Chaehan So
Yonsei University, Information & Interaction Design, Faculty Member
- Humboldt Universität zu Berlin, Institut fuer Psychologie, AlumnusIDAS/Hongik University - Seoul, Design Managment, Faculty MemberYonsei University, Information and Interaction design, Faculty Memberadd
- Chaehan So is an Assistant Professor of Information & Interaction Design at Yonsei University, South Korea. His main ... moreChaehan So is an Assistant Professor of Information & Interaction Design at Yonsei University, South Korea. His main research interest lies in applying psychological and artificial intelligence methods to analyze design-related phenomena, and to create psychologically-grounded ways of user experience. Dr. So has a strong interdisciplinary intent spanning from his Ph.D. in Social Psychology from Humboldt University of Berlin, Germany, M.S. in electrical engineering from Technical University Berlin, Germany, and Master of Management from ESCP, France.edit
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Research Interests: Psychology and Teamwork
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Objective The present work explores how the horizontal viewing angle of a virtual character’s face influences perceptions of credibility and approachability. Background When encountering virtual characters, people rely both on credibility... more
Objective The present work explores how the horizontal viewing angle of a virtual character’s face influences perceptions of credibility and approachability. Background When encountering virtual characters, people rely both on credibility and approachability judgments to form a first impression of the depicted virtual character. Research shows that certain perceptions are preferred either on frontal or tilted faces, but not how approachability or credibility judgments relate to horizontal viewing angles in finer granularity between 0° and 45°. Method 52 participants performed a two-alternative forced choice (2AFC) task rating 240 pairwise comparisons of 20 virtual character faces shown in four horizontal viewing angles (0°, 15°, 30°, and 45°) on approachability and credibility. They also rated scales on individual differences based on the BIS-BAS framework (behavioral inhibition system, drive, and reward responsiveness), self-esteem, and personality traits (neuroticism, loneliness)....
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Research on data science has largely viewed data as an abstract input in service of algorithms developed by data scientists. In this view, data science activities are made sustainable by the efficient flow of data to improve the... more
Research on data science has largely viewed data as an abstract input in service of algorithms developed by data scientists. In this view, data science activities are made sustainable by the efficient flow of data to improve the algorithms. Recent studies in CSCW and HCI, however, point to how the effectiveness of algorithms critically depends on sustainably collecting reliable, complete data situated in domain experts’ practices and settings. Drawing on ethnographic fieldwork and a pilot machine learning project at a craft brewery, we describe three types of situations where brewers’ data practices led to unreliable, incomplete data, and how such data practices limited the effectiveness of data science activities. We analyze sources of misalignment between their data practices and data science activities, which we use to offer design implications for sustainability. Extending research on end-user software development that views sustainability as driven by domain experts as ‘owners ...
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The present study investigated the specific life circumstances that determine designers' happiness measured in terms of life satisfaction and subjective well-being. To this aim, self-reports of 252 participants in an online survey... more
The present study investigated the specific life circumstances that determine designers' happiness measured in terms of life satisfaction and subjective well-being. To this aim, self-reports of 252 participants in an online survey were analysed using psychological measurement instruments for pressure, self-aspects, happiness and mindfulness. The findings highlight that social pressure and time pressure are negatively related to designers' happiness and to positive self-aspects (self-esteem, creative self-efficacy) by a small to medium effect size (r = .21). Judging partially mediates this detrimental effect by a small effect size (r = .09), is highly related to social comparison frequency (r = .50) and global happiness (r = –.40). Present focus and present value relate more positively to global happiness than future focus and future value (r = .17). A positive implication for designers derives from the result that creative self-efficacy closely relates to positive emotions (r = .43) and life satisfaction (r = .40) but is unaffected by social and time pressure.
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Purpose This paper aims to present a conceptual framework of how software teams can leverage the implicit information of implemented acceptance tests to cater to the needs of decision makers. The research questions on this framework were... more
Purpose This paper aims to present a conceptual framework of how software teams can leverage the implicit information of implemented acceptance tests to cater to the needs of decision makers. The research questions on this framework were how business stakeholders can receive project status information in an intuitive way and how this framework can guarantee the traceability of tests to requirements. Design/methodology/approach The conceptual framework delineates the design of an acceptance test framework in three aspects: how the requirements model reflects the evolving states of requirement maturity over a project, how the acceptance test model becomes synchronized with the requirements model without a traceability matrix and how the acceptance test model communicates business value to the decision makers. Findings In an industrial case study, the presented framework yielded the positive effects of intuitive understanding by business stakeholders, high test coverage of requirements...
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Summary. Rising interest on social-psychological effects of agile prac-tices necessitate the development of appropriate measurement instru-ments for future quantitative studies. This study has constructed such instruments for eight agile... more
Summary. Rising interest on social-psychological effects of agile prac-tices necessitate the development of appropriate measurement instru-ments for future quantitative studies. This study has constructed such instruments for eight agile practices, namely iteration planning, itera-tive ...
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Neural style transfer is a popular deep learning algorithm to generate images to mimic human artistry. This work applies the psychological method of the two-alternative forced choice (2afc) task to measure aesthetic preferences for neural... more
Neural style transfer is a popular deep learning algorithm to generate images to mimic human artistry. This work applies the psychological method of the two-alternative forced choice (2afc) task to measure aesthetic preferences for neural style generated images. Portrait photos of three popular celebrities were generated by varying three parameters of neural style transfer in five configuration levels. Participants had to choose the image they preferred aesthetically from all pairwise combinations of configurations per style. The rate of being chosen was calculated for each neural style transfer configuration level. The findings show a differentiated picture of aesthetic preferences. On the one side, they indicate that people prefer images rendered with 500 iterations and a learning rate of 2e1, i.e. configurations that allow them to recognize the structure of the portrait image despite the stylization. On the other side, aesthetic preferences peak for two distinctly different conte...
The purpose of this study was to investigate whether and to what degree the creative performance of an ideation session depends on the time configuration of an ideation process. To this aim, the present study applied psychological... more
The purpose of this study was to investigate whether and to what degree the creative performance of an ideation session depends on the time configuration of an ideation process. To this aim, the present study applied psychological research methodology and yielded the first quantitative insight into this research question. Fifty-six graphic design students produced 13,195 ideas in six experimental sessions of brain-writing, averaging 36.1 ideas per person and experimental session after removal of outliers. The quantitative outcomes of these sessions were combined for pairwise comparisons to test the effect of session type (long session vs. sequenced session), warm-up session (sequenced session vs. long session), interval duration (decreasing intervals vs. shortened overall interval). Results revealed positive effects with large effect sizes in the range of 57–72 percent increase in creative performance for sequenced sessions over long and continuous sessions (H1), for long sessions following a sequenced session (H2a), and for extremely short interval duration over short interval duration of 3 minutes (H3b). Decreasing the interval duration in three subsequent sessions showed a moderate increase (+21%) over short interval duration (H3a). These results are relevant for design educators and design thinking practitioners as they provide consistent evidence for optimized creative performance if ideation sessions are structured in several intervals of extremely short duration.
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Meta-learning has emerged as a new paradigm in AI to challenge the limitation of conventional deep learning to acquire only task-specific knowledge. Meta-learning transcends this limitation by extracting the general concepts when learning... more
Meta-learning has emerged as a new paradigm in AI to challenge the limitation of conventional deep learning to acquire only task-specific knowledge. Meta-learning transcends this limitation by extracting the general concepts when learning tasks to apply these concepts later when learning new tasks. One popular meta-learning approach is model-agnostic meta-learning (MAML) which learns tasks by optimizing parameters towards highest generalizability of future tasks. The present paper applied a practical implementation of MAML to conduct an image classification task. Results showed that performance on learning new tasks neared training performance without overfitting. Furthermore, optimal values for inner-loop and outer-loop learning rate were close to default parameter values. Smaller batch sizes with more epochs improved learning in earlier epochs compared to larger batch sizes with fewer epochs. These findings show that MAML is able to transfer the concepts extracted during training ...
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When people buy products online, they primarily base their decisions on the recommendations of others given in online reviews. The current work analyzed these online reviews by sentiment analysis and used the extracted sentiments as... more
When people buy products online, they primarily base their decisions on the recommendations of others given in online reviews. The current work analyzed these online reviews by sentiment analysis and used the extracted sentiments as features to predict the product ratings by several machine learning algorithms. These predictions were disentangled by various meth-ods of explainable AI (XAI) to understand whether the model showed any bias during prediction. Study 1 benchmarked these algorithms (knn, support vector machines, random forests, gradient boosting machines, XGBoost) and identified random forests and XGBoost as best algorithms for predicting the product ratings. In Study 2, the analysis of global feature importance identified the sentiment joy and the emotional valence negative as most predictive features. Two XAI visualization methods, local feature attributions and partial dependency plots, revealed several incorrect prediction mechanisms on the instance-level. Performing t...
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Demands on more transparency of the backbox nature of machine learning models have led to the recent rise of human-in-the-loop in machine learning, i.e. processes that integrate humans in the training and application of machine learning... more
Demands on more transparency of the backbox nature of machine learning models have led to the recent rise of human-in-the-loop in machine learning, i.e. processes that integrate humans in the training and application of machine learning models. The present work argues that this process requirement does not represent an obstacle but an opportunity to optimize the design process. Hence, this work proposes a new process framework, Human-in-the-learning-loop (HILL) Design Cycles - a design process that integrates the structural elements of agile and design thinking process, and controls the training of a machine learning model by the human in the loop. The HILL Design Cycles process replaces the qualitative user testing by a quantitative psychometric measurement instrument for design perception. The generated user feedback serves to train a machine learning model and to instruct the subsequent design cycle along four design dimensions (novelty, energy, simplicity, tool). Mapping the fou...
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This study uses an organizational psychology lens to gain a fundamental understanding of how Agile Practices positively influence socio-psychological mechanisms that lead to a successful project. The theoretical framework of this study is... more
This study uses an organizational psychology lens to gain a fundamental understanding of how Agile Practices positively influence socio-psychological mechanisms that lead to a successful project. The theoretical framework of this study is based on a causal model of teamwork derived from innovation research with the major constructs of coordination capability and knowledge growth. We will investigate the impact of Agile Practices on these constructs through well-researched teamwork variables such as goal commitment and social support, and by the new constructs adaptivity and open communication. We will then determine the impact of these constructs on project performance. In addition, we will analyze the moderating effect of team autonomy on the relationship between coordination capability and project performance. The quantitative field study will be conducted in Nov 2008 and targets a sample size of 60 agile projects.
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People often rely on online reviews to make purchase decisions. The present work aimed to gain an understanding of a machine learning model's prediction mechanism by visualizing the effect of sentiments extracted from online hotel... more
People often rely on online reviews to make purchase decisions. The present work aimed to gain an understanding of a machine learning model's prediction mechanism by visualizing the effect of sentiments extracted from online hotel reviews with explainable AI (XAI) methodology. Study 1 used the extracted sentiments as features to predict the review ratings by five machine learning algorithms (knn, CART decision trees, support vector machines, random forests, gradient boosting machines) and identified random forests as best algorithm. Study 2 analyzed the random forests model by feature importance and revealed the sentiments joy, disgust, positive and negative as the most predictive features. Furthermore, the visualization of additive variable attributions and their prediction distribution showed correct prediction in direction and effect size for the 5-star rating but partially wrong direction and insufficient effect size for the 1-star rating. These prediction details were corro...
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On a constant quest for inspiration, designers can become more effective with tools that facilitate their creative process and let them overcome design fixation. This paper explores the practicality of applying neural style transfer as an... more
On a constant quest for inspiration, designers can become more effective with tools that facilitate their creative process and let them overcome design fixation. This paper explores the practicality of applying neural style transfer as an emerging design tool for generating creative digital content. To this aim, the present work explores a well-documented neural style transfer algorithm (Johnson 2016) in four experiments on four relevant visual parameters: number of iterations, learning rate, total variation, content vs. style weight. The results allow a pragmatic recommendation of parameter configuration (number of iterations: 200 to 300, learning rate: 2e-1 to 4e-1, total variation: 1e-4 to 1e-8, content weights vs. style weights: 50:100 to 200:100) that saves extensive experimentation time and lowers the technical entry barrier. With this rule-of-thumb insight, visual designers can effectively apply deep learning to create artistic visual variations of digital content. This could...
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Bibliographic Information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the internet at... more
Bibliographic Information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the internet at http://dnb.d-nb.de. Zugl.: Berlin, Humboldt-Univ., Diss., 2009 Cover ...
This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3... more
This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.
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This paper investigates how the immersion into a real-world user context influences design students' design inspiration and mood improvement. To this aim, the present study analyzed the perception of 16 design students who immersed... more
This paper investigates how the immersion into a real-world user context influences design students' design inspiration and mood improvement. To this aim, the present study analyzed the perception of 16 design students who immersed themselves into a dance studio and compared it with the external evaluation of 74 professional designers. Results indicate that (a) design students can attribute the immersion experience to multiple stimulus sources; (b) the mood improvement and design inspiration induced by the immersion experience predict their satisfaction level; and (c) the design inspiration induced by the immersion experience can be recognized by external observers. These findings can be harnessed for leveraging designers' user research effectiveness by embedding it in an immersion experience.
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This paper investigates how non-experts perceive digital design, and which psychological dimensions are underlying this perception of design. It thus constructs a measurement instrument to analyse user response to online displayed design... more
This paper investigates how non-experts perceive digital design, and which psychological dimensions are underlying this perception of design. It thus constructs a measurement instrument to analyse user response to online displayed design and to predict design preference. Study 1 let non
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Purpose-This paper aims to present a conceptual framework of how software teams can leverage the implicit information of implemented acceptance tests to cater to the needs of decision makers. The research questions on this framework were... more
Purpose-This paper aims to present a conceptual framework of how software teams can leverage the implicit information of implemented acceptance tests to cater to the needs of decision makers. The research questions on this framework were how business stakeholders can receive project status information in an intuitive way and how this framework can guarantee the traceability of tests to requirements. Design/methodology/approach-The conceptual framework delineates the design of an acceptance test framework in three aspects: how the requirements model reflects the evolving states of requirement maturity over a project, how the acceptance test model becomes synchronized with the requirements model without a traceability matrix and how the acceptance test model communicates business value to the decision makers. Findings-In an industrial case study, the presented framework yielded the positive effects of intuitive understanding by business stakeholders, high test coverage of requirements and distinctly reduced manual quality assurance (QA) work by automated testing for browsers and mobile devices. Practical implications-The presented framework can help to convince business stakeholders to approve the budget for building a testing framework because it delivers them value as a status reporting tool. Originality/value-This paper is the first to describe a step-by-step approach to solving a critical problem that IT departments frequently face. The solution consists in a new way of transforming the perception of a technical framework into a reporting tool for business information by intuitive design. The idea of mapping hierarchically corresponding abstraction layers can be transferred to other engineering domains.
The purpose of this study was to test whether the priming of a brainstorming task by a per-sona increases ideational fluency and originality, i.e. the quantitative and qualitative dimensions of creative performance. We conducted a... more
The purpose of this study was to test whether the priming of a brainstorming task by a per-sona increases ideational fluency and originality, i.e. the quantitative and qualitative dimensions of creative performance. We conducted a preliminary (n = 18) and final (n = 32) experiment with international students of business. These experiments revealed that priming of brainstorming by a persona increases originality of ideas by a large effect size (Cohen's d = .91, p = .02), and not significantly ideational fluency by a medium effect size (Cohen's d = .33, p = .39). As an alternative explanation to empathy, the found creativity effect may be attributed to priming that retrieves related memory items and thereby facilitates idea generation. As practical implications, design thinking practitioners can expect more original ideas and overcome design fixation if they brainstorm on a persona which is modelled in a concise and consistent way that caters to understanding the user need.
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The purpose of this study was to investigate whether and to what degree the creative performance of an ideation session depends on the time configuration of an ideation process. To this aim, the present study applied psychological... more
The purpose of this study was to investigate whether and to what degree the creative performance of an ideation session depends on the time configuration of an ideation process. To this aim, the present study applied psychological research methodology and yielded the first quantitative insight into this research question. Fifty-six graphic design students produced 13,195 ideas in six experimental sessions of brain-writing, averaging 36.1 ideas per person and experimental session after removal of outliers. The quantitative outcomes of these sessions were combined for pairwise comparisons to test the effect of session type (long session vs. sequenced session), warm-up session (sequenced session vs. long session), interval duration (decreasing intervals vs. shortened overall interval). Results revealed positive effects with large effect sizes in the range of 57–72 percent increase in creative performance for sequenced sessions over long and continuous sessions (H1), for long sessions following a sequenced session (H2a), and for extremely short interval duration over short interval duration of 3 minutes (H3b). Decreasing the interval duration in three subsequent sessions showed a moderate increase (+21%) over short interval duration (H3a). These results are relevant for design educators and design thinking practitioners as they provide consistent evidence for optimized creative performance if ideation sessions are structured in several intervals of extremely short duration.
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Rising interest on social-psychological effects of agile practices necessitate the development of appropriate measurement instruments for future quantitative studies. This study has constructed such instruments for eight agile practices,... more
Rising interest on social-psychological effects of agile practices
necessitate the development of appropriate measurement instruments for future quantitative studies. This study has constructed such instruments for eight agile practices, namely iteration planning, iterative development, continuous integration and testing, stand-up meetings, customer access, customer acceptance tests, retrospectives and co-location.
The methodological approach followed the scale construction process elaborated in psychological research. We applied both qualitative methods for item generation, and quantitative methods for the analysis of reliability and factor structure (principal factor analysis) to evaluate critical psychometric dimensions.
Results in both qualitative and quantitative analyses indicated high psychometric quality of all newly constructed scales. The resulting measurement instruments are available in questionnaire form and ready to be used in future scientific research for quantitative analyses of socialpsychological
effects of agile practices.
necessitate the development of appropriate measurement instruments for future quantitative studies. This study has constructed such instruments for eight agile practices, namely iteration planning, iterative development, continuous integration and testing, stand-up meetings, customer access, customer acceptance tests, retrospectives and co-location.
The methodological approach followed the scale construction process elaborated in psychological research. We applied both qualitative methods for item generation, and quantitative methods for the analysis of reliability and factor structure (principal factor analysis) to evaluate critical psychometric dimensions.
Results in both qualitative and quantitative analyses indicated high psychometric quality of all newly constructed scales. The resulting measurement instruments are available in questionnaire form and ready to be used in future scientific research for quantitative analyses of socialpsychological
effects of agile practices.
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How does good teamwork emerge? Can we control mechanisms of teamwork? The author has analyzed these questions in a study involving 227 participants of 55 software development teams. First, he empirically confirmed his teamwork model... more
How does good teamwork emerge?
Can we control mechanisms of teamwork?
The author has analyzed these questions in a study involving 227 participants of 55 software development teams. First, he empirically confirmed his teamwork model based on innovation research, goal setting and control theory. Second, he measured the impact of a wide selection of agile practices on these teamwork mechanisms. Third, he explained these impacts based on a thorough review of current psychological research.
This book is intended for people working in agile contexts as they will gain insight into the complexity of how «good teamwork» emerges. This insight on team dynamics may also prove valuable for upper management for calibrating agile practices and «soft factors», thus increasing the effectiveness of software teams.
Can we control mechanisms of teamwork?
The author has analyzed these questions in a study involving 227 participants of 55 software development teams. First, he empirically confirmed his teamwork model based on innovation research, goal setting and control theory. Second, he measured the impact of a wide selection of agile practices on these teamwork mechanisms. Third, he explained these impacts based on a thorough review of current psychological research.
This book is intended for people working in agile contexts as they will gain insight into the complexity of how «good teamwork» emerges. This insight on team dynamics may also prove valuable for upper management for calibrating agile practices and «soft factors», thus increasing the effectiveness of software teams.
This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3... more
This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.
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This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series "Game of Thrones". The respondents... more
This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series "Game of Thrones". The respondents answered which character they anticipated to win in the final episode of the series, and explained their choice of the final candidate in free text from which sentiments were extracted. These sentiments were compared to feature sets derived from candidate likeability and candidate personality ratings. In our benchmarking of 10-fold cross-validation in 100 repetitions, all feature sets except the likeability ratings yielded a 10-11% improvement in accuracy on the holdout set over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased holdout set performance by 20-34% but surprisingly not testing set performance. Taken together, our study provides a quantified estimation of the additional predictive value of psychological predictors. Likeability ratings were clearly outperformed by the feature sets based on personality, emotional valence, and basic emotions.