Exploring `Designer Context' in Engineering Design: The Relationship Between Self, Environment, and Design Methods

Engineering design methods support engineers’ decision-making throughout a design process in order to improve design outcomes. The selection and implementation of suitable design methods are therefore critical to project success. Prior engineering design research has focused on designers' professional experiences or the problem context for guiding method choices. Perhaps due to disciplinary norms of objectivity, individual characteristics outside of an engineer's professional expertise are not seen as influential on design outcomes. In contrast, theories from other design disciplines define aspects of a designer's experience outside of their professional self as central to design practice. This dissertation seeks to reconcile these two paradigms by exploring whether `designer context' factors, that are often not discussed in engineering design but are found in other design fields (i.e. - organizational culture, gender, race) can impact design outcomes via method selection and implementation. Results from practitioner interviews on designer context and prototyping methods, as well as an empirical study of a novel design method, suggest that a broad range of designer context factors can influence design method selection and implementation, ultimately impacting the efficiency and efficacy of a design process. Therefore, if engineering designers were to consider their holistic designer context and its influence on their work, as occurs in other design fields, better engineering outcomes could be achieved. An exploratory study consisting of qualitative interviews formalized designer context and illustrated how these contextual factors impacted methods used by practitioners in the medical device industry. This study provides an initial foundation of designer context factors for exploration in future research and practice. These factors were categorized into the Design Environment, or the external factors surrounding a designer when they are designing, and the Designer's Self, or the internal factors related to a designer. Interviews with design practitioners from small-to-medium sized enterprises in Rwanda and Kenya revealed specific resource constraints impacting the implementation of prototyping methods. Many of the identified constraints were related to the practitioners’ context. Limited access to quality materials or fabricators, often due to difficulties navigating a decentralized market, added time and cost to the process. Practitioners reported trying to develop simple, functional, and physical prototypes with increasing fidelity through a highly iterative process. However, these constraints negatively impacted the chosen prototyping method, suggesting that alternative methods could be beneficial. In an empirical study, our team proposed and implemented a new method for considering multiple stakeholder preferences, the Stakeholder Agreement Metric (SAM) framework, to support the design of a hand tool to reduce injuries for informal electronic-waste (e-waste) recyclers in rural Thailand. This method was compared to the Analytical Hierarchy Process (AHP), an existing method that supports similar decisions. Results showed that the SAM framework outperformed AHP in this informal setting due to the failed completion of AHP by participants. The study highlights how designer context not only influenced the implementation of design methods but also their development. This dissertation expands the boundaries of what factors should be considered influential on design processes and their outcomes. Across all three studies, designer context was shown to influence method selection and implementation. The findings suggest that contextual factors affect design methods in practice and should be included in future research to enable the selection and implementation of more suitable and effective design methods.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163100/1/suzchou_1.pd

The Use of Prototypes to Engage Stakeholders in Low- and Middle-Income Countries During the Early Phases of Design

Human-centered design processes have been leveraged to help advance solutions to the world’s most pressing problems. Early and frequent engagement with stakeholders is a key activity of early-stage human-centered design processes that leads to better alignment of product requirements with the needs of stakeholders and the context of the artifact. There are many tools to support early stakeholder engagement. A subset of methods includes the use of prototypes – tangible manifestations of design ideas. However, prototypes are underutilized in early design activities to engage stakeholders, notably during cross-cultural design in Low and Middle-Income Countries (LMICs). In such contexts, prototypes have the potential to bridge contextual and cultural differences, which is especially critical when designing for LMICs where many proposed solutions have failed to meet people’s needs. To investigate the roles of prototypes to engage stakeholders in LMICs, I used both qualitative and quantitative research methods emphasizing both engineering design and economics theory and methods. Specifically, I conducted an interview-based study with industry practitioners and investigated two prototype-based stakeholder engagement methods in practice in LMICs. I conducted semi-structured interviews focused on the use of prototypes to engage stakeholders in early design stages with 24 medical device design practitioners from multinational and global health companies. Practitioners described the types of stakeholders, prototypes, and settings leveraged during front-end design and the associations of engagement strategies, stakeholders, prototypes, and/or settings. I further studied the practices of global health design practitioners working on medical devices for use in LMICs and described their approaches to tackle stakeholder remoteness, explore the environment of use, bridge cultural gaps, adjust the engagement activities to stakeholders, and work with limited resources. My analysis of requirements elicitation interviews with 36 healthcare practitioners from two hospitals in Ghana revealed participant preferences when viewing three, one, or no prototypes. The findings indicate that stakeholders preferred interviews with prototypes and in the absence of a prototype, stakeholders referenced existing or imaginative devices as a frame of reference. I investigated the preferences for, willingness to pay for, and usage of a novel tool for electronic-waste recycling with 105 workers in North-Eastern Thailand. Workers were assigned to one of two conjoint experiments that leveraged different prototype forms. Workers further completed baseline and endline surveys and participated in a Becker-Degroot-Marschak auction experiment. The results showed that the prototype form used in the conjoint experiment affected the valuation of product features. One-month evaluation of usage revealed that participants who received the new tool decreased their injury rates and increased productivity. This research provides new insights into the practices and teachings of prototype usage for stakeholder engagement during early design stages, contributes to the developing body of literature that recognizes the unique design constraints associated with designing for LMICs, and advances approaches for promoting more inclusive design practices. The description of the types of stakeholders, prototypes, settings, and strategies leveraged by industry practitioners when engaging stakeholders in LMICs are potentially transferable to, and can have a broader impact on, other contexts in which prototypes are used to engage stakeholders. Furthermore, both applied studies illustrate the effect of using different numbers of prototypes and different prototype forms on the outcomes of the two commonly used stakeholder engagement methods – interviewing and conjoint analysis. The applied studies provide examples of stakeholder engagement methods with prototypes in LMIC settings in practice.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162996/1/mjcoul_1.pd

Conjoint analysis of researchers' hidden preferences for bibliometrics, altmetrics, and usage metrics

The amount of annually published scholarly articles is growing steadily, as is the number of indicators through which impact of publications is measured. Little is known about how the increasing variety of available metrics affects researchers' processes of selecting literature to read. We conducted ranking experiments embedded into an online survey with 247 participating researchers, most from social sciences. Participants completed series of tasks in which they were asked to rank fictitious publications regarding their expected relevance, based on their scores regarding six prototypical metrics. Through applying logistic regression, cluster analysis, and manual coding of survey answers, we obtained detailed data on how prominent metrics for research impact influence our participants in decisions about which scientific articles to read. Survey answers revealed a combination of qualitative and quantitative characteristics that researchers consult when selecting literature, while regression analysis showed that among quantitative metrics, citation counts tend to be of highest concern, followed by Journal Impact Factors. Our results suggest a comparatively favorable view of many researchers on bibliometrics and widespread skepticism toward altmetrics. The findings underline the importance of equipping researchers with solid knowledge about specific metrics' limitations, as they seem to play significant roles in researchers' everyday relevance assessments

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.Comment: 32 pages, 12 figure

Language, Ethnical Identity and Consumer Behavior: A Cross-Cultural Study of Marketing Communication in the Region FVG

In the multiethnic EU contest, the European Institutions encourage all citizens to be multilingual by learning at least two languages in addition to their mother tongue, including the local languages to maintain alive their cultural backgrounds and preserve their ethnic identity. The objective of this study is to verify whether the local language used in marketing communication strategies could significantly affect the consumers’ preferences for food product and their decisions. The theoretical framework is given by the consumer motivational approach that enlarges the traditional consumer theory including other determinants of consumers decisions as the language and ethnicity; the empirical analysis is performed with the multivariate conjoint analysis to evaluate the influence of the cross cultural influence in consumer’s choice. A number of students from the university of Udin have been interviewed with a questionnaire focusing on preferences for a simulated packed sandwich product distributed by vendor machine inside the University space to test the influence of local language. The results suggest some reactions to the messages reported by package in different languages depending on the level of language knowledge and suggest their use for potential demand segmentation to generate niche markets. These results can be generalized to the many regional markets in the EU where the identity construct evocated by the local language can be used in market communication strategies to increase the local food demand and customization

Affective design using machine learning : a survey and its prospect of conjoining big data

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design

Perceived Quality in the Automotive Industry

The supremacy of the automotive manufacturers in the modern world is no longer driven by them achieving a superior manufacturing quality but increasingly depends on the customer’s quality perception. The premium sector of the automotive industry is facing tough international competition. Studies within the automotive industry have identified that the perceived quality has become an important purchase decision factor. In practice, this means that the car manufacturers need to develop products that not only meet their customer’s expectations but also exceed them. It is necessary to close the gap between engineering and customer perceptions of the final product. Under such conditions, design process tasks are difficult in implementation because the evaluation of the perceived quality attributes is often subjective and intuitive rather than objective. The automotive industry demands methods and tools that allow the definition and validation of perceived quality related requirements.Developing methods for objective assessment of the perceived quality attributes is a task with a very high level of complexity. The vehicle itself is a very complex product. This fact leads to the information asymmetry because the actual quality of the product is not always visible to the customer. This thesis is a step towards closing the information asymmetry gap and bringing subjectively assessed perceived quality attributes to the objective side, supported by structured quantification methods. The author reviewed and structured product quality paradigms from the past, defined perceived quality attributes, described their properties regarding the premium automotive sector. The proposed comprehensive perceived quality framework is the major result of the thesis

Relations between comprehensibility and adequacy errors in machine translation output

This work presents a detailed analysis of translation errors perceived by readers as comprehensibility and/or adequacy issues. The main finding is that good comprehensibility, similarly to good fluency, can mask a number of adequacy errors. Of all major adequacy errors, 30% were fully comprehensible, thus fully misleading the reader to accept the incorrect information. Another 25% of major adequacy errors were perceived as almost comprehensible, thus being potentially misleading. Also, a vast majority of omissions (about 70%) is hidden by comprehensibility. Further analysis of misleading translations revealed that the most frequent error types are ambiguity, mistranslation, noun phrase error, word-by-word translation, untranslated word, subject-verb agreement, and spelling error in the source text. However, none of these error types appears exclusively in misleading translations, but are also frequent in fully incorrect (incomprehensible inadequate) and discarded correct (incomprehensible adequate) translations. Deeper analysis is needed to potentially detect underlying phenomena specifically related to misleading translations

- 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