Social Informatics

October 10, 2020

Contents

• The User
• Usability
  • Principles and Guidelines
  • Evaluation Without Users
  • Evaluation With Users
• User Interfaces
• Interaction, Work, and Technology
• Socio-Technical Systems

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The User

Modelling Individual User Behaviour

Perceptual, Motor, and Cognitive sub-systems can be characterised by storage capacity, decay time, and processor cycle time. Performance can depend on the conditions of the task.

Long Term Memory (LTM)

• Contains declarative knowledge (knowing ‘that’) and procedural knowledge (knowing ‘how’).
  • Declarative knowledge can be further split into semantic knowledge (general knowledge) and episodic memories (personal recollections).
• Appears to have infinite capacity, but recall depends on forming effective encodings.
• Contents becomes lost if not used.

Working Memory (WM)

• ‘Registers’ of the cognitive processor.
• Data can only be processed when in the Working Memory.
• Data can be sourced from the perceptual sub-systems and ‘activated’ chunks of long-term memory.
• Has limited capacity, 7 +/-2 chunks.

Chunks refer to individual meaningful blocks - for example long numbers may be split into smaller blocks, allowing them to be remembered.

Motor Sub-System

• There are differences in learned (open loop) and unlearned (closed loop) performance.
• Movement in learned motor tasks is continuous.
• Movement in unlearned tasks is iterative, and involves successive cycles of action and judgement of outcome.
• There are several relations relating to motor control:
  • Simple Reaction Time: $T_r = T_p + T_c + T_m$.
  • Rationality Principle: Goals + Tasks + Operators + Inputs + Knowledge + Process Limits = Behaviour.
  • Power Law of Practice: $T_n = T_1 n^{-a} = 0.4[0.2 – 0.6]$, where $n$ is the trial number, $a$ is a constant.
  • Yerkes-Dodson Law: Performance peaks at a certain level of arousal.
  • Hick’s Law: Time to decide $T_d = I_c H$, where $H = \log_2 (n+1)$ for equally probable alternatives.
    • Average reaction time to decide between options increases logarithmically with the number of options.
  • Fitt’s Law: $T_{pos} = I_m \log_2 (2d / s)$.
    • Where $d$ is distance to target, $s$ is width of target.

Factors Affecting Performance

Data, task, and environment:

• Slow changing, repetitive data.
• Boring tasks.
• Attention diversion.
• Stress.
• Fatigue.

This has implications for when designing systems:

• Don’t overload working memory with complicated procedures for carrying out tasks.
• Design interfaces that promote recognition rather than recall.
• Provide users with various ways of encoding information to help them remember.
  • E.g. categories, colour, flagging, time stamping.
• Use different kinds of output to convey feedback.
• Provide break points or ‘closure events’ in long tasks.

Keystroke Level Modelling

The following times have been seen in typical users:

Operator	Time (s)
Keystroke	0.10-1.20
Point with Mouse	1.10 avg. (Fitt’s Law)
Mouse Button Press	0.10 down/up, 0.20 click
Home Hands	0.40
Mentally Prepare	1.35
Response Time	Calculated ($n$)

An example of a Keystroke Level Model is as follows:

• GOAL: ICONISE-WINDOW
  • [select GOAL: USE-CLOSE-METHOD
    • MOVE MOUSE TO WINDOW BAR
    • CLOCK ON MENU
    • CLICK OVER CLOSE OPTION
  • [ GOAL: USE L7-METHOD
    • HOME
    • PRESS L7 KEY

The selection rule may reflect either experience or where the user’s hands happen to be before performing the task.

Cognitive Modelling Limitations

• Predictions sometimes do not match experimental findings unless the task and exact methods are dictated to subjects.
• A barrier to predicting execution times is that users do not adopt methods predicted.
  • Users do not generate and follow fixed plans of the kind suggested in theory.
• We cannot except fixed and predictable behaviour from users even at low levels.
• Cognitive psychology theory is informative only about isolated effects.

Knowledge, Errors, and Behaviour

Primary Knowledge

Knowledge contains information about:

• Domain - Goals relevant to the application domain and tasks required to accomplish them.
• Semantics - Conceptual knowledge of entities and operations.
• Syntax - Dialogue rules, input/output language.
• Lexical - Physical actions.

Studies have found that having device model information (knowledge about how a process works) can improve performance when using a system.

Secondary Knowledge

Secondary knowledge includes:

• Knowledge of natural language.
• Knowledge of using other systems.
  • E.g. email, telephone, post…

Secondary knowledge may encourage analogical mapping - the use of metaphor in UI design.

• In designing interactive systems we are constantly trying to describe a new domain.
• Can use metaphor to describe new domain in terms of something more familiar.
• Different metaphors lead to different conceptions and designs.
• Metaphors are not meant to suggest an interface looks like the metaphorical thing, idea is to think about activities in different ways.
• Interface is designed to be similar to a physical entity but also have its own properties.
• Can be based on activity, object, or combination of both.
• Exploit user’s familiar knowledge, helping them to understand ‘the unfamiliar’.

Problems with Interface Metaphors

• May break conventional and cultural rules.
• Can constrain designers in the way they conceptualise the problem space.
• May conflict with design principles.
• Forces users to only understand system in terms of the metaphor.
• Designers can inadvertently use bad existing designs and transfer the bad parts over.
• Lints designers’ imagination in coming up with new conceptual models.

Affordances - Knowledge in the World

• Affordances are the perceived and actual properties of artefacts.
• Illustrate the importance of:
  • Visibility,
  • Feedback,
  • Clues for action, supporting improvisation.
• Where users are domain experts, focus on their knowledge of the application domain.
• Where users are not expects, base the ‘system image’ on a suitable metaphor.
• Encourage learning by exploration.
  • Provide safeguards and make errors easy to reverse.
• Reduce memorisation and support improvisation by exploiting affordances.

Errors

• Account for up to 30% of interaction time.
• Are frustrating and sometimes dangerous.
• Can be categorised:
  • Errors of Perception - Failing to interpret information correctly, or failing to notice information.
  • Mistakes: Wrong Intention - Faulty conceptual models lead to incorrect plans or interpretations.
  • Slips: Correct Intention, Faulty Execution - Occur when plans are executed incorrectly.
    • Capture errors are failures of activation where a more frequently used command ‘takes over’ execution of a less well-known one.
    • Position errors are failures of control. A step in a plan may be missed out or repeated, etc.
• Errors can have constructive results - allow users to learn by doing.

Design Implications

• Provide prompt and relevant feedback for each action.
• Provide ability to diagnose errors and recover.
  • Use meaningful error messages.
  • Allow cancellation of operations in progress.
  • Require confirmation for actions which have drastic consequences.
• Match UI to users’ knowledge and tasks.

Empirical Studies of User Behaviour

Re-Enactment Protocol involves having a user interact with a system, videoing this interaction, and then showing the user a reply of the video and asking them to comment on their actions.

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Usability

Principles and Guidelines

Usability principles are generic rules for user interface design. Usability guidelines are more specific advice for how a usability principle might be achieved in practice.

Guidelines often require careful interpretation with respect to context and may conflict.

Principles of Universal Design

• Equitable Use - Design does not disadvantage any group of users.
• Flexibility in Use - Design accommodates wide range of individual preferences and abilities.
• Simple, Intuitive Use - Design is easy to understand, regardless of users’ experience, knowledge, and language skills.
• Perceptible Information - Design communicates necessary information effectively to users, regardless of ambient conditions or users’ sensory abilities.
• Tolerance for Error - Design minimises hazards and adverse consequences of accidental or unintended actions.
• Low Physical Effort - Design can be used efficiently, comfortably, with minimum fatigue.

Usability Principles Basics

• Learnability - Ease with which new users can begin effective interaction and achieve maximal performance.
  • Predictability - System behaviour is observably deterministic.
  • Synthesisability - User can assess effect of past actions.
  • Familiarity - Match interface to users’ expectations.
• Flexibility - Multiplicity of ways user and system exchange information.
  • Dialogue Initiative - Give user control of dialogue flow. Permit users to abandon, suspend, and resume tasks at any stage.
  • Multi-Threading - Provide support for simultaneous tasks.
  • Task Migratability - Negotiability of function allocation between user and system.
  • Substitutability - Equivalence for different forms of input expression.
  • Customisability - Interface is capable of being adapted to suit different needs.
• Robustness - Level of support provided to the user in determining successful achievement and assessment of goal-directed behaviour.
  • Observability - Relationship between system state and its presentation.
    • Where am I? (Immediate honesty)
    • How did I get here? (Synthesisability)
    • What can I do here? (Operation visibility)
    • Where can I go from here? (Predictability)
    • How do I get there? (Operation visibility)
  • Recoverability - Support for undoing errors.
    • Help users understand the nature of errors.
  • Task Conformance - Interface functionality should match common user tasks.
  • Responsiveness - Feedback should be commensurate with action.
    • Provide time affordances where delays are unpredictable.

Time Affordances

• Where delays are inevitable and unpredictable, users need reassurance that an operation will complete.
• User sensitivity to delay depends on context.
• User interface should provide time affordances such that delays become part of the accountable behaviour of the system.

The following affordances can be used:

• Acceptance - Tell whether a user’s request was valid.
• Scope - Judge size and potential time required to complete the operation, and whether it can be interrupted.
• Initiation - Tell whether to and how to start a task, and determine when it has actually started.
• Progress - Tell that overall task is being carried out, and estimate rate of completion.
• Heartbeat - Quickly observe whether a task is still alive or has stalled.
• Exception - Tell whether errors have occurred and how the user needs to intervene.
• Remainder - Tell how long and how much is left before a task is completed.
• Completion - Tell how to terminate a task and when it has terminated (along with any errors).

Guidelines for Menus

Consider the ordering of items:

• Alphabetical, semantic, random.
• Frequency of use.
• Order of use.
• Importance.
• Positional consistency for re-occurring entries.
• Allow customisation.

For hierarchical menus, empirical studies suggest the optimum number of entries per level is 8 - consistent with the idea of working memory capacity. Guideline is minimise depth to reduce the chance of getting lost. May be difficult to implement if it means ignoring natural categories.

More recently, gesture interfaces have begun to abandon existing principles. There are a lack of established guidelines for gesture control, which some argue has led to usability problems.

Evaluation Without Users

Evaluation without users broadly has two forms:

• Model-based, analytic.
• Exploratory.

Metrics

Usability Objective	Effectiveness Measure	Efficiency Measure	Satisfaction Measure
Task Conformance	Percentage of Goals Achieved	Time to Complete a Task	Rating Scale for Satisfaction
Appropriate for Trained Users	Number of Power Features Used	Relative Efficiency compared with Expert User	Rating Scale for Satisfaction with Power Features
Learnability	Percentage of Functions Learned	Time to Learn Criterion	Rating Scale for Ease of Learning
Error Tolerance	Percentage of Errors Corrected Successfully	Time Spent on Correcting Errors	Rating Scale for Error Handling

Guideline-Based Evaluation

• UI Designers apply set of explicit guidelines, for example:
  • Use simple and natural dialogue design.
  • Speak the user’s language.
  • Minimise user memory load.
  • Be consistent.
  • Provide feedback.
  • Provide clearly marked exits.
  • Provide shortcuts.
  • Prevent errors.
  • Provide error recovery.

Guidelines to consider are:

• Visibility.
• Consistency.
• Familiarity.
• Affordance.
• Navigation.
• Control.
• Feedback.
• Recovery.
• Constraints.
• Flexibility.
• Style.
• Conviviality.

Expert Heuristic Evaluation

• Usability specialist study the interface in depth.
• Look for properties they know will lead to usability problems.
• Check compliance with recognised usability principles and guidelines.
• Different evaluators may find different problems.

Cognitive Walkthrough

• Usability analyst steps through cognitive tasks that must be carried out in interacting with the system.
• Similar to structured code walkthroughs.
• Focuses on user goals and knowledge required to complete them.
• Examines how interface supports user in generating a correct goal sequence and choosing appropriate actions.
• Core tasks must be identified and specified.
• Intended for use early in design cycle.
• Assumes user behaviour follows a goal-directed exploration model.
• Particularly suitable for ‘walk-up and use’ interfaces.

In general, we can assume a four-stage model:

1. Goal - Is the current goal valid for the task and the interface?
2. Search - Is the desired action visible at the interface?
3. Select - Will users be able to select the desired action from others currently visible.
4. Perform - Will users understand the feedback after the action is performed.

The forms used for task plans follow the following basic structure:

1. Task Acquisition/Goal.
2. Planning/Search.
3. Action Specification/Selection.
4. Action Execution.
5. Perception of Outcome.
6. Interpretation of Outcome.
7. Evaluation of Outcome.

Actions/choices should be ranked according to the the number of users who have experienced problems (0 = none, 3 = most).

Comparison of Techniques

	Advantages	Disadvantages
Expert Heuristic Evaluation	Identifies many more problems. Identifies more serious problems.	Requires UI expertise. Requires several evaluators.
Non-expert, Guideline-based Evaluation	Identifies recurring problems. Can be used by interface developers.	Misses some severe problems.
Cognitive Walkthrough	Helps define users’ goals and assumptions. Can be used by interface developers.	Need task definition methodology. Tedious. Misses recurring problems.

The overall advantages of evaluation without users are:

• Usable early.
• Few resources required.
• Strongly diagnostic.
• Overview of whole system.
• High potential return.

The overall disadvantages are:

• Narrow focus.
• Broad assumptions of users’ cognitive behaviour.
• Subject to bias.
• Problems getting experts.
• Cannot capture real behaviour.

Evaluation With Users

Interviews

Can be unstructured or structured:

• Unstructured - Not directed by a script.
  • Gain rich information but not replicable.
• Structured - Tightly scripted, essentially spoken questionnaire.
  • Replicable but lack rich results.
• Semi-Structured - Guided by script but interesting issues can be explored in more depth:
  • Can provide good balance between richness and replicability.

There are two main types of interview questions:

• Closed Questions - Have predetermined answer format.
• Open Questions - Do not have any predetermined format for answers.

Closed questions are easier to analyse but of course may lack detail.

When running an interview, the basic structure should roughly follow:

1. Introduction - Introduce yourself, explain goals, ethical obligations etc.
2. Warm-Up - Make first questions easy and non-threatening.
3. Main Body - Present questions in logical order.
4. Cool-Off Period - Include some easy questions to defuse tension at the end.
5. Closure - Thank interviewee etc.

Questionnaires

As with interviews, questions can be closed or open. Can be disseminated to large populations, though sampling can be a problem if the size of the population is unknown (as is the case with online surveys).

There are several considerations to take into account when designing a questionnaire:

• Impact of questions can be influenced by question order.
• May need different versions for different populations.
• Provide clear instructions on how to complete questionnaire.
• Strike balance between using white space and keeping questionnaire compact.
• Avoid very long questionnaires.
• Decide on whether process will all be positive, all negative, or mixed.

Response rates to questionnaires vary, but good response rate can be encouraged by:

• Making sure purpose of study is clear.
• Promise anonymity.
• Ensure questionnaire is well designed.
• Offer short version fo those who do not have time to complete long questionnaires.
• Follow-up with emails, phone calls, letters etc.
• Provide incentives.

Laboratory Studies

In a typical lab usability test, a user attempts to complete a task or set of tasks, each which has a specified goal with effectiveness, efficiency, and satisfaction identified in the specified context of use.

When designing a study, you should consider:

• Determine hypotheses.
• Experimental design.
• Choice of subjects.
• Determine independent and dependent variables.
• Different conditions.
• Organisation:
  • Within subjects to eliminate training.
  • Between subjects to eliminate bias.

Setting Usability Targets

• Generally, targets will be set for specific usability goals:
  • Time to perform task.
  • Time to learn.
  • Errors committed.
  • User satisfaction.
• Targets may be defined in terms of performance ranges:
  • Excellent.
  • Acceptable.
  • Unacceptable.

Statistical Tests

Parametric tests assume normal distribution, and are robust. Non-Parametric tests do not assume normal distribution, are less powerful, but are more reliable as fewer assumptions are made about the data.

Test	Independent Variable	Dependent Variable	Parametric?
t-Test	2 valued	Normal	Yes
ANOVA	Discrete	Normal	Yes
Wilcoxon	2 valued	Continuous	No

Web Analytics

Tools can be used for optimising web usage by looking at web data. Typically focuses on number of visitors and page views.

For example, Google Analytics.

Cooperative Evaluation

• Encourages dialogue between evaluators and users.
• Helps provide intentional context necessary to interpret user behaviour.
• May affect normal behaviour.
  • May be recorded for subsequent analysis using re-enactment protocol.

The procedure to follow with cooperative evaluation is:

1. Get users to ask for help when they get stuck.
2. Ask users what the commands mean.
3. After users read tasks, ask them how they might solve it.
4. As users consider each command, ask what they think it does.
5. When users have entered a command, ask what they think it has done and what the response indicates.

Ethnographic Methods

• Dedicated to observing in detail everyday working practices and seeks to explicate the numerous, situated ways in which these are actually achieved.
• Ethnography is attentive to ways in which people actually perform activities.
• Brings to light informal and usually unnoticed, undocumented nature of users’ practices.
• What is often observed is an emergent and flexible work organisation.
• Co-operation of people being observed is essential.

Planning and Conducting Observation

• Decide on how involved you will be - passive observer to active participant.
• How to gain acceptance.
• How to handle sensitive topics.
• How to collect the data:
  • What data to collect.
  • What equipment to use.
  • When to stop observing.

Guiding Observation

• Three easy-to-remember parts:
  • The person - who?
  • The place - where?
  • The thing - what?
• A more detailed framework:
  • Space - What is the physical space like and how is it laid out?
  • Actors - What are the names and relevant details of the people involved?
  • Activities - What are the actors doing and why?
  • Objects - What physical objects are present, such as furniture.
  • Acts - What are specific individual actions?
  • Events - Is what you observe part of a special event?
  • Time - What is the sequence of events?
  • Goals - What are the actors trying to accomplish?
  • Feelings - What is the mood of the group and of individuals?

Interpreting Results

Some important factors to consider when interpreting results are:

• Reliability - Does method produce same results on separate occasions?
• Validity - Does method measure what it is intended to measure?
• Ecological Validity - Does evaluation environment distort results?
• Biases - Are there biases that distort results?
• Scope - How generalisable are results?

Summary of Advantages & Disadvantages

Method	Advantages	Disadvantages
Analytical	Usable early. Few resources required. High potential return.	Narrow focus. Broad assumptions of users’ cognitive behaviour. Cannot capture real behaviour.
Observational	Quickly highlights problems. Valuable, rich data source.	Can affect user performance. Analysis can be time-consuming.
Survey	Addresses opinions and understanding. Can be used on large groups.	Low response rates. Possible interviewer bias. Time-consuming.
Laboratory Studies	Powerful, quantitative data. Good reliability and repeatability.	High resource demands. Time-consuming. Artificial - questionable validity.

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User Interfaces

Basic Guidelines

Physiological Guidelines

• Colours must be considered in context.
• Avoid simultaneous display of highly saturated, spectrally extreme colours.
• Avoid pure blue for text, thin lines, and small shapes.
• Avoid red and green in periphery of large displays.

Cognitive Guidelines

• Use colour sparingly.
• Use colour to draw attention.
• Use colour to reflect organisation and establish relationships between objects.
• Be consistent, follow conventions, but investigate first.
• Design initially in monochrome and don’t rely on colour alone.

Guidelines for Text

• All upper case is harder to read than standard case.
• Don’t make edges of text too interesting.
• Avoid mismatching typefaces.
• Make sure text is not too small.

Icons

Consider:

• Semantics - Does icon represent its meaning well?
• Visual Syntax - Rules of composition (how does it look, how does it relate to others, …).
• Pragmatics - Is it easy to distinguish and select?
• Boundaries should be solid, closed, and contrast-bounded.
• Technological icons minimise culturally variable interpretations.
• Use representation hierarchy to direct recognition and interpretation.
• Keep them simple - over-elaborate effects may hinder interpretation.
• Supplement with labels where appropriate.
• Colour aids identification.
• Animation aids comprehension.

Layout

• Include all (but only) essential information.
• Consistency of placement aids interpretation.
• Proximity effect suggests relationships so group items semantically.
• Make boundaries clear.
• Establish a consistent visual syntax/hierarchy.
• Where data volume is large, provide means for user to:
  • Navigate.
  • Control level of detail.
  • Problems of integrating detail and context.

Interactivity

• Distinguish active elements of display from passive ones.
• Make it clear how to activate controls and what will happen.

Animation

• Good attention getter.
• Good for signalling changes.
• Use for effecting display overlaps.

Sound

• Confirmatory feedback.
• Monitoring state.
• Reducing reliance on visual system.
• Grabbing attention.
• Interface sound design typically arbitrary and synthetic.

Neilsen’s Ten Good Deeds

1. Place name and logo on every page and make logo link to home page.
2. Provide search if site has more than 100 pages.
3. Write straightforward and simple headlines and page titles that clearly explain what page is about and that will make sense when read out-of-context.
4. Structure page to facilitate scanning and help users ignore large chunks of page in single glance.
5. Don’t cram everything into single, infinite page - use hypertext to structure content space.
6. Use product photos, but avoid cluttered and bloated product family pages with lots of photos.
7. Use relevance-enhanced image reduction when preparing small photos and images.
8. Use link titles to provide users with preview of where each link will take them, before they have clicked on it.
9. Ensure all important pages are accessible for users with disabilities.
10. Do the same as everyone else.

Design Patterns Paradigms

Menus

There are a number of menu interface styles: flat lists, drop-down, pop-up, contextual, expanding, scrolling, cascading.

Expanding Menus

• Enables more options to be shown on single screen than possible with a single flat menu.
• More flexible navigation, allowing for selection of options to be done in the same window.
• Cascading menus most popular.

Radial Menus

• Empirical studies confirm Fitts’ Law derived assumptions about pie and linear menus.
• In studies, participants don’t prefer pie menus, however.

Icons

• Assumed to be easier to learn and remember than commands.
• Can be designed to be compact and variably position on a screen.
• Mapping between representation and underlying referent can be:
  • Similar - e.g. picture of file to represent object file.
  • Analogical - e.g. picture of a pair of scissors to represent ‘cut’.
  • Arbitrary - e.g. use of an X to represent ‘delete’.
• Many operations are actions making it more difficult to represent them.
• When designing icons, consider:
  • Does it represent its meaning well?
  • How does it look?
  • How does it relate to others?
  • Is it consistent in use of orientation, scale, colour?
  • Is it easy to distinguish?
  • Is it easy to select?

Design Guidelines

• Boundaries should be solid, closed, and contrast-bounded.
• Technological icons minimise culturally variable interpretations.
• Use representation hierarchy to direct recognition and interpretation.
• Keep them simple,
• Colour aids identification.
• Animation aids comprehension.
• Wealth of resources now so do not have to draw or invent new icons from scratch.
• Text labels can be used alongside icons to help identification for small icon sets.
• Consider representativeness and discriminability.

Interface Patterns

Command Line

• Commands such as abbreviations.
• Some hard wired at keyboard, others can be assigned to keys.
• Efficient, precise, and fast.
• Large overhead to learning set of commands.

GUIs

• Windows - Scrolled, stretched, overlapped, opened, closed, and moved around screen using mouse.
• Icons - Represented applications, objects, commands, and tools opened when clicked on.
• Menus - Offering lists of options that could be scrolled through and selected.
• Pointing Device - Moue controlling cursor as point of entry to windows, menus, and icons.

Challenges now are to design GUIs best suited for tablet, smartphone, and smartwatch interfaces.

Guidelines for Small Devices

• Make usefulness immediately apparent.
• Structure interface to task.
• Short cuts and flexibility.
• Minimise memory load.
• Use consistent screen templates.
• Provide a back function on every screen.
• Selection better than writing.

‘Dark’ Patterns

A ‘dark’ pattern is a solution that should be avoided, because it has been proven to represent a bad practice. Nevertheless, some of these patterns are now common (usually shady things regarding privacy settings etc…).

Emerging Paradigms

Gesture

• Uses camera recognition, computer vision techniques.
• Movements mapped onto variety of gaming motions such as swinging, bowling, hitting, and punching.
• Applications include gaming, and situations where contact-based interfaces present risk (e.g. operating theatres).

Agent-Based Interaction

• Agents - Autonomous, active computer processes that possess some ability to communicate with people and adapt their behaviour.
• Agent-based interaction has been seen as a solution to many usability problems, but its record has been mixed.
• Very difficult to infer user intentions accurately from a finite set of data - sometimes agents help, but are often wrong and can be worse than no help at all.

Speech Interfaces

• Speech synthesis and speech recognition.
• Useful whenever user cannot type.
• More flexible systems allow the user to take the initiative in a conversation (though with more chance of error).
• Problems are usually related to open-ended questions, and where natural language can be misleading.
• Design issues include considering how to keep conversation on track, and the types of voice etc.

Augmented and Mixed Reality

• Augmented Reality - Visual representation superimposed on physical devices and objects.
• Mixed Reality - Views of real world are combined with views of virtual environment.
• Applications include medicine and gaming.

Virtual Reality

• Virtual reality involves immersing users in a computed world.
• Useful for training and teleoperation purposes.
• Issues include mismatch of vision and proprioception, leading to health issues such as nausea etc.

Other

• Sharable UIs
  • Designed for more than one person to use.
  • Provide multiple inputs and allow simultaneous input.
  • Large wall displays etc.
• Mobile UIs
  • Handheld devices intended to be used on the move.
  • Have become pervasive, increasingly used in all aspects of everyday and working life.
• Wearables
  • Head and eyewear mounted cameras.
  • Jewellery, smart fabrics, glasses, shoes…
  • Need to consider comfort, hygiene, ease of wear, usability etc…
  • Linked to idea of Ubiquitous Computing - ways of embedding digital systems into spaces and objects.

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Interaction, Work, and Technology

Technology Acceptance Model

Theoretical model of effect of system characteristics on user acceptance of IT systems. The aims are:

• Improve understanding of user acceptance process.
• Provide theoretical basis for user acceptance testing.

The claim is this can be used to predict potential IT usage by measuring users’ beliefs after they are exposed to the system.

Based on the assumption that two important user beliefs influence the use of IT:

• Perceived Usefulness (PU) - Degree to which person believes that using a particular system would enhance job performance.
• Perceived Ease of Use (PEOU) - Degree to which person believes using particular system would be free of effort.

Method

• Devise set of hypotheses to express expected outcomes:
  • System has significant effect on ease of use.
  • PEOU has significant effect on PU and attitude.
  • PU has significant effect on attitude.
  • Attitude has direct effect on usage behaviour.
• Design survey to rate PEOU, PU, and attitudes towards the system and distribute to users.
• Analyse data for correlations to test if hypotheses confirmed.

Measuring Perceived Usefulness

Four items most commonly used are:

1. ‘Using the application increases my productivity.’
2. ‘Using the application increases my job performance.’
3. ‘Using the application enhances my effectiveness on the job.’
4. ‘Overall, I find the application useful in my job.’

Measuring Perceived Ease of Use

Four items most commonly used are:

1. ‘Learning to operate the application is easy for me.’
2. ‘I find it easy to get the application to do what I want to do.’
3. ‘The application is rigid and inflexible to interact with.’
4. ‘Overall, I find the application easy to use.’

However, some studies have shown perceived ease of use has no significant effect on attitude and perceived usefulness. Might suggest ‘no amount of perceived ease of use will compensate for low usefulness’.

Critiques of TAM

• Question heuristic value, limited explanatory and predictive power, triviality, and lack of practical value.
• Attempts to expand TAM and adapt it to constantly changing IT environments has led to confusion.
• Focuses on individual ‘user’, with concept of ‘perceived usefulness’, with extension to bring in more and more factors to explain how a user perceives usefulness.
• Ignores essentially social process of IT development and implementation, without questioning if more technology is actually better.

Alternative Conceptualisations

• TAM predicted on a stable set of social, economic, and technical forces, which serve to generate uni-directional adoption trajectory.
• In practice, there may be tensions between conception and implementation of technology.
• Initial technological trajectories may fail, become fragmented, or be reversed.
• Implementation may be important site of innovation.

Organisational Factors

• Structure.
  • Hierarchical, matrix.
• Purposeful activity.
  • Collective goals pursued through ‘rational action’.
• Defined roles, responsibilities, relationships.
  • Division of labour.
  • Patterns of accountability.
  • Rules of governance.
• Process changes.
  • Does the system require changes to work processes in the environment?
• Work changes.
  • Does system override ad-hoc procedures?
  • Does system de-skill users in environment or cause them to change the way they work?
• Organisational changes.
  • Does system change organisational structures, patterns of collaboration?

Social Shaping

• Technology and organisation cannot be treated as entirely separated categories.

Gartner ‘Hype Cycle’

1. Technology Trigger - Product launch.
2. Peak of Inflated Expectations - Over-enthusiasm, unrealistic projections.
3. Trough of Disillusionment - Technology becomes unfashionable because of failure to live up to expectations.
4. Slope of Enlightenment - Experimentation and hard work produces realistic understanding of applicability, risks, and benefits.
5. Plateau of Productivity - Potential to produce real-world benefits is maximised.

Technological Determinism

• Technology is an autonomous force ‘outside’ of society.
  • Linear model of innovation, predictable path.
• Technology is the key driver of social, economic, and political change.
  • Inevitability of impacts - ‘You can’t stop progress’.

Social Determinism

• Technologies reflect different degrees of social, political, and economic power possessed by stakeholders.
• Stakeholders with greatest economic and political power are able to dictate technological development.
• Select and sponsor technologies that work to keep them wealthy and powerful.

Understanding Innovation

• Technological determinism assumes technology follows self-evident trajectory of improvement.
• Technologies require particular forms of social organisation to be successful.
• Multiple choices at all stages of development and use of a technology.
• Technological systems evolve over time, growing more complex and interconnected.
• Complex networks of artefacts, organisations, and regulations, requiring many people to maintain and operate.
• Exercising control over technological change becomes more difficult as we move towards large, complex interdependent systems.
• Different stakeholders shape evolution through collaboration and competition.
• New technologies may strengthen position of established stakeholders or provide opportunities for new competitors - disruptive technologies.

Social Shaping of Technology

• Technology Shapes Society
  • Technologies impose their own politics on users of technologies.
• Technology is Shaped by Society
  • Social patterns impose themselves in the design and use of technologies.
  • Different social groups through collaboration and competition shape evolution of technologies.

Technologies have affordances - evident, inherent properties:

• Social Affordances
  • Used in different ways by different people.
  • Enable and constrain social actions and relationships.
• Technical Affordances
  • Communicative and computational roles.
  • Temporal and spatial consequences.
• Societal Affordances
  • Provide means to alter/challenge existing control structures.
  • Can lead to negative consequences for some stakeholders.

Ethnography and Design

Ethnomethodology - Study of how social order is accomplished in situ, with emphasis on uncovering work as set of social, collaborative practices.

• Dedicated to observing in detail everyday working practices.
• Is attentive to how work actually gets done, including recognition of tactic skills and cooperative activities.
• It brings to light informal and usually unnoticed, undocumented nature of work and making these processes and practices ‘visible’.
• What is often observed is an emergent and flexible work organisation thorough which local ordering of activities is achieved despite ‘normal, natural troubles’.

For interactive systems design, relevance is for understanding the social context in which work of individuals is typically embedded and how this influences its accomplishment. The Ethnographer’s role is to ‘bring users back to designers’.

Lessons of Ethnography for Design

• Supporting work in all its contingent aspects requires designers to pay attention to the occasioned character of the logic of work.
• Understanding importance of how people make their activities visible so as to sustain mutual awareness and support informal collaboration.
• Current design and development methodologies have taken on board the idea of development as an evolutionary process, bur what is missing is the connection of design and development with actual working practice.
• Successful systems may require day-to-day collaboration between users and designers/developers as together they track down troubles with the system and work to come up with solutions.

Ethnography and Design

• Matching ethnographic data and software engineering.
• Ethnography is concerned with analysis - gathering and interpreting data.
• Software engineering is concerned with synthesis - designing and building systems.
  • Wants to know what is important in what people do.
• Ethnography is concerned with detail of ‘how things are done now’.

However, Ethnographic enquiry is often lengthy and therefore expensive. There may also be problems getting access to some settings because people may feel uncomfortable while being observed.

ETHICS Methodology

Aim was to find ways to design and use technology that is ethically acceptable.

ETHICS Principles

• Encourage Participation - System design for, by, and with the users.
• Improve general conditions of work under label ‘quality of work life improvements’.
• Produce systems that are technically efficient and have social characteristics which lead to high job satisfaction.
• Follow socio-technical philosophy of trying for joint optimisation, making the best use of people and best use of technology.

Participatory Design (PD)

Involves direct participation of these whose working lives will change as a consequence of introduction of the new system. PD potentially relates to all aspects, phases, and activities:

• Decision-making.
• Designing.
• Developing.
• Deployment.
• Further development in use.

There are political and pragmatic arguments to consider:

• Political arguments - Industrial democracy and right of workers to determine working conditions, including their means of labour.
• Pragmatic reasons - Workers, as experts in work practices, work organisation and means of labour, able to contribute expertise to discussions about activities shaping future work, ensuring technology is appropriate.

Challenges for Participatory Design

• Users often don’t know what they want.
• Users and designers lack a common language and perspective.
• Users may have conflicting opinions about requirements.
• Picking representative sample of user community.
• Some users may be more powerful or articulate than others.
• Users’ commitment to project may be weak or decline if progress is slow.
• User participation increases uncertainty and uncertainty creates a desire for control.

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Socio-Technical Systems

Internet of Things

Has several potential application domains:

• Healthcare - monitoring vital signs, fitness.
• Energy - monitoring consumption, demand management.
• Transport - connected cars, traffic management.
• Smart homes - remote control of services.

The internet of things also presents some challenges:

• Security
  • Spam, viruses, botnets etc…
  • Unpatched systems, browsers…
• Freedom of Information vs Privacy
  • Google has data on all your searches etc.
  • Facebook may know who your friends are, where you live, political views…
  • Government censorship.

Net Neutrality

Net Neutrality is the principle that internet service providers should treat all internet traffic equally and not discriminate or charge differently by user, content, website, platform, application, type of attached equipment, or method of communication.

• Important because control over traffic flow gives ISPs tremendous power.
• A recent US Federal Communications Commission ruling has abandoned this principle.
• Abandoning Net Neutrality would mean companies would be free to charge extra fees to access certain services.
  • Some claim it would enable the internet to be dominated by a few existing content producers to the detriment of new providers and so stifle innovation.

Disruptive Technologies

Disruptive technologies can be defined as:

1. An innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market leading firms, products and alliances.
2. An innovation that creates ‘significant social impact’.

Technologies have affordances - evident, inherent properties. Social and economic factors may have strong influence on innovation outcomes.

eCommerce

• Rise of eCommerce has hit traditional high street retailers very hard.
• Low cost base (since no physical shop) makes eCommerce so profitable.
  • Sells goods directly to customers.
• Amazon offers a platform for other retailers to sell products too, keeping a commission.
  • Controversially, also pays low task since no business rates.
  • Low pay and poor working conditions.

Uber

• Online transport company.
• App allows consumers to request car transportation or food delivery.
• Pioneer in the ‘sharing economy’.
• Drivers allocated using an algorithm-driven dispatch system.
• Categorises drivers as self-employed and therefore avoid employee rights, minimum wage etc.
• Defines itself as a computer services company, therefore avoiding health and safety regulation.

Airbnb

• Online marketplace and hospitality service, enabling people to offer short-term lodging, including vacation rentals, apartment rentals.
• Receives percentage service fees (commissions) from guests and hosts with every booking.
• Right to be a host or guest is determined by a scoring system.
• Acts as a broker, and allows it to bypass zoning laws on uses of property.
• Hosts able to avoid regulations on health and safety and security associated with letting a property required of hotels and hostels.
• Loss of tax revenue for cities if people choose to stay in Airbnb accommodation.

General ‘Rule Book’

Somewhat controversially…

• Devise business model to which existing regulations are not well-matched.
• Exploit regulatory loop-holes to reduce costs.
• Exploit global reach to reduce tax liabilities.

Artificial Intelligence

• Significant productivity gains from AI raises prospect of increased unemployment.
• Equally possible productivity can grow alongside employment, assuming economic output also increases.
• Proportion of jobs estimated to be at risk in developed economies range between 10% and 50%, over next 10-20 years.
  • Low skilled, repetitive, less creative jobs most at risk.
• Concerns about impact of new technology on jobs is not new.
• On the whole, job creation typically followed the introduction of new technology, however.
• AI may also fail to deliver anyway…
  • Faith in technology can be misplaced.
  • Many examples of how technology solutions fail to deliver.
  • Human intervention often needed to ensure things run smoothly.
  • Unknown to what extent AI will be adopted, how its implementation will affect work, and exactly who will be affected.

Challenges for ‘Platform Workers’

• Most platforms have generic features enabling workers to meet new clients, but often have limited functionality and provide little support for the workers.
• Uber drivers use external services to share tips and help them work securely and efficiently.
  • Works for ad-hoc problems, but cannot address larger structural challenges.

Data and Ethics

• Research data ethics is built around two key principles:
  • Anonymity - No data will be collected or released that contains identifiable information.
  • Informed Consent - Who is collecting, what is being collected, how information is being used and shared.
• Both principles are proving increasingly difficult to sustain with advent of big data and data science.
• Research process assumes identification of individuals is practically impossible.
  • Data collection handled by a small number of people, data and findings are anonymised.
  • Uses of data can be specified in advance.
• Capacity to link data is built into architecture of the internet, social media, and IoT.

Dataveillance

• Governments, law-enforcement agencies and companies can start tracking in new ways.
• Individual autonomy regulated and restricted.
• Governments can ‘nudge’ us to make good decisions.
• Who owns your data?
• Principles of informed consent may be impossible to apply.

Rumour and Fake News

• Social media platforms such as Facebook, Instagram, and Twitter enable users to share content instantly.
• Recent surveys suggest very large proportions of people use social media as their primary news source.
  • Can this lead to increasing polarisation and create conditions that promote confirmation bias?

Fake News

• Traditional media is no longer the only source for information and news.
• More and more people turn to social media to get updated on what is happening.
• Since anyone can publish and share news online, social media can be and is often used for spreading hoaxes, dissemination of rumours, and false information.
• As there is no quality control on user-generated content on social media, it becomes essential to recognise credible information and distinguish true statements from misinformation online.
• Russia’s IRA began targeting US voters as early as 2012.

Impact

• Recent past highlights influential role of digital social networking platforms in shaping public debate on current affairs and political issues.
• Disinformation and the new digital media distort societal debates, increase polarisation, and threaten participatory democracy.
• Not only does fake news get significant attention, but their narratives try to gain credibility by undermining reader confidence in mainstream media.

Response

• Emergence of fact-checking sites such as FactCheck and Snopes.
• Following controversy over fake news during US presidential election in 2016, platforms have bowed to pressures to tackle fake news and have introduced mechanisms for its detection and moderation.
  • Such approaches not scalable, however.
• Rise of ‘deep fakes’, involving using AI to produce high-quality fake video footage, has led to further challenges.