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Organizing for the future
- 1. ORGANIZING FOR THE FUTURE Cognitive Experience Design for HR Hamed Abdi | April 2021 1
- 3. FOUR MACRO TRENDS More Connection Unprecedented Automation Lower Transaction Costs Demographic Shifts 3 Source: McKinsey & Company • Rising interconnectivity speeds, disruption, upending the principles for disruptive innovation • Free-moving information bypasses-and challenges, existing hierarchies • Increased automation undercuts the mechanistic thinking upon which organizations were created • 200 years of management thinking on control and predictability become obsolete • Barriers to entry and costs to achieve scale are evaporating • Internal bureaucracy presents more friction than external • Gen Z and beyond will have new, fundamentally different career aspirations • Expect more variety and learning, more leadership and promotion opportunities, more social impact, and more career mobility
- 4. 9 KEYS FOR FUTURE 4 Who are we? 1. Define a resonant purpose 2. Sharpen your value agenda 3. Create a special culture How do we operate? 4. Radically flatten your structure 5.Turbocharge decision making 6.Treat talent as the scarcer capital How do we grow? 7.Take an ecosystem view 8. Build a data-rich technology platform 9.Accelerate learning Source: McKinsey & Company Purpose Culture Value Structure Tech Platform Talent Learning Decision Making Ecosystem
- 5. 9 KEYS FOR FUTURE 5 Source: McKinsey & Company
- 6. HUMAN CAPITAL TRENDS 6 Source: Deloitte Insights
- 7. 7 What is Society 5.0? A human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.
- 8. SOCIETY 5.0 & INDUSTRY 4.0 8
- 9. What is the next step? 9
- 10. 10 THE EVOLUTION OF HR
- 14. 14 DESIGN Design is not about following processes. It’s about being mindful of understand the problem, the people, and the system, and do iterative. Keep focus on people and the entire system to solve the right problems. Don Norman
- 15. 15 DESIGNTHINKING Co-Design Empathic Systemic Stanford D.School ZURB The Double Diamond IBM What x 4 LUMA IDEO
- 17. 17 EMPATHY MAP CANVAS Source: gamestorming.com
- 18. What people say, what people do, and what they say they do are entirely different things. 18
- 20. Black Box Self Report Noise Input Output Decision Making Memory Learning Mental Models Attention Language . . . 20 COGNITIVE APPROACH
- 22. Human Processor Model (HPM) 22
- 23. Human Processor Model Parameter Mean Range Eye movement time 230 ms 70-700 ms Decay half-life of visual image storage 200 ms 90-1000 ms Visual Capacity 17 letters 7-17 letters Decay half-life of auditory storage 1500 ms 90-3500 ms Auditory Capacity 5 letters 4.4-6.2 letters Perceptual processor cycle time 100 ms 50-200 ms Cognitive processor cycle time 70 ms 25-170 ms Motor processor cycle time 70 ms 30-100 ms Effective working memory capacity 7 chunks 5-9 chunks Pure working memory capacity 3 chunks 2.5-4.2 chunks Decay half-life of working memory 7 sec 5-226 sec Decay half-life of 1 chunk working memory 73 sec 73-226 sec Decay half-life of 3 chunks working memory 7 sec 5-34 sec 23
- 24. Neuroscientific Methods Neuronal Activity Within The Brain (CNS) Electroencephalogram (EEG) Magnetoencephalography (MEG) Steady State Topography (SST) Functional Magnetic Resonance Imaging (fMRI) Positron-emission Tomography (PET) Neuronal Activity of The Peripheral Nervous System (NPS) Electrocardiogram (ECG) Galvanic Skin Response (GSR) Facial Coding (FC) Facial Electromyography (FEMG) OtherTechniques EyeTracking (ET) Stationary Eye-tracker Eye-tracker Glasses Eye-trackers in VR Glasses Eye-tracking Through Webcams Implicit Response Test (IRT) Semantic Priming Visual Priming Indoor Positioning Systems Beacons (GPS Based) Video (Camera Location) Neckly (GPS Based on UWB) 24
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- 26. Facial Action Coding System (FACS) Motion Action Units Description Happiness 6+12 Cheek Raiser, Lip Corner Puller Sadness 1+4+15 Inner Brow Raiser, Brow Lowerer, Lip Corner Depressor Surprise 1+2+5+26 Inner Brow Raiser, Outer Brow Raiser, Upper Lid Raiser, Jaw Drop Fear 1+2+4+5+7+20+26 Inner Brow Raiser, Outer Brow Raiser, Brow Lowerer, Upper Lid Raiser, Lid Tightener, Lip Stretcher, Jaw Drop Anger 4+5+7+23 Brow Lowerer, Upper Lid Raiser, Lid Tightener, Lip Tightener Disgust 9+15+16 Nose Wrinkler, Lip Corner Depressor, Lower Lip Depressor Contempt 12+14 (on one side of the face) Lip Corner Puller, Dimpler 26
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- 30. Emotion Recognition API Tools Tool Class label Technique Attributes Number of face Accuracy Microsoft cognitive services anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise Cloud-Based Emotion Recognition Algorithm. Face rectangle top, left, Height, and width. Individual face, Multiple faces High Kairos smile, dislike, surprise, and attention - Eyes, eyebrows, nose, mouth Moderate Eyris EmoVu facial detection Deep Learning Architecture Individual face, static image - Sky biometric application facial attributes including gender, smile, closed eyes, open mouth, glasses and dark glasses Deep Neural Networks Closed eyes, open mouth, glasses and dark glasses. - Affectiva engaged, amused, surprised or confused. Machine-Learning Algorithms Eyes, nose, mouth, eyebrows, Individual face, Multiple faces, Real time images 90% Emotient joy, surprise, sadness, anger, disgust, contempt, fear, Machine-Learning Algorithms Eyes, eyebrows, nose, mouth 30
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- 33. Mental Models Mental model is knowledge structure that individuals construct to understand and explain their experiences. The models are constrained by the individuals’ implicit theories about these experiences, which can be more or less accurate. Johnson-Laird 33
- 35. Brain as Complex System Information Processing System • Brain as Prediction Machine 35
- 37. Cognitive LoadTheory Cognitive load refers to the total amount of mental activity imposed on working memory in any one instant. Cognitive Load Intrinsic Load • Manage with good instructional sequencing Extraneous Load • Reduce with good instructional design Germane Load • Maximize this 37
- 38. CounterfactualThinking Counterfactual thinking is thinking about a past that did not happen.This is often the case in “if only…” situations, where we wish something had or had not happened. • Upward vs. Downward • Additive vs. Subtractive • Self vs. Other 38
- 39. CounterfactualThinking Why do we have counterfactual thoughts? • Risk Aversion • Behavior Intention • Goal-Directed Activity • Collective Action • Benefits and Consequences Change Behavior 39
- 40. Behavioral ChangeTheories Fogg Behavior Model: B = MAT • Motivation: • Pleasure or Pain • Hope or fear • Social acceptance or rejection • Ability We are fundamentally lazy • Triggers 40
- 44. 44 COGNITIVE BIASES Biases Decision-making & behavior These biases affect people’s decision-making abilities, behavior and the decisions they make based on the different information they get. Thinking & problem solving These biases can change the way people think or solve problems and lead them to come up with wrong conclusions. Memories & recalling These biases can influence choices by either enhancing or impairing the recall of a memory or altering the content of a reported memory. Interview & user testing These biases can directly influence designer, during interviews or user testing, and may change the outcome of our research.They influence the behavior of people we interview or people who will test your products and services. Team work, social & meetings These biases can change the way groups of people work collectively and interact with each other, whether in a meeting room or in their daily lives in general. Source: Stephanie Walter
- 45. 45 DECISION-MAKING & BEHAVIOR DECISION-MAKING & BEHAVIOR Anchoring Availability Heuristic Default Effect Denomination Effect Loss Aversion Forer / Barnum Effect IKEA Effect Illusory Truth Effect Mere Exposure Effect Money Illusion Status Quo Bias Unit Bias Authority Bias
- 46. 46 THINKING & PROBLEM SOLVING THINKING & PROBLEM SOLVING Automation Bias Availability Bandwagon Confirmation Bias Curse of Knowledge Hyperbolic Discounting Law of The Instrument Pro-innovation Bias Rhyme as Reason Effect Fear of Missing Out
- 47. 47 MEMORIES & RECALLING MEMORIES & RECALLING Information Bias Pareidolia Bizarreness Effect Context Effect Google Effect Humor Effect Picture Superiority Effect Primacy Effect Spacing Effect Verbatim Effect Restorff Effect
- 48. 48 INTERVIEW & USER TESTING INTERVIEW & USER TESTING Blind Spot Bias Congruence Bias Courtesy Bias Hindsight Bias Illusion of Validity Negativity Bias Observer-Expectancy Effect Stereotyping Peak-end Rule
- 49. 49 TEAMWORK, SOCIAL & MEETINGS TEAM WORK, SOCIAL & MEETINGS Dunning–Kruger Effect Framing Effect NIH Effect Planning Fallacy Reactance Reactive Devaluation Group Attribution Error Self-Serving Bias System Justification Cheerleader Effect
- 51. From Chaos to Pattern and Beyond 51
- 54. Pattern Recognition Classification Recall Clustering Learning Logistic Regression Decision Tree Neural Networks Naive Bayes K-Nearest Neighbors Support Vector Machines K-means K-medoids Density Based Hierarchical 54
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- 56. Decision 56
- 58. Features Selection Wrapper SFS/GSFS (Forward Selection) SBS (Backward Elimination) Filter Embedded Extraction Linear PCA LDA MDA ICA Factor Analysis Multidimensional Scaling Non-Linear Kernel PCA ISOMAP LLE Neural Networks (MLP) SOM 58
- 60. Structural Equation Modeling CB-SEM and PLS-SEM 60
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- 62. The statistical methods First Generation Exploratory Cluster Analysis Exploratory Factor Analysis Multidimensional Scaling Confirmatory Analysis of Variance Logistic Regression Multiple Regression Confirmatory factor Analysis Second Generation Exploratory PLS-SEM (Partial Least Square) Confirmatory CB-SEM (Covariance-Based) 62
- 63. Considerations in using SEM 1. Composite Variables 2. Measurement 3. Measurement Scales 4. Coding 5. Data Distributions 1. Nominal 2. Ordinal 3. Interval 4. Ratio 63
- 65. Assessment Measurement Model Reflective Internal Consistency Convergent Validity Discriminant Validity Formative Convergent Validity Collinearity among Indicators Significant and Relevance of Outer Weight Structural Model Coefficient of Determination (R2) Effect Sizes (f2) Predictive Relevance (Q2) Size and Significant of Path Coefficients 65
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- 70. Put the human/humanity back in HR! 70
- 71. 71 HR LIFE CYCLE Business strategy HR strategy Organizational design Job & team design HR planning Vision and culture Recruitment & selection Onboarding and induction Assessment and appraisal Training & development Engagement & reward Career management Exit Source: Ulrich (2019)
- 72. 72 EMPLOYEE JOURNEY Before During After
- 73. 73