23. FEEDBACK (Cross-checking, Cross-Referring, Refering Back, Reverting): (A) Introduce feedback or facilitate detection or measurement, (B) If the feedback already exists change (or reverse or adjust) it
SYNONYMS: Cross-checking, Cross-Referring, Refering Back, Reverting
EXAMPLE: Automatic Process, Temperature, Pressure, Signal and Volume Measurement / Detectors / Control Devices – Thermostat, River/ Reservoir / Tank Water Marks, Budget, Automated (& Signal Sensitivity Driven) instead of Manual Control – Auto-Pilot, Smar Lighting System, Robotics, Traffic Control System, Smart Agriculture, Home Automation Systems, Health Monitoring System etc
ACB:
The Feedback principle refers to the idea of utilizing feedback loops or mechanisms in a system to improve its performance or achieve a desired result. The Feedback principle involves introducing or optimizing feedback loops within a system to enhance its functionality, control, or efficiency. The primary purpose of implementing feedback is to continuously monitor and adjust the system based on its output. This helps in maintaining stability, improving performance, and achieving desired outcomes. Feedback mechanisms are prevalent in various engineering and technological systems. Temperature control systems that adjust heating or cooling based on the feedback of the current temperature. In vehicles, feedback systems adjust steering based on the vehicle’s position relative to a desired path. Many industrial processes use feedback to maintain specific conditions, such as pressure, speed, or temperature. Feedback allows a system to self-regulate and adapt to changes, making it more robust and capable of responding to variations in input or external conditions.
The Feedback principle often addresses contradictions related to maintaining stability and precision in a system while adapting to changing conditions. It helps balance the need for control with the need for flexibility. Feedback can work in conjunction with other principles, such as the Dynamicity or Segmentation etc to achieve more sophisticated solutions. Innovations based on the Feedback principle might involve improving the accuracy of control systems, optimizing the responsiveness of automated processes, or enhancing the stability of a system in the face of external disturbances. Negative feedback, which opposes or reduces the deviation from a desired condition, is a common form of feedback used for stability and regulation in systems. It encourages engineers and problem solvers to incorporate feedback loops into systems, enabling them to adjust and improve their performance over time. By doing so, the system becomes more adaptive, responsive, and capable of maintaining desired conditions or achieving specific goals.
Introduce feedback (closed-loop systems)” involves incorporating mechanisms into technical systems that allow for the monitoring and adjustment of system parameters based on real-time data or input: Feedback loops enable systems to self-regulate and optimize performance by continuously comparing actual output with desired targets and making necessary adjustments. Example: Thermostat Control System: A thermostat control system in heating, ventilation, and air conditioning (HVAC) systems is an example of a technical system that utilizes feedback to regulate indoor temperature. The thermostat continuously monitors the ambient temperature and compares it to the desired setpoint. If the actual temperature deviates from the setpoint, the thermostat activates the heating or cooling system to adjust the indoor temperature accordingly. Once the temperature reaches the desired setpoint, the thermostat signals the heating or cooling system to stop, maintaining the desired temperature within the space. This closed-loop feedback mechanism ensures that the indoor environment remains comfortable while minimizing energy consumption. In this example, the feedback loop consists of the thermostat sensing the temperature, comparing it to the setpoint, and sending signals to the HVAC system to adjust heating or cooling output as needed. This continuous monitoring and adjustment process exemplifies the use of feedback in technical systems to maintain desired performance levels and optimize efficiency.
If feedback already exists, change it. Increase its degree of automation, intelligence, intensity, accuracy, reliability, documentation, applicability, or scope, controllability, auditability, and adaptiveness, etc.: This principle suggests enhancing existing feedback mechanisms in technical systems to improve their effectiveness and performance. By upgrading and optimizing feedback systems, engineers can ensure better control, monitoring, and adaptability, leading to overall system improvements. By upgrading the feedback mechanism with RFID technology, the automated inventory management system achieves significant improvements in automation, intelligence, accuracy, reliability, documentation, applicability, scope, controllability, auditability, and adaptiveness, leading to enhanced efficiency and performance.
Automated Inventory Management System: An automated inventory management system in a warehouse is an example of a technical system where feedback can be enhanced to increase efficiency and accuracy. In a traditional inventory management system, manual processes may be prone to errors, delays, and inefficiencies. To improve the feedback mechanism in the inventory management system, engineers can introduce RFID (Radio-Frequency Identification) technology. RFID tags attached to inventory items allow for automated tracking and monitoring of item movement throughout the warehouse. RFID readers installed at various checkpoints continuously collect data on inventory levels, location, and movement in real-time. By upgrading the feedback mechanism with RFID technology, the inventory management system achieves increased automation, accuracy, and reliability. The system can accurately track inventory levels, reduce stockouts and overstocks, and optimize inventory replenishment processes. Additionally, the system’s scope and applicability are expanded, as RFID technology can track a wide range of inventory items across different warehouse locations. Furthermore, the introduction of RFID technology enables better documentation, as detailed records of inventory movement and transactions are automatically generated and stored in the system. The enhanced feedback mechanism also improves controllability, as warehouse managers have better visibility and control over inventory operations.
Introduce diverse feedback mechanisms, including multiple homogeneous or heterogeneous types, incorporating past or incremental information, associated data, facts, assumptions, evidence, contexts, experiences, opinions, viewpoints, suggestions, recommendations, etc.: This principle advocates for the incorporation of various types of feedback mechanisms into a technical system, encompassing both homogeneous (similar) and heterogeneous (different) sources. These feedback mechanisms should utilize past or incremental information, along with associated data and contextual factors, to provide a comprehensive understanding of system performance and facilitate informed decision-making. By combining diverse feedback mechanisms, the smart home energy management system optimizes energy usage, reduces costs, and enhances user comfort while promoting sustainability and environmental conservation. This multifaceted approach to feedback integration exemplifies the principle of introducing diverse feedback mechanisms to improve technical system performance.
Smart Home Energy Management System: A smart home energy management system exemplifies the application of diverse feedback mechanisms in a technical system. This system integrates multiple sources of feedback, including sensor data, historical energy usage patterns, user preferences, environmental conditions, and utility pricing information, to optimize energy consumption and enhance efficiency. Sensor-based feedback: The system employs sensors throughout the home to monitor factors such as temperature, occupancy, lighting levels, and appliance usage in real-time. These sensors provide continuous feedback on energy usage patterns and environmental conditions, allowing the system to adjust heating, cooling, lighting, and appliance settings dynamically based on occupancy and user behavior. Historical data analysis: The system analyzes past energy usage data to identify trends, patterns, and anomalies in household energy consumption. By leveraging historical data, the system can predict future energy demands, anticipate peak usage periods, and optimize energy scheduling to minimize costs and reduce waste. User feedback and preferences: The system incorporates user feedback and preferences to personalize energy management strategies according to individual needs and priorities. Users can set energy-saving goals, adjust temperature settings, schedule appliance operations, and receive recommendations for optimizing energy usage based on their preferences and lifestyle. Environmental and external feedback: The system integrates external factors such as weather forecasts, utility pricing, and grid demand-response signals to adapt energy management strategies in response to changing conditions. For example, the system may adjust thermostat settings based on weather forecasts to optimize heating and cooling efficiency or participate in utility demand-response programs to reduce energy consumption during peak periods.
The endowment effect is a cognitive bias where individuals ascribe higher value to objects they own or possess compared to equivalent objects they do not own. In other words, people tend to overvalue the things they own simply because they own them. An understanding of the endowment effect can inform various aspects of system design, from interface design and personalization to retention strategies and user engagement. By recognizing and leveraging users’ tendency to overvalue their possessions, designers can create more engaging, effective, and user-centered technical systems. This bias has been demonstrated in various contexts, including experimental economics and behavioral psychology. For example, studies have shown that individuals are often unwilling to part with possessions they own, even when offered fair compensation for them. Similarly, people may be more reluctant to trade an item they own for an equivalent item they do not own, simply because they perceive the owned item to be more valuable.
The endowment effect can have important implications for decision-making, consumer behavior, and market outcomes. For example, it can lead to inefficiencies in markets, as individuals may be unwilling to sell their possessions at prices that reflect their true market value. It can also influence pricing strategies, negotiation tactics, and marketing techniques, as businesses seek to capitalize on consumers’ tendency to overvalue their possessions. In terms of designing technical systems, an understanding of the endowment effect can inform the design of interfaces, features, and user experiences. For example: User Interfaces: Designers can create interfaces that highlight users’ ownership of digital assets, such as personalized profiles, collections, or virtual goods. By emphasizing users’ ownership and attachment to these assets, designers can enhance users’ sense of identity and engagement within the system. Personalization and Recommendations: Designers can leverage the endowment effect to personalize recommendations, promotions, and offers based on users’ ownership history and preferences. By tailoring content to align with users’ existing possessions and interests, designers can increase the perceived value of recommended items and encourage users to engage with additional content. Retention Strategies: Designers can implement retention strategies that capitalize on the endowment effect to encourage ongoing engagement and usage. For example, loyalty programs, achievement systems, and virtual rewards can reinforce users’ attachment to their possessions and incentivize continued interaction with the system.
In the context of business contradictions, it refers to the strategic use of feedback mechanisms within an organization or business processes to improve overall performance, adapt to changes, and achieve desired outcomes. It encourages organizations to establish mechanisms that enable them to learn, adapt, and innovate continuously. By actively seeking and utilizing feedback from customers, employees, and the market, businesses can navigate challenges, seize opportunities, and optimize their overall performance. Regularly assessing potential risks, gathering feedback from stakeholders, and adjusting risk management strategies based on the evolving business environment.
Regularly collecting customer feedback, analyzing performance metrics, and using this information to make adjustments and enhancements to products or services. Gathering customer reviews, conducting surveys, and using the feedback to identify areas for improvement, leading to increased customer satisfaction. Regularly assessing market trends, customer preferences, and competitor activities to adjust business strategies accordingly. Establishing feedback channels for employees to contribute ideas, address concerns, and improve overall engagement. Launching pilot programs or minimum viable products, collecting user feedback, and using insights to refine and enhance products before a full-scale release. Conducting regular employee surveys, feedback sessions, or suggestion boxes to foster a culture of continuous improvement. Employing feedback mechanisms for quality control and process optimization. Implementing quality control checks at various stages of production, with feedback loops to identify and address defects promptly. Using real-time data and feedback to streamline inventory management, reduce lead times, and improve overall supply chain performance.
One notable example in the history of engineering and technical systems where a lack of feedback contributed to a major accident is the case of the Hyatt Regency walkway collapse in Kansas City, Missouri, in 1981. The Hyatt Regency Hotel had multiple suspended walkways, and during a tea dance event in July 1981, the second and fourth-floor walkways collapsed, resulting in a tragic disaster. The root cause was traced to a design change that altered the method of supporting the walkways. The original design used separate support rods for each walkway, but the change combined the loads of the second and fourth floors onto a single set of rods. The critical issue was a lack of proper feedback and communication during the design change. The engineers responsible for the modification failed to recognize the significant increase in load on the supporting connections. The collapse resulted in the death of 114 people and numerous injuries, making it one of the deadliest structural failures in U.S. history.
The consequences of the design change were not thoroughly evaluated or communicated, and the failure to recognize the increased load on the walkway supports had severe and tragic repercussions. Since then, the Hyatt Regency walkway collapse has been studied extensively as a case highlighting the importance of clear communication, thorough engineering analysis, and feedback loops in design and construction projects. It serves as a reminder of the need for robust feedback mechanisms and communication channels to identify and address potential risks and issues in complex technical systems.
One of the pivotal ways in which Amazon transformed the landscape of customer reviews was by seamlessly integrating them into the shopping journey. In the era prior to Amazon’s intervention, customer reviews were not readily accessible for a majority of products, posing a challenge for shoppers seeking reliable insights into their potential purchases. Amazon introduced a transformative change in 1995 by empowering customers to share reviews for products directly on its platform, thus furnishing shoppers with a wealth of valuable information pertaining to their desired items. This innovation substantially eased the decision-making process for shoppers, solidifying Amazon’s position as a dependable wellspring of product-related information. To establish an effective feedback mechanism, Amazon adeptly facilitated the process for customers to provide reviews. Following a purchase, customers automatically receive a request to review the product, accompanied by clear instructions on how to do so. The review submission form is readily accessible and user-friendly, enabling customers to effortlessly compose and submit their reviews with a few clicks. This simplified approach empowers even the most technologically hesitant customers to contribute their feedback, thereby encouraging a broader participation and enabling Amazon to amass a substantial volume of customer input. In 2019, Amazon introduced its innovative “One Tap Review” system, which allows customers to bestow a star rating without the obligation of composing a full-fledged review. This feature effectively eliminates additional barriers within the feedback process
Amazon utilizes customer feedback as a valuable tool to aid sellers in enhancing their performance and driving sales. This practice encompasses multiple strategies aimed at improving customer experience and boosting seller success. For instance, Amazon leverages customer feedback to assist sellers in pinpointing prevalent issues associated with their offerings, such as subpar packaging or delayed shipping. By identifying such challenges through customer feedback, sellers can initiate improvements that enhance the overall customer journey, resulting in improved sales.
Furthermore, Amazon harnesses customer feedback to guide sellers in optimizing their product listings. This involves emphasizing the most attractive features and advantages of a product to entice a larger customer base, thereby augmenting sales prospects. However, Amazon has encountered a challenge in ensuring the authenticity of customer reviews, guarding against fabricated or manipulated content. Since reviews wield substantial influence over product sales and reputation, there have been instances of deceptive practices, including falsified positive reviews to boost a product’s image or fake negative reviews to tarnish competitors. To address this concern, Amazon has implemented a range of measures, including the application of machine learning algorithms to identify anomalous patterns in review behavior, allowing the detection and removal of fraudulent reviews. In summary, Amazon strategically employs customer feedback to facilitate seller performance enhancement and sales growth. This involves identifying product-related issues, refining product listings, and safeguarding review authenticity against deceptive practices.
In technical systems and engineering contexts, providing effective feedback is crucial for optimizing processes, enhancing performance, and addressing issues promptly. The choice of a specific method depends on the nature of the system, the objectives, and the type of feedback required.
Closed-Loop Feedback Control System: A system where feedback is continuously measured, compared to a reference value, and adjustments are made to maintain or regulate the system’s output. Maintain system stability, accuracy, and responsiveness by making real-time adjustments based on feedback. PID Controller (Proportional-Integral-Derivative): A control system feedback loop that uses three components (proportional, integral, and derivative) to regulate the system output. Minimize error, reduce overshooting, and improve the overall performance of a control system. Fault Detection and Diagnosis (FDD): Monitoring and analyzing data to detect and diagnose faults or anomalies in a technical system. Early identification of issues, reducing downtime, and facilitating proactive maintenance. Condition Monitoring: Continuous monitoring of the condition of machinery or equipment to detect potential issues before they lead to failure. Predictive maintenance, improved reliability, and increased equipment lifespan.
Failure Mode and Effects Analysis (FMEA): A systematic method for evaluating potential failure modes of a system, assessing their effects, and prioritizing corrective actions. Identify and mitigate potential risks and failures in a technical system. Key Performance Indicators (KPIs): Establishing measurable indicators to assess the performance of technical systems. Quantify and evaluate various aspects of system performance, providing a basis for improvement. Root Cause Analysis (RCA): A method for identifying the underlying causes of a problem or failure in a technical system. Address the root cause of issues to prevent recurrence and enhance system reliability. Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control): A structured problem-solving approach that includes a phase for analyzing and improving processes based on data-driven feedback. Reduce defects, optimize processes, and enhance overall system performance.
Reliability Centered Maintenance (RCM): A maintenance strategy that uses feedback data to determine the most effective maintenance approach for each component within a system. Optimize maintenance efforts, prioritize critical components, and maximize system reliability. Model Predictive Control (MPC): A control method that uses mathematical models to predict future behavior and make optimal control decisions based on feedback. Improve control system performance by considering future system states in decision-making. Process Hazard Analysis (PHA): Identifying and analyzing potential hazards and risks associated with a process or system. Enhance safety, prevent accidents, and improve overall system reliability. Telemetry and Remote Monitoring: Using sensors and communication technology to monitor and collect data from remote locations. Enable real-time monitoring, early fault detection, and proactive maintenance in distributed technical systems. Control Charts: Statistical tools for monitoring and analyzing process variations over time. Identify trends, variations, and abnormal patterns in data, allowing for timely adjustments.
Several popular frameworks and methods are used in business management to provide constructive feedback to individuals and teams. These frameworks aim to facilitate effective communication, promote professional development, and enhance overall performance. When applying these frameworks, it’s crucial to tailor the feedback approach to the individual, context, and the nature of the feedback being delivered. Additionally, creating a culture that values regular and constructive feedback contributes to ongoing professional development and organizational success.
360-Degree Feedback: Involves collecting feedback from various sources around you, including peers, supervisors, subordinates, and even customers. It provides a comprehensive view of an individual’s performance. Enhances self-awareness, identifies your strengths and areas for improvement, and promote a more holistic understanding of an individual’s contributions. It is normally used for career guidance and development. It is also known as multi-rater feedback or 360-degree feedback, is a process in which feedback on an individual’s performance is gathered from multiple sources, including supervisors, peers, direct reports, and sometimes external stakeholders. The name “360-degree” reflects the idea that feedback is obtained from individuals all around the person being evaluated, providing a comprehensive and holistic view of their performance. Selection of Raters/Feedbacl Providers: The individual undergoing the review, often referred to as the “ratee” or “subject,” selects a group of raters who will provide feedback. Raters typically include supervisors, peers, direct reports (if applicable), and sometimes external stakeholders such as clients or partners. The anonymity of raters is often preserved to encourage honest and candid feedback. Questionnaire or Assessment: Raters are asked to complete a questionnaire or assessment form that includes a series of questions about the ratee’s performance, skills, competencies, and behaviors. These questions are usually designed to gather feedback on specific areas such as communication, leadership, teamwork, problem-solving, and interpersonal skills.
Feedback Collection: The completed questionnaires are collected and compiled by an administrator or HR representative. The feedback is anonymized to protect the confidentiality of the raters and encourage honest responses. The collected feedback is then aggregated and summarized into a report. Feedback Discussion: The feedback report is shared with the ratee during a feedback discussion or coaching session. The ratee has an opportunity to review the feedback, reflect on the comments provided, and discuss the results with their supervisor, coach, or mentor. The focus is on identifying strengths, areas for improvement, and development opportunities. Action Planning: Based on the feedback received, the ratee and their supervisor or coach collaboratively develop an action plan for addressing areas of improvement and further developing strengths. This may involve setting specific goals, identifying training or development activities, and establishing timelines for follow-up. Follow-Up and Monitoring: The progress of the ratee is monitored over time, and follow-up reviews may be conducted periodically to assess growth and development. Continuous feedback and support are provided to help the ratee achieve their goals and maximize their potential. 360-degree reviews offer several benefits, including a more comprehensive and balanced assessment of performance, increased self-awareness, enhanced communication and teamwork, and opportunities for professional growth and development. However, they also present challenges, such as ensuring confidentiality, managing feedback biases, and effectively using feedback for actionable insights. Overall, when implemented thoughtfully and effectively, 360-degree reviews can be valuable tools for fostering continuous learning and improvement in organizations.
SBI Model (Situation-Behavior-Impact): Focuses on specific situations, observable behaviors, and the impact of those behaviors. It provides a structured approach to delivering feedback. Offer clear and specific feedback, avoid generalizations, and help individuals understand the consequences of their actions. The Feedback Sandwich or Praise-Improve-Praise: Involves framing feedback with positive comments or praise, followed by constructive criticism or improvement areas, and concluding with positive reinforcement. Balance constructive feedback with positive reinforcement to maintain motivation and morale. STAR Method (Situation-Task-Action-Result): Particularly useful for performance reviews, the STAR method involves describing the Situation, Task, Action taken, and Result achieved in a specific scenario. Provide concrete examples of performance, highlight achievements, and offer insights into an individual’s contributions.
GROW Model (Goal-Reality-Options-Way Forward): Originally a coaching model, GROW can be adapted for feedback discussions. It involves exploring goals, assessing the current reality, discussing options for improvement, and establishing a way forward/will. Aligns feedback with individual or team goals, identify actionable steps for improvement, and foster a collaborative approach. The GROW model is a popular framework used in coaching and mentoring to facilitate goal setting, problem-solving, and personal development. It consists of four key stages, each represented by a letter in the acronym GROW: Goal (what is to be achieved by coachee ex. wants to lead a project): In the first stage, the focus is on establishing clear and specific goals or objectives. This involves defining what the individual wants to achieve, both in the short term and the long term. The goal should be SMART (Specific, Measurable, Achievable, Relevant, Time-bound) to ensure clarity and focus. Reality (assess curent situation and issues ex. no relevant experience to lead a project): In this stage, the individual examines the current reality or situation. They assess their current circumstances, resources, strengths, weaknesses, and any obstacles or challenges that may be hindering progress toward their goal. This step involves an honest and objective evaluation of the present reality. Options (explore possible actions ex. shadow a project and learn): Once the individual has a clear understanding of their goal and current reality, they explore different options or strategies for achieving their goal. This involves brainstorming ideas, considering alternative approaches, and evaluating the pros and cons of each option. The focus is on generating creative and practical solutions. Way Forward/Will (commit to a specific way forward ex. plan for chosen option and review progress agains the goal): In the final stage, the individual develops a plan of action or way forward based on the options identified. They select the most promising strategies and create a step-by-step action plan outlining specific tasks, milestones, and timelines. The plan should be realistic, achievable, and aligned with the individual’s goals and resources. The GROW model provides a structured framework for coaching conversations, guiding individuals through a process of self-discovery, problem-solving, and goal achievement. By following the four stages of GROW, coaches and mentors can help individuals clarify their goals, identify obstacles, explore solutions, and create actionable plans for success.
The COIN model is another framework used in feedback conversations to provide structured and effective feedback for behavior corrections. It stands for: Context (describe where and when the behavior occured): This involves setting the scene or providing the context for the feedback conversation. It’s important to establish why the feedback is being given, what specific situation or behavior it pertains to, and any relevant background information that may be necessary for the recipient to understand the feedback. Observation (state behavior was observed): In this step, the feedback giver provides specific observations or examples of the behavior or performance they are addressing. These observations should be factual, objective, and focused on observable behaviors rather than interpretations or assumptions. Impact (explain the effects of the behavior): Here, the feedback giver explains the impact of the observed behavior or performance, both on the individual receiving the feedback and on others involved. This step helps the recipient understand the consequences of their actions and why the feedback is important. Next Steps (suggest actions for improvement): Finally, the feedback giver discusses potential next steps or actions for improvement. This may involve offering suggestions, guidance, or resources to support the individual in addressing the feedback and making positive changes. The COIN model provides a structured framework for delivering feedback that is clear, specific, and actionable. By addressing each component of the model, feedback givers can ensure that their feedback is well-received, understood, and effectively applied by the recipient.
The CEDAR framework is a structured approach used in feedback and performance reviews to provide constructive and effective feedback. Each letter in CEDAR represents a key component of the framework: Context (set the stage ex. this year your project leadership stood out) : Provide context for the feedback by explaining the specific situation, project, or behavior being addressed. This helps ensure that the feedback recipient understands the circumstances surrounding the feedback and its relevance. Example (Be specific – give examples of how you handled XYZ project and how it was exceptional): Offer specific examples or instances to illustrate the feedback being given. Concrete examples help clarify the feedback and make it more actionable for the recipient. They provide evidence to support the feedback and enable the recipient to understand exactly what behavior or performance is being discussed. Data/Diagnosis (Share he reasons – you have put a lot of effort into improving your skills) : Use data and evidence to support the feedback whenever possible. This may include quantitative metrics, qualitative observations, or feedback from others. Data-driven feedback enhances objectivity and credibility, making it more compelling and easier for the recipient to understand and accept. Action (Plan next steps – lets discuss how you can build on this strength): Discuss potential actions or strategies for improvement based on the feedback provided. Offer constructive suggestions, resources, or support to help the recipient address the feedback and make positive changes. Encourage the recipient to develop a plan of action and set goals for improvement. Response (Agree on timeline – we can meet again next month to see how you are doing): Finally, encourage an open dialogue and invite the recipient to respond to the feedback. Allow them to share their perspective, ask questions, and provide input on how they plan to address the feedback. This promotes two-way communication and collaboration, fostering a positive feedback culture. By following the CEDAR framework, feedback givers can deliver feedback that is clear, specific, and actionable, while also promoting a constructive and supportive feedback environment.
The FEED framework is a structured approach used for providing constructive feedback. Each letter in FEED represents a step in the process of giving effective feedback: Facts (what was observed?): Start by presenting the facts or specific observations related to the behavior or performance being addressed. Provide concrete examples and avoid making assumptions or generalizations. Focusing on factual information helps ensure that the feedback is objective and based on observable behaviors. Effects (what were the effects?): Describe the effect or impact of the behavior or performance on individuals, teams, or outcomes. Explain how the behavior or performance contributes to success or detracts from it. Emphasize the consequences of the behavior or performance to help the recipient understand its significance. Expectations (what was expected?): Clarify expectations for future behavior or performance. Clearly communicate the desired outcomes, standards, or goals that the recipient should strive to meet. Provide specific guidance on what changes or improvements are expected and how they align with organizational objectives or values. Dialogue/Development (what needs to happen next?): Encourage open dialogue and two-way communication. Create a safe and supportive environment where the recipient feels comfortable discussing the feedback and asking questions. Listen actively to their perspective, concerns, and ideas for improvement. Engaging in a constructive dialogue fosters mutual understanding and collaboration. By using the FEED framework, feedback givers can provide feedback that is specific, impactful, and actionable, while also promoting a culture of continuous improvement and development.
The BOOST model is a feedback framework designed to provide positive reinforcement and support for individual development and growth. Each letter in BOOST represents a step in the feedback process: Balanced (Provide positive and constructive feedback): The feedback provided should be balanced, focusing on both strengths and areas for improvement. It’s important to acknowledge and celebrate the individual’s achievements and successes while also providing constructive feedback on areas where they can grow and develop. Observational (Base feedback on observable feedback): Feedback should be based on specific observations of the individual’s behavior, actions, and outcomes. Provide concrete examples and evidence to support your feedback, making it more credible and actionable for the recipient. Objective (Describe specific behaviors seen): Feedback should be objective and focused on observable behaviors and outcomes rather than subjective opinions or judgments. Avoid making assumptions or interpretations about the individual’s intentions or motivations. Instead, focus on providing factual and evidence-based feedback. Specific (Be precise about what was done well): Feedback should be specific and targeted, addressing particular behaviors, actions, or outcomes. Avoid vague or general statements and provide clear examples to illustrate your points. Specific feedback helps the recipient understand exactly what they are doing well and what areas they can improve upon. Timely (Give feedback soon after the event): Feedback should be delivered in a timely manner, ideally soon after the observed behavior or event occurs. Timely feedback allows the individual to reflect on their actions while the experience is still fresh in their mind and provides an opportunity for immediate course correction or reinforcement. By following the BOOST model, feedback givers can provide positive reinforcement and support for individual development in a constructive and effective manner. This model emphasizes the importance of providing balanced, observational, objective, specific, and timely feedback to promote growth and success.
Nonviolent Communication (NVC): Focuses on empathetic and compassionate communication, emphasizing observations, feelings, needs, and requests. Foster open and non-defensive communication, build understanding, and create a positive environment for feedback. Critical Incident Technique: Involves documenting specific events or incidents that demonstrate exemplary or problematic behavior, forming the basis for feedback. Provide concrete examples of behavior, making feedback more tangible and actionable. Feedforward: Shifts the focus from past behavior to future development, providing suggestions for improvement and growth. Encourage forward-looking discussions, promote a growth mindset, and emphasize continuous learning. Management by Objectives (MBO): Establishes clear, measurable objectives for individuals or teams, and feedback is provided based on the achievement of these objectives. Aligna feedback with specific performance goals, facilitate goal-setting, and drive results-oriented discussions. Appreciative Inquiry: Focuses on identifying and building on strengths and positive aspects rather than focusing on weaknesses. Foster a positive and strengths-based approach to feedback, inspire confidence, and encourage continuous improvement.
The safety sensor on elevator doors is a critical feature designed to enhance the safety of passengers entering and exiting elevators. Elevator doors are often equipped with infrared sensors or other technologies that can detect the presence of objects, including people, in the doorway area. When the elevator doors are in the process of closing, the sensors continuously monitor the space in front of the doors. If the sensors detect the presence of an object, such as a person, in the doorway, they send a signal to the elevator control system. Upon receiving the signal that an object is detected, the elevator control system takes preventive action. In many cases, this action involves automatically reversing the door’s closing movement or holding the doors open to ensure the safety of the person in the doorway. The safety sensor serves as a crucial precautionary measure to prevent the doors from closing on passengers. This is especially important in situations where someone might be entering or exiting the elevator, and the doors need to remain open until it is safe to close.
The incorporation of safety sensors makes elevator systems more user-friendly and reduces the risk of accidents or injuries related to closing doors. Elevator safety standards often require the implementation of such sensors to ensure that the elevator doors adhere to safety regulations and provide a secure environment for passengers. In emergency situations, such as when a passenger is entering slowly or has mobility challenges, the safety sensor ensures that the doors do not close prematurely, allowing sufficient time for safe entry or exit. The safety sensor is typically integrated into the overall safety system of the elevator, which may include features such as emergency brakes, door reopening buttons, and emergency communication systems. Overall, the safety sensor on elevator doors is a fundamental component that contributes to the overall safety and reliability of elevator systems, prioritizing passenger well-being during the boarding and disembarking process.
The reserve fuel marking and indicator in vehicles serve several purposes and provide benefits to both drivers and the vehicle itself. In many vehicles, there is a designated portion of the fuel tank that is marked as the “reserve” or “low fuel” level. The fuel tank is equipped with a fuel level sensor that constantly measures the amount of fuel in the tank. When the fuel level drops to the reserve marking, the fuel level sensor triggers an indicator on the vehicle’s dashboard. The dashboard indicator alerts the driver that the fuel level is low, prompting them to refuel. The reserve fuel indicator notifies drivers when it’s time to refuel, preventing unexpected fuel depletion during a journey. Running a vehicle on very low fuel levels can lead to the pickup of sediments or debris from the bottom of the fuel tank, potentially causing damage to the engine. The reserve indicator helps avoid such situations. Knowing that the fuel level is low allows drivers to plan and refill the tank before the fuel runs out, preventing the engine from stalling. In emergency situations, drivers are aware of the remaining fuel and can plan accordingly.
Maximizing fuel efficiency may lead to the risk of running out of fuel without warning. The reserve fuel system addresses this by providing a warning indicator, balancing the need for efficiency with the driver’s awareness. The implementation of a reserve fuel system aligns with the principles such as “Cushioning In Advance”, “Prior Counteraction” and “Feedback.” It acts as a preventive measure to avoid engine damage and provides feedback to the driver, promoting timely action to refill the fuel tank.
There are diverse applications of sensor-based control and feedback systems across industries, contributing to automation, efficiency, and improved decision-making processes: Sensors detect ambient temperature, and the thermostat adjusts heating or cooling systems to maintain a set temperature. Users can receive feedback on energy usage and control the system remotely. Proximity sensors detect the presence or absence of components, ensuring precise positioning and triggering automated actions. Feedback is used to optimize production efficiency and detect faults. Wheel speed sensors monitor individual wheel speeds, providing real-time feedback to the ABS system. This enables the system to modulate brake pressure, preventing wheel lockup during braking. Sensors track movement and heart rate, providing feedback to users about their activity levels, sleep patterns, and overall health.
Some devices can also send data to healthcare professionals for monitoring. Gas sensors measure pollutant levels in the air, providing feedback on air quality. This information can be used for environmental management and public health purposes. Soil moisture sensors provide feedback on soil conditions, helping farmers optimize irrigation schedules and conserve water resources. Sensors detect the presence of vehicles at intersections, providing feedback to the traffic light control system. This allows for adaptive signal timings based on real-time traffic conditions. Energy consumption sensors provide feedback on electricity usage in real-time, allowing consumers to monitor and manage their energy consumption more efficiently. Infrared or ultrasonic sensors detect obstacles in the cleaning path, providing feedback to the robotic vacuum to adjust its trajectory and avoid collisions. Ambient light sensors measure natural light levels, providing feedback to adjust artificial lighting accordingly. This helps in energy savings and creating comfortable lighting conditions.
Suggestibility bias is a cognitive bias where individuals are inclined to accept and internalize suggestions or information from external sources, even if that information is inaccurate or misleading. This bias can lead individuals to incorporate false or misleading information into their beliefs, memories, or perceptions, particularly when those suggestions come from authority figures, peers, or other influential sources. There are several factors that can contribute to suggestibility bias: Authority Figures: People are often more susceptible to suggestions from individuals perceived as authority figures or experts in a particular domain. This can include teachers, leaders, or professionals who are seen as knowledgeable and credible. Group Dynamics: In group settings, individuals may be influenced by the opinions or suggestions of others, leading to conformity and the adoption of shared beliefs or perspectives. This can occur even when the information provided is inaccurate or contradicts one’s own beliefs. Leading Questions: The way information is presented can influence how it is perceived and remembered. Leading questions that contain suggestions or assumptions can bias individuals’ responses and lead them to adopt the suggested information as their own. Repeated Exposure: Repeated exposure to suggestions or information can increase the likelihood of acceptance and internalization. Over time, individuals may come to believe false information simply because it has been presented to them frequently or consistently. Emotional State: Emotional factors, such as stress, anxiety, or a desire for approval, can influence suggestibility. Individuals may be more susceptible to suggestions when they are in a heightened emotional state or seeking reassurance from others. Suggestibility bias has important implications in various domains, including eyewitness testimony, interrogation techniques, therapeutic practices, and educational settings. It underscores the importance of critical thinking, skepticism, and independent verification of information to mitigate the impact of suggestibility on decision-making and belief formation.
Self-consistency bias is a cognitive bias where individuals tend to perceive themselves as more consistent in their attitudes, beliefs, and behaviors over time than they actually are. This bias leads people to underestimate or overlook inconsistencies in their thoughts, feelings, and actions, instead preferring to maintain a sense of coherence and stability in their self-concept. Individuals experiencing self-consistency bias may resist changing their attitudes, beliefs, or behaviors, even when presented with new information or alternative solutions that challenge their existing mindset. This resistance to change aligns with the concept of psychological inertia, where people tend to maintain their current state due to a reluctance to disrupt familiar patterns or routines.
Overcoming self-consistency bias is essential for fostering creative problem-solving and innovation. By challenging entrenched beliefs and assumptions, individuals can break free from psychological inertia and explore new possibilities for solving technical problems and achieving breakthroughs in technical systems. Self-consistency bias can create barriers to innovation by causing individuals to dismiss or ignore novel ideas or approaches that conflict with their established ways of thinking or working. This reluctance to embrace change can impede the adoption of innovative solutions and hinder progress in problem-solving efforts. Self-consistency bias refers to the tendency for individuals to perceive themselves as more consistent in their attitudes, beliefs, and behaviors over time than they actually are. It pertains to how individuals perceive and interpret information about themselves. On the other hand, self-service refers to a mode of interaction or delivery where individuals have control over accessing services or performing tasks without relying on assistance from others. It pertains more to the operational aspects of systems or processes.
The backfire effect is a cognitive bias where individuals, when presented with evidence that contradicts their existing beliefs or attitudes, may become even more entrenched in those beliefs or attitudes rather than changing them. Essentially, instead of updating their beliefs in response to new information, individuals may react defensively and reject the evidence, strengthening their original beliefs in the process. By addressing the backfire effect proactively, individuals and organizations can improve their ability to adapt to new information, foster innovation, and achieve better outcomes in technical systems and environments.
In technical systems or problem-solving contexts, the backfire effect can manifest in several ways: Resistance to Change: When individuals are presented with evidence that challenges established practices or beliefs within a technical system, they may resist adopting new methods or technologies, preferring to maintain the status quo even in the face of evidence suggesting potential improvements. Confirmation Bias: The backfire effect can exacerbate confirmation bias, where individuals selectively seek out and interpret information that confirms their existing beliefs while dismissing contradictory evidence. In technical systems, this bias can lead to a narrow focus on information that aligns with preconceived notions, hindering innovation and problem-solving. Decision-Making: The backfire effect can influence decision-making processes within technical organizations, as individuals may prioritize information that supports their existing beliefs or agendas, disregarding evidence that suggests alternative courses of action. Team Dynamics: In team-based technical projects, the backfire effect can contribute to conflicts and disagreements, as team members may become defensive when their ideas or approaches are challenged. This defensiveness can impede collaboration and hinder progress toward shared goals.
To mitigate the impact of the backfire effect in technical systems and problem-solving, it is essential to: Foster a culture of open-mindedness and intellectual humility, where individuals are encouraged to consider alternative perspectives and engage with evidence that challenges their beliefs. Provide training and education on cognitive biases, including the backfire effect, to help individuals recognize and address their own biases in decision-making and problem-solving. Promote interdisciplinary collaboration and diversity of thought within technical teams, encouraging constructive debate and exchange of ideas to foster innovation and creativity. Implement processes for evaluating evidence and making decisions that prioritize objective analysis and critical thinking over personal biases or agendas. Involve communication strategies such as providing clear explanations, avoiding overly persuasive language, and offering options for further exploration without imposing new beliefs on users. Implement features such as contextual feedback, user-controlled customization, and transparent data visualization to support users in evaluating evidence and adjusting their beliefs accordingly.y By remaining responsive to emerging evidence and adjusting system features accordingly, designers can ensure that technical systems are aligned with the latest insights into human cognition and behavior. By observing user behavior and gathering feedback, designers can identify potential sources of resistance or misunderstanding and iterate on system design to address these challenges effectively.
The backfire effect refers to the phenomenon where individuals, upon being presented with evidence contradicting their beliefs, may reject the evidence and become even more steadfast in their original beliefs. Coined by Brendan Nyhan and Jason Reifler in 2010, subsequent research has failed to consistently replicate findings supporting this effect. A study involving 10,100 participants from Ohio State University and George Washington University examined 52 different issues expected to trigger the backfire effect, but no instances of backfire were observed. These findings suggest that while individuals may indeed exhibit resistance to information that challenges their beliefs, the extreme response of strengthening those beliefs in the face of contradictory evidence may be less prevalent than previously believed. It’s important for researchers and practitioners to consider these nuances when discussing cognitive biases and their implications for decision-making and behavior. As a result, the backfire effect is now understood to be a rare occurrence rather than a common one.
The Ostrich Effect is a cognitive bias where individuals avoid or ignore negative or threatening information, preferring to bury their heads in the sand like an ostrich instead of confronting the reality of a situation. This bias is rooted in the desire to avoid stress, anxiety, or discomfort that may arise from acknowledging unpleasant truths. The term “Ostrich Effect” is derived from the popular but inaccurate myth that ostriches bury their heads in the sand when faced with danger. While ostriches do not actually exhibit this behavior, the metaphorical image has become synonymous with the tendency of humans to avoid facing difficult or distressing realities. In financial contexts, the Ostrich Effect may manifest when investors refuse to check their investment portfolios during periods of market volatility, preferring to ignore potential losses rather than confront them. Similarly, individuals may ignore warning signs of health problems or personal issues, choosing temporary relief from anxiety over addressing the underlying problems. Understanding the Ostrich Effect is important because it highlights how cognitive biases can influence decision-making and lead to irrational behaviors. By being aware of this bias, individuals can take proactive steps to confront difficult situations, seek out relevant information, and make more informed choices. Recognizing the tendency to avoid negative information can prompt individuals to actively seek out potential risks or threats in technical systems. By acknowledging the possibility of problems, engineers and designers can proactively identify and address vulnerabilities, leading to more robust and reliable solutions. In the case of the Ostrich Effect, the intermediary could be processes or mechanisms that encourage transparency, open communication, and proactive problem-solving, serving as a buffer against the tendency to ignore or avoid negative information.
The “magical number 7 ±2” refers to a concept in cognitive psychology related to the capacity of human working memory. Working memory is the system responsible for temporarily storing and manipulating information needed to perform cognitive tasks, such as problem-solving, reasoning, and comprehension. The magical number 7±2 suggests that the typical capacity of working memory is limited to about 7 (±2) items or chunks of information. This means that people can usually hold and process around 5 to 9 pieces of information simultaneously. However, this capacity can vary among individuals and is influenced by factors such as age, cognitive ability, and the complexity of the information being processed.
The concept was proposed by psychologist George A. Miller in his influential paper “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” published in 1956. Miller argued that the capacity of working memory is constrained by the limited number of discrete chunks of information that can be held in memory at any given time. For example, when remembering a list of random numbers, individuals typically find it easier to remember sequences that are broken down into smaller chunks or groups (e.g., 123-456-789) rather than a single long sequence (e.g., 123456789). This chunking process allows individuals to maximize their working memory capacity by organizing information into meaningful units. The concept of the magical number ±2 has implications for various aspects of cognitive psychology and human behavior, including learning, problem-solving, decision-making, and communication. It underscores the importance of managing cognitive load and structuring information in a way that optimizes memory and cognitive performance.
Social comparison bias is a cognitive bias in which individuals tend to evaluate their own abilities, attributes, or circumstances by comparing themselves to others. This bias can manifest in various ways, influencing perceptions, emotions, and behaviors. Some key characteristics of social comparison bias include: Upward Comparison: Individuals may compare themselves to others who are perceived as superior or more successful, leading to feelings of inadequacy, envy, or inferiority. This can contribute to low self-esteem and dissatisfaction with one’s own abilities or achievements. Downward Comparison: Conversely, individuals may engage in downward comparison by comparing themselves to others who are perceived as less fortunate or successful. Downward comparison can boost self-esteem and feelings of superiority but may also lead to complacency or underestimation of personal challenges and opportunities for improvement. Selective Comparison: People tend to selectively compare themselves to others in ways that reinforce their self-image or beliefs. For example, someone may choose to compare their athletic abilities to individuals who are less skilled to maintain a positive self-concept. Impact on Behavior: Social comparison bias can influence various behaviors, such as consumption choices, career decisions, and interpersonal relationships. For instance, individuals may be motivated to pursue certain goals or adopt specific behaviors based on comparisons with others, whether to emulate successful role models or differentiate themselves from perceived inferior peers.
Social comparison bias can have both positive and negative consequences. While it can serve as a source of motivation and inspiration, it can also contribute to feelings of inadequacy, envy, and social pressure. Recognizing the influence of social comparison bias can help individuals cultivate a healthier perspective on self-evaluation and develop strategies to mitigate its negative effects, such as focusing on personal growth and fostering authentic connections with others.
The decoy effect, also known as the asymmetric dominance effect or the attraction effect, is a phenomenon in decision-making where the introduction of an irrelevant or inferior option (the “decoy”) influences the choice between two or more options in a predictable way. This effect occurs when the decoy makes one of the original options appear more attractive or favorable by comparison. Key characteristics of the decoy effect include: Context Dependence: The decoy effect depends on the context in which options are presented. The introduction of the decoy can alter the perceived value or attractiveness of the original options relative to each other. Preference Shifting: The presence of the decoy can lead individuals to shift their preferences toward one of the original options, even if they would not have chosen that option in the absence of the decoy.
Asymmetric Dominance: The decoy is typically designed to be inferior to one of the original options but superior to the other. This creates an asymmetry in the comparison process, making one of the original options appear more appealing. Manipulation of Choice: The decoy effect can be used strategically to influence decision-making in various contexts, such as marketing, pricing, and negotiation, by steering individuals toward specific choices.
An example of the decoy effect can be seen in consumer decision-making. Suppose a customer is considering purchasing a medium-sized popcorn at a movie theater. The theater offers two options: a small popcorn for $5 and a large popcorn for $8. The customer may initially lean toward the small popcorn due to its lower price. However, if a third option, a medium popcorn for $7.50, is introduced as a decoy, the large popcorn may suddenly seem like a better value, as it appears to offer more popcorn for only $0.50 more than the medium option. This can lead the customer to choose the large popcorn, influenced by the presence of the decoy. The decoy effect is a powerful cognitive bias that demonstrates how the presentation of options can significantly impact decision-making processes. Recognizing this effect can help individuals make more informed choices by being aware of how external factors, such as the introduction of decoy options, can influence their decisions.
Information bias, also known as information-selection bias or observational bias, is a type of cognitive bias that occurs when there are systematic differences in the information gathered between comparison groups in a study or when information is collected and interpreted in a manner that distorts the true relationship between variables. Key characteristics of information bias include: Selection Bias: Information bias can arise during the selection or recruitment of study participants, leading to differences in the characteristics of the study groups. This can occur when certain groups are more likely to be included or excluded from the study population, resulting in a biased sample that does not accurately represent the target population. Measurement Bias: Information bias can also occur due to errors or inconsistencies in the measurement or assessment of study variables. This may include inaccuracies in data collection methods, subjective interpretation of measurements, or differential misclassification of exposure or outcome variables between groups. Misclassification Bias: Misclassification bias occurs when individuals are incorrectly classified into categories of exposure or outcome status. This can lead to underestimation or overestimation of the true association between variables, depending on the direction and magnitude of the misclassification. Reporting Bias: Information bias can be influenced by differences in the reporting or disclosure of information between groups. This may include differences in the accuracy, completeness, or timing of data reporting, which can affect the validity and reliability of study findings. Information bias can have significant implications for research validity and the interpretation of study results. It can lead to distorted estimates of effect size, misleading conclusions about causal relationships, and inaccurate assessments of the effectiveness of interventions or treatments. Recognizing and minimizing information bias is essential for ensuring the reliability and validity of research findings and promoting evidence-based decision-making in various fields, including healthcare, epidemiology, and social sciences.
The humor effect, also known as the humor impact or humor advantage, refers to the phenomenon where information presented with humor is more likely to be remembered, positively evaluated, and perceived as more persuasive compared to information presented in a non-humorous manner. This effect demonstrates the persuasive power of humor in communication and its ability to influence attitudes, perceptions, and behaviors. Key aspects of the humor effect include: Enhanced Memory Retention: Humorous content is often more memorable than non-humorous content. This is because humor captures attention, engages audiences, and stimulates cognitive processing, leading to improved encoding and retention of information in memory. As a result, people are more likely to remember information that is presented with humor. Positive Evaluation: Humor can enhance the likability and attractiveness of a message or communicator. When information is presented in a humorous manner, it tends to evoke positive emotions, such as amusement and enjoyment, which can lead to more favorable evaluations of the message and the communicator. This positive evaluation can influence attitudes and perceptions related to the message content. Increased Persuasiveness: Humor can increase the persuasiveness of a message by making it more engaging and relatable to the audience. Humorous messages are often perceived as less threatening and more approachable, which can reduce resistance to persuasion and increase receptivity to the message’s intended goals or outcomes. Attention and Engagement: Humor captures attention and sustains engagement by creating an element of surprise or incongruity. This heightened attention can lead to increased message comprehension and processing, as individuals are more likely to actively engage with and pay attention to humorous content.
While the humor effect can be a powerful tool in communication and persuasion, it is important to note that not all humor is equally effective, and inappropriate or offensive humor can backfire and undermine the intended message. Additionally, the effectiveness of humor may vary depending on individual differences, cultural norms, and the context in which it is used. By understanding the mechanisms underlying the humor effect and using humor strategically in communication, individuals can enhance the effectiveness of their messages and improve audience engagement and receptivity.
Attention bias refers to the tendency of individuals to pay more attention to certain stimuli or information compared to others. This bias can influence perception, memory, decision-making, and behavior, as individuals may prioritize or focus on particular cues or aspects of their environment while neglecting others. Key aspects of attentional bias include: Selective Attention: Attentional bias involves selective attention, where individuals allocate their attentional resources to specific stimuli or information while ignoring or minimizing attention to others. This selective attention can be influenced by various factors, including personal relevance, emotional salience, and previous experiences. Emotional Bias: Attentional bias can be particularly pronounced for emotionally charged stimuli. For example, individuals may be more likely to attend to and dwell on negative or threatening information, such as images or memories associated with fear or anxiety, compared to neutral or positive information. This emotional bias in attention can contribute to the maintenance of negative emotional states and increase susceptibility to mood disorders, such as anxiety and depression. Impact on Perception and Memory: Attentional bias can shape how individuals perceive and remember information. By selectively attending to certain cues or features of a situation, individuals may encode and retrieve memories in a biased manner, leading to distorted perceptions or memories of events. For example, individuals with social anxiety may selectively attend to social cues indicating rejection or disapproval, leading to heightened sensitivity to social threats and negative interpretations of social interactions. Clinical Implications: Attentional bias has important implications for understanding and treating various psychological disorders, including anxiety disorders, post-traumatic stress disorder (PTSD), and addiction. Therapeutic interventions that target attentional bias, such as attention bias modification training (ABMT) or cognitive-behavioral therapy (CBT), aim to modify maladaptive patterns of attention and reduce symptoms associated with these disorders. Attentional Control: While attentional bias may be automatic and unconscious, individuals can also exert control over their attentional focus through effortful processes. Techniques such as mindfulness meditation and cognitive reappraisal can help individuals regulate their attention and reduce the impact of attentional bias on emotional states and behavior. By understanding attentional bias and its effects on perception, cognition, and behavior, individuals can become more aware of their attentional patterns and develop strategies to effectively manage their attentional focus in different contexts.
Lake Wobegon Effect: The Lake Wobegon effect is the tendency for individuals to overestimate their own abilities or characteristics relative to others. In a technical context, this bias might lead designers or engineers to overestimate the quality or effectiveness of their work compared to their peers, potentially resulting in complacency or a lack of critical self-evaluation. When solving technical problems, individuals might be overly confident in their ability to find solutions, leading to underestimation of the complexity of the problem or the need for additional expertise.
Defensive Attribution Hypothesis: The defensive attribution hypothesis is the tendency for individuals to attribute their own successes to internal factors while attributing their failures to external factors. In designing a technical system, this bias might lead designers or engineers to take credit for successful outcomes while blaming external factors such as time constraints or resource limitations for failures or shortcomings. When solving technical problems, individuals might be quick to attribute failures to external factors rather than acknowledging their own role or responsibility, potentially hindering learning or improvement.
Illusory Superiority: Illusory superiority, also known as the superiority bias or the above-average effect, is the tendency for individuals to overestimate their own abilities or characteristics relative to others. In a technical context, this bias might lead designers or engineers to overestimate the quality or effectiveness of their designs or solutions compared to those of their peers, potentially resulting in complacency or a lack of critical self-evaluation. When solving technical problems, individuals might be overly confident in their ability to find solutions, leading to underestimation of the complexity of the problem or the need for additional expertise.
Fundamental Attribution Error: The fundamental attribution error is the tendency for individuals to attribute the behavior of others to internal characteristics or traits while attributing their own behavior to external factors or situational influences. In a technical context, this bias might lead designers or engineers to attribute design flaws or failures to external factors such as time constraints or resource limitations, while attributing successes to their own abilities or efforts. When solving technical problems, individuals might be quick to blame external factors for failures or setbacks rather than acknowledging their own role or responsibility, potentially hindering learning or improvement
3: Length of the moving object: [’23: Material loss’]
5: Area of the moving object: [’38: Level of automation’]
6: Area of the non-moving object: [’38: Level of automation’]
9: Speed: [’30: Harmful external factors’]
10: Force: [’28: Accuracy of measurement’]
13: Stability of the object: [’16: Action time of the non-moving object’, ’37: Complexity of control and measurement’, ’39: Productivity’]
16: Action time of the non-moving object: [’13: Stability of the object’]
19: Energy consumption of the moving object: [’11: Tension, Pressure’, ’26: Amount of substance’]
20: Energy consumption of the non-moving object: [’27: Reliability’]
22: Energy loss: [‘7: Volume of the moving object’, ’36: Complexity of the structure’, ’37: Complexity of control and measurement’]
23: Material loss: [‘1: Mass of the moving object’, ’39: Productivity’]
24: Information loss: [’27: Reliability’, ’39: Productivity’]
27: Reliability: [’20: Energy consumption of the non-moving object’, ’28: Accuracy of measurement’]
28: Accuracy of measurement: [’27: Reliability’]
29: Accuracy of manufacturing: [‘7: Volume of the moving object’, ’33: Convenience of use’, ’38: Level of automation’]
30: Harmful external factors: [‘7: Volume of the moving object’, ’28: Accuracy of measurement’]
31: Harmful internal factors: [‘9: Speed’]
32: Convenience of manufacturing: [’26: Amount of substance’]
33: Convenience of use: [’29: Accuracy of manufacturing’]
38: Level of automation: [‘4: Length of the non-moving object’, ’22: Energy loss’, ’29: Accuracy of manufacturing’]
39: Productivity: [’23: Material loss’, ’24: Information loss’]
3/23 5/38 6/38 9/30 10/28 13/16 13/37 13/39 16/13 19/11 19/26 20/27 22/7 22/36 22/37 23/1 23/39 24/27 24/39 27/20 27/28 28/27 29/7 29/33 29/38 30/7 30/28 31/9 32/26 33/29 38/4 38/22 38/29 39/23 39/24
EXAMPLE: One notable case leading to a medication error occurred at the Denver Health Medical Center in Colorado in 2008. In this case, a nurse intended to administer a pain medication called hydromorphone to a patient. However, instead of administering the correct medication, the nurse mistakenly administered vecuronium, a paralyzing agent used during surgery. This error led to the patient experiencing respiratory distress, and the mistake was discovered only when the patient’s condition deteriorated. This underscores the critical role of technology or corss-referring systems, in healthcare settings to improve patient safety and prevent medication errors. Bar Code Medication Administration (BCMA) is one such system that acts as a feedback mechanism, introducing an additional layer of verification in the medication administration process to ensure accuracy and reduce the risk of adverse events. The BCMA system is an example of how feedback mechanisms can be integrated into healthcare processes to prevent errors and improve patient outcomes. By addressing contradictions related to accuracy, speed, simplicity, and reliability, BCMA exemplifies the application of technology to enhance safety in medication administration.
Solution: A patient received a medication error due to the lack of a feedback mechanism or referring back or cross checking system like a barcode scanning system during the medication administration process. The absence of a barcode scanning system like BCMA contributed to the medication error. Without a barcode scanning system, there was no automated verification process to match the prescribed medication with the patient’s identity and the medication being administered.
BCMA is a technology-driven system used in healthcare settings to verify and administer medications to patients. It involves the use of barcodes on medication packaging and patient identification bands. When a nurse is about to administer medication, the BCMA system requires the nurse to scan both the patient’s identification bracelet and the barcode on the medication. The system then compares this information with the electronic medication administration record (eMAR) to ensure accuracy. The primary goal of BCMA is to prevent medication errors, such as administering the wrong medication, the wrong dose, or the wrong route. By incorporating barcode scanning into the medication administration process, BCMA aims to enhance patient safety and reduce the risk of adverse events related to medication.
The incident highlighted the importance of implementing feedback and cross-referring technologies like BCMA to prevent medication errors. Barcode scanning systems can significantly reduce the risk of administering the wrong medication, wrong dose, or wrong route by providing a systematic verification process. Following this incident, Denver Health Medical Center and many other healthcare institutions recognized the need to enhance medication safety. The implementation of BCMA systems became more widespread as a result, aiming to prevent similar errors in the future. BCMA has been shown to significantly reduce medication errors, improve patient safety, and streamline the medication administration workflow. It provides an additional layer of verification, helping healthcare providers deliver the right medication to the right patient at the right time.
