Author Archives: alfredo

Exploring Relationships Between Interaction Attributes and Experience

Eva Lenz, Sarah Diefenbach and Marc Hassenzahl proposed the Interaction Vocabulary, obtained after evaluating different interactions through the why-, what- and how- framework (a simplistic form of Goal Oriented Analysis).

 

Experience Interaction Attributes Experience
Esteem, focus on the interaction itself, significance of the present moment, relaxing, calming, accuracy, care, appreciation of interaction/product slow fast Animating, stimulating, activating, efficiency, focus on instrumental goal of interaction, expression of willpower.
Ritualization, every step is meaningful, rewarding, emphasis on progress and advance of the process, approaching a goal step by step, clear structure, being guided through the process stepwise fluent Autonomy, continuous influence, power and right to change what’s happening at anytime of the process, no barriers, fluent integration in running process, spurring instead of interrupting
Instant feedback makes own effect experiential, competence, feeling of own impact creates a feeling of security, you see what you do, makes immediate correction possible, nothing in between, you experience what you do, increase of competence, the instant feedback creates a feeling of recognition. instant delayed Emphasizing the moment of interaction, creating awareness. Centering on the interaction itself rather than its instrumental effect.
Influence by intuition, control uniform diverging Unusual, unnatural, amplified, grasping for attention
Creates feeling of security constant inconstant Liveliness, suspense, you can’t adapt yourself to it, unreliable, chance as an idea generator
Uncertainty, ambiguity, magic, handing over the responsibility (the interaction happens somewhere else), you don’t put much of yourself in it m mediated direct Significance of your own doing, face-to-face contact, experiencing affinity, self-made, close relation to the product, feeling of constant control
not feeling as a part of it, feeling of distance spatial separation spatial proximity Personal contact, feeling of relatedness, safety (you know exactly what you did), being a part of it, intensive examination of details
Deeper analysis is needed, room for variation = room for competence, room for new ideas, exploration approximate precise Safety, no changes = room to concentrate on something else/competence in other fields, exact idea of result, always exact the same
Carefulness, awareness, appreciation, making a relationship with the thing (being gentle with it), being a part of it, revaluation of the action, raises the quality, allows to perform a loving gesture gentle powerful Archaic interaction, sign of strength, power, effectiveness
Low challenge, no room to experience competence, no room for improvement, becomes side issue, doesn’t matter incidental targeted Appreciation, significance of interaction, worthy of attention, high challenge, high concentration, room for competence
Conscious of the significance of your own doing, assurance, security, goal-mode, seeing what is going on, expressive, very easy apparent covered magic, excitement, exploration, action-mode, witchcraft, deeply impress somebody

Sources:

E. Lenz, S. Diefenbach, and M. Hassenzahl, “Exploring relationships between interaction attributes and experience,” in Proceedings of the 6th International Conference on Designing Pleasurable Products and Interfaces, 2013, pp. 126–135.

Proportionality Design Method

The principle of Data Quality from the Fair Information Practices insinuates that the information that is obtained from the users should be applied to their benefit:

“Personal data should be relevant to the purposes for which they are to be used, and, to the extent necessary for those purposes, should be accurate, complete and kept up-to-date.”

Giovanni Iachello and Gregory D. Abowd use this as a starting point and elaborate the principle of proportionality:

“Any application, system, tool or process should balance its utility with the rights to privacy (personal, informational, etc.) of the involved individuals”

Based on this principle, they propose the Proportionality design method:

Proportionality Design Method

Proportionality Design Method

During the whole development cycle of the application, the different parts need to verify the legitimacy, appropriateness and adequacy of the application:

  • Legitimacy: Verify that the application is useful to the user. What is the function that the application cover?
  • Appropriateness:Analyse if the alternative implementations with the different technologies satisfy the goal of the application without supposing a risk for the privacy of the users?
  • Adequacy: Analyse if the different alternative technologies are correctly implemented.

Sources:

G. Iachello and G. D. Abowd, “Privacy and proportionality: adapting legal evaluation techniques to inform design in ubiquitous computing,” in Proceedings of the SIGCHI conference on Human factors in computing systems, 2005, pp. 91–100.

Privacy Risk Models

Jason Hong, Jennifer D. Ng, Scott Lederer and James A. Landay present their framework for modelling privacy risks in ubiquitous computing environments.

The privacy risk models framework consists of two parts: privacy risk analysis, that proposes a list of questions to help defining the context of use of the future application and the privacy risk management, which is a cost-benefit analysis that is used to prioritise the privacy risks and develop the system.

Privacy risk analysis

The privacy risk analysis starts with the formulation of the following questions grouped in the categories Social and Organisational Context and Technology:

Social and Organizational Context

  • Who are the users of the system? Who are the data sharers, the people sharing personal information? Who are the data observers, the people that see that personal information?
  • What kinds of personal information are shared? Under what circumstances?
  • What is the value proposition for sharing personal information?
  • What are the relationships between data sharers and data observers? What is the relevant level, nature, and symmetry of trust? What incentives do data observers have to protect data sharers’ personal information (or not, as the case may be)?
  • Is there the potential for malicious data observers (e.g., spammers and stalkers)? What kinds of personal information are they interested in?
  • Are there other stakeholders or third parties that might be directly
    or indirectly impacted by the system?

Technology

  • How is personal information collected? Who has control over the
  • computers and sensors used to collect information?
  • How is personal information shared? Is it opt-in or is it opt-out (or do data sharers even have a choice at all)? Do data sharers push personal information to data observers? Or do data observers pull personal information from data sharers?
  • How much information is shared? Is it discrete and one-time? Is it continuous?
  • What is the quality of the information shared? With respect to space, is the data at the room, building, street, or neighborhood level? With respect to time, is it real-time, or is it several hours or even days old? With respect to identity, is it a specific person, a pseudonym, or anonymous?
  • How long is personal data retained? Where is it stored? Who has access to it?

Privacy Risk Management

This part consists on the prioritisation of privacy risks applying the inequality known as the Hand’s rule.

C < L×D

Being:

  • L: The likelihood that an unwanted disclosure of personal information occurs
  • D: The damage that will happen on such a disclosure
  • C: The cost of protecting this privacy in an adequate manner

References

J. I. Hong, J. D. Ng, S. Lederer, and J. A. Landay, “Privacy risk models for designing privacy-sensitive ubiquitous computing systems,” in Proceedings of the 5th conference on Designing interactive systems: processes, practices, methods, and techniques, 2004, pp. 91–100.