Performance and Accountability Reporting (PAR) is a comprehensive document that outlines an organization's performance in achieving its goals and its accountability in managing resources. This report is essential for transparency, governance, and continuous improvement.
The tendency for individuals to put in less effort when working in a group compared to when working alone, due to reduced accountability. Crucial for understanding group dynamics and designing systems that ensure individual accountability.
Average Revenue Per Account (ARPA) is a metric used to measure the average revenue generated per user or account. Crucial for understanding and optimizing revenue streams in subscription-based businesses.
A dark pattern where users are forced to sign up for an account to complete a basic task. Designers should avoid this practice and provide optional account creation to respect user preferences.
Responsible, Accountable, Consulted, and Informed (RACI) is a responsibility assignment framework that clarifies roles and responsibilities in a projec. Crucial for ensuring clear communication and accountability in project management.
The practice of being open and honest about operations, decisions, and business practices, fostering trust and accountability. Essential for building trust with users and stakeholders and ensuring ethical business practices.
A psychological state where individuals feel as though the success and well-being of a project or task is their personal responsibility, akin to having an "owner's mentality.". Essential for fostering accountability, motivation, and proactive engagement within a product design team.
A behavioral economics concept where people categorize and treat money differently depending on its source or intended use. Crucial for understanding financial behavior and designing systems that align with users' mental accounting practices.
Return on Investment (ROI) is a performance measure used to evaluate the efficiency or profitability of an investment or compare the efficiency of different investments. Crucial for assessing the financial effectiveness of business decisions, projects, or initiatives.
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 cross-functional team that is given the autonomy, resources, and authority to make decisions and take ownership of the product's success, focusing on solving user problems and achieving business outcomes. Important for fostering innovation, accountability, and agility, leading to more effective product development and higher user satisfaction.
A concept in behavioral economics that describes how future benefits are perceived as less valuable than immediate ones. Important for understanding user preferences and designing experiences that account for time-based value perceptions.
A phenomenon where the success or failure of a design or business outcome is influenced by external factors beyond the control of the decision-makers, akin to serendipity. Important for recognizing and accounting for external influences in performance evaluations to ensure fair assessments and informed decisions.
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 degree to which the operations and decisions of an AI system are understandable and explainable to users. Crucial for building trust and ensuring ethical AI use.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
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.
A cognitive bias where people underestimate the influence of emotional states on their own and others' behavior. Crucial for designers to account for varying user emotional states in experience design.
A psychological state where individuals lose their sense of self-awareness and personal responsibility in groups, often leading to atypical behavior. Crucial for understanding group dynamics and designing experiences that promote positive group interactions.
Drivers, Approvers, Contributors, and Informed (DACI) is a responsibility assignment framework that clarifies roles and responsibilities. Essential for making clear and effective decisions in collaborative environments.
A cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
A regular meeting in Agile project management where team members discuss progress, plan work, and identify any obstacles, typically held daily. Crucial for maintaining communication, transparency, and accountability within Agile teams.
The study of strategic decision making, incorporating psychological insights into traditional game theory models. Useful for understanding complex user interactions and designing systems that account for strategic behavior.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
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.
Explainable AI (XAI) are AI systems that provide clear and understandable explanations for their decisions and actions. This transparency is crucial for building trust and confidence in AI applications across various domains.
A phenomenon where vivid mental images can interfere with actual perception, causing individuals to mistake imagined experiences for real ones. Important for ensuring that marketing and product design set realistic user expectations to avoid disappointment and maintain brand integrity.
Trust, Risk, and Security Management (TRiSM) is a framework for managing the trust, risk, and security of AI systems to ensure they are safe, reliable, and ethical. Essential for ensuring the responsible deployment and management of AI technologies.
Guidelines and principles designed to ensure that AI systems are developed and used in a manner that is ethical and responsible. Crucial for building trust and ensuring the responsible use of AI technologies.
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights. Crucial for ensuring that AI technologies are developed responsibly and ethically.
A cognitive bias where people tend to believe that others are more affected by media messages and persuasive communications than they are themselves. Important for understanding media influence and designing communications that account for this bias in user perception.
A holistic approach to analysis that focuses on the way that a system's constituent parts interrelate and how systems work over time and within the context of larger systems. Essential for solving complex problems and designing systems that account for interdependencies and dynamics.
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.
A theoretical framework in economics that assumes individuals act rationally and seek to maximize utility, used to predict economic behavior and outcomes. Important for understanding traditional economic theories and designing systems that account for rational decision-making.
The tendency for individuals to recall information that is consistent with their current mood. Important for understanding how mood affects memory and designing experiences that account for emotional states.
A concise statement of what the team aims to achieve during a sprint, providing direction and a shared understanding of the sprint's purpose. Crucial for ensuring team alignment and focus on the most important outcomes during a sprint.
A meeting at the end of a sprint where the development team presents their completed work to stakeholders. Crucial for gathering feedback and demonstrating progress.
A clear and concise list of criteria that a product or task must meet to be considered complete, ensuring alignment and understanding within a team. Essential for maintaining quality and consistency in agile project management.
Lifetime Value (LTV) is a metric that estimates the total revenue a business can expect from a single customer account throughout their relationship. Crucial for informing customer acquisition strategies, retention efforts, and overall business planning by providing insights into long-term customer profitability.
A list of tasks and deliverables that a team commits to completing during a sprint, providing a clear focus and scope for the sprint's duration. Essential for organizing and prioritizing work within an Agile sprint.
A cognitive bias where the pain of losing is psychologically more powerful than the pleasure of gaining. Important for designing user experiences that account for and mitigate loss aversion.
A cognitive bias where people allow themselves to indulge after doing something positive, believing they have earned it. Important for understanding user behavior and designing systems that account for self-regulation.
A cognitive bias where people wrongly believe they have direct insight into the origins of their mental states, while treating others' introspections as unreliable. Important for designing experiences that account for discrepancies between user self-perception and actual behavior.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
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.
Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) Goals are a framework for setting and achieving clear objectives. Essential for setting clear and actionable objectives in personal and professional contexts.
A cognitive bias where individuals overestimate how well their thoughts, feelings, and emotions are understood by others. Crucial for designing communication and user interfaces that account for and mitigate this bias.
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
A web-based interface that allows customers to find information and perform tasks without needing assistance from a customer service representative. Essential for improving customer experience and reducing support costs.
A graphical representation showing the amount of work remaining versus time, used in agile project management to track progress. Crucial for managing project progress and ensuring timely completion of tasks.
Guidelines that dictate how a brand should be presented across various media to ensure consistency. Crucial for maintaining brand integrity and ensuring uniformity in brand communications.
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
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.
The commitment a brand makes to its customers about the quality and experience they can expect. Essential for building trust and setting customer expectations.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
A set of rules and standards that define how a brand should be represented across all media and platforms. Crucial for ensuring brand consistency and maintaining brand integrity.
A concept that humans make decisions within the limits of their knowledge, cognitive capacity, and available time, leading to satisficing rather than optimal solutions. Crucial for designing systems and processes that account for human cognitive limitations and decision-making processes.
A cognitive bias where people remember scenes as being more expansive than they actually were. Important for understanding how users perceive and recall visual information, aiding in better visual design decisions.
The tendency for people to believe that others are telling the truth, leading to a general assumption of honesty in communication. Important for understanding communication dynamics and designing systems that account for this bias.