- Case of next-generation transportation market -

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 이종수.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.본 연구는 기계학습 기반의 인공지능망과 기존의 통계적 마케팅 선택모형을 통합적으로 활용하여 제품 및 서비스 수용 이론으로 정의된 소비자들의 제품 수용 행위를 분석하였다. 기존의 제품 수용 이론들은 소비자들의 선택에 끼치는 영향을 단계별로 정의하였지만, 대부분의 이론은 제품 특성이 소비자 선택에 미치는 영향을 분석하기 보다는 소비자들의 의향, 제품의 대한 의견, 지각 수준과 소비자 선택의 관계 분석에 집중하였다. 따라서 본 연구는 소비자의 제품 수용 의향, 대안 평가 그리고 제품 및 사용량 선택을 포함하여 더욱 포괄적인 측면에서 소비자 제품 수용 행위를 분석하였다. 본 연구에서는 소비자의 제품 수용 관련 선택을 총 세 단계로 분류하였다. 첫 번째는 소비자의 제품 사용 의향을 결정하는 단계, 두 번째는 제품들의 대안을 평가하는 단계, 세 번째는 제품의 사용량을 선택하는 단계로, 각 단계를 분석하기 위해서 본 연구는 인공지능망과 통계적 마케팅 선택모형을 활용하였다. 인공지능망은 예측과 분류하는 작업에서 월등한 성능을 가진 모형으로 소비자들의 제품 수용 의향을 예측하고, 의향 선택에 영향을 주는 주요 변수들을 식별하는 데 활용되었다. 본 연구에서 제안한 주요 변수 식별을 위한 인공지능망은 기존의 변수 선택 기법 보다 모형 추정 적합도 측면에서 높은 성능을 보였다. 본 모형은 향후 빅데이터와 같이 많은 양의 소비자 관련 데이터를 처리하는데 활용될 가능성이 클 뿐만 아니라, 기존의 설문 설계 기법을 개선하는데 용이한 방법론으로 판단된다. 소비자 선호를 기반으로 한 대안 평가 및 사용량을 분석하기 위해서 통계적 선택 모형 중 계층적 베이지안 모형과 혼합 MDCEV 모형을 활용하였다. 계층적 베이지안 모형은개별적인 소비자 선호를 추정할 수 있는 장점이 있고, 혼합 MDCEV 모형의 경우 소비자들의 선호를 기반하여 선택된 대안들로 다양한 포트폴리오를 구성할 수 있고, 각 대안에 대한 사용량을 분석할 수 있다. 제안된 모형들의 실증 연구를 위해 차세대 자동차 수송 서비스에 대한 소비자들의 사용 의향, 서비스 대안에 대한 선호, 수송 서비스별 사용량을 분석하였다. 실증 연구에서는 차세대 자동차 수송 서비스를 수용하기까지 소비자들이 경험하는 단계별 선택 상황을 반영하였으며, 각 단계에서 도출된 결과를 통해 향후 차세대 자동차 수송 서비스의 성장 가능성과 소비자들의 이동 행위 변화에 대해 예측하였다. 본 연구를 통해 인공지능망이 소비자 관련 연구에서 유용하게 활용될 수 있음을 보였으며, 인공지능망과 통계적 마케팅 선택모형이 결합될 경우 소비자들의 제품 선택 행위뿐만 아니라, 제품 선택 의사결정 과정 전반에 걸쳐 소비자 선호를 포괄적으로 분석할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objective 7 1.3 Research Outline 12 Chapter 2. Literature Review 14 2.1 Product and Technology Diffusion Theory 14 2.1.1. Extension of Adoption Models 19 2.2 Artificial Neural Network 22 2.2.1 General Component of the Artificial Neural Network 22 2.2.2 Activation Functions of Artificial Neural Network 26 2.3 Modeling Consumer Choice: Discrete Choice Model 32 2.3.1 Multinomial Logit Model 32 2.3.2 Mixed Logit Model 34 2.3.3 Latent Class Model 37 2.4 Modeling Consumer Heuristics in Discrete Choice Model 39 2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39 2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42 2.4.3 Modeling Consumer Usage: MDCEV Model 50 2.5 Difference between Artificial Neural Network and Choice Modeling 53 2.6 Limitations of Previous Studies and Research Motivation 58 Chapter 3. Methodology 63 3.1 Artificial Neural Network Models for Prediction 63 3.1.1 Multiple Perceptron Model 63 3.1.2 Convolutional Neural Network 69 3.1.3 Bayesian Neural Network 72 3.2 Feature Identification Model through Attention 77 3.3 Hierarchical Bayesian Model 83 3.4 Multiple Discrete-Continuous Extreme Value Model 86 Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98 4.1 Empirical Analysis Framework 98 4.2 Data 101 4.2.1 Overview of the Survey 101 4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110 4.3.1 Research Motivation and Goal 110 4.3.2 Data and Model Setup 114 4.3.3 Result and Discussion 123 4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142 4.4.1 Research Motivation and Goal 142 4.4.2 Data and Model Setup 144 4.4.3 Result and Discussion 149 4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163 4.5.1 Research Motivation and Goal 163 4.5.2 Data and Model Setup 164 4.5.3 Result and Discussion 166 Chapter 5. Discussion 182 Bibliography 187 Appendix: Survey used in the analysis 209 Abstract (Korean) 241Docto

Preference Modeling in Data-Driven Product Design: Application in Visual Aesthetics

Creating a form that is attractive to the intended market audience is one of the greatest challenges in product development given the subjective nature of preference and heterogeneous market segments with potentially different product preferences. Accordingly, product designers use a variety of qualitative and quantitative research tools to assess product preferences across market segments, such as design theme clinics, focus groups, customer surveys, and design reviews; however, these tools are still limited due to their dependence on subjective judgment, and being time and resource intensive. In this dissertation, we focus on a key research question: how can we understand and predict more reliably the preference for a future product in heterogeneous markets, so that this understanding can inform designers' decision-making? We present a number of data-driven approaches to model product preference. Instead of depending on any subjective judgment from human, the proposed preference models investigate the mathematical patterns behind users’ choice and behavior. This allows a more objective translation of customers' perception and preference into analytical relations that can inform design decision-making. Moreover, these models are scalable in that they have the capacity to analyze large-scale data and model customer heterogeneity accurately across market segments. In particular, we use feature representation as an intermediate step in our preference model, so that we can not only increase the predictive accuracy of the model but also capture in-depth insight into customers' preference. We tested our data-driven approaches with applications in visual aesthetics preference. Our results show that the proposed approaches can obtain an objective measurement of aesthetic perception and preference for a given market segment. This measurement enables designers to reliably evaluate and predict the aesthetic appeal of their designs. We also quantify the relative importance of aesthetic attributes when both aesthetic attributes and functional attributes are considered by customers. This quantification has great utility in helping product designers and executives in design reviews and selection of designs. Moreover, we visualize the possible factors affecting customers' perception of product aesthetics and how these factors differ across different market segments. Those visualizations are incredibly important to designers as they relate physical design details to psychological customer reactions. The main contribution of this dissertation is to present purely data-driven approaches that enable designers to quantify and interpret more reliably the product preference. Methodological contributions include using modern probabilistic approaches and feature learning algorithms to quantitatively model the design process involving product aesthetics. These novel approaches can not only increase the predictive accuracy but also capture insights to inform design decision-making.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145987/1/yanxinp_1.pd

Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive "theme clinic" costs between \$100,000 and \$1,000,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner-7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs-38% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, suggest that machine learning offers significant opportunity to augment aesthetic design

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping

On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN

A neural network and rule based system application in water demand forecasting

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation

AN EYE-TRACKING SUPPORTED INVESTIGATION INTO THE ROLE OF FORMS OF REPRESENTATION ON DESIGN EVALUATIONS AND AFFORDANCES OF ORIGINAL PRODUCT FEATURES

AbstractThe paper investigates the relationship between the forms through which products are represented and the outcomes of evaluations made by observers. In particular, the study focuses on perceived affordances of creative designs, meant as the capability of capturing original elements and corresponding functions, for products presented through static images or videos. Also thanks to the use of Eye Tracking, the experimental results show how dynamic effects that involve salient aspects of products, as well as creative features, are critical to observers' capability of capturing design intentions

Focusing on Updating Expectations and Perceptions in Platform Service Use

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2022. 8. 이종수.The marketing literature clearly defines that consumers’ intention to repurchase a product or continue to use a service depends primarily on their prior experience of using them, and that continued user satisfaction is considered the key to building and retaining a loyal base of long-term customers. However, most existing studies use static utility models to explain consumer behavior in platform services and therefore do not adequately reflect the time-varying effects of continued use of the service. In addition, cross-sectional studies of consumers’ continued use of services cannot provide an accurate view of how customers’ expectations and perceptions of the product/service may change over time. Therefore, dynamic longitudinal studies are needed to determine how customers update their expectations and perceptions through experience and how this may affect customer satisfaction and/or behavior. This study aims to fill this gap by employing a dynamic utility model to explain consumer behavior in a platform economy where services are used repeatedly. Through an empirical study, we examine the time-varying effects of covariates in explaining consumers' use of ride-hailing platforms by first identifying the effect of updating expectations and perceptions with repeated use, thereby extending upon the expectation-confirmation theory. In the second part of this study, we observe the temporal effects on consumers' usage behavior through semiparametric modeling. The results of this study are expected to add to the literature on consumer behavior by presenting how the discrepancy between updated service expectations and actual service delivery, as well as updated perceptions, affect consumer behavior in platform services and by demonstrating seasonality in services with repeated use.서비스에 대한 고객 충성도를 야기하고 유지하기 위해서는 지속적으로 소비자를 만족시켜야 하고, 소비자의 서비스 재이용 여부는 해당 서비스와 관련하여 축적된 소비자의 이용 경험에 따라 달라진다는 것은 마케팅 문헌에서 익히 알려져 있다. 그러나 기존 연구는 재이용이 빈번한 플랫폼 서비스에서의 사용자 행태를 분석하는데 있어 정적 효용 모형을 사용하므로, 서비스의 지속 사용에 따른 시간 변동 효과를 적절하게 보이지 못하고 있다. 또한 소비자의 지속적인 사용에 따른 고객의 서비스에 대한 기대치 및 인식이 변화할 수 있음을 반영하지 못한다. 본 연구에서는 동적 효용 모형을 채택함으로써 플랫폼 사용자가 서비스 이용 경험에 기반하여 서비스에 대한 기대치 및 인식을 조정할 수 있음을 반영하고, 서비스에 대한 기대치와 실제 경험의 차이가 서비스의 만족도에 어떻게 영향을 미치는지를 알아보고자 한다. 또한 반모수 모델링을 통해 소비자의 서비스 이용 행태에서의 공변량의 시간적 특성을 알아본다. 분석 결과, 서비스에 대한 ‘서비스 격차’ 및 ‘인식 격차’는 서비스 만족도에 영향을 미치며, 그 영향 수준은 경험이 누적됨에 따라 변화함을 알 수 있었다. 또한, 누적된 경험에 기반하여 조정된 소비자의 서비스에 대한 인식과 실제 서비스 이용 경험 간의 차이가 서비스 만족에 대한 경험 누적 효과를 가장 잘 설명함을 알 수 있었다. 마지막으로 소비자의 서비스 이용에 있어 계절적 특성이 있음을 알 수 있었다. 이에 마케팅 관점에서 공변량에 대한 시간적 효과를 반영하지 못하면 소비자의 행동 변화를 잘못 감지할 가능성이 있으므로, 마케팅 전략 수립에 있어 서비스 재이용에 따른 특성 및 계절성을 적절히 반영할 필요가 있음을 알 수 있었다.Chapter 1. Introduction 12 1.1 Research Background 12 1.2 Research Objectives 15 Chapter 2. Literature Review 17 2.1 Studies on Consumer Behavior 17 2.1.1 Expectation-Confirmation Theory 17 2.1.2 Studies on Consumer's Continued Use of IT Service 20 2.1.3 The GAP Model of Service Quality 23 2.1.4 Studies on Seasonality of Consumer Behaviors 26 2.1.5 Studies on Online Platform Service Use 27 2.2 Models with Time Effect 33 2.2.1 Fitting Data with Spline 33 2.2.2 Varying Coefficient Models 35 2.2.3 Discrete Choice Models with Time Effect in Attributes 37 2.3 Deep Learning Models for Data Prediction 39 2.3.1 Recurrent Neural Network (RNN) 39 2.3.2 Long Short-Term Memory (LSTM) 42 2.3.3 Applications of Deep Learning in Consumer Studies 44 2.4 Limitations of Previous Literature and Research Motivation 45 Chapter 3. Methodology 47 3.1 Methodological Framework 47 3.2 Model Specification 48 3.2.1 Generic Model 48 3.2.2 Functions with Time-Varying Parameters 49 3.2.3 Smoothing Splines and Penalized Regression 51 3.2.4 Estimation Method 57 3.2.5 Parameter Selection 60 Chapter 4. Simulation Study 66 4.1 Validation of P-spine Implementation 66 4.2 Functional Case Studies 70 4.3 Comparison of Fit by Parameter Selection Method 77 Chapter 5. Empirical Study 83 5.1 Research Background 84 5.2 Data 90 5.3 Model Specification 92 5.3.1 Covariates of Time and Cost 92 5.3.2 The Interaction of Trip Distance and Travel Speed 93 5.3.3 Formation of Consumer Expectations 95 5.3.4 Estimation of Smoothing Coefficient for Error Adaption 99 5.4 Estimation Results 101 5.4.1 The Generic Model 101 5.4.2 Accumulated Experience Effect 121 5.4.3 Stream-of-Time Effects (by Times of the Day) 176 Chapter 6. Conclusion 217박

Deep visual generation for automotive design upgrading and market optimising

The rising levels of homogeneity of modern cars in terms of price and functions has made exterior styling increasingly vital for market success. Recently, researchers have attempted to apply deep learning, especially deep generative models, to automotive exterior design, which has enabled machines to deliver diverse novel designs from large-scale data. In this thesis, we argue that recent advancements in deep learning techniques, particularly in deep generation, can be utilised to facilitate different aspects of automotive exterior design, including design generation, evaluation, and market profit predicting. We conducted three independent studies, each providing tailored solutions to specific automotive design scenarios. These include: a study focused on adapting the latest deep generative model to achieve regional modifications in existing designs, and evaluating these adjustments in terms of design aesthetic and prospective profit changes; another study dedicated to developing a predictive model to assess the modernity of existing designs regarding the future fashion trends; and a final study aiming to incorporate the distinctive shape characteristics of a cheetah into the side view designs of cars. This thesis has four main contributions. First, the developed DVM-CAR dataset is the first large-scale automotive dataset containing designs and marketing data over 10 years. It can be used for different types of research needs from multiple disciplines. Second, given the inherent constraints in automotive design, such as the need to maintain “family face”, and the fact that unconstrained design generation can be seen as a special form of regional modification, our research distinctively focuses on the regional modifications to existing designs, a departure from existing studies. Third, our studies are the first works that integrate the design modules with market profit optimisation. This reforms the traditional product design optimisation frameworks by replacing the abridged design profiles with graphical designs. Finally, the proposed data-driven measures offer effective approaches for automotive aesthetic evaluation and market forecasting, including approaches that can make assessments from a dynamic perspective by examining the evolving fashion trends

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence