It seams that the innovations in digital advertising and thus the discussed questionable business models have evolved primarily because advancements in technology allowed their realization. Organizations with their innovation teams and data scientists are notably productive in developing use cases, experiments and eventually solutions to increase marketing effectiveness, to improve operations and even enhance products and services offered. However, the iceberg approach to trust requires putting the customer first. It is crucial to understand the digital customer and it’s mind in particular. Hence, the focus of this chapter lies on analyzing changing customer characteristics and crucial findings from Neuroeconomics and Behavioral Economics that describe how customers make decisions in uncertainty – the most basic mechanism that requires and therefore develops trust.
Giving away personal data in a situation of uncertainty and without adequate compensation seams to be an irrational behavior. This contrasts sharply with the traditional economic theory of a rationally acting individual, the Homo Economicus. Modern behavioral economists and neuroeconomists have demonstrated that consumers are, in fact, not rational in their decision-making. This chapter gives a basic understanding of how the human brain works when making decisions. Knowledge about the limitations we face when processing information under uncertainty helps to understand the importance of trust.
Enabled by new technologies customers have become more elusive than ever before. In order to stay competitive, companies must enhance their limited view of the customer and understand them in the context of their social networks. While information and communication technologies are about to reach broad acceptance and ever higher levels of maturity, business need to adapt their marketing strategies to take new customer characteristics into account: In recent years technology have given rise to a more empowered global consumer:
Consumers became more knowledgeable. They became much more tech savvy and more active in controlling the consumption cycle. In addition, the ever-increasing number of online consumer is well connected and creates content and social networks. In a first wave of the digital transformation users have been given access (deliberately or not) to rich information that may effectively support their decision-making. In the current state of the transformation however, information overload and deliberately caused confusion (e.g. through dynamic pricing) provides new opportunities for businesses to build strong, valuable relationships with customers by aligning their marketing strategies towards transparency and trust. Regulators protecting privacy and other consumer interests more than ever before also drive the need for clarity and transparency.
Digital consumers are more demanding than traditional consumers. Today, online users benchmark prices and quality of service providers and products across industries. They are expecting comparable standards as well as rewards for loyalty. Convenience, flexibility and personalization are a given. A more hedonistic attitude of consumers requires putting the user first.
The ability to establish a mutual, honest dialog and a long-term partnership with customers will be central to defining the winners of tomorrow. A short review of the evolution of marketing outlines that businesses need to adapt again to the described, new needs to empowered customer.
A company that acts according to a Push-Strategy provides incentives for the markets to include its products in their product range. In contrast, companies following the Pull-Strategy advertise their products directly to the consumer to gain audience awareness.
CRM puts the individual and its data in the center of communication strategies. It is a business process in which client relationships, loyalty and brand value are built.
Customer Advocacy aims to build deeper customer relationships by earning new levels of trust and commitment. It’s all about the development of mutual transparency, dialogue and partnership with customers.
Classical mass markets that have been driven by monopolistic structures once required a marketing approach that is widely know as the Push-Pull Marketing. A company that acts according to a Push-Strategy provides monetary and non-monetary incentives for the markets to include its products in their product range. Markets can then increase the visibility of these products. This differentiation from products of competitors is an additional stimulus for the consumer to eventually buy the product (Szeliga, 1996). In contrast, companies following the Pull-Strategy advertise their products directly to the consumer to gain audience awareness. The resulting demand from consumers then motivates markets to add the products to their product range. Obviously, this strategy is only relevant to businesses of a certain growth and financial power.
A successful strategy usually shows elements of both the push and pull promotional methods. Push-Pull Marketing is most relevant in monopolistic markets with a supply of products and services that is limited. It therefore lost traction with the rise of saturated markets. However, the pull strategy is still relevant in digital markets. It shifts the emphasis and attention onto the user and its needs. Streaming platform like Spotify and Apple Music discovered music playlists as highly effective pull marketing resources. Whereas music listeners were forced over years to buy full albums of their preferred artist they can now download single songs and combine them in playlists. These lists can then be shared easily on the streaming platform or on other social media channels.
The rise of saturated markets and decreased efficiency of mass advertising are only two of many factors that forced marketers to evolve their strategy. A more individual approach to marketing was required. Capabilities for individual communication to customers become increasingly relevant with decreasing possibilities to differentiate by quality, service or price. New communications technologies such as the Internet not only allowed to start a true dialogue with individual customers, they also provided access to relevant data about consumer behavior and preferences. Customer Relationship Marketing (CRM) puts the individual (and its data) in the center of communication strategies. It is a business process in which client relationships, loyalty and brand value are built. In contrast to Push-Pull Marketing, CRM focuses on long-term customer engagement and customer loyalty rather than shorter-term goals like audience awareness and individual sales. This new marketing paradigm required new highly integrated IT systems, which focus on knowledge about the customers and support all customer oriented business processes.
“Far from being foolish, the honesty of advocacy reflects the reality that customers will learn the truth anyway. If a company is distorting the truth, customers will detect the falsehoods and act accordingly.” (Urban 2005).
In order to engender trust and to build a true partnership with its customers, companies need to go a step further in the evolution of marketing. MIT Sloan Professor Glen Urban has conceived an interesting approach that is fully oriented to the characteristics and needs of the empowered customer. Professor Urban describes an advanced form of market-orientation that responds to the new drivers of consumer choice, involvement and knowledge. Customer Advocacy aims to build deeper customer relationships by earning new levels of trust and commitment (Lawer et al., 2006). It’s all about the development of mutual transparency, dialogue and partnership with customers.
Customer Advocacy uses the mindset and tools of the CRM-approach as well as orientation towards total quality and customer satisfaction as a foundation. Companies following this strategy act as an honest and unselfish advocate for the customer. This includes providing the customer with the best offer even if it comes from a competitor (Urban 2005b). In order to effectively engender online trust it is mandatory to follow this approach for trust-based marketing.
The discussed advances in information and communications technology have given rise to a new, more empowered consumer. 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 to explain customer behavior in a digital world.
In order to understand the mind of the digital customers – and eventually to understand 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 shall give an easy to understand introduction into the field of neuroscience and the core concepts around human decision-making.
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 names these systems the cool and hot thinking (Mischel, 2014) or simply system 1 and system 2 (Kahneman, 2011).
Research has come a long way to analyze and differentiate these two systems. Whereas the neurobiological approach divides the roles of information processes using an anatomical division (into the limbic system and pre-frontal cortex), the neuropsychological approach uses a dual mode of thinking and is reluctant to assign the information processing functions to a specific organ within the brain. When thinking about the brain, the important part is to understand the research on both sides of the issue and create a mental model that helps to understand how individuals process information.
The two systems of the brain can very easily be explained when 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 were able to wait for a short period. These experiments showed that children that demonstrated a high level of for self-control were more successful in life than children with low levels of self-control. In addition, Mischel showed that this ability can easily be trained and that there are effective strategies to increase one’s level of self-control. However, we are going to focus on the brain systems that are active when we are exposed to a stimulus and have to 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 is responding on the basis of 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 taking about the limbic system. The area of the brain arose in an early phase of the evolution. For our early ancestors to, the limbic system was key to an effective flight behavior. In a dangerous situation it is often more effective to react quickly and run then to elaborate a deliberate escape plan. The hot system resembles the unconscious, psychic instance defined by Freud as the “id”. A prominent area of the limbic system is the amygdala. It plays a primary role in the processing of memory, decision-making, and emotional reactions such as 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 for 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 to resist stimuli and to make future-oriented decisions.
The knowledge about these two very distinct systems of our brain helps us to understand the consumers mind. When prompted to provide personal data online, a customer will always process information though the two systems. It is key to know that the cool and the hot system always have an influence on and compete with each other. If we face a stressful situation, the cognitive system powers down and resources are allocated to the hot system. Alike the children in the Stanford experiments, digital consumers need to be able to resist temptations and to cool allures down in order to be able to make rational, deliberate decisions. Professor Mischel identifies a set of powerful strategies that allow individuals to increase their level of self-control. Among these is the strategy to reflect one’s situation from the perspective of another person. The cool system can more easily be activated when a hot decision is made for another person. This may explains the success of online recommendations and third party endorsements. Users may make wrong purchase decisions, but their online reviews (e.g. on Tripadvisor) are very specific and helpful for others to prevent the same mistakes. Reviewers are able to conceive meaningful recommendations, even if they have not bought or consumed the service or product themselves.
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 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 truly strong password instead of a 4-digit number – which was probably the user’s birthday.
Building initial trust relies heavily on the evaluation of 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 information 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 to develop these schemata’s.
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 the decision about the trustworthiness of another individual. Next to more transparent signals such as quality seals and recommendation, human beings consider the hidden mental-states or traits when making a decision 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 for higher social cognition. The outmost layer of the brain is known to support social interaction by enabling conscious thought, language, behavioral and emotion regulation, as well as empathy. It provides individuals with the ability to understand the feelings and intentions of others. 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 contends that the human brain is only capable of managing relationships with about 150 people. According to a study conducted by Dunbar on data from Facebook, this number retains its full relevance in the digital space.
To better understand the social brain, neuroscientists draw on modern research approaches such as the application of computational neuroimaging. I had the pleasure to meet with Professor John P. O’Doherty within the scope of 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 reflection of 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).
Observational-reward learning: An agent learns not trough direct experience but instead by observing the stimuli and consequences experienced by another agent.
Action-observational learning: the mere choice of action made by an observee that lead to consequences influence expectations of the observers.
Strategic learning about traits and hidden mental states of others. Only this learning system engages typically social brain areas such as the anterior dorsomedial prefrontal cortex (dmPFC) and the temporoparietal junction (TPJ) as well as the posterior superior temporal sulcus (pSTS).
A third interesting source of insights from neuroeconomics research comes the findings from INSEAD Marketing Assistant Professor Hilke Plassmann. While traditional economic theory draws on utility theory to explain purchase decisions, behavioral economic theories suggest an additional hedonic process that considers payment of a price as an immediate displeasure: The pain of paying. In the context of trust, the provision of personal data can also be considered an act of paying. Hence, providing personal data online is an actual affective experience that guides online behavior.
Plassmann’s recent research provides empirical evidence of a higher order affective pain-experience (not yet published). The pain of paying appears to be associated with affective pain and not with somatosensory pain. fMRI results show that affective and somatosensory pain take affect in different areas of the brain. The more intense this experience of pain, 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 result 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 mind. Obviously, marketing specialists have always been aware of the fact, that customers dislike the process of paying. Transaction data provides evidence for the fact that 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 travelers.
In the context of online trust, marketing specialists need to consider the fact that providing personal data is associated with affective pain. Hence, this perception of affective pain needs to be facilitated in order to get the customers consents and therefore access to the data. A promising strategy is the deliberate timing of data collection. Data can or should be collected along phases on the digital customer lifecycle when a low level of affective pain can be assumed. This topic is subject to the section about 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 transaction online. When buying a product online or providing personal data, a customer can never be perfectly sure that the transaction will fulfill his 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.
In order to understand this process, an overview of scientific theories about decision-making under uncertainty is helpful.
Behavioral economics provides an interesting set of theories that contribute to an answer on how people act under uncertainty. This discipline has been dominated for years by the picture of the homo economicus – an individual that acts fully rational without being influenced by any 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 brain is “hard-wired” to socially interact with other human beings. And all these interactions lead to information and eventually schemata that influence our decisions by the hot system even before the cool system kicks in. Empowered by technology, consumers have become networked minds, 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”:
The most basic economic theory relies in a purely mathematical view on probability theory. According to the expected value theory an agent aligns his behavior precisely to 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 19th 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 leads to wrong results and that also the relationship between the psychological value and actual money obeys rather a logarithmic 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.
Consider a theoretical game in which a fair coin is tossed. If tail appears the player is paid $2 (assuming an investment of $1). If head appears the coin is tossed again, until tail appears, doubling the initial gain every time the coin is tossed. The paradox is the discrepancy between what people are willing to pay to enter the game and the expected value. According to the expected value theory, the expected gain would be infinite and a rationally acting individual would be willing to play the game at any price if offered the opportunity. People tend to distrust this result.
Bernoulli observed that most people neglect unlikely events that yield the high prizes that lead to an infinite expected value. Most people dislike risk and will choose a sure thing that is less than expected value. Bernoulli’s utility function also simply explains why poor people buy insurance and why richer people sell it to them. Depending on the relative wealth an individual is happy (low wealth) to a premium to transfer risk to another individual (high wealth). The risk aversion is explained by the diminishing marginal value of wealth. Not only the expected value is subject to individual, subjective consideration but also the probability of occurrence.
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.
Kahneman and Tversky found that people’s preferences systematically violate the principle the expected utility theory (1979). People just can’t think in terms of a continuum of probabilities. Objective probabilities should rather be considered subjective decision weights. Most people that board an airplane round the probability of a crash to zero. But some people don‘t. They overrate the probability of crashing. As shown in the picture below, decision weights slightly defer from objective probabilities. Kahneman and Tversky suggest that small probabilities are overrated while middle and large probabilities are underrated. This implies that probabilities of risky options are underestimated compared to certain probabilities of winning (certainty-effect). The effect increases preference for the certain positive option (win), which in turn leads to risk-aversive behavior in decision-making. When faced with a certain negative option (loss), people tend to act risk seeking. A sure loss is very aversive, and this drives people to take the risk. People tend to become risk seeking when all their options are bad – they have “nothing to loose”.
In their prospect theory, Kahneman and Tversky extend simple utility theory by three mayor elements (1979, 2001):
The principle of diminishing sensitive as described by findings in psychophysics is adapted to the evaluation of changes in wealth.
Drawing on evolutionary history Kahneman and Tversky suggest that negative expectations loom larger than positive expectations. Survival and reproduction is better assured when threats are treated more urgently than opportunities. This results in a principle of loss aversion.
Thirdly, evaluation according to the prospect theory is relative to a neutral reference point. A decision maker considers prospects using a function that values all prospects relative to a reference point.
As a result of these principles the value function according to the prospect theory is S-shaped. It shows the psychological value gains and losses, which are the “carriers” of value (and not states of wealth). On the left side of the steeply sloping graph indicates that the response to losses is stronger than the response to corresponding gains while both sides of the reference point show diminishing sensitivity. This implies that people are very conscious about little changes around the reference point. Sensitivity to losses or gains is maximal in the very first unit of gains or loss. This explains the commercial success of slightly insignificant insurance policies for diamond rings, single flights or funerals. These are all easier to sell than significant policies like life insurance.
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 both an insurance policy and a lottery ticket. Furthermore, the prospect theory highlights implication of the following selection of effects (cognitive bias) that significantly influences decision-making:
Ambiguity effect
People prefer to choose an option with a known probability of a favorable outcome over an option where this probability is unknown. Even if the stock market lures with high returns over time, a risk-averse investor tends to invest money into conservative investments such as government bonds and bank deposits, as opposed to more volatile investments such as stocks and funds.
Certainty effect
As elaborated above when discussing the subjective probability weights, people tend to overweight outcomes that are certain relative to those that are probable. Certain probabilities of winning are preferred compared to risky options. A reduction of a chance of winning results in larger psychological effect when it is done from certainty than from uncertainty. Most people would pay more to remove the only bullet in the gun in a game of Russian Roulette than they would to remove one bullet when there were four in the gun.
Anchoring effect
Individuals judge the frequency, probability, or value of items often in comparison to an anchor point. A customer that considers a product that has a price tag with three different prices considers this a bargain, if the two highest prices on the tag are crossed out. The highest price on the tag may be seen as an anchor.
Framing effect
The framing effect is an example of a cognitive bias, in which people react to a particular choice in different ways depending on how it is presented; e.g. as a loss or as a gain. People tend to avoid risk when a positive frame is presented but seek risks when a negative frame is presented. Risky prospects can be framed in different ways as gains and losses. Framing a prospect as a loss rather than a gain, by changing the reference point, changes the decision due to the properties of the value function. German politicians preferred talking about an austerity package (“Sparpaket”) rather than budget cutting measures that have been inflicted on Greek people in response to recent economic crisis.
Endowment effect
People adjust to their level of ownership. This new level becomes the baseline by which they judge future gains and losses. When we own something, we focus on what we would give up rather than what we could gain. This effect explains the success of sales tactics such as the provision of trail versions and a “30 day money-back guarantee”
To sum things up:
The traditional economic concept of the rationally acting individual turns out to be incorrect. Decision-making must be best explained with theory of bounded rationality (Simon, 1997). People tend to make decisions with particular biases. Understanding these biases gives insights into the mind of the consumers and eventually reveals strategies for marketing to influence costumer decisions. The prospect theory gives important hints for potential strategies to engender and maintain digital trust:
Pay attention to detail.
Prevent customers from even smallest damages (losses) to trust that can occur for example through minor Data leakage. Sensitivity to losses or gains is maximal in the very first unit of gains or loss. People are very concise about little changes. It is easy to loos someone’s trust but hard to engender trust.
Pay attention to the reference point and framing.
The way agents subjectively frame an outcome or transaction in their mind affects the utility they expect or receive. Companies can actively define – or to a certain extent – manipulate framing.
Pay attention to anchoring.
The estimation of subjective probabilities and values is severely biased by anchoring. Third party endorsements and recommendations can support online trust. This works best when the own brand is compared to an already trustworthy brand (as an anchor). Pay attention to your main message as this would often be the first piece of information and therefore considered an anchor.
Pay attention to ambiguity.
Be transparent whenever possible and provide warranties. Demonstrate accountability. Online customers prefer to choose options with a known probability of a favorable outcome over options where this probability is unknown.
The prospect theory unveils some important insights about the bounded rationally 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 obvious by intuition that more specific factors like environmental situation and individual needs have to power to 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 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:
Current need
Average expected value
of each option
The variability of an option
According to the Risk-Sensitive Foraging Theory animals are trying to maximize the probability to reach a goal. An animal acts risk-averse if (1) it faces two options with different variability and (2) the risk of not satisfying it’s need if it chooses the option with high variability (while the option with low variability satisfies its need for certain). Conversely, an animal acts risk-prone if its need is higher than the maximum value offered by the option with low variability. It then chooses the option with higher variability and hopes that its needs will be satisfied also with lower probability.
This behavioral ecology theory contributes to a better understanding of digital trust by raising the awareness for the following details:
Pay attention to the context, actual value and transparency.
An individual tries to satisfy its individual, current needs. Whether an online user is willing to provide personal data highly depends on the goal that he or she wants to reach by using a service or making a transaction. In the digital work we most certainly don’t directly face option that puts survival in jeopardy. However, we face affluence problems that can reach significant individual weight. When faced with a threatening deadline, a service provided by an online productivity tool can contribute significant value. Furthermore, a customer needs to understand how the value offered correlates with the risk invested (reciprocity).
Pay attention to subjective variability.
The amount or expected value may differ from individual to individual. A young, digital native may have another interpretation of expected value of an option than a digital immigrant. Hence, younger users may act more risk-prone just because they attribute lower subjective variability to an option. They just don’t care whether a service provides value or not. It’s always worth a try.
Pay attention to information asymmetry.
Incomplete, false or partial information is one of the key challenges of online transactions. A service provider usually has more or better information than a customer. This information asymmetry needs to be removed though active communication (signaling). In particular, a service provider can signal consistent service quality – and therefore a low variability in value – by enabling customer rating and commenting. The more ratings provided by customers the lower the perceived variability in service quality.