. In a dangerous situation, it is often more effective to react quickly and run than to elaborate a deliberate escape plan. The hot system resembles the unconscious, psychic instance, as defined by Freud, the “id”. A prominent area of the limbic system is the amygdala. It plays a primary role in memory processing, decision-making, and emotional reactions, including appetitive behavior.

The cool, cognitive system developed later in evolution. It is specialized for complex spatiotemporal and episodic representation and thought. The cognitive system is located in the prefrontal cortex. Research shows that this brain region plays an important role in planning complex cognitive behavior, personality expression, decision-making, and moderating social behavior. It orchestrates thoughts and actions in accordance with internal goals. The cool system arose in a later phase of evolution. In the context of the marshmallow experiments, the prefrontal cortex is responsible for the children’s self-control. It allows one to resist stimuli and to make future-oriented decisions.

Knowledge of these two very distinct systems of the brain helps us understand the consumer’s mind. When prompted to provide personal data online, a customer will always process it through both systems. It is key to recognize that the cool and hot systems always influence and compete with each other. When we face a stressful situation, the cognitive system powers down and allocates resources to the hot system. As with the children in the Stanford experiments, digital consumers need to be able to resist temptations and cool allures to make rational, deliberate decisions. Professor Mischel identifies a set of powerful strategies that help individuals increase their self-control. Among these is the strategy of reflecting on one’s situation from another person’s perspective. The cool system can more easily be activated when a hot decision is made for another person. This may explain the success of online recommendations and third-party endorsements. Users may make incorrect purchase decisions, but their online reviews (e.g., on TripAdvisor) are particular and helpful to others in preventing the same mistakes. Reviewers can offer meaningful recommendations, even if they have not purchased or used the service or product themselves.

Increased sensitivity of online customers regarding privacy and higher competence in dealing with personal data support the activation and, therefore, the weight of the cognitive, so the cold, system. The evolution of the digital mind will make it increasingly more difficult to extract an email address from a visitor with promises of a chance to win an iPhone. Already, users have become more willing to deliberate a robust password instead of a 4-digit number – which was probably the user’s birthday.

An increased sensitivity of online customers regarding privacy and higher competence in dealing with personal data support the activation and therefore the weight of the cognitive, so the cold, system. The evolution of the digital mind will make it increasingly difficult to extract an email address from a visitor who is promised a chance to win an iPhone. Already, users are more willing to use a strong password rather than a 4-digit number, which was probably their birthday.

Building initial trust relies heavily on evaluating existing schemata. Such schemata describe an organized pattern of thought or behavior that organizes categories of information and the relationships among them. Schemata can be seen as memory traces. Incoming data is matched with these available patterns and evaluated within the limbic system. The iceberg trust framework considers the importance of behavioral schemata and the fact that the mentioned mechanism within the hot limbic system can hardly be influenced (see chapter 3). Part of the journey towards a more sustainable approach to personal data is to benevolently support customers in building new competence in dealing with data and, eventually, in developing these schemata.

B. The social brain

Trusting other people or an organization is a highly social act. Our brain must be able to process an enormous amount of information to make a decision about the trustworthiness of another individual. Next to more transparent signals, such as quality seals and recommendations, people consider hidden mental states or traits when deciding to trust. This largely lies within the responsibility of the cool system. However, a more anatomical approach is required to better understand these mechanisms.

As a result of the evolution, the human brain is much larger than those of primates and mammals of similar size. It is particularly the “cool” neocortex that enables human beings to have higher social cognition. The outermost layer of the brain is known to support social interaction by enabling conscious thought, language, behavioral and emotional regulation, and empathy. It enables individuals to understand others’ feelings and intentions. A phenomenon referred to as theory of mind.

The mere size of the neocortex is said to determine the size of the group an individual can socially act in. Robin Dunbar, a professor of evolutionary anthropology at Oxford University, found this correlation between primate brain size and average social group size. He defined a cognitive limit to the number of people with whom one can maintain stable social relationships (Dunbar, 1992). His theory holds that the human brain can manage relationships with only about 150 people. According to a study by Dunbar based on Facebook data, this number remains highly relevant in the digital space.

To better understand the social brain, neuroscientists draw on modern research approaches, such as computational neuroimaging. I had the pleasure of meeting Professor John P. O’Doherty during a PhD seminar in neuroeconomics at the University of Zurich. His research on social cognition shows which brain areas are responsible for social interaction and mentalizing – the process of reflecting on the hidden mental states and intentions of others – in particular (O’Doherty et al., 2013). The study identifies three distinct learning systems that may contribute to social cognition and map onto distinct neuroanatomical substrates (as shown in the picture below).

C. The pain of paying

A third interesting source of insights from neuroeconomics research comes from the findings of INSEAD Marketing Assistant Professor Hilke Plassmann. While traditional economic theory draws on utility theory to explain purchase decisions, behavioural economic theories suggest an additional hedonic process that considers price payment as an immediate displeasure: The pain of paying. In the context of trust, providing personal data can also be considered an act of paying. Hence, providing personal data online is an effective experience that guides online behaviour.

Plassmann’s recent research provides empirical evidence of a higher-order affective pain experience (Plassmann, 2017). “Marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness” (Plassmann et al., 2008). The pain of paying appears to be associated with affective, rather than somatosensory, pain. fMRI results show that affective and somatosensory pain affect different brain areas. The more intense the pain experience, the lower a consumer’s Willingness-to-Pay (WTP). Manipulation of affective pain (e.g., through conceptual priming or drugs) can influence the consumer’s WTP.

These results show the power of contribution neuroscience can make to traditional economic disciplines such as marketing. It gives new and detailed insights into the mechanism of the consumer’s mind. Obviously, marketing specialists have always been aware that customers dislike the process of paying. Transaction data proves consumers spend more when they can pay with credit cards. In addition, the concept of all-inclusive hotels is widely spread and highly appreciated by travellers.

In the context of online trust, marketing specialists need to consider that providing personal data is associated with affective pain. Hence, this perception of affective pain needs to be facilitated to get the customer’s consent and access to the data. A promising strategy is the deliberate timing of data collection. Data can or should be collected along phases of the digital customer lifecycle when a low level of affective pain can be assumed. This topic is covered in the section on timing in Chapter 3.

Trust plays a central role in helping consumers overcome perceptions of risk and insecurity (McKnight, 2002). It is essential to enable business transactions online. When buying a product online or providing personal data, a customer can never be perfectly sure that the transaction will fulfill their expectations and the data provided will not be abused. The information available is always incomplete. Information asymmetry is omnipresent on the World Wide Web. Trust reduces this complexity by facilitating the customer’s decision-making process under uncertainty.

To understand this process, an overview of scientific theories of decision-making under uncertainty is helpful.

Behavioral economics offers an interesting set of theories that help explain how people act under uncertainty. This discipline has been dominated for years by the picture of the homo economicus – an individual who acts rationally, without being influenced by emotion. The homo economics draws on the cool system as described above to align his decisions fully on the maximization of benefit and minimization of cost. However, friends, feelings, and preferences influence even the coolest character in his behavior. As outlined above, our brains are “hard-wired” to interact socially with other human beings. And all these interactions lead to information and eventually to schemata that influence our decisions through the hot system, even before the cool system kicks in. Empowered by technology, consumers have become networked minds and social decision-makers more than ever before (Helbing, 2015). The following section provides a summary of the key concepts that describe economic behavior within the context of the transition from “homo economicus” to “homo socials”:

A. Expected value and utility

The most basic economic theory relies on a purely mathematical view of probability theory. According to the expected value theory, an agent aligns his behavior precisely with the predictions of mathematical probability theory. Behavior is a function of the expectancies one has and the value of the goal toward which one is working. The expected value of an outcome equals the objective probability of occurrence multiplied by the objective value of this outcome.

Although mathematically correct, this equation is not able to reflect reality. Basic findings in psychophysics first suggested that in the real world there is no linear relationship between a stimulus and an outcome – a subjective sensation in this case. Turning on a weak light has a large effect in a dark room. In a fully illuminated room such an increment of light may remain unnoticed. Prominent research in 19^th century suggests that for many dimensions the function is logarithmic (Weber-Fechner Law) or a power function (Stevens’ Power-Law)

Following this reasoning, economists agreed that a purely mathematical consideration of expected money yields incorrect results and that the relationship between the psychological value and actual cash follows a logarithmic rather than a linear function. It was Swiss scientist Daniel Bernoulli who enhanced expected value theory to the more subjective expected utility theory. Individuals no longer consider the objective Value of an option but rather a subjective equivalent to this value. Utility is a logarithmic function of wealth. This correlation can best be explained by examining the St. Petersburg paradox.

B. The prospect theory

The subjective expected utility theory is not fully capable of explaining why people will always choose a known probability of winning over an unknown probability of winning even if the known probability is low and the unknown probability could be a guarantee of winning – a key finding also known as the Ellsberg paradox or ambiguity effect.

In their prospect theory, Kahneman and Tversky extend simple utility theory by three mayor elements (1979, 2001):

The prospect theory helps to explain several behaviours observed in economics. The different perspectives on looming gains and losses may explain why the same person may buy an insurance policy and a lottery ticket. Furthermore, the prospect theory highlights the implication of the following selection of effects (cognitive bias) that significantly influence decision-making:

To sum things up:

The traditional economic concept of the rationally acting individual is incorrect. Decision-making must be best explained with the theory of bounded rationality (Simon, 1997). People tend to make decisions with particular biases. Understanding these biases provides insights into consumers’ minds and eventually reveals strategies for marketing that influence customer decisions. The prospect theory gives important hints for potential strategies to engender and maintain digital trust:

C. Perspectives from behavioral ecology

The prospect theory offers essential insights into the bounded rationality of human decision-making. However, as a descriptive theory, its ability to explain situations like the Ellsberg Paradox is limited.

Decision-making is often much more complex than described in mathematical models of the discussed theories. It is evident from intuition that more specific factors, such as environmental conditions and individual needs, can influence human decision-making.

An interesting approach to decision-making originates from research in behavioral ecology. Studies of the foraging behavior of birds showed that animals not only consider utilities and probabilities but also their individual need. Behavioral ecology shows that choosing a risky option can be regarded as an evolutionary stable strategy if the need is higher than the average, expected value of two options (Stephens 1981, Rode et al. 1999). It’s all about minimizing the probability that a current need remains unsatisfied – or in other words: it’s all about survival.

Three parameters seem to be relevant for decision-making when facing two options under uncertainty:

According to the Risk-Sensitive Foraging Theory, animals are trying to maximize the probability of reaching a goal. An animal acts risk-averse if (1) it faces two options with different variability and (2) the risk of not satisfying its need if it chooses the option with high variability (while the option with low variability satisfies its need for certain). Conversely, an animal is risk-prone if its need exceeds the maximum value offered by the low-variability option. It then chooses the option with higher variability and hopes that its needs will be satisfied with a lower probability.

This behavioral ecology theory contributes to a better understanding of digital trust by raising awareness of the following details: