2. Inside the Digital Consumer Mind

Chapter 1: Learn how pervasive consumer concerns about data privacy, unethical ad-driven business models, and the imbalance of power in digital interactions highlight the need for trust-building through transparency and regulation.

Chapter 2: Learn how understanding the digital consumer’s mind, influenced by neuroscience and behavioral economics, helps businesses build trust through transparency, personalization, and adapting to empowered consumer behaviors.

Chapter 3: Learn how the Iceberg Trust Model explains building trust in digital interactions by addressing visible trust cues and underlying constructs to reduce risks like information asymmetry and foster consumer confidence.

Chapter 4: Learn how trust has evolved from personal relationships to institutions and now to decentralized systems, emphasizing the role of technology and strategies to foster trust in AI and digital interactions.

Chapter 5: Learn that willingness to share personal data is highly contextual, varying based on data type, company-data fit, and cultural factors (Western nations requiring higher trust than China/India).

Chapter 6: Learn about the need to reclaim control over personal data and identity through innovative technologies like blockchain, address privacy concerns, and build trust in the digital economy.

Chapter 7: Learn how data privacy concerns, questionable ad-driven business models, and the need for transparency and regulation shape trust in the digital economy.

Chapter 8: Learn how AI’s rapid advancement and widespread adoption present both opportunities and challenges, requiring trust and ethical implementation for responsible deployment. Key concerns include privacy, accountability, transparency, bias, and regulatory adaptation, emphasizing the need for robust governance frameworks, explainable AI, and stakeholder trust to ensure AI’s positive societal impact.

Towards Trust-Based Marketing

Consumers have evolved into more sophisticated and knowledgeable market participants, demonstrating increased technological literacy and greater control over their consumption processes (Kozinets, 2019; Hennig-Thurau et al., 2010). Digital transformation has facilitated the emergence of interconnected consumer networks that actively generate content and form digital communities, fundamentally altering traditional market dynamics (Belk, 2013).

The initial digital transformation phase provided consumers unprecedented access to information, ostensibly enhancing their decision-making capabilities (Acquisti et al., 2016). However, the current digital landscape presents a paradox: while information is abundant, the complexity of market practices – such as algorithmic pricing and personalization – has created new forms of information asymmetry and consumer confusion (Martin & Murphy, 2017). This evolution allows organisations to differentiate themselves through transparent practices and trust-building initiatives, particularly as consumers struggle with information overload and decision fatigue (Waldman, 2018).

Digital consumers demonstrate heightened expectations and more sophisticated demands than traditional consumers (Rose et al., 2012). Research indicates that modern consumers routinely use cross-industry price comparison and service quality benchmarking enabled by digital platforms and comparison tools (Kumar & Reinartz, 2016). Studies show these consumers expect consistent service standards across channels and tangible recognition for their loyalty, with convenience, flexibility, and personalization now considered basic requirements rather than differentiators (Wilson et al., 2017).

Recent research in consumer psychology reveals an increasing shift toward hedonistic consumption patterns in digital environments, characterized by expectations of immediate gratification and seamless experiences (Holbrook & Hirschman, 1982). This evolution necessitates a fundamental shift toward human-centred design and service delivery approaches (Parasuraman et al., 2005). Data suggests that organizations failing to prioritize user experience and emotional satisfaction face significant customer attrition risks in today’s competitive digital marketplace (Duhachek, 2005).

Empowered customers have given rise to a new area
Empowered customers have given rise to a new area
  • Push-Pull Marketing

    Marketing literature distinguishes between two fundamental strategic approaches: Push and Pull strategies (Kotler & Keller, 2023). In push strategies, companies provide incentives to channel members to stock and promote their products, while pull strategies focus on direct consumer advertising (Armstrong & Cunningham, 2024).

  • Customer Relationship Marketing

    Customer Relationship Management (CRM) represents a data-driven strategic approach that places individual customer interactions at the core of organizational communication strategies (Payne & Frow, 2023).

  • Customer Advocacy

    Customer Advocacy represents a strategic evolution in relationship marketing, focused on building deeper customer connections through trust and commitment (Urban, 2022). Advocacy depends on three core elements: mutual transparency, authentic dialogue, and collaborative partnerships (Kumar & Pansari, 2023).

Successful strategies often blend push and pull promotional methods, though their relevance has evolved significantly with market conditions (Kumar & Palmatier, 2016). While traditional Push-Pull Marketing was most effective in monopolistic markets with limited supply, digital transformation has particularly revitalized pull strategies through new channels and consumer touchpoints (Wilson, 2018).

The digital marketplace has enabled innovative applications of pull marketing, which is particularly evident in the music streaming industry. Research examining platforms like Spotify and Apple Music demonstrates how user-generated playlists are powerful pull marketing tools (Morris, 2015;  Morris & Powers, 2015). This shift from traditional album-based distribution to playlist-centric consumption represents a fundamental transformation in how consumers discover and share music, creating organic promotion through social networks. Studies show that this user-driven content curation significantly influences consumer behaviour and engagement patterns in digital entertainment markets.

Playlist Pull
“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).

Building authentic trust and developing genuine customer partnerships requires organizations to advance beyond traditional marketing approaches (Urban, 2005a; Morgan & Hunt, 1994). MIT Sloan Professor Glen Urban pioneered a significant advancement in marketing theory through the concept of Customer Advocacy, which represents an evolution beyond traditional relationship marketing approaches (2005b). This framework responds directly to the characteristics of increasingly empowered, knowledgeable consumers in digital markets by emphasizing trust-building and authentic partnerships (Lawer & Knox, 2023).

The Mind of the Digital Consumer

brain

Let’s address  the Elephant in the room

The Elephant and Rider metaphor, introduced by psychologist Jonathan Haidt in his book The Happiness Hypothesis, illustrates the relationship between our emotional, intuitive mind and our rational, reasoning mind (2006).

The Elephant and Rider metaphor can be effectively applied to the context of trust in artificial intelligence (AI) by understanding how emotional intuition (Elephant) and rational reasoning (Rider) influence our perception and acceptance of AI. Trust in AI systems involves both the intuitive, emotional response to the technology and the rational evaluation of its performance, reliability, and ethical alignment.

The Elephant and Rider metaphor
The Elephant and Rider metaphor
The Elephant (emotions):

People’s trust in AI often begins with emotional reactions—fear, excitement, or scepticism. Concerns about job displacement, bias, or lack of transparency can evoke resistance, regardless of the system’s actual capabilities.

The Rider (reason):

The rational mind evaluates AI through technical metrics like accuracy, fairness, and explainability. Even if the system performs well, trust may falter if people feel emotionally uneasy about its use or implications.

Trust-building requires alignment:

Successful adoption of AI hinges on aligning the emotional (Elephant) and rational (Rider) perspectives. For instance, intuitive fears of bias can be addressed by rationally demonstrating fairness through transparent algorithms and policies.

Emotional trust precedes rational trust:

Without addressing emotional concerns – such as fears about privacy or loss of control – rational explanations of AI’s benefits may fail to persuade. People need to feel safe before they engage with the logical aspects of the technology.

Overcoming skepticism:

Transparency, relatable design, and user-friendly interfaces can calm the Elephant, reducing emotional barriers. For example, humanizing AI through voice, visuals, or explanations can make it feel less alien.

Preventing blind trust:

The Rider plays a crucial role in ensuring that emotional trust in AI doesn’t lead to complacency. Rational scrutiny ensures that systems are genuinely trustworthy and not just perceived as such.

Bias in both Elephant and Rider:

Cognitive biases influence both the emotional and rational responses to AI, potentially distorting trust. Awareness and education about these biases can help calibrate perceptions.

The ultimate goal:

To build trust in AI, organizations must design systems that appeal to both the intuitive and rational dimensions of human nature, creating technologies that are not only effective but also emotionally acceptable and ethically sound.

Shared decision-making:

Framing AI as a collaborative tool rather than a replacement can help balance the roles of the Elephant and Rider, fostering trust and acceptance.

Building a feedback loop:

Trust grows when users see AI delivering consistent and fair outcomes, reinforcing the Rider’s logical confidence and the Elephant’s emotional comfort.

Marshmallow Test
Our mind uses different strategies depending on its familiarity with the situation and what is at stake
Our mind uses different strategies depending on its familiarity with the situation and what is at stake
1

Observational-reward learning: An agent learns not trough direct experience but instead by observing the stimuli and consequences experienced by another agent.

2

Action-observational learning: the mere choice of action made by an observee that lead to consequences influence expectations of the observers.

3

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).

Social Brain

Online Decision-Making Science

Weber-Fechner Law
Stevens Law
St. Petersburg Paradox

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.

Decision Weights
1

The principle of diminishing sensitive as described by findings in psychophysics is adapted to the evaluation of changes in wealth.

2

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.

3

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.

Prospect Theory
Effects

Ambiguity effect

Certainty effect

Anchoring effect

Framing effect

Endowment effect

Cognitive biases
Hundreds of identified cognitive biases

Pay attention to detail.

Pay attention to the reference point and framing.


Pay attention to anchoring.

 


Pay attention to ambiguity.

 

 


1

Current need

2

Average expected value

of each option

3

The variability of an option

Risk-sensitive foraging theory

Pay attention to the context, actual value and transparency.

Pay attention to subjective variability.

Pay attention to information asymmetry.

Show references used in the chapter
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