The study of how people make choices about what and how much to do at various points in time, often involving trade-offs between costs and benefits occurring at different times. Crucial for designing systems that account for delayed gratification and long-term planning.
The behavior of seeking information or resources based on social interactions and cues. Important for understanding how users gather information in social contexts and designing systems that support collaborative information seeking.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
The cognitive bias where people treat a set of items as more significant when they are perceived as a cohesive group. Important for understanding user perception and decision-making.
An action in a user interface that, once performed, cannot be undone and typically involves deleting or removing content. Important for emphasizing the severity of the action and ensuring user confirmation to prevent accidental data loss.
A psychological phenomenon where people do something primarily because others are doing it. Important for understanding social influences on user behavior and trends.
A cognitive bias that causes people to believe they are less likely to experience negative events and more likely to experience positive events than others. Crucial for understanding user risk perception and designing systems that account for unrealistic optimism.
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
The use of algorithms to generate new data samples that resemble a training dataset, often used in AI for creating realistic outputs. Important for developing creative and innovative solutions in digital product design, such as content generation and simulation.
Marketing Qualified Lead (MQL) is a prospective customer who has shown interest in a company's product or service and meets specific criteria indicating a higher likelihood of becoming a customer. Essential for prioritizing leads and optimizing the efficiency of sales and marketing efforts by focusing resources on prospects most likely to convert.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
Research conducted in natural settings to collect data on how people interact with products or environments in real-world conditions. Crucial for gaining authentic insights into user behaviors and contexts.
A cognitive architecture model that explains how humans can learn and adapt to new tasks. Useful for understanding user learning and behavior adaptation, informing better user experience design.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
A situation in which an individual is unable to make a decision due to the overwhelming number of options available. Important for designing interfaces that streamline decision-making processes for users.
A framework that combines multiple theories to explain and predict behavior, focusing on intention, knowledge, skills, environmental constraints, and habits. Crucial for designing interventions that effectively change user behavior.
A step-by-step guide that helps users complete a complex task by breaking it down into manageable steps. Crucial for improving usability and ensuring users can successfully complete multi-step processes.
A term used to describe an organization focused on continuously shipping new features, often at the expense of quality, user experience, or business value. Crucial for recognizing and addressing the pitfalls of prioritizing quantity over quality in feature development.
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.
A set of fundamental principles and guidelines that inform and shape user research practices. Crucial for maintaining consistency and ensuring high-quality user insights.
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.
A theory that emphasizes the role of emotions in risk perception and decision-making, where feelings about risk often diverge from cognitive assessments. Important for designing systems that account for emotional responses to risk and improve decision-making.
The phenomenon where people remember information better when it is presented through multiple sensory modalities rather than a single modality. Crucial for enhancing memory retention and understanding through multimodal presentations.
A cognitive process used to generate creative ideas by exploring many possible solutions, often contrasted with convergent thinking. Essential for fostering creativity and innovation in problem-solving and design.
The study of the principles that govern human behavior, including how people respond to stimuli and learn from their environment. Crucial for designing user experiences that anticipate and influence user behavior.
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.
AI systems designed to generate creative content, such as art, music, and literature. Important for exploring new forms of artistic expression and automating creative processes.
The practice of protecting systems, networks, and programs from digital attacks, unauthorized access, and data breaches. Essential for safeguarding sensitive information, maintaining user trust, and ensuring the integrity and functionality of digital products and services.
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.
Minimum Viable Product (MVP) is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development. Essential for validating product ideas quickly and cost-effectively, allowing teams to learn about customer needs without fully developing the product.
A risk management model that illustrates how multiple layers of defense (like slices of Swiss cheese) can prevent failures, despite each layer having its own weaknesses. Crucial for understanding and mitigating risks in complex systems.
A logical fallacy where people assume that specific conditions are more probable than a single general one. Important for understanding and addressing cognitive biases in user behavior.
The phenomenon where people continue a failing course of action due to the amount of resources already invested. Important for recognizing and mitigating biased decision-making.
A theory suggesting that information processed at a deeper, more meaningful level is better remembered than information processed at a shallow level. Crucial for designing educational and informational content that enhances retention and understanding.
The stages a customer goes through from awareness to purchase and post-purchase activities. Important for designing strategies that optimize customer acquisition, retention, and satisfaction.
The process of continuously improving a product's performance, usability, and value through data-driven decisions and iterative enhancements. Crucial for ensuring that a product remains competitive and meets evolving user needs.
A cognitive bias where people are less likely to spend large denominations of money compared to an equivalent amount in smaller denominations. Useful for designers to understand consumer behavior and design pricing strategies that consider spending biases.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
Numeronym for the word "Internationalization" (I + 18 letters + N), enabling localization for different languages, regions, and cultures without requiring extensive rework. Important for expanding product reach to global markets.
An established company or market leader that holds a significant market share and has a strong presence in the industry. Important for understanding the dynamics between established players and new entrants in a market.
Also known as Expert Review, a method where experts assess a product or system against established criteria to identify usability issues and areas for improvement. Essential for leveraging expert insights to enhance product quality and usability.
An ongoing process of learning about user needs and validating assumptions through continuous research and experimentation. Crucial for staying responsive to user needs and improving products iteratively.
A usability testing method that measures the first click users make on a webpage to determine if they can successfully navigate to their goal. Essential for evaluating and improving the navigational structure of a website.
Product Development is the process of bringing a new product to market or improving an existing one. Crucial for innovation, meeting customer needs, and maintaining a competitive edge.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
The phenomenon where the credibility of the source of information influences how the message is received and acted upon. Crucial for designing communication strategies that leverage trusted sources.
The process of tailoring a product or experience to meet the individual needs and preferences of users. Essential for enhancing user engagement and satisfaction by delivering relevant experiences.
An event where developers, designers, and other stakeholders collaborate intensively on software projects, typically within a short timeframe. Important for fostering innovation, team collaboration, and rapid prototyping of new ideas in digital product development.
A type of artificial intelligence capable of generating new content, such as text, images, and music, by learning from existing data. Important for automating creative processes and generating novel outputs.
Redundant, outdated, or unnecessary code or design elements that accumulate over time in a system. Important for identifying and removing to maintain clean, efficient, and maintainable systems and interfaces.
A statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Essential for predicting outcomes and understanding relationships between variables in digital product design and analysis.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
A dark pattern where the product asks for the user's social media or email credentials and then spams all the user's contacts. Recognizing the harm of this practice is important to protect user trust and avoid spamming their contacts.
The process of breaking down decisions into smaller, manageable stages to simplify the decision-making process. Useful for guiding users through complex decisions in a structured manner.
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.
A tendency to avoid making decisions that might lead to regret, influencing risk-taking and decision-making behaviors. Crucial for understanding decision-making processes and designing systems that minimize regret.
A psychological phenomenon where the desire for harmony and conformity in a group results in irrational or dysfunctional decision-making. Crucial for recognizing and mitigating the risks of poor decision-making in teams.
Activities that give the appearance of innovation but do not produce tangible results. Important for recognizing and avoiding ineffective innovation efforts.
Domain-Driven Design (DDD) is an approach to software development that focuses on modeling the business domain and its logic. Essential for aligning software development with business needs and creating maintainable systems.
A research method that involves observing subjects in their natural environment. Crucial for gathering authentic data and insights into real-world behaviors and interactions.