Berichter:
Univ.-Prof. Dr.-Ing. Robert Heinrich Schmitt
Univ.-Prof. Dr.-Ing. Dipl.-Wirt.-Ing. Christopher Marc Schlick
Tag der mündlichen Prüfung: 20. Mai 2014
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Alexander Walter van Laack
Measurement of Sensory and Cultural Influences on Haptic Quality Perception of Vehicle Interiors
1. Auflage 2014
Umschlagseite gestaltet von Martin van Laack B.Sc. (RWTH-Aachen)
© 2014 van Laack GmbH, Aachen, Buchverlag
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D 82 (Diss. RWTH Aachen University, 2014)
The scientific research leading to this dissertation was conducted during my time as a Research Engineer at the Ford Research Center in Aachen, Germany.
I would like to express my special appreciation and thanks to my advisor Prof. Dr.-Ing. Robert Schmitt, director of the Institute for Production Metrology and Quality Management within the WZL at the RWTH Aachen University, for offering me the unique opportunity to write this dissertation. During our constructive discussions about my research project he gave me invaluable advice and support that strongly influenced the success of this project. I would also like to express my gratitude to Prof. Schmitt’s research assistants for their excellent cooperation over the past years. In particular I would like to thank Dipl.-Ing Dipl.-Wirt. Ing. Björn Falk, Chief Engineer at the WZL, for his support during the last stages of my thesis.
I would also like to thank Prof. Dr.-Ing. Dipl.-Wirt.-Ing Christopher Schlick, Director of the Institute of Industrial Engineering and Ergonomics (IAW), for his professional interest in my dissertation and for taking the role of the co-supervisor.
At Ford I am foremost grateful to my former mentor, Dr.-Ing. Mark Spingler and my former colleague, Dipl.-Ing. Marc Galonska, for guiding and assisting me during the past years and creating a constructive and positive atmosphere to work in. Additionally I would like to show my appreciation to Dr.-Ing. Florian Golm, his management and his team for the outstanding support I received. I also thank my students for their dedication and great work.
My sincere appreciation also goes to my good friends Dipl.-Ing. Dipl.-Wirt. Ing. Sean Humphrey and Dipl.-Ing. Dipl.-Wirt. Ing. Simon Müller for the constructive discussions we had and the intensive review of my thesis. I would also like to thank my cousin Dipl.-Wirt.-Inf. (FH) Marc Oliver van Laack for offering me the IT-infrastructure to conduct the online surveys for my research.
Foremost I am grateful to my family and I would like to thank my parents, Carla and Prof. Dr. med. Walter van Laack, for believing in me and giving me their continuous and unconditional love, support and encouragement throughout my life. I am also thankful to thank my brother, Martin van Laack B.Sc., for his help designing the artwork of this book, and of course to my girlfriend, Melania Mateias M.Sc., for her motivation, thoughtfulness and patience over the last years. I would like to express my profound appreciation and admiration to my beloved grandparents, Doris and Werner van Laack, who sadly could not witness the end of my dissertation in person.
Aachen, May 2014
Alexander van Laack
Meinen Großeltern
Meinen Eltern
Produkte verkaufen sich schon längst nicht mehr ausschließlich aufgrund ihrer rein technischen Überlegenheit. Eine Differenzierung erfolgt meist nach subjektiven Kriterien, zu denen auch die wahrgenommene Qualität (engl. Perceived Quality) zählt. Seit einigen Jahren ist dieser Trend auch in der Automobilindustrie zu beobachten. So zählt hier die wahrgenommene Qualität mittlerweile zu den bedeutendsten Kaufkriterien, die neben der visuellen auch stark von der haptischen Wahrnehmung sowie von kulturellen Einflüssen geprägt ist.
Um die Kundenwahrnehmung von Autoinnenräumen zu verbessern und die Kommunikation zwischen Automobilherstellern und Zulieferern zu unterstützen, ist es notwendig Messmethoden einzusetzen, welche die vom Kunden wahrgenommene Qualität objektiviert und in Messwerten ausdrückt.
Zielsetzung dieser Arbeit ist es daher, robuste Messmethoden zu entwickeln, mit deren Hilfe die haptische Qualitätswahrnehmung von Fahrzeuginnenräumen unter Berücksichtigung kultureller Einflüsse zuverlässig gemessen werden kann. Die zu entwickelnden Methoden müssen dabei zerstörungsfrei sein und sowohl im Labor als auch im Fahrzeug eingesetzt werden können.
Zur Identifizierung der Einflussfaktoren auf die Wahrnehmung von haptischen Qualitätsmerkmalen werden Probandenstudien durchgeführt und statistisch ausgewertet. Unter Berücksichtigung von physischen und psychophysischen Gesetzmäßigkeiten werden die subjektiven Bewertungen mit objektiven Messwerten korreliert und passende Transferfunktionen entwickelt. Kulturelle Unterschiede in der Wahrnehmung werden zusätzlich durch zwei internationale Kundenstudien abgefragt und ihr Einfluss auf die Entwicklung globaler Autos bewertet.
| μ | Friction Coefficient |
| μaverage | Average Friction Coefficient |
| μk | Kinetic Friction Coefficient |
| μpeak | Peak Friction Coefficient |
| μs | Static Friction Coefficient |
| A | Surface Area [mm2] |
| a | Amplitude |
| ANOVA | Analysis of Variance |
| as | Acceleration [m/s2] |
| AS | Asia |
| b | Exponent Variable |
| c | constant |
| C | Heat capacity [J/(kg*K)] |
| C1 | Constant 1 |
| C2 | Constant 2 |
| cp | Specific Heat Capacity [kJ/(kg*K)] |
| csv | Comma-Separated Values |
| CTD | Contact Temperature Device |
| DIN | German Institute for Standardization |
| e | Thermal Effusivity [Ws0,5/m2K] |
| EEG | Electroencephalography |
| eH | Thermal Effusivity of Human Hand [Ws0,5/m2K] |
| eM | Thermal Effusivity of Material [Ws0,5/m2K] |
| EMG | Electromyography |
| ENSMM | École Nationale Supérieure de Mécanique et des Microtechniques |
| ESTIEM | European Students of Industrial Engineering and Management |
| EU | Europe |
| F | Force [N] |
| Fabs | Absolute Force [N] |
| FCPA | Ford Consumer Product Audit |
| Fabs | Absolute Force [N] |
| FM | Force of Mass m [N] |
| FN | Normal Load [N] |
| FR | Resistance Friction Force [N] |
| Fres | Restoring Force [N] |
| Fs | Static Friction Force [N] |
| FSP | Spring Force [N] |
| fStick-Slip | Stick-Slip Frequency [Hz] |
| FT, reference | Tack Force of Reference Sample [N] |
| FT, sample | Tack Force of Sample [N] |
| FVPA | Final Vehicle Product Audit |
| Fxy | Force in XY-Direction [N] |
| Fz | Force in Z-Direction [N] |
| HMI | Human Machine Interface |
| HVAC | Heating, Ventilation and Air Conditioning |
| ICP | Integrated Control Panel |
| IP | Instrument Panel |
| ISO | International Organization for Standardization |
| k | Constant |
| kF | Spring Constant [N/m] |
| L | Sample Thickness [mm] |
| LSD | Least Significant Difference |
| m | Mass [kg] |
| MDS | Multi-Dimensional Scaling |
| MLE | Maximum-Likelihood-Estimation |
| mT | Slope of the Temperature Curve |
| NA | North America |
| NR | Natural Rubber |
| OEM | Original Equipment Manufacturer |
| OZ | Quality Number |
| p | Probability Value |
| P | Perception |
| P/T | Precision to Tolerance Ratio |
| PC | Pacinian Corpuscle |
| PP | Polypropylene |
| PUR | Polyurethane |
| PVC | Polyvinyl Chloride |
| Q | Induced Energy [J] |
| QFD | Quality Function Deployment |
| QPC | Quality Perception Chain |
| QT | Tack Quotient |
| OZ | Quality Number |
| R&R | Repeatability and Responsibility |
| R2 | Coefficient of Determination |
| RA | Rapidly Adapting |
| RPZ | Risk Priority Number |
| RUTH | Robotized Unit for Tactility and Haptics |
| S | Sensory Warmth |
| SA | Slowly Adapting |
| SBR | Styrene Butadiene Rubber |
| SW | Steering Wheel |
| t | Time [s] |
| T | Temperature [°C] |
| Tcs | Contact Temperature between Surfaces [°C] |
| Ti | Immediate Increased Temperature on the Back-Side [K] |
| TiniH | Initial Temperature of Hand [°C] |
| TiniM | Initial Temperature of Material [°C] |
| Tm | Maximum Temperature Increase on the Back-Side [K] |
| TPC | Temperature Perception Chain |
| TPO | Thermoplastic Olefin |
| Troom | Room Temperature [°C] |
| txt | Text-File |
| U.S. | United States |
| UST | Universal Surface Tester |
| UX | User Experience |
| v | Velocity [m/s] |
| V | Dimensionless Increased Temperature on the Back-Side |
| vl | ‘van Laack’ Value Correlating to the Thermal Effusivity |
| VR | Virtual Reality |
| VWI | Verband Deutscher Wirtschaftsingenieure |
| WMDS | Weighted Multi-Dimensional Scaling |
| WZL | Laboratory for Machine Tools and Production Engineering |
| α | Temperature Diffusivity [m2/s] |
| ΔT | Temperature Increase on the Sample Surface |
| Δφ | Difference Threshold |
| λ | Thermal Conductivity [W/(m*K)] |
| ρ | Density [kg/m3] |
| φ | Stimulus |
| ψ | Sensation Intensity |
Figure 1.1: Customer relevance of vehicle attributes in Germany 2006
Figure 1.2: Three levels of uniqueness in human mental programming
Figure 1.3: Spectrum of Science
Figure 1.4: Heuristic framework
Figure 1.5: Thesis structure
Figure 2.1: Maslow's pyramid of needs in comparison with the car buying process
Figure 2.2: Kano model
Figure 2.3: Perceived quality information sources
Figure 2.4: Quality Perception Black Box
Figure 2.5: Transition from sensory perception to cognitive perception
Figure 2.6: Influence of personality, culture, and human nature
Figure 2.7: Quality Perception Chain (QPC)
Figure 2.8: Break-down of quality attributes
Figure 3.1: Weber’s law
Figure 3.2: Connection between Weber coefficient and stimulus
Figure 3.3: Illustration of Fechner’s law
Figure 3.4: Empiric relation between sound and loudness
Figure 3.5: Multimodality of knocking perception
Figure 3.6: Maximum-Likelihood-Estimation of object size perception
Figure 3.7: The structure of human skin
Figure 3.8: Action potential frequencies of cold and warm receptors
Figure 3.9: Stribeck curve. I: Solid friction, boundary friction, II: Mixed friction, III: Fluid friction
Figure 3.10: Friction mechanisms
Figure 3.11: Newcastle friction meter
Figure 3.12: Schematic setup with two load cells
Figure 3.13: Universal surface tester by Innowep
Figure 3.14: Artificial robot finger to measure surface friction
Figure 3.15: Types of friction
Figure 3.16: Simplified stick-slip model
Figure 3.17: Three kinds of sliding – (a) uniform sliding – (b) periodic stick-slip – (c) chaotic motion
Figure 3.18: Measuring principle according to Ziegler Instruments
Figure 3.19: Example of a stickiness diagram
Figure 3.20: Stickiness measurement of Grestenberger
Figure 3.21: Comparison of stickiness measurement results and the haptic panel assessment
Figure 3.22: Contact temperature for two semi-infinite bodies touching
Figure 3.23: Handy Tester
Figure 3.24: Contact Temperature Device (CTD)
Figure 3.25: Input and output data of a product clinic
Figure 3.26: Etymology of Kansei
Figure 3.27: Comprehensive view of Kansei
Figure 3.28: Comparison between Quality Perception Chain and Kansei
Figure 3.29: Descriptor classes of the Sensotact® reference frame
Figure 3.30: Müller-Lyer Illusion. Although both distances AB and CD have the same length, people will judge AB as being longer than CD.
Figure 3.31: Sander's illusion. Line AE and EC have in fact the same length. However, line AE is mostly perceived longer than EC.
Figure 3.32: The vertical-horizontal illusion leads to an overestimation of the vertical line length, although both lines AB and CD are identical.
Figure 4.1: Quality Perception Chain of Chapter 4
Figure 4.2: Variance change of the confidence interval for different sample sizes
Figure 4.3: Haptic rig used during the customer clinics
Figure 4.4: MoistSense® skin moisture measurement device
Figure 4.5: Clinic set-up overview
Figure 4.6: Haptic rig setup with video cameras
Figure 4.7: Spearman correlation between the customer perception ranking and the ranking according to the measured friction coefficient.
Figure 4.8: Ranking for least friction of sample A and B
Figure 4.9: Friction evaluation for dry and moist fingers
Figure 4.10: Video analysis of finger angle while perceiving surface friction
Figure 4.11: Spearman correlation between the customer perception ranking and the ranking according to the measured force peaks
Figure 4.12: Influence of finger moisture on stick-slip perception
Figure 4.13: Schematic diagram of the stickiness buck
Figure 4.14: Spearman correlation between the customer perception ranking and the ranking according to the measured lifting forces
Figure 4.15: Correlation between customer stickiness ranking and Sensotact scale
Figure 4.16: Influence of finger moisture level on distinguishing between sticky samples
Figure 4.17: Clinic Samples
Figure 4.18: Thermography pictures of two participants, one with cold hands (left), the other one with warm hands (right)
Figure 4.19: Material association for the evaluated four samples
Figure 4.20: Sample perception by different senses
Figure 4.21: Visual comparison between shiny, brushed and mat aluminum of block 2
Figure 4.22: Average standard deviation of the temperature ranking at different finger temperatures (block 3)
Figure 4.23: Perception ranking vs. thickness of aluminum samples (block 3)
Figure 5.1: Quality Perception Chain in Chapter 5
Figure 5.2: Friction finger setup
Figure 5.3: Correlation between human perception and measured friction µ
Figure 5.4: Friction measurement of clinic samples A, B, C and D
Figure 5.5: Gage R&R ANOVA results for the Friction Finger
Figure 5.6: Stick-slip finger setup
Figure 5.7: Stick-slip measurement
Figure 5.8: Correlation between human stick-slip perception and measurement values
Figure 5.9: Gage R&R results for stick-slip measurement
Figure 5.10: Stick-slide finger setup
Figure 5.11: Gage R&R for stick-slide measurements
Figure 5.12: Stickiness methodology
Figure 5.13: Correlation between the Log(Fz) and the stickiness Sensotact® value
Figure 5.14: Gage R&R for stickiness measurements
Figure 5.15: Temperature Perception Chain: from thermal effusivity to contact temperature
Figure 5.16: Logarithmic correlation between contact temperature difference and temperature perception S (left) and Sensotact® scale (right)
Figure 5.17: Cold receptor activity frequency as a function of temperature
Figure 5.18: Temperature Perception Chain: from thermal effusivity to receptor frequency
Figure 5.19: Correlation between receptor frequency and Sensotact® scale
Figure 5.20: Temperature Perception Chain to determine human temperature perception based on contact temperature
Figure 6.1: Quality Perception Chain of Chapter 6
Figure 6.2: Correlation between measured friction coefficient and human perception
Figure 6.3: Friction measurement of two instrument panel samples
Figure 6.4: Stickiness measurement results for all 17 samples
Figure 6.5: Results for stickiness measurements for stickiness samples S-A to S-G
Figure 6.6: Results of the stick-slip measurements for stick-slip samples S-S-A to S-S-G
Figure 6.7: Correlation between human stick-slip rating and measured stick-slip value
Figure 6.8: Correlation between human evaluation and measured stickiness value
Figure 6.9: Correlation between measured perception and customer evaluation
Figure 6.10: E-coating thickness of Sample 1
Figure 6.11: E-coating thickness of Sample 2
Figure 7.1: Quality Perception Chain of Chapter 7
Figure 7.2: Vehicle interiors 1, 5, 6 and 8
Figure 7.3: Boxplot of the interior ranking
Figure 7.4: Participating cultural regions
Figure 7.5: Purchase factors as percentage of customer relevance
Figure 7.6: Understanding of the term "quality"
Figure 7.7: Interior parts of concern for the evaluation
Figure 7.8: Interior assessment based on region
Figure 7.9: Preferences regarding surface haptics
Figure 7.10: Preferences of steering wheel spokes
Figure 7.11: Steering wheels from left to right: 4A, 4B, 4C, 3A, 3B, 3C
Figure 7.12: Steering wheel perception based on region
Figure 7.13: Selected air register styles (from left to right: A, B, C, D)
Figure 7.14: Air registers perception by culture
Figure 7.15: Different grains as they were used in the online survey. From left to right, grain 1 to 5.
Figure 7.16: Color perception of different cultures
Figure 7.17: Interior photos of the used mid-size vehicles. From left to right, C1 to C4
Figure 7.18: Interior ranking evaluation for mid-size vehicles
Figure 7.19: Interior photos of the used full-size vehicles. From left to right, D1 to D4
Figure 7.20: Interior ranking evaluation for full-size vehicles
Figure 7.21: Box-Plot of correlation coefficients for all three regions
Figure 7.22: Kansei evaluation for vehicle C1
Figure 7.23: Kansei evaluation for vehicle C2
Figure 7.24: Kansei evaluation for best ranked mid-size car
Figure 7.25: Kansei evaluation for best ranked full-size car
Figure 7.26: Color influence on interior perception for preferred and non-preferred colors of European participants
Figure 7.27: Transfer function for quality attribute “smooth”
Figure 7.28: Transfer function for quality attribute “soft”
Figure 7.29: Transfer function for quality attribute “sticky”
Figure 7.30: Transfer function for “high quality”
Figure 9.1: Age distribution of the friction clinic
Figure 9.2: Age distribution of the stick-slip clinic
Figure 9.3: Example of measured stick-slip between finger and sample for high, medium and low stick-slip behavior
Figure 9.4: Age distribution for the stickiness clinic
Figure 9.5: Age distribution for the temperature perception clinic
Figure 9.6: Friction finger DoE main effects for underlying foam and friction partner
Figure 9.7: Friction finger DoE interaction plot between underlying foams and friction partners
Figure 9.8: Friction finger DoE main effects for force, angle, and speed
Figure 9.9: Friction finger DoE interaction plot between force, angle, and speed
Figure 9.10: Residual plots for friction perception vs. friction coefficient µ
Figure 9.11: Stick-Slip finger DoE main effects for force and friction material
Figure 9.12: Stick-Slip finger DoE interaction plot between force and friction material
Figure 9.13: Residual plots for stick-slip perception
Figure 9.14: Measurement of samples with different peak forces
Figure 9.15: Main effects of DoE for stickiness force Fz
Figure 9.16: Interaction effects of DoE for stickiness force Fz
Figure 9.17: Residual plots for stickiness Log(Fz) vs. Sensotact® value
Figure 9.18: Boxplot of friction evaluation
Figure 9.19: Residual plots for friction rating
Figure 9.20: Customer clinic results on instrument panel preferences
Figure 9.21: Customer clinic results on instrument panel friction
Figure 9.22: Residual plots for human perceived stick-slip rating
Figure 9.23: Residual plots for stick-slip correlation
Figure 9.24: Residual plots for human perceived stickiness rating
Figure 9.25: Residual plots for stickiness correlation
Figure 9.26: Residual plots for temperature ranking
Figure 9.27: Residual plots for temperature rating
Figure 9.28: Residual plots for temperature perception
Figure 9.29: Vehicle interiors from 2 to 14 (without Interior 1, 5, 6 and 8)
Figure 9.30: Percentage of participating cultural regions
Figure 9.31: World Gasoline, Diesel prices in Euro/Liter
Figure 9.32: Proportion of the most important purchase decision factor for people that define quality with aesthetics and perception
Figure 9.33: Steering wheel thickness preferences by gender and culture
Figure 9.34: Preferred transmission by culture
Figure 9.35: Demand for acoustic switch feedback
Figure 9.36: Steering wheel material preferences by culture
Figure 9.37: Seat material preference by culture
Figure 9.38: Decoration material preferences by culture
Figure 9.39: Grain perception for grain samples G1 to G5 by culture
Figure 9.40: Vehicle C1
Figure 9.41: Vehicle C2
Figure 9.42: Vehicle D1
Figure 9.43: Vehicle D2
Figure 9.44: Vehicle D3
Figure 9.45: Kansei differential correlation between Europe and Asia
Figure 9.46: Kansei differential correlation between North America and Asia
Figure 9.47: Kansei differential correlation between Europe and North America
Figure 9.48: Kansei evaluation for vehicle D1
Figure 9.49: Kansei evaluation for vehicle D2
Figure 9.50: Kansei evaluation for vehicle D3
Figure 9.51: Color influence on interior perception for preferred and non-preferred colors of Asian participants
Figure 9.52: Color influence on interior perception for preferred and non-preferred colors of North American participants
Figure 9.53: Residual plots for transfer function for smooth
Figure 9.54: Residual plots for transfer function for soft
Figure 9.55: Residual plots for transfer function for sticky
Figure 9.56: Residual plots for transfer function for high quality
Table 3.1: Types of mechanoreceptors
Table 5.1: Decision matrix
Table 5.2: Comparison of friction methodologies regarding the requirements of Chapter 5.1
Table 5.3: Comparison of stick-slip methodologies regarding requirements presented in Chapter 5.1
Table 5.4: Comparison of stickiness methodologies regarding requirements presented in Chapter 5.1
Table 5.5: Sensotact® temperature reference samples
Table 5.6: Comparison of requirements for temperature perception measurement presented in Chapter 5.1
Table 6.1: Evaluation pattern for the assessment of surface friction
Table 6.2: Evaluation pattern for stick-slip (S-S) and stickiness (S) assessment
Table 6.3: Cool-touch results of the interior benchmark
Table 7.1: Summarizing results of the online survey
Table 7.2: Kansei questionnaire for each sample (English)
Table 7.3: Correlation coefficients R2 between the Kansei words and cultures
Table 7.4: Correlation results between the transfer function for “smooth” and cultural data
Table 7.5: Correlation results between the transfer function for “soft” and cultural data
Table 7.6: Correlation results between the transfer function for “sticky” and cultural data
Table 7.7: Correlation results between the transfer function for “high quality” and cultural data
Table 9.1: One-way ANOVA: Friction evaluation of Sample A; Sample B; Sample C; Sample D
Table 9.2: Least significant difference (LSD) calculation for friction evaluation
Table 9.3: One-way ANOVA: Friction measurement of Sample A; Sample B; Sample C; Sample D
Table 9.4: Least significant difference (LSD) calculation for friction measurement
Table 9.5: One-way ANOVA: Stick-slip evaluation of sample A; C; B; D; E
Table 9.6: Least significant difference (LSD) calculation for stick-slip evaluation
Table 9.7. One-way ANOVA: Sample A; Sample B; Sample C; Sample D
Table 9.8: Least significant difference (LSD) calculation for stickiness evaluation (p=0.1)
Table 9.9: Least significant difference (LSD) calculation for stickiness evaluation (p=0.05)
Table 9.10: Residual plots for stickiness evaluation vs. sensotact value
Table 9.11: Regression Analysis: Sensotact value versus stickiness evaluation
Table 9.12: One-way ANOVA: Aluminum samples with 0,1mm and 0,2mm thickness
Table 9.13: One-way ANOVA: Aluminum samples with 0,2mm and 0,3mm thickness
Table 9.14: One-way ANOVA: Aluminum samples with 0,3mm and 0,4mm thickness
Table 9.15: One-way ANOVA: Aluminum samples with 0,4mm and 0,7mm thickness
Table 9.16: One-way ANOVA: Aluminum samples with 0,7mm and 1mm thickness
Table 9.17: One-way ANOVA: 0,1mm steel (S); 0,1mm aluminum (A)
Table 9.18: One-way ANOVA: 0,2mm steel (S); 0,2mm aluminum (A)
Table 9.19: One-way ANOVA: 0,4mm steel (S); 0,4mm aluminum (A)
Table 9.20: Regression Analysis: Friction perception versus friction coefficient µ
Table 9.21: Gage R&R study for friction measurement
Table 9.22: Regression Analysis: Stick-slip perception versus stick-slip measurement
Table 9.23: Gage R&R study for stick-slip measurement
Table 9.24: Gage R&R study for stick-slide measurement
Table 9.25: Regression Analysis: Sensotact value versus Log(F_z)
Table 9.26: Gage R&R study for stickiness Log(F_z)
Table 9.27: One-way ANOVA: Friction ranking for Sample 9; Sample 6; Sample 5; Sample 3; Sample 1
Table 9.28: Least significant difference (LSD) calculation for friction ranking
Table 9.29: One-way ANOVA: Sample 6 (in comparison to 5); Sample 6 (in comparison to 3)
Table 9.30: One-way ANOVA: Sample 5 (in comparison to 6); Sample 5 (in comparison to 3)
Table 9.31: One-way ANOVA: Sample 3 (in comparison to 6); Sample 3 (in comparison to 5)
Table 9.32: Regression Analysis: Evaluation versus friction
Table 9.33: One-way ANOVA: Stick-slip rating versus samples
Table 9.34: Regression Analysis: Human evaluation versus stick-slip
Table 9.35: One-way ANOVA: Stickiness rating versus samples
Table 9.36: Regression Analysis: Human evaluation versus stickiness
Table 9.37: One-way ANOVA: Temperature ranking versus sample
Table 9.38: One-way ANOVA: Temperature rating versus sample
Table 9.39: Regression Analysis: Customer evaluation versus Temperature Perception Chain values
Table 9.40: One-way ANOVA: Sample 1 ranking versus region
Table 9.41: One-way ANOVA: Sample 10 ranking versus region
Table 9.42: One-way ANOVA: Sample 11 ranking versus region
Table 9.43: One-way ANOVA: Sample 12 ranking versus region
Table 9.44: One-way ANOVA: Sample 13 ranking versus region
Table 9.45: One-way ANOVA: Sample 14 ranking versus region
Table 9.46: One-way ANOVA: Sample 2 ranking versus region
Table 9.47: One-way ANOVA: Sample 3 ranking versus region
Table 9.48: One-way ANOVA: Sample 4 ranking versus region
Table 9.49: One-way ANOVA: Sample 5 ranking versus region
Table 9.50: One-way ANOVA: Sample 6 ranking versus region
Table 9.51: One-way ANOVA: Sample 7 ranking versus region
Table 9.52: One-way ANOVA: Sample 8 ranking versus region
Table 9.53: One-way ANOVA: Sample 9 ranking versus region
Table 9.54: Survey question: which of the following definitions fits to your personal quality definition best?
Table 9.55: Comparison of ANOVA results of sample ranking for all three cultures
Table 9.56: Average Kansei evaluation of Asian participants
Table 9.57: Average Kansei evaluation of European participants
Table 9.58: Average Kansei evaluation of North American participants
Table 9.59: ANOVA p-values for cultural differences
Table 9.60: ANOVA results for "Exclusive"
Table 9.61: Regression Analysis: Smooth versus stick-slip; roughness
Table 9.62: Regression Analysis: Europe versus transfer function smooth
Table 9.63: Regression Analysis: North America versus transfer function smooth
Table 9.64: Regression Analysis: Asia versus transfer function smooth
Table 9.65: Regression Analysis: Soft versus stick-slide; stick-slip
Table 9.66: Regression Analysis: Asia versus transfer function soft
Table 9.67: Regression Analysis: Europe versus transfer function soft
Table 9.68: Regression Analysis: North America versus transfer function soft
Table 9.69: Regression Analysis: Sticky versus stick-slide; stickiness
Table 9.70: Regression Analysis: Asia versus transfer function sticky
Table 9.71: Regression Analysis: Europe versus transfer function sticky
Table 9.72: Regression Analysis: North America versus transfer function sticky
Table 9.73: Regression Analysis: High quality versus friction; roughness
Table 9.74: Regression Analysis: Asia versus transfer function high quality
Table 9.75: Regression Analysis: Europe versus transfer function high quality
Table 9.76: Regression Analysis: North America versus transfer function high quality
The visionary Apple co-founder Steve Jobs once described the esthetics of his products with the words: “We made the buttons on the screen so good you’ll want to lick them”1. He was an expert in making customers to fans and expressed with this statement that not only functionality but especially the overall product experience has a major impact on customer perception. This perceived quality describes the consumer’s judgment about a products overall excellence or superiority.2
Figure 1.1: Customer relevance of vehicle attributes in Germany 20063
Studies within the automotive industry have identified that perceived quality is a major purchase decision factor (compare Figure 1.1).4 The success of today’s automotive companies is, therefore, no longer only determined by their technical superiority or robustness, but especially by subjective factors that convince potential customers to purchase a vehicle.5 “Quality is in the eye of the beholder”6, and comparable to the Gap model7 a substantial difference exists between the customer quality perception of products and the intended quality of the engineers. To develop products that exceed customer expectations and create the customer satisfaction needed, it is necessary to bring engineering as close to the customer side as possible and, therefore, focus on the customer experience. In this context the showroom effect plays a significant role: because the consumer cannot know how long a car will last, it has to appear solid and worthy and satisfy the emotional expectations the potential buyer has in regard to the vehicle.8 Besides the visual impression of a car, the haptic of surfaces has an enormous impact on the customer’s quality perception.9 Although the average customer only touches certain areas such as the steering wheel or the gear shift frequently during the usage of the car, other surfaces like the dashboard or door trims are still taken into consideration during the purchase decision. This results in an enormous chance for the Original Equipment Manufacturer (OEM) to manage the perceived quality of its products by improving and developing cars, which meet and exceed expectations of their customers from an aesthetic as well as perceptual point of view.
To improve customer perception and to support communication within the company and to suppliers, it is crucial for automotive companies to implement processes that quantify the quality perception reproducible with robust metrologies. In contrast to many state of the art methodologies, this means that quantification has to be feasible outside the lab and inside the car itself. OEMs that manage to measure and optimize their perceived quality can also improve their brand perception and create a considerable competitive advantage.10 Comparing automotive perceived quality studies and brand image studies from different markets revealed substantial differences in consumer brand perception, which suggest a connection between regional background and perceptual preferences. From a perceived quality perspective Lexus is perceived as benchmark for US customers, while it lacks emotionality for German customers and therefore its perceived quality is not as convincing. Audi’s perceived quality rating, on the other hand, is only slightly above average in comparison to other luxury brands in the U.S., while Audi is perceived as high quality brand in Germany.11 The ongoing globalization within the car industry and the financial pressure of today’s markets forces automotive OEMs to profit from economies of scale and sell very similar vehicles globally. The knowledge about differences, preferences and expectations of customers from various cultures is, therefore, crucial information to secure the future success of cars on a global market.
Culture is a fuzzy construct12, which is used in many different ways and with a multitude of meanings, such as organizational culture, dining culture, culture of arts, and many more.13 Although numerous culture definitions exist due to various publications about this topic, the majority of authors describe culture as a pattern of thinking and behaving. Kluckhohn defines culture as a patterned way of thinking, which essential core consists of traditional elements and values14. Kroesber and Parsons proposed a cross-disciplinary definition that describes culture as patterns of values and ideas that are created and transmitted leading to the shaping of human behavior15. In 1968 Harris wrote that “the culture concept comes down to behavior patterns associated with particular groups of people that is to ‘customs’ or to a people’s ‘way of life’.”16 A different meaning of culture was presented by Goodenough, in which he describes it as an organization of people, behaviors and emotions. He further explains that culture is “the form of things that people have in mind, their models for perceiving, relating and otherwise interpreting them”17. Later Simpson and Goodenough distinguish between two different types of culture. Based on previous definitions they describe the first one as a recurring pattern that characterizes a community as a balanced system. The second kind of culture they define as people’s standards for perceiving, behaving, and judging.18 Triandis introduces the term of “subjective culture” and defines it as “a cultural group’s characteristic way of perceiving the man-made part of its environment”19.
Figure 1.2: Three levels of uniqueness in human mental programming20
Hofstede defines culture as “the collective programming of the human mind that distinguishes the members of one human group from those of another. Culture, in this sense, is a system of collectively held values.”21 He identified three levels of uniqueness in human mental programming (see Figure 1.2). The first and most basic one is human nature. It is a universal level of mental programming that is inherited and shared by all mankind and can be understood as the biological “operating system” of the human body. The second level is culture. It is part of a collective level of mental programming, which is shared by people belonging to a certain group or category and people from different groups are programmed differently. Hofstede shares Binford’s opinion that culture is not under direct genetic control22 and therefore not inherited, but learned instead. The individual level of mental programming, such as personality, is very unique and partly inherited and partly learned.23 Hofstede argues that culture is to a human collective such as for societies or nations what personality is to an individual.24 In the 1990s Schein defined culture as “[…] what a group learns over a period of time as that group solves its problems […]”.
In context of this thesis, cultural groups can be understood as customer collectives of different societies and economic markets. Because culture is mostly learned over the years, the ethnic background is less important than the social environment a person spent the last years in. For the automotive industry three major markets can be identified in particular, the Asian, the European, and the North American market. For this research project the term ‘culture’ is, therefore, used to describe perception and preference patterns as well as standards of people belonging to one of these three markets.
The basis for the scientific orientation of this research thesis is its classification in the spectrum of science. In general this spectrum can be divided into formal and empirical sciences (compare Figure 1.3). Formal science is concerned with languages, such as their character sets and associated rules for their usage. Examples include philosophy, logic and mathematics.25 Formal science has no real-world objects; hence, the verification of research results is limited to the search for logical contradictions.26
The object of the empirical science, however, is the description, explanation and design of empirically perceptible and verifiable relationships. These are divided into fundamental science and applied science. Findings are thereby obtained both theoretically and empirically from experience.27 Fundamental science emphases a better understanding of nature, and includes natural sciences.28 Applied science, on the other hand, focuses on human behavior of individuals and collectives. This category includes psychology, social psychology and sociology.29
This work is based on the understanding of engineering as an applied science.30 It is devoted to a specific practical problem to measure sensory and cultural influences on haptic quality perception of vehicle interiors. The research activities aim to solve the identified problem. Therefore, the applicability and effectiveness of methodologies and models are investigated.31 The foundation for this thesis was laid out by Spingler’s research32, and it aims to improve and expend his investigations on different haptic methodologies. The outcome has considerable practical relevance to measure and support the improvement of vehicle interior quality perception. In addition to Spingler’s work this research also investigates the impact of specific physical surface parameters on human perception as well as the influence of cultural differences during the perception and evaluation of interior quality.
Figure 1.3: Spectrum of Science33
The ongoing research project consists of theoretically and practically challenging aspects. Due to its complexity, this thesis requires the implementation of different research methods.
Literature Research
Literature analysis and desk research are essential methods to gain knowledge and elaborate the scientific basics of new research fields.34 The evaluation of already established approaches is utilized to extract scientific knowledge and further develop valid hypothesizes.35 Within this thesis, state of the art research is based on literature studies and it presents a toolbox for the following research project.
Empirical Research
Empirical research creates knowledge by evaluating experiences systematically.36 From this point of view the researcher establishes hypotheses and theoretical models that have to be tested in reality by scientific experiments.37 This basic principle can be further split into deductive and inductive methodologies. By using deductive methodologies, a general theory is used to derive a certain statement, which then needs to be validated by empirical research.38 In contrast, inductive methodologies are applied, when general relationships are drawn from a certain statement or specific observation. In context of this thesis, inductive methods are used to derive transfer functions between certain physical characteristics and their human perception.
Heuristic Research
Heuristic methodologies invert the scientific research process and describe a data-driven theory development.39 It is an analytical approach, in which conjecture are made about a system based upon limited knowledge and time effort.40
The groundwork is established by applying the heuristic framework according to Kubicek (1977)41. During an iterative detailing process three levels are established that lead to the final synthesis as visualized in Figure 1.4.
The first level of detail includes the different categories, quality, metrology, perception and culture that frame the scope of research and are further specified and evaluated using desk research.
The second level identifies a number of physical parameters that influence the human perception of surface haptics. According to previous research42