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.
A research method that involves observing subjects in their natural environment. Crucial for gathering authentic data and insights into real-world behaviors and interactions.
Numeronym for the word "Observability" (O + 11 letters + N), the ability to observe the internal states of a system based on its external outputs, facilitating troubleshooting and performance optimization. Crucial for monitoring and understanding system performance and behavior.
A theory that suggests people learn behaviors, skills, and attitudes through observing and imitating others, as well as through direct experiences. Crucial for understanding how users acquire new behaviors and designing educational or training programs.
A research method where participants take photographs of their activities, environments, or interactions to provide insights into their behaviors and experiences. Important for gaining in-depth, visual insights into user contexts and behaviors.
Behavior-Driven Development (BDD) is a software development approach where applications are specified and designed by describing their behavior. Important for ensuring clear communication and shared understanding between developers and stakeholders.
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.
A research method where participants record their activities, experiences, and thoughts over a period of time, providing insights into their behaviors and needs. Important for gaining in-depth, longitudinal insights into user experiences.
The tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
A field research method where researchers observe and interview users in their natural environment to understand their tasks and challenges. Crucial for gaining authentic insights into user behavior and needs.
A research method that involves repeated observations of the same variables over a period of time. Crucial for understanding changes and developments over time.
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
A research approach that starts with observations and develops broader generalizations or theories from them. Useful for discovering patterns and generating new theories from data.
A qualitative research method that studies people in their natural environments to understand their behaviors, cultures, and experiences. Crucial for gaining deep insights into user behaviors and contexts.
A psychological phenomenon where people follow the actions of others in an attempt to reflect correct behavior for a given situation. Essential for designing interfaces and experiences that leverage social influence to guide user behavior and increase trust and engagement.
The observed tendency of humans to quickly return to a relatively stable level of happiness despite major positive or negative events or life changes. Useful for designing experiences that maintain user engagement and satisfaction over time.
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.
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.
The study of how colors affect perceptions and behaviors. Important for designing experiences that evoke desired emotional responses from users.
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.
A statistical method used to predict a binary outcome based on prior observations, modeling the probability of an event as a function of independent variables. Essential for predicting categorical outcomes in digital product analysis and user behavior modeling.
The economic theory that suggests limited availability of a resource increases its value, influencing decision-making and behavior. Important for creating urgency and increasing perceived value in marketing.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
A common pattern of eye movement where users scan web content in an "F" shape, focusing on the top and left side of the page. Crucial for designing web content that aligns with natural reading patterns to improve engagement.
A statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
A practice of performing testing activities in the production environment to monitor and validate the behavior and performance of software in real-world conditions. Crucial for ensuring the stability, reliability, and user satisfaction of digital products in a live environment.
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 usability testing method where users interact with a system they believe to be autonomous, but which is actually operated by a human. Essential for testing concepts and interactions before full development.
The process of evaluating a product by testing it with real users to gather feedback and identify usability issues. Essential for validating design decisions and ensuring the product meets user needs.
A statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.