Innovation Theory
The study of how new ideas, products, and processes are developed and brought to market. Essential for fostering creativity and ensuring the continuous improvement and relevance of products.
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“contextual understanding”
Behavioral Learning Theory
The theory that all behaviors are acquired through conditioning, often used to understand and influence behavior change. Important for designing interventions that promote positive behavior change.
Product Brief
A document that provides a high-level overview of a product, including its objectives, target market, key features, and requirements, used to guide development efforts. Essential for ensuring that all stakeholders have a clear and consistent understanding of the product.
Iconic Memory
A type of sensory memory that briefly holds visual information for a fraction of a second. Useful for understanding how users process visual information and designing interfaces accordingly.
Multimodal Theory
A theory that explains how information is processed through different sensory modalities, such as visual, auditory, and tactile. Important for designing user experiences that engage multiple senses for better interaction and understanding.
Use Case
A detailed description of a system's behavior as it responds to a request from one of its stakeholders, often used to capture functional requirements. Essential for understanding and documenting how users will interact with a system to achieve their goals.
VUCA
Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) is an acronym for describing the challenging conditions of the modern world. Important for understanding and navigating dynamic and unpredictable environments.
Allais Paradox
A decision-making paradox that shows people's preferences can violate the expected utility theory, highlighting irrational behavior. Important for understanding inconsistencies in user decision-making and designing better user experiences.
Maslow’s Hierarchy of Needs
A psychological theory proposed by Abraham Maslow that outlines a five-tier model of human needs, ranging from basic physiological needs to self-actualization. Crucial for designing products and services that address various levels of user needs.
5V’s of Big Data
Characteristics of big data defined as Volume, Velocity, Variety, Veracity, and Value. Important for understanding the complexities and potential of big data in driving business insights and innovation.
Information Processing
The series of actions or operations involved in the acquisition, interpretation, storage, and retrieval of information. Crucial for understanding how users handle information and designing systems that align with cognitive processes.
Motivation Theory
A framework for understanding what drives individuals to act, involving theories such as Maslow's hierarchy of needs. Important for designing products and experiences that align with users' intrinsic and extrinsic motivations.
Problem Space
The context and set of conditions surrounding a problem that needs to be solved. Essential for understanding the full scope of a problem and identifying potential solutions.
Omission Bias
A cognitive bias where people judge harmful actions as worse, or less moral, than equally harmful omissions (inactions). Important for understanding user decision-making and designing systems that mitigate this bias.
Declarative Memory
A type of long-term memory involving information that can be consciously recalled, such as facts and events. Important for understanding how users retain and recall information in design.
Decision Utility
The value or satisfaction derived from a decision, influencing the choices people make. Crucial for understanding user preferences and designing experiences that maximize satisfaction.
Information Ecology
The study of the relationships between people, practices, values, and technologies within an information environment. Helps in understanding and designing systems that are sustainable and adaptive to human and environmental changes.
Pessimism Bias
A cognitive bias that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
Ratio Bias
A cognitive bias where people judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
Category Size Bias
A cognitive bias where people judge the likelihood of an event based on the size of its category rather than its actual probability. Crucial for designers to understand how category size influences user perception and decision-making processes.
Winner’s Curse
A phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
Grit
The perseverance and passion for long-term goals, often seen as a key trait for success. Important for understanding and fostering resilience and persistence in design and product development.
Normalcy Bias
The tendency to believe that things will always function the way they normally have, often leading to underestimation of disaster risks. Important for understanding risk perception and designing systems that effectively communicate potential changes.
Product vs. Program
Products are individual items or services designed to meet specific customer needs, while programs are collections of related projects and products managed together to achieve broader strategic goals. Essential for understanding the different scopes and objectives involved, helping to manage and align efforts effectively within an organization.
Seven Stages of Action
A model by Don Norman outlining the cognitive steps users take when interacting with a system: goal formation, planning, specifying, performing, perceiving, interpreting, and comparing. Important for designing user-friendly and effective products by understanding and supporting user behavior at each stage.
Normal Distribution
A statistical distribution where most occurrences take place near the mean, and fewer occurrences happen as you move further from the mean, forming a bell curve. Crucial for data analysis and understanding variability in user behavior and responses.
Homo Economicus
A theoretical concept in economics that portrays humans as rational and self-interested agents who aim to maximize their utility. Important for understanding economic decision-making and designing systems that align with rational behavior.
Ontology vs. Taxonomy
Ontology is a comprehensive model that includes entities, their attributes, and the complex relationships between them, while taxonomy is a hierarchical classification system that organizes entities into parent-child relationships. Essential for understanding the depth and scope of data organization, helping to choose the appropriate structure for information management and retrieval.
Schema Markup
Code added to a webpage to help search engines understand the content and provide more informative results for users, enhancing SEO. Essential for improving SEO and ensuring that search engines can accurately interpret webpage content.
Data Distribution
The spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.
Gambler’s Fallacy
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
Qualitative Research
A research method that focuses on understanding phenomena through in-depth exploration of human behavior, opinions, and experiences, often using interviews or observations. Essential for gaining deep insights into user needs and behaviors to inform design and development.
Peltzman Effect
The hypothesis that safety measures may lead to behavioral changes that offset the benefits of the measures, potentially leading to risk compensation. Crucial for understanding risk behavior and designing systems that account for compensatory behaviors.
Zero Price Effect
A phenomenon where people perceive an item as more valuable when it is free, leading to an increased likelihood of choosing the free item over a discounted one. Important for understanding consumer behavior and designing effective marketing strategies.
Affect Heuristic
A mental shortcut where current emotions influence decisions, often bypassing logic and reasoning. Important for understanding how emotions impact user decisions, aiding in more effective design and marketing.
Representativeness
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
Customer Journey
The complete set of experiences that customers go through when interacting with a company, from initial contact to post-purchase. Essential for understanding and optimizing each touchpoint in the customer lifecycle.
Feynman Technique
A learning method that involves teaching a concept to a novice to identify gaps in understanding and reinforce knowledge. Important for enhancing comprehension and retention of complex subjects.
Impact Bias
The tendency to overestimate the duration or intensity of the emotional impact of future events. Important for understanding user expectations and satisfaction.
Preference Reversal
A phenomenon where individuals' preferences between options change when the options are presented in different ways or contexts. Important for understanding and designing around inconsistencies in user choices.
Worse-Than-Average Effect
A cognitive bias where individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
Proportionality Bias
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
Sentiment Analysis
The use of natural language processing to identify and extract subjective information from text, determining the sentiment expressed. Crucial for understanding public opinion and customer feedback.
Regulatory Focus Theory
A theory that describes how individuals pursue goals using either a promotion focus (seeking gains) or a prevention focus (avoiding losses). Crucial for designing motivation strategies and understanding user behavior in goal pursuit.
Herd Behavior
The tendency for individuals to mimic the actions of a larger group, often leading to conformity and groupthink. Crucial for understanding social influence and designing experiences that consider group dynamics.
Behaviorism
A theoretical approach that focuses on observable behaviors and dismisses internal processes, emphasizing the role of environmental factors in shaping behavior. Foundational for understanding how external factors influence user behavior and for designing behavior-based interventions.
User Research
The process of understanding user behaviors, needs, and motivations through various qualitative and quantitative methods. Essential for designing user-centered products and ensuring they meet actual user needs.
Salience Bias
A cognitive bias where people focus on the most noticeable or prominent information while ignoring less conspicuous details. Important for understanding user decision-making and ensuring balanced presentation of information.
Illusion of Control
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
Bubble Sort
A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. Important for understanding basic algorithmic principles and their applications.
Projection Bias
The tendency to overestimate how much our future preferences and behaviors will align with our current preferences and behaviors. Important for understanding user behavior and designing experiences that account for changes over time.
B2B
Business-to-Business (B2B), a business model where products or services are sold from one business to another. Crucial for understanding business markets and developing inter-business strategies.
Naive Allocation
A heuristic where individuals evenly distribute resources across all options, regardless of their specific needs or potential. Useful for understanding and designing around simplistic decision-making strategies.
Annual Recurring Revenue
A metric that shows the revenue that a company can expect to receive annually from its customers for subscriptions or services. Essential for understanding business performance and growth potential.
Ostrich Effect
A cognitive bias where people avoid negative information or situations, preferring to remain uninformed or ignore problems. Important for understanding user behavior and designing systems that encourage proactive engagement.
Web Crawler
An automated program used by search engines to browse the internet and index web pages, aiding in the retrieval of relevant information during a search query. Crucial for understanding how search engines discover and index web content.
Demographic
Statistical data relating to a particular population and groups within it. Crucial for market research and understanding target audiences.
Myopic Loss Aversion
A cognitive bias where individuals tend to avoid risks when they perceive potential losses more acutely than potential gains. Important for understanding decision-making behavior in users and designing systems that mitigate risk aversion.
Sales Funnel
Often referred to as "marketing funnel", a model that represents the user journey from awareness to purchase used to analyze and optimize conversion of prospects to customers. Essential for understanding and improving the customer journey and conversion process.
Subadditivity Effect
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.