The Hidden Structure of Mental Maps Brent Zenobia Department of Engineering and Technology Management Portland State University bcapps@hevanet.com Charles Weber Department of Engineering and Technology Management Portland State University webercm@gmail.com
Abstract Most prior work on mental maps has focused on techniques for their elicitation and representation; what can be gleaned from investigating their structure? This study applies the Motive-Technology-Belief (MTB) framework to analyze structural relationships in mental maps to gain insight into the underlying processes of learning and technology adoption. A previously unsuspected feedback loop operates between bounded rationality and intuition; implications for technology management are discussed.
Emotions Drive Adoption Q. Why would anybody but a professional photographer want a $3300 camera? A. Because it s FUN! Fun is its own justification. But how do emotions drive technology adoption? Source: Hume (1739/40); Frankfurt (1988); Schroeder (2004); Irvine (2006)
Emotions and Tacit Knowledge The emotional roots of adoption are unconscious and impossible to examine directly. Consumers aren t always aware of their own motives. The reasons they give for adoption are often after-the-fact justifications. Sophisticated techniques such as the Zaltman Metaphor Elicitation Technique (ZMET) are needed to to reveal tacit knowledge structures. Source: Levy (2001); Arnould and Epp (2006)
One Person s Mental Map of a Detergent Brand Softens clothes Feel comfortable ZMET is a qualitative method used in advertising to capture mental maps of brands. Dependable Fresh clothes Feel refreshed Selfconfidence Self-image Strong Clean clothes No worries What do these maps reveal? Source: Coulter and Zaltman (1994)
Problem Mental mapping techniques like ZMET are necessarily ad hoc and subjective. They are difficult to compare across informants and lack predictive power. How can we enhance these techniques so as to ease comparison and improve predictive power? We need a causal theory of adoption that reveals how consumers construct and use mental maps.
The Motive-Technology-Belief Framework The Motive-Technology-Belief (MTB) adoption framework provides a theoretical context for decoding the internal structure of mental maps. Motives, representations of inner mental reasons Selecting uses beliefs Technologies, tools pertaining to motives Auxiliary Processes (perceiving, categorizing, focusing, framing, acting) change motives Beliefs, judgments about cause and effect Maintaining changes technologies Evaluating changes beliefs
MTB: Background MTB was derived from a qualitative empirical field study (see Zenobia, 2008 dissertation.) MTB reveals the deep structure of technology adoption. Structural building blocks (motives, technologies, beliefs) Behavioral processes (selecting, evaluating, maintaining) MTB is a simulation-ready theory of adoption developed for use with agent-based artificial markets. MTB is useful as an analytical framework in its own right. To glean hidden insights from mental maps. To analyze the value proposition for new products and services. As a measurement model for adoption surveys. We will illustrate with a branding analysis.
The Deep Structure of Adoption Adoption and rejection emerge from the behavioral interactions of motives, technologies, and beliefs. Motives, representations of inner mental reasons Selecting uses beliefs Technologies, tools pertaining to motives Auxiliary Processes (perceiving, categorizing, focusing, framing, acting) change motives Beliefs, judgments about cause and effect Maintaining changes technologies Evaluating changes beliefs
Motives: Inner Mental Reasons Motives Needs are means. Desires are ends. Plans are conscious, short-term, pragmatic needs. Images are unconscious, long-term values. Hedonic desires are unconscious motives to seek rewards and avoid penalties. Volitional desires are conscious exertions of willpower.
Technologies: Tools that Pertain to Motives Technologies are relevant only to the extent that they help or hinder human purposes. They can be unbundled into more finely-grained sub-technologies. e.g., cameras have lenses Technologies have embedded capabilities and requirements.
Capabilities and s Car: Technology Move Cargo: Capabilities and requirements are the pegs and sockets that hold the framework together. Move Myself: Move Passengers: Driver: Fuel: Parking:
Beliefs: Judgments About Cause and Effect Beliefs are constructed as technologies are evaluated. They have three parts: A requirement end R A capability end C A valence property: positive, negative, mixed, or unknown Positive belief: C satisfies R Mixed belief: C partly satisfies R ± Negative belief: C does not satisfy R Unknown whether C satisfies R?
The Structure of Mental Maps Returning now to the detergent example, let s see how the MTB framework sheds new light on the ZMET mental map. Softens clothes Feel comfortable Dependable Fresh clothes Feel refreshed Selfconfidence Self-image Strong Clean clothes No worries The The informality of of this this map map makes it it hard to to spot hidden structure.
Mental Map: The MTB Version Brand: Technology Self-Image: Image Detergent: Technology Socializing: Plan Softens clothes: Feel comfortable: Self-Confidence: Hedonic Desire Dependable: Dependable: Fresh clothes: Feel refreshed: Strong: Strong: Clean clothes: No worries: What was missing from the original ZMET version?
Feedback Loops Were Ignored Self-Image: Image Socializing: Plan Self-Confidence: Hedonic Desire Feel comfortable: Feel refreshed: No worries: Brand: Technology Detergent: Technology Dependable: Softens clothes: Clean clothes: Strong: Fresh clothes: Dependable: Strong:
Only Positive Valence Was Shown Self-Image: Image Socializing: Plan Self-Confidence: Hedonic Desire Feel comfortable: Feel refreshed: No worries: Brand: Technology Detergent: Technology Dependable: Softens clothes: Clean clothes: Strong: Fresh clothes: Dependable: Strong:
Brands are Technologies Brand: Technology Dependable: Detergent: Technology Softens clothes: A brand is a kind of mental tool that emerges from familiarity with a product or service. Strong: Fresh clothes: It supplies a external capability that is required to complete one s s self-image image. Clean clothes:
When Does Branding Help? Branding involves complex feedback loops. Therefore, negative and mixed valence must be considered. (Sterman, 2000) Branding is only effective if it satisfies one or more self-image requirements. Socializing: Plan Feel comfortable: Feel refreshed: Self-Image: Image Self-Confidence: Hedonic Desire Dependable: Otherwise, branding just provides excess capability. (Christensen and Rosenbloom, 1995) No worries: Strong:
The Cycle of Ends-Means Reasoning The The MTB MTB framework explains how how brands close close the the feedback loop loop of of ends-means reasoning. Conscious Plans Means (Bounded Rationality) Willpower Unconscious Images Ends (Intuition) Unconscious Desires Emotion
Conclusions MTB was derived from qualitative empirical field research. MTB reveals the deep structure of technology adoption. Structural building blocks (motives, technologies, beliefs) Behavioral processes (selecting, evaluating, maintaining) MTB is a simulation-ready theory of adoption developed for use with agent-based artificial markets. MTB is useful as an analytical framework in its own right. To glean hidden insights from mental maps. To analyze the value proposition for new products and services. As a measurement model for adoption surveys.
Questions?
Bibliography Arnould, E. and Epp, A., 2006. Deep engagement with consumer experience, in: Grover, R. and Vriens, M., (Eds.) The Handbook of Marketing Research. Sage, Thousand Oaks, CA., pp. 51-82. Coulter, R. H. and Zaltman, G., 1994. Using the Zaltman metaphor elicitation technique to understand brand images, in: Allen, C. T. and John, D. R., (Eds.) Advances in Consumer Research, vol. 21. Association for Consumer Research, Provo, UT, pp. 501-508. Christensen, Clayton M. and Richard S. Rosenbloom, 1995. Explaining the Attacker s Advantage: Technological Paradigms, Organizational Dynamics and the Value Network. Research Policy, Vol. 24, pp. 233-257 Frankfurt, H., 1988. Necessity and Desire, in:the Importance of What We Care About: Philosophical Essays. Cambridge University Press, Cambridge, pp. 104-116. Hume, D., 1739/1981. A Treatise of Human Nature 2nd ed. Oxford University Press, Oxford, UK. Irvine, W. B., 2006. On Desire. Oxford University Press, New York, NY. Levy, S. J., 2001. Discussion, in: Gilly, M. and Myers-Levy, J., (Eds.) Advances in Consumer Research, vol. 28. Association for Consumer Research, Valdosta, GA, pp. 253-254. Schroeder, T., 2004. Three Faces of Desire. Oxford University Press, New York. Sterman, J. D., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill, Boston, MA. Thompson, D. V., Hamilton, R. W., and Rust, R. T., 2005. Feature fatigue: When product capabilities become too much of a good thing. Journal of Marketing Research 42, 431-442.
Back Slides
What Do Customers Want? What they need (basic capability: $100) vs. What they desire (top-of-the-line: $3300)
Why Technology is Never Good Enough Novice and experienced users do not place the same weight on capability and usability Novices emphasize capability over usability and tend to choose feature-rich products (even though these do not satisfy them for very long.) As they gain experience, they learn to place greater emphasis on usability and seek an appropriate level of capability (as opposed to surplus capability) But by that time, a new generation of novices has come along, demanding new bells and whistles Source: Thompson, Hamilton and Rust (2005)