Today’s digital economy has an enormous potential to transform most of the aspects of our live. The World Wide Web removes barriers for market entry, develops new, disruptive business models and eventually dissolves traditional values such as brick and mortar shops. Technology breaks boarders between organizations, countries and time zones. The way to modernity is characterized by a social change that “disembeds” individuals out of their traditional social relationships and localized interaction context (Giddens, 1995).
Every day, fresh start-up companies develop new perspectives on issues of our daily life and propose often disrupting solution. The consumer is faced with technologies and social concepts (such as the sharing economy) that are new and unknown. Digital trust disengages itself to an ever larger extent from the know concept of trust in persons and generalizes in institution-based trust (Luhmann, 1989). The iceberg approach to digital trust pays attention to different component of trust and provides a framework that allows marketing professionals to understand how trust is engendered.
Ever since the World Wider Web became a global phenomenon, scientists from a wide range of disciplines try to make the trust construct comprehensible. These studies all build on the broad scientific work that origins from a more analogue world. Deutsch has developed one of the most fundamental definitions for trust (1962): He defines a framework in which an individual faces two aspects. First, the individual has a choice between multiple options that result in outcomes that it perceives as either positive or negative. Second, the individual acknowledges that the actual result depends on the behavior of another person. Deutsch also mentions that the trusting individual perceives the effect of a bad result as stronger than the effect of the positive outcome. This corresponds to the findings of the prospect theory discussed in chapter 2.
Trust is the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party“.
(Mayer et al.)
Thus, trust is built if a person assumes that the aspired beneficial result is more likely to occur than a bad outcome. In this context there is no possibility to influence the process. The following example illustrates this situation; a mother leaved her baby to a babysitter. She is aware of the fact that the consequences of her choice depend heavily on the behavior of the babysitter. In addition, she knows that the damage from a bad outcome of this engagement carries more weight than the benefit of a good outcome. Important factors in the trust equation are missing control, vulnerability and the existence of risk (Petermann, 1985).
The existence of multiple options and diverse scenarios lead to ambiguity and risk. According to Lumann, individuals must reduce complexity in order to eventually make a decision in such a situation. Trust is such a mechanism that reduces social complexity. This context is best captured in the definition of trust developed by Mayer, Davis and Schoorman (1995: 712).
Trust can be an efficiently help to overcome the agency dilemma. In economics the principal-agent problem describes a situation where a person or entity (the agent) acts on behalf of another person (the principal). Due to information asymmetry, which is omnipresent in digital markets, the agent can either act in the interest of the principal or not by acting for example selfishly. Trust can solve such problems by absorbing behavioral risks (Ripperger, 1998). In situations where information asymmetry is present, screening and signaling activities are often inefficient due to high information cost. Trust can reduce such agency-cost (including imminent utility losses). It can increase the agent’s intrinsic motivation to act in the principal’s interest.
You must trust and believe in people, or life becomes impossible.
(Anton Chekhov)
A trust relationship as such can be seen as a principal-agent relationship. The relationship between a trusting party and a trusted party build on an implicit contract. Trust is provided as a down payment by the principal. The accepting agent can either honor this ex ante payment or disappoint the principal.
According to the principle-agent theory a trusting party faces three risks:
Adverse Selection: When selecting an agent, the principal faces the risk of choosing of an unwanted partner. Hidden characteristics of an agent or his service are not transparent to the principal before the contract is made. These leaves room for the agent to act opportunistically.
Moral Hazard: If information asymmetry incurs after the contact has been closes (ex post) the risk of moral hazard arises. The principal has insufficient information about the exertion level of the agent that fulfills the service. External effect such as environmental conditions can also influence the agent’s actions.
Hold Up: This type of risk is particularly relevant for the discussion about the use of personal data. It describes the risk that occurs if the principal makes specific investment such as providing sensitive data. After closing of the contract, the agent can abuse this one-sided investment to the detriment of the principal. The subjective insecurity about the integrity of the agent based on potential hidden intentions.
The described risks can be reduced through signaling and screening activities. Signaling is a strategy for agents to communicate their very nature, their true character. The provision of Certification and quality seals are expels for signaling activities. On the other hand, a principal tries to identify the true nature of an agent by applying screening activities. However, screening is only effective if signals are valid (the agent actually owns this characteristic) and if the absence of such as signal indicates the lack of this trait.
Our trust model resembles the shape of an iceberg. The iceberg metaphor works in many different ways:
First of all, trust must be seen as a precious good. It is hard to build, but can be lost very quickly. This makes it both a key differentiator to win against the competition and a potential pitfall that can easily destroy organizations. The Volkswagen emissions scandal has demonstrated how quickly trust is lost. It showed that even strong brands from traditional brick-and-mortar businesses can become severely damaged. The Internet accelerates this process. Bad news or reviews are spread at light speed around the globe and once negligible pieces of information can cause harmful shitstorms to spiral out of control. Like an iceberg at open sea, trust issues must be recognized early and marketers need to sail elegantly around such perils. On the other hand, trust is vital. Ongoing melting of the polar ice caps will inevitably raise the sea level and put coastal areas in danger. Similarly, if the trust factor is neglected, organizations are missing out on potential business opportunities and putting themselves at risk to be sunk by the competition.
Second, the sheer size of an iceberg usually remains unknown to the observer. This is due to ice’s density being less than liquid water’s density. Hence, only one-tenth of the volume of an iceberg is typically above the water level. This resembles the fact that most of the determinants of trust are less known, not understood or simply invisible. Moreover, it is difficult to manipulate those constructs. Trust therefore is often built when no one is looking. For companies this means that there are limited but important options to influence the information asymmetry in digital markets.
Just like the processes of freezing water and forming an iceberg take a lot of time, the process of building trust usually takes time. The primary way of gaining trust is to earn it by developing and nurturing a relationship with customers and future prospects. Companies can and must have control over the quality and intensity of the customer experience if they want to influence the level of trust of the customer. Opportunities to shape the experience exist at any touchpoints in the customer decision lifecycle. This model however focuses on understanding and engendering initial trust.
T he iceberg trust model differentiates between two major components that influence formation of initial trust. Trust constructs and cues/signals. Trust constructs refer to elements of the iceberg that are usually under water. They represent the schemata that unconsciously steer our behavior. Constructs represent mechanisms driven by our hot system (refer to chapter 2 for more details on insights in the mind of the digital consumer).
Cues or signals refer to strategic areas and design patterns companies apply to engender trust. They are above the surface and allow for reducing information asymmetry as outlined in the discussion of the principal-agent theory.
The model described hereafter is in large parts influence by recent research from local institutions, the University of St. Gallen (Hoffman et. al, 2015) and the Swiss Federal Institute of Technology in Zurich (Helbing, 2015). Furthermore, it is built on the fundament of social cognitive theory. As outlined in the last chapter, cognitive structures allow for orientation in complex decision-making situation. The resulting behaviours are not always rational because individuals are influenced by cognitive biases.
McKnight et. al. provide a very useful model for initial trust (2002). While analyzing the broad literature on trust in context of new communication technologies, they found that what most of the established models have in common is some combination of trusting disposition, cognition and willingness/intentions. Furthermore, they draw on the Theory of Reasoned Action to structure their argumentation. The theory shows that trust formation follows a catenation process. Beliefs lead to attitudes, which lead to behavioral intentions, which lead to the behavior itself.
The base of the iceberg is characterized by four major constructs. Together, they describe the catenated process that forms trust und leads to trusting behaviours:
Institution-based trust:
The first element that constitutes the underwater body of the iceberg is the belief that needed structural conditions to achieve a positive outcome are present. This element includes requirements for a structure that make an environment trustworthy. Such a structural element is legal protection. A decent legal framework that protects privacy supports the belief in a favorable system. McKnight compares this mechanism that can make the World Wide Web a more trusted environment with the effects of the establishment of justice and protection in the “wild, wild west” of the 19th century. This structural assurance makes users belief that structures are in place to promote success. A second dimension of institution-based trust is situation normality. Online users that perceive high situation normality would belief that, in general vendors in the environment have the required trust building attitudes; competence, benevolence and integrity.
Disposition to trust:
The individual disposition to trust describes the extent to which an individual is willing to depend on the actions of others. This propensity is inherent in any actor in a very individual way. “All trust begins and ends with the self […]” (Kahre/Rifkin, 1997: 81). Reception of stimuli and information is influenced by a person’s attitude. In addition, individual characteristics such as age, education and income can influence the disposition to trust.
McKnight distinguishes two subconstructs of this disposition: Faith in humanity and trusting stance. With high faith in humanity a person assumes that others are usually upright, well meaning and dependable. Trusting stance means that an actor assumes optimistically a better outcome from an interaction than he would guess by assessing the characteristics of an agent. Principals may trust until they are proven wrong. Obviously, trust-building strategies may be different depending on the level of disposition to trust. Some users are more suspicious than others. The level of disposition to trust influences institution-based trust.
Trusting beliefs:
These beliefs describe the actual perceptions of specific attributes of web vendors. According to the cognitive-based trust literature, trusting beliefs may form quickly. This process is heavily influenced by the individual disposition to trust as well as by the level of institution-based trust. A user believes that the trustee has attributes that are beneficial to him. Obviously, there are many attributes that can be considered beneficial. The most prominent one’s are beliefs about competence, benevolence and integrity. These are the main, specific attributes E-Commerce users tend to focus on to gauge vendors. Empirical data shows that trusting beliefs demonstrate statistical separation. “[…] it is possible for a consumer to believe quickly that a Web-based vendor is honest and benevolent, but not competent enough to earn the consumer’s business or vice-versa (McKight, 2002, 352). The trusting beliefs differentiate over time. This gives room to shape customer experience as the relationship matures.
Trusting intentions and behaviors:
Influenced by all three constructs mentioned above, a user may build an intention to engage in a trust-related behavior and eventually acts. The provision of personal data to an online vendor is considered such a trust related behavior.
The iceberg model suggests four clusters of trust cues that sit at the tip of the iceberg: Reciprocity, brand, social adaptor and social protector. Each cluster holds again a set of trust signals or trust design patterns that can be considered by marketing professionals in order to engender trust. Chapter two and the discussion of the principle-agent problem highlights the importance of trust cues in online transactions. “Perceptions of a trust cue triggers preestablished cognitive and affective associations in the user’s mind, which allows for speedy information processing” (Hoffmann et al., 2015, 142).
Reciprocity:
Reciprocity is a social construct that describes the act of rewarding kind actions by responding to a positive action with another positive action. The benefits to be gained from transactions in the digital space originate in the willingness of individuals to take risks by placing trust in others that are regarded to act competently, benevolently and morally. A fair degree of reciprocity in the exchange of data, money, products and services reduces user’s concerns and eventually induces trust (Sheehan/Hoy, 2000). A user that provides personal data to an online service – either actively or passively –perceives this to be an input into an exchange. They expect an outcome of adequate value. A fair level of reciprocity is reached through transparent exchange of information for appropriate compensation. The table bellow shows the most relevant signals or strategic elements that establish positive reciprocity.
Value
A business needs to offer fair reciprocal benefits that are directly relevant to the data the business is collecting and storing. If the business if taking advantage of information that is not necessary to the service being provided, additional compensation needs to be considered. Because of their bounded rationality, consumers often are likely to trade off long-term privacy for short-term benefits.
Transparency
Fair and open information practices are an essential enabler for reciprocity. Users must be able to find all relevant information quickly. This leads to a reduction in real or perceived information asymmetry. Customer data advocacy can require altruistic information practices.
Terms & Conditions
Standard legal information such as Terms & Condition as well as security and privacy policies must be provided proactively and in an accessible way. Users need to be made aware of information collection as well of the way information is used. Consistency of this content over time is an important signal that facilitates trusting intentions.
Warranties
Warranties and guarantees support the perception of fair reciprocity and therefore signal trustworthiness. Opportunistic behavior will entail expenses for the agent.
Contact & Service
Pre- and aftersales service as well as any other touch point allowing a user to get in contact with an agent is a terrific opportunity to shape customer experience. Failures in this strategic element are penalized with distrust and unfavorable feedback.
3rd Party Sharing
Sharing data with 3rd parties has a very strong effect on the user’s willingness to provide data. Avoid data sharing behind the back of the user – even if legal compliance is given.
Social Mirror
In particular in social (e.g. non profit) situation, users can often not easily see benefits from providing data. A social mirror can facilitate this process by supporting awareness of the situation and the context in which this data adds value. In addition, data can be used to generate insights that motivate users to participate (e.g. shared health data).
Accountability
Users expect that access to their data will be used responsibly and in his best interests. If a company cannot meet these expectations or if an unfavorable incident occurs, businesses must demonstrate accountability. This requires processes and organizational precautions that allow reacting responsibly and fast.
Brand
A second powerful signal that promotes trusting beliefs is the brand of an entity. A company makes a certain commitment when investing in its brand, reputation and awareness. Due to the fact that brand building is a very expensive endeavor, consumers perceive this signal as very trustworthy. Capital invested into a brand can be considered a pledge that is at stake with every customer interaction and every transaction. Whether the investment pays of or whether it’s lost depends heavily on the true competency of a company. Strategic elements such as brand recognition, image and website design are supposed to trigger associations in the user’s cognitive system that prompt a feeling of familiarity.
Brand Image
The identity of a company triggers associations that together constitute the brand image. It is the impression in the consumers’ mind of a brand’s total personality. Brand image is developed over time through advertising campaigns with a consistent theme, and is authenticated through the consumers’ direct experience.
Recognition & Reach
Brand recognition is the extent to which a consumer or the general public is able to identify a brand by its attributes such as the logo of a product or service, tag line, packaging or advertising campaign. Familiarity through frequent exposure has been shown to elicit positive feelings towards the brand stimulus.
Consumers are more willing to rely on large and well-established providers. Digital consumer prefer brands with a broad reach. Search engine marketing is a relevant element that influences a brand’s relevance and reach. Many new business models rely on a competitive advantage in the ability to generate leads through search engine optimization (SEO) and search engine advertising (SEA).
Design Principles
Privacy by Design advances the view that the future of privacy cannot be assured solely by compliance with legislation and regulatory frameworks; rather, privacy assurance must become an organization’s default mode of operation. It is an approach to systems engineering, which takes privacy into account throughout the whole engineering process. Privacy needs to be embedded, by default, during the architecture, design and construction of the processes.
Design Thinking describes a paradigm – not a method – for innovation. It integrates human, business and technical factors in problem forming, solving and design. The user (human) centered approach to design has been extended beyond user needs and product specification to include the „human-machine-experience“.
Relevance
Digital customers are very demanding regarding the relevance of a product, service or information as such. Mass customization and personalization foster a good customer experience. The ability to process large amount of data allows to individualize transactions and to align production on pseudo-individual customer requirements.
Trough personalization a customer gets the feeling of being treated as a segment of one. Service provider can offer individual solutions and therefore increase perceived competency by giving better and faster access to relevant information. Personalization works best in markets with fragmented customer needs.
Site Quality
The quality of a company’s web presence can foster brand reputation and enhance brand recognition. High site quality signals that the company has the required level of competence. An important element of site quality is usability and the resulting user experience.
Social adaptor
The social adaptor is a strategic element that connects the cues that are processed by the cognitive system with the base of the iceberg. It is an interface to the construct of institution-based trust. The social adaptor holds innovative strategic elements in the technological space that influence how consumers perceive structural assurance and situation normality. As outlined in chapter 1, system complexity in a modern digital world increases on a factorial scale. Increasing system complexity in turn requires more decentralized control mechanisms (Helbing, 2015). This is why digital consumers must be given more control over their personal data. It is a matter of time before decentralized user control becomes a legal requirement.
Drawing on the iceberg metaphor, most elements of the social adaptor already lie underwater. This means that companies cannot directly influence these developments. Hover, they can engender trust by adopting effective concepts of the social adaptor early and conscientiously.
User Control
Companies can signal willingness to empower users by providing measures of user control. Such measures can create the impression that an individual can actively reduce the risk of data abuse. Hence, a consumer perceives lower cost of a transaction. This makes the transaction more attractive and therefore engenders trust.
An essential element of user control is the application of permission-based systems. Permissions must base on the user’s dynamic consent. This consent involves a continuing obligation to comply with the user’s choice.
Trust-less Systems
Trust-less Systems can replace confidence in the trust of an organization or the government with the cryptographic security of mathematics. Recent innovations around cryptographic currencies and the blockchain protocol in particular provide the ability to avoid the need for a trusted third party. Agency problems such as moral hazard and hold up arise from imperfections of the human nature. The cryptographic security of mathematics makes human interaction – the weakest link – obsolete.
The blockchain protocol enables the trustless exchange of any kind of digital asset from domain name signatures, digital contracts, digital titles to physical assets like cars and houses.
Trust Frameworks
In digital identity systems, a trust framework is a certification program that enables a party who accepts a digital identity credential to trust the identity, security, and privacy policies of the party who issues the credential and vice versa.
Social protector
The empowered customer has identified online review systems as a very intuitive, robust and therefore useful source of information for decision-making. Uncountable amounts of rating and reviews are posted every day. With the enormous size of their communities, consumer websites relying on reputation systems have become some of the most frequently visited pages of the World Wide Web. Cues within the cluster of social protectors can support users to avoid lossy interactions. They can help to overcome ‘tragedies of the commons’ and create social benefits.
Affiliation
In contrast to a lock-in strategy, companies can gain customer loyalty by focusing on values a customer can relate to. Whereas the element of personalization is highly effective from a signaling perspective, the development of a feeling of affiliation and belonging can be successful from a screening perspective. “The feeling of belonging to a community, which deepens over time, leads to positive feelings and positive evaluation of the group members generating a communal sense of trust (Einwiller et. al. 2000: 2).
Reputation Systems
Reputation can be considered as a collective measure of trustworthiness – in the sense of reliability – based on the referrals or ratings from members in a community (Josang et. al. 2007). Signals from reputation systems are among the most popular elements screened by web users. The grown familiarity of users with these mechanisms is not only a benefit but also a risk. Reputation systems are not immune to manipulation.
3rd Party Endorsements
Third-party endorsements are primarily independent statements of certification authorities or other experts that attest honesty and trustworthiness. They usually facilitate the transfer of positive cognitive associations with the endorser to the endorsee. Signaling objective security measures through 3rd party certificates or guarantees enhance the feeling of security and generate trust.
Because of their high potential to increase the persuasiveness of the communication, companies can also draw on heuristic cues as communicators (Eagly/Chaiken, 1993). These are cues that are based on experience or common sense. Their use is meant to save time and reduce demands for thinking. Examples are experts, attractive persons or majorities.
Knowledge about fundamental trust components and trust cues allow marketers to understand how trust is built and how businesses can interfere in this process to further engender trust. However, the discussion about customer data advocacy requires broadening the horizon and extending the strategic framework with perspectives that are specific to data. This “data context” dimension analyses additional drivers for data advocacy. It identifies factors that individuals consider when determining if a given use of data is acceptable and that determine how users define the context of their data sharing. This research questions have been gaining a lot of attention from political and governmental institutions. They are hotly discussed in the US White House, the US Federal Trade Commission and the World Economic Forum.
An interesting study to establish insights into these issues and to inform development of appropriate policy frameworks has been conducted by Microsoft in 2012 and 2013 (Nguyen et al., 2013). The following objective variables have been recognized to impact user sensitivity in sharing personal data or trust in entities they interact with:
The results reinforced the relevance of context, indicating that what is considered acceptable use of data is personal, subject to change, and reflects differences in cultural and social norms (S. 231).
1. Type of data – what type of data it is (e.g., location, financial, medical)
2. Type of entity – who is using the data (e.g., retailer, employer, government)
3. Collection method – how the data is collected (e.g., actively provided by the user, passively collected without user awareness, inferred through analytics)
4. Device type – what kind of device is being used (e.g., mobile phone, PC)
5. Data usage – level of user involvement in the data use, from express consent to autonomous use
Paying attention to the fact that trust attribution varies significantly across cultural segments (refer to “consumer concerns in numbers“), the Microsoft team validated the importance of each driver across the globe.
Collection method and data usage are the two primary drivers that determine user sensitivity. This reflects the desire of users particular from developed markets to control personal data. In emerging markets, users lay greater emphasis on value exchange – a factor that is considered a subjective trust clue within the iceberg model – than data usage. This reflects the relatively higher importance in emerging markets of perceived value for personal data provided over benefits from control.
The importance of the two factors of data context “collection method” and “data use” equates to today’s notice/consent model. It is best practice to collect personal data from users actively participating in the transaction and upon informed consent by these users. However, “in the world of big data, most data will be passively collected or generated, i.e., without active user awareness, and it would be impractical if not impossible for users to give express consent with respect to all data collected” (Nguyen et al., 2013, S. 233). The remaining context variables as well as the trust cues can and must be leveraged to increase user acceptance in order to harness the potential of big data.
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