Law

Hick-Hyman Law

The time needed to make a choice tends to increase as the number of possible choices and the uncertainty between them increase.

Explore

Why it matters

Interfaces become harder to navigate when people must evaluate many unfamiliar or poorly differentiated options at once.

In practice

Structure choices rather than blindly removing them. Group related options, establish hierarchy, use sensible defaults and reveal complexity when it becomes relevant.

Origin

William Edmund Hick, 1952, with closely related work by Ray Hyman.

Sources

W. E. Hick, “On the rate of gain of information”, 1952.

Finding

Miller’s Law

Human capacity for processing and immediately remembering unfamiliar information is limited.

Explore

Why it matters

Interfaces that require people to hold lots of unfamiliar information in their heads create unnecessary cognitive effort.

In practice

Prefer recognition to memorisation, group information meaningfully and keep important information available when it is needed.

Origin

George A. Miller, 1956.

Sources

George A. Miller, “The Magical Number Seven, Plus or Minus Two”, 1956.

Rule

Peak-End Rule

People tend to judge an experience disproportionately by how they felt at its most intense point and at its end.

Explore

Why it matters

The remembered experience of a product or service is not necessarily an average of every moment in the journey.

In practice

Pay particular attention to emotionally significant moments and endings: completion, confirmation, delivery, cancellation and recovery from failure.

Origin

Associated with research by Barbara Fredrickson and Daniel Kahneman and colleagues in the 1990s.

Sources

Barbara L. Fredrickson and Daniel Kahneman, “Duration neglect in retrospective evaluations of affective episodes”, 1993.

Effect

Von Restorff Effect

When similar things are presented together, an item that is noticeably different is more likely to be remembered.

Explore

Why it matters

Difference creates salience. Visual hierarchy can make an important action, message or object easier to notice and remember.

In practice

Use contrast deliberately and sparingly. If everything is visually exceptional, nothing is.

Origin

Hedwig von Restorff, 1933. Also known as the isolation effect.

Sources

Hedwig von Restorff, “Über die Wirkung von Bereichsbildungen im Spurenfeld”, 1933.

Observation

Conway’s Law

Organisations tend to produce systems whose structures reflect the communication structures of the organisations that created them.

Explore

Why it matters

Product architecture is shaped by organisational boundaries as well as by user needs and technical decisions.

In practice

When a product feels fragmented, examine the teams and communication patterns behind it as well as the interface itself.

Origin

Melvin E. Conway, 1967.

Sources

Melvin E. Conway, “How Do Committees Invent?”, 1968.

Effect

Aesthetic-Usability Effect

People often perceive aesthetically pleasing designs as easier to use than less attractive designs.

Explore

Why it matters

Visual quality influences perception of usability and can make people more tolerant of minor usability problems.

In practice

Treat aesthetics as part of the experience, but not as a substitute for usability. Attractive friction is still friction.

Origin

Associated with research by Masaaki Kurosu and Kaori Kashimura, 1995.

Sources

Masaaki Kurosu and Kaori Kashimura, “Apparent Usability vs. Inherent Usability”, 1995.

Effect

Barnum Effect

People tend to accept vague, broadly applicable personality descriptions as uniquely accurate accounts of themselves.

Explore

Why it matters

Personalised experiences can feel convincing even when the underlying message would fit almost anyone. That can inflate trust in quizzes, recommendations and generated insights.

In practice

Make personalisation specific, explain what evidence it uses and test whether different inputs produce meaningfully different outputs. Avoid flattering ambiguity presented as diagnosis.

Origin

Psychologist Bertram Forer demonstrated the effect with students in 1948. The name refers to showman P. T. Barnum’s supposed appeal to everyone.

Sources

Bertram R. Forer, “The fallacy of personal validation”, 1949.

Effect

Serial Position Effect

Items near the beginning and end of a sequence are often remembered better than items in the middle.

Explore

Why it matters

Order influences attention and memory. Important content can disappear when it sits in the undifferentiated middle of a long list, flow or presentation.

In practice

Put genuinely important material where it is likely to be encountered and understood, but do not use order as a substitute for hierarchy, labels or search.

Origin

Research on free recall distinguishes a primacy effect for early items and a recency effect for later ones. Bennet Murdock’s 1962 experiments are a well-known demonstration.

Sources

Bennet B. Murdock Jr., “The serial position effect of free recall”, 1962.

Effect

Zeigarnik Effect

Interrupted or unfinished tasks can remain more mentally accessible than completed ones.

Explore

Why it matters

Visible incompleteness can help people resume work, but it can also create tension or distraction. Progress cues draw attention because they represent unfinished activity.

In practice

Preserve drafts, show meaningful progress and make the next step clear. Do not manufacture anxiety with arbitrary completion meters or persistent reminders.

Origin

Bluma Zeigarnik reported experiments on interrupted tasks in 1927, following work in Kurt Lewin’s research group.

Sources

Bluma Zeigarnik, “Das Behalten erledigter und unerledigter Handlungen”, 1927.

Finding

Doherty Threshold

Interactive work becomes more productive when a system responds quickly enough for the exchange to feel continuous.

Explore

Why it matters

Delay interrupts concentration and makes each action feel costly. Immediate acknowledgement can preserve momentum even when the full result takes longer.

In practice

Respond to input at once, show honest progress for longer work and keep common interactions fast. Optimise the delays people repeatedly encounter before polishing rare paths.

Origin

Walter J. Doherty and Arvind J. Thadani described IBM research linking sub-second response with productivity in the early 1980s.

Sources

Walter J. Doherty, “Computing as a tool”, 1986.

Principle

Occam’s Razor

When several explanations fit the evidence, prefer the one that depends on fewer unsupported assumptions.

Explore

Why it matters

Product decisions often accumulate speculative features, rules and edge cases. Simpler explanations and models are easier to test, communicate and revise.

In practice

Start with the smallest model that explains observed behaviour. Remove assumptions before adding machinery, but retain complexity that the problem genuinely requires.

Origin

The principle is associated with fourteenth-century philosopher William of Ockham, though its familiar wording is later.

Sources

Stanford Encyclopedia of Philosophy, “Simplicity”.

Observation

Pareto Principle

A large share of an outcome is sometimes produced by a relatively small share of its causes.

Explore

Why it matters

Use, revenue, defects and support demand are often unevenly distributed. Finding the concentrated portion can focus limited research and delivery effort.

In practice

Measure the actual distribution, then prioritise high-impact tasks or causes. Revisit the data because concentration changes as products and audiences change.

Origin

Named for economist Vilfredo Pareto’s observations of unequal distributions; management thinker Joseph Juran later generalised the idea as the “vital few”.

Sources

Joseph M. Juran, “The Non-Pareto Principle; Mea Culpa”.

Law

Fitts’s Law

The time required to reach a target depends on its size and distance: nearer, larger targets are generally faster to acquire.

Explore

Why it matters

Interactive controls are physical targets. Small or distant controls demand more precision, especially with touch, limited dexterity or movement.

In practice

Give frequent and important actions generous hit areas, include label space in the target and avoid crowding destructive actions beside routine ones.

Origin

Psychologist Paul Fitts modelled the speed–accuracy trade-off in aimed movement in 1954. The law was later applied extensively to human-computer interaction.

Sources

Paul M. Fitts, “The information capacity of the human motor system in controlling the amplitude of movement”, 1954.

Principles

Gestalt Principles

People organise visual elements into perceived groups and wholes using cues such as proximity, similarity, continuity and enclosure.

Explore

Why it matters

Layout communicates relationships before words are read. Spacing, alignment and similarity can imply structure more strongly than borders or labels.

In practice

Keep related controls close, make peers visually consistent and separate unrelated groups. Check that visual grouping agrees with the underlying information architecture.

Origin

Gestalt psychology developed in the early twentieth century through researchers including Max Wertheimer, Wolfgang Köhler and Kurt Koffka.

Sources

Encyclopaedia Britannica, “Gestalt psychology”.

Adage

Hofstadter’s Law

Work tends to take longer than expected, even when previous underestimation has been considered.

Explore

Why it matters

Plans often account for known tasks but not discovery, coordination, revision and surprise. Complex product work exposes uncertainty as it proceeds.

In practice

Use ranges rather than false precision, separate estimates from commitments and update forecasts as evidence arrives. Make uncertainty visible instead of hiding it inside padding.

Origin

Douglas Hofstadter coined the self-referential adage in his 1979 book Gödel, Escher, Bach.

Sources

Douglas R. Hofstadter, Gödel, Escher, Bach, 1979.

Bias

Survivorship Bias

Looking only at cases that remain visible can hide the failures that would change the conclusion.

Explore

Why it matters

Successful products, active customers and completed journeys are easier to study than abandoned attempts. Their visibility can make success factors look more reliable than they are.

In practice

Seek people who left, failed, declined or never started. Include deleted experiments and unsuccessful competitors when reconstructing why an outcome occurred.

Origin

The bias is commonly illustrated by Abraham Wald’s Second World War work estimating aircraft vulnerability from damage observed on returning planes.

Sources

Abraham Wald, “A Method of Estimating Plane Vulnerability Based on Damage of Survivors”, 1943.

Bias

Confirmation Bias

People tend to seek, interpret and remember information in ways that support what they already believe.

Explore

Why it matters

Teams can turn research into a search for approval, favour supportive metrics and explain away evidence that threatens a preferred direction.

In practice

Write down what would disprove a belief, test credible alternatives and invite someone outside the decision to review the evidence and interpretation.

Origin

Peter Wason’s experiments in the 1960s helped establish the study of confirmatory reasoning, though related observations are much older.

Sources

Peter C. Wason, “On the failure to eliminate hypotheses in a conceptual task”, 1960.

Bias

Fundamental Attribution Error

Observers often overemphasise personal character and underemphasise circumstances when explaining another person’s behaviour.

Explore

Why it matters

Users can be labelled careless, resistant or incapable when the real causes are poor information, time pressure, policy constraints or an unforgiving interface.

In practice

Investigate the conditions surrounding behaviour before assigning motives. Reproduce the task, context and incentives rather than relying only on reported attitudes.

Origin

Lee Ross introduced the term in 1977, building on earlier attribution research by Edward Jones and Victor Harris.

Sources

Lee Ross, “The intuitive psychologist and his shortcomings”, 1977.

Adage

Parkinson’s Law

Work expands to fill the time made available for its completion.

Explore

Why it matters

Open-ended schedules invite additional discussion, polish and coordination whether or not those activities improve the outcome. Scope can quietly grow around available capacity.

In practice

Timebox decisions and explorations, define what “done” means and review whether extra work changes the result. Deadlines should create focus, not unsafe pressure.

Origin

C. Northcote Parkinson opened a satirical 1955 essay in The Economist with the observation before expanding it into a book.

Sources

C. Northcote Parkinson, “Parkinson’s Law”, 1955.

Theory

The Innovator’s Dilemma

Successful organisations can rationally favour existing customers and margins, leaving them vulnerable to initially weaker technologies that improve in new markets.

Explore

Why it matters

Good management of today’s product can conflict with exploring propositions whose early users, economics and quality measures look unattractive by existing standards.

In practice

Separate exploratory bets from core-product expectations, identify who values the new trade-off and test whether performance is improving along a different trajectory.

Origin

Clayton M. Christensen developed the theory in research on disk-drive industries and popularised it in his 1997 book.

Sources

Clayton M. Christensen, The Innovator’s Dilemma, 1997.

Heuristic

Jakob’s Law

People spend most of their time using other products, so they bring established expectations to yours.

Explore

Why it matters

Familiar conventions reduce learning effort. An unconventional interaction must overcome habits formed across many other services, not only explain itself locally.

In practice

Use established patterns for routine behaviour and spend novelty where it creates real value. Research the conventions your audience actually knows rather than assuming one universal norm.

Origin

Usability researcher Jakob Nielsen formulated the observation from decades of studying web behaviour.

Sources

Nielsen Norman Group, “Jakob’s Law of Internet User Experience”.

Adage

Tesler’s Law

Every system contains some irreducible complexity, and design determines whether the product or the person carries it.

Explore

Why it matters

Simplifying the interface may require the organisation to absorb complexity through automation, operations, policy or better defaults rather than merely hiding it.

In practice

Move repeated calculation and coordination into the system when it can do so reliably. Keep meaningful choices visible when people need judgment or control.

Origin

The “law of conservation of complexity” is attributed to computer scientist and interaction designer Larry Tesler.

Sources

Donald A. Norman, Living with Complexity, 2010.

Effect

Goal-Gradient Effect

Motivation and effort often increase as progress towards a visible goal becomes more immediate.

Explore

Why it matters

A clear sense of advancement can sustain activity through a multi-step process. A distant or ambiguous finish makes effort harder to judge.

In practice

Show honest progress, divide long tasks into meaningful stages and make completion criteria clear. Avoid artificial head starts or moving finish lines that undermine trust.

Origin

Clark Hull described the effect in animal learning in 1932. Later studies examined related patterns in human reward programmes.

Sources

Ran Kivetz, Oleg Urminsky and Yuhuang Zheng, “The Goal-Gradient Hypothesis Resurrected”, 2006.

Bias

Anchoring Bias

An initial value or reference point can pull later estimates and judgements towards it, even when it is arbitrary.

Explore

Why it matters

First prices, estimates, examples and suggested values can shape subsequent decisions. The order in which options appear is therefore not neutral.

In practice

Use defaults and reference prices responsibly. When estimating, form an independent view before seeing others’ numbers and examine a credible range, not one starting point.

Origin

Amos Tversky and Daniel Kahneman described anchoring and insufficient adjustment in their 1974 account of judgement under uncertainty.

Sources

Amos Tversky and Daniel Kahneman, “Judgment under Uncertainty: Heuristics and Biases”, 1974.

Effect

Framing Effect

Equivalent information can lead to different choices when it is expressed in different ways, such as gains rather than losses.

Explore

Why it matters

Headlines, comparison labels and success rates establish a frame before people evaluate the underlying facts. Wording can alter risk perception without changing outcomes.

In practice

Present consequential information in balanced forms, including absolute numbers where possible. Test comprehension, not just which wording produces more conversion.

Origin

Amos Tversky and Daniel Kahneman demonstrated framing effects in decisions involving risk in the early 1980s.

Sources

Amos Tversky and Daniel Kahneman, “The Framing of Decisions and the Psychology of Choice”, 1981.

Effect

Default Effect

People are disproportionately likely to keep a preselected option rather than actively change it.

Explore

Why it matters

Defaults remove effort and may be read as a recommendation. They can powerfully shape privacy, payment, notification and consent outcomes.

In practice

Choose defaults that serve the person’s likely interest, make consequences clear and make change straightforward. Treat high-impact defaults as policy decisions, not cosmetic settings.

Origin

Default effects have been studied across economics and psychology; Eric Johnson and Daniel Goldstein’s organ-donation research is a prominent demonstration.

Sources

Eric J. Johnson and Daniel Goldstein, “Do Defaults Save Lives?”, 2003.

Bias

Loss Aversion

In many decisions, losing something is felt more strongly than gaining an equivalent amount.

Explore

Why it matters

Changes that remove access, status or accumulated work can provoke stronger reactions than equivalent additions attract. The current state becomes a reference point.

In practice

Explain what changes and preserve work where possible. Do not exploit fear with misleading countdowns, exaggerated warnings or manufactured scarcity.

Origin

Daniel Kahneman and Amos Tversky formalised loss aversion within prospect theory in 1979.

Sources

Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk”, 1979.

Effect

Endowment Effect

Possessing something can make people value it more highly than they did before they owned it.

Explore

Why it matters

People may resist losing saved work, customisation, status or access once it feels like theirs, even when the original acquisition required little effort.

In practice

Handle migrations and removals as losses, provide export and recovery, and let people experience value before purchase without using deceptive ownership cues.

Origin

Richard Thaler named the effect; Daniel Kahneman, Jack Knetsch and Thaler later demonstrated it experimentally through exchange studies.

Sources

Daniel Kahneman, Jack L. Knetsch and Richard H. Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem”, 1990.

Heuristic

Availability Heuristic

Events that come easily to mind can seem more common or likely than events that are harder to recall.

Explore

Why it matters

Recent complaints, vivid failures and memorable anecdotes can outweigh quieter base rates. Teams may overreact to what is salient rather than what is representative.

In practice

Pair stories with frequency data, use consistent research sampling and compare incidents with the number of opportunities for them to occur.

Origin

Amos Tversky and Daniel Kahneman described availability as a judgement heuristic in 1973.

Sources

Amos Tversky and Daniel Kahneman, “Availability: A heuristic for judging frequency and probability”, 1973.

Effect

Priming

Recent exposure to a stimulus can influence how quickly or readily related information is processed.

Explore

Why it matters

Labels, examples and preceding questions activate concepts that shape interpretation. Context therefore affects what people notice and how they understand later material.

In practice

Order research questions carefully, keep examples neutral and check whether introductory language biases later choices. Use priming as an explanation to test, not a persuasion trick.

Origin

David Meyer and Roger Schvaneveldt’s 1971 lexical-decision experiments are foundational to semantic priming research.

Sources

David E. Meyer and Roger W. Schvaneveldt, “Facilitation in recognizing pairs of words”, 1971.

Effect

Picture-Superiority Effect

Under many conditions, pictures are remembered better than corresponding words.

Explore

Why it matters

Concrete imagery can create an additional route to recognition and recall. It is especially useful when appearance or spatial relationships carry information.

In practice

Use relevant images with labels where each adds meaning. Avoid decorative imagery that competes with the task, and always provide textual alternatives for essential content.

Origin

Allan Paivio and colleagues studied superior recall for pictures in the 1960s, contributing to later dual-coding explanations.

Sources

Allan Paivio, Thomas B. Rogers and Padric C. Smythe, “Why are pictures easier to recall than words?”, 1968.

Heuristic

Recognition over Recall

It is generally easier to recognise visible information than to retrieve the same information from memory without a cue.

Explore

Why it matters

Remembering commands, formats and earlier choices consumes attention that could be spent on the task. Visible options and context reduce that burden.

In practice

Keep relevant information available, offer history and suggestions, label icons and provide examples of required formats. Avoid forcing people to memorise information between screens.

Origin

Recognition versus recall is a longstanding distinction in memory research and appears as one of Jakob Nielsen’s usability heuristics.

Sources

Nielsen Norman Group, “Recognition vs. Recall in User Interfaces”.

Theory

Cognitive Load

Working memory has limited capacity, so learning and problem-solving suffer when a task demands too much mental processing at once.

Explore

Why it matters

Unfamiliar terminology, split attention, unnecessary decisions and remembered dependencies all compete for limited working-memory resources.

In practice

Remove irrelevant demands, integrate information needed together and sequence learning so complexity grows with understanding. Do not erase necessary complexity or useful context.

Origin

John Sweller developed cognitive load theory in research on learning and problem solving during the 1980s.

Sources

John Sweller, “Cognitive Load During Problem Solving: Effects on Learning”, 1988.

Strategy

Chunking

People can treat several familiar elements as one meaningful unit, making complex information easier to process and remember.

Explore

Why it matters

Meaningful groups reveal structure and reduce the number of separate units a person must consider. Expertise itself relies partly on recognising larger patterns.

In practice

Group information by real relationships, use headings and spacing, and format long identifiers into readable segments. Test whether the grouping makes sense to newcomers.

Origin

George Miller discussed recoding information into chunks in 1956; later research developed chunking as a theory of learning and expertise.

Sources

Fernand Gobet and colleagues, “Chunking mechanisms in human learning”, 2001.

Design principle

Progressive Disclosure

Show the information and controls needed now, while keeping less common or advanced choices available when they become relevant.

Explore

Why it matters

Presenting every possibility at once can obscure the common path. Staged complexity helps people form an initial understanding without permanently removing capability.

In practice

Lead with frequent tasks, reveal detail in context and make hidden options easy to discover and revisit. Never conceal costs, consequences or information needed for consent.

Origin

The interaction-design term developed in early human-computer interaction work, including John Carroll and Mary Beth Rosson’s research at IBM in the 1980s.

Sources

IBM, “Progressive disclosure”.

Effect

Mere-Exposure Effect

Repeated exposure to something can increase liking or preference for it, particularly when it is initially neutral or unfamiliar.

Explore

Why it matters

Familiarity can make a brand, pattern or proposition feel easier and safer. Repetition may influence preference before people can explain why.

In practice

Build consistency across repeated encounters and introduce unfamiliar patterns gradually. Do not confuse recognition with quality, and avoid repetition that becomes intrusive or irritating.

Origin

Robert Zajonc’s 1968 review and experiments established mere exposure as an important topic in social psychology.

Sources

Robert B. Zajonc, “Attitudinal effects of mere exposure”, 1968.

Effect

Halo Effect

A strong impression in one area can influence judgements about other, unrelated qualities.

Explore

Why it matters

A polished visual identity, famous founder or excellent first interaction can colour assessments of reliability, usability and value before those qualities are examined.

In practice

Evaluate important qualities separately, use explicit criteria and gather behavioural evidence. In research, avoid letting one striking success or failure dominate the whole session.

Origin

Edward Thorndike named the pattern in a 1920 study of how officers rated soldiers on different traits.

Sources

Edward L. Thorndike, “A constant error in psychological ratings”, 1920.

Bias

Negativity Bias

Negative events and information often have a stronger psychological impact than equally intense positive ones.

Explore

Why it matters

One failed payment, lost document or dismissive message can outweigh many routine successes. Harm and recovery deserve disproportionate design attention.

In practice

Prevent severe failures, communicate clearly when they occur and make recovery humane. When reviewing feedback, distinguish the importance of negative reports from their frequency.

Origin

The idea spans several research traditions. Roy Baumeister and colleagues synthesised evidence under the phrase “bad is stronger than good” in 2001.

Sources

Roy F. Baumeister and colleagues, “Bad is Stronger than Good”, 2001.

Bias

Status Quo Bias

People often prefer the current state, even when alternatives could produce a better outcome.

Explore

Why it matters

Change carries effort, uncertainty and potential loss. Existing settings, suppliers and workflows therefore have an advantage beyond their objective quality.

In practice

Make benefits and trade-offs concrete, support reversible trials and provide migration help. Do not interpret non-switching as enthusiastic preference without investigating barriers.

Origin

William Samuelson and Richard Zeckhauser named and experimentally studied status quo bias in 1988.

Sources

William Samuelson and Richard Zeckhauser, “Status quo bias in decision making”, 1988.

Effect

Sunk-Cost Effect

Past investments of time, money or effort can make people continue a course of action even when future costs outweigh future benefits.

Explore

Why it matters

Teams can keep funding weak features because they were expensive to build, while users persist with tools because setup and learning took effort.

In practice

Ask what you would choose if deciding today with current evidence. Set stopping criteria before investment and assess only costs and benefits that can still change.

Origin

Hal Arkes and Catherine Blumer’s 1985 experiments helped establish the sunk-cost effect as a subject of behavioural research.

Sources

Hal R. Arkes and Catherine Blumer, “The psychology of sunk cost”, 1985.

Bias

Curse of Knowledge

Once people know something, they find it difficult to imagine what the world looks like to someone who does not.

Explore

Why it matters

Product teams know the vocabulary, structure and history behind an interface. That knowledge makes missing context and ambiguous labels harder for them to see.

In practice

Test with people who lack insider knowledge, ask them to explain what they expect and keep domain terms only where the audience genuinely uses them.

Origin

Colin Camerer, George Loewenstein and Martin Weber introduced the term in a 1989 study of economic judgement.

Sources

Colin Camerer, George Loewenstein and Martin Weber, “The Curse of Knowledge in Economic Settings”, 1989.

Bias

False-Consensus Effect

People tend to overestimate how widely others share their own beliefs, preferences and behaviour.

Explore

Why it matters

Teams can mistake their habits for normal behaviour and interpret agreement inside a company as evidence about the market.

In practice

Recruit beyond colleagues and enthusiastic customers, quantify assumptions and examine meaningful audience differences. Treat “people like us” as a hypothesis, not a population.

Origin

Lee Ross, David Greene and Pamela House named the false-consensus effect in experimental work published in 1977.

Sources

Lee Ross, David Greene and Pamela House, “The false consensus effect”, 1977.

Heuristic

Social Proof

When uncertain, people often use other people’s behaviour or choices as evidence about what is appropriate or worthwhile.

Explore

Why it matters

Reviews, popularity signals and examples from peers can reduce uncertainty. Their influence grows when the situation is ambiguous and the other people seem relevant.

In practice

Use authentic, specific evidence from comparable users. Explain sample sizes and moderation where needed, and never fabricate activity, scarcity, ratings or testimonials.

Origin

The term was popularised by psychologist Robert Cialdini in his 1984 book Influence, drawing on wider conformity research.

Sources

Influence at Work, “The Principles of Persuasion”.

Hypothesis

Choice Overload

Under some conditions, adding options can make choosing harder and reduce satisfaction or action.

Explore

Why it matters

Large sets are difficult when options are unfamiliar, trade-offs are unclear or preferences are weak. In other contexts, breadth is useful and expected.

In practice

Improve comparison, grouping, recommendations and filtering before simply removing options. Test with realistic decisions and measure decision quality as well as completion.

Origin

Sheena Iyengar and Mark Lepper’s 2000 studies made the idea widely known; subsequent research has focused on when the effect does and does not appear.

Sources

Benjamin Scheibehenne, Rainer Greifeneder and Peter M. Todd, “Can There Ever Be Too Many Options?”, 2010.

Effect

IKEA Effect

People can value an object more highly when they have contributed effort to creating it successfully.

Explore

Why it matters

Meaningful participation can build attachment to plans, profiles and creations. But effort imposed without agency or success is more likely to feel like friction.

In practice

Let people shape outcomes where their contribution matters, show the result of their work and provide enough support for successful completion. Do not add labour merely to manufacture attachment.

Origin

Michael Norton, Daniel Mochon and Dan Ariely named the effect after experiments involving assembled products, including IKEA boxes.

Sources

Michael I. Norton, Daniel Mochon and Dan Ariely, “The IKEA effect”, 2012.

Index