Jiwon Jung
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1
Navigating the future of mobility user experience
Eliciting user values
Creating successful future mobility products and services requires a deep understanding of user values and requirements. Past research studies are limited in that they have predominantly focused on a single mobility context, such as private car use, leaving areas like shared mobility services and air travel insufficiently explored. The lack of comprehensive understanding of how user values and requirements vary and interrelate across different mobility contexts creates a significant knowledge gap. This gap impedes not only the design of context-specific user experiences but also the cohesive integration of various mobility forms at a systemic level. Addressing this, this study delved into the user values and functional requirements across four distinct mobility contexts: private cars, shared mobility services, airplanes, and public transportation. Through expert interviews, unique user values with corresponding functional requirements were identified for each mobility context. The study findings would inform the user experience design for future mobility systems.
...
Creating successful future mobility products and services requires a deep understanding of user values and requirements. Past research studies are limited in that they have predominantly focused on a single mobility context, such as private car use, leaving areas like shared mobility services and air travel insufficiently explored. The lack of comprehensive understanding of how user values and requirements vary and interrelate across different mobility contexts creates a significant knowledge gap. This gap impedes not only the design of context-specific user experiences but also the cohesive integration of various mobility forms at a systemic level. Addressing this, this study delved into the user values and functional requirements across four distinct mobility contexts: private cars, shared mobility services, airplanes, and public transportation. Through expert interviews, unique user values with corresponding functional requirements were identified for each mobility context. The study findings would inform the user experience design for future mobility systems.
Journal article
(2026)
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Caroline A. Figueroa, Kathleen W. Guan, Dimpy Gupta, Neslihan Can, Kayla Green, Jiwon Jung, Eva Thalassinou, Gerben Kuiper, Niko Vegt
Introduction: Mental health issues among young people have surged post-COVID-19. Mental health apps can offer accessible preventive support on a large scale, yet the perspective of minoritized youth–such as those from low socioeconomic and ethnic/racial backgrounds–are underexplored. This risks low uptake and effectiveness, and exacerbating health inequities. This study aimed to understand the needs and concerns of minoritized youth in the Netherlands using a participatory approach. Methods: We conducted 3 co-creation sessions with 17 adolescents (16 females, majority Dutch Moroccan background) aged 11–22 years, recruited through community centers in lower-income neighborhoods in The Netherlands, with the help of community workers. We also organized a discussion session with 26 preventive youth workers to explore their perspectives regarding implementation. A subset of youth (n = 10) analyzed the data in 2 co-thematic analysis workshops. We compared youth and researcher themes. Results: Youth saw data-driven mental health apps as useful for short-term stress relief through motivational quotes, social activity suggestions, and homework support, but unable to solve more severe issues. In the co-analysis, youth analyzed based on emotion and functions, whereas researchers employed a more technical lens. Key themes included identity-based (such as religion, gender, and age) and contextual tailoring (to school/home schedules), compassionate communication as opposed to fake support (robots), safety, and the role of social media. Conclusion: These findings highlight the need to examine how app design for young people can prioritize authentic, compassionate communication, safety–including transparency about data–tailoring to identify aspects, adapting the timing and frequency of notifications, and integrating social connections and social media. Participatory approaches are promising to better understand the needs of youth from minoritized backgrounds for digital mental health technologies, with the aim of equitable digital solutions.
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Introduction: Mental health issues among young people have surged post-COVID-19. Mental health apps can offer accessible preventive support on a large scale, yet the perspective of minoritized youth–such as those from low socioeconomic and ethnic/racial backgrounds–are underexplored. This risks low uptake and effectiveness, and exacerbating health inequities. This study aimed to understand the needs and concerns of minoritized youth in the Netherlands using a participatory approach. Methods: We conducted 3 co-creation sessions with 17 adolescents (16 females, majority Dutch Moroccan background) aged 11–22 years, recruited through community centers in lower-income neighborhoods in The Netherlands, with the help of community workers. We also organized a discussion session with 26 preventive youth workers to explore their perspectives regarding implementation. A subset of youth (n = 10) analyzed the data in 2 co-thematic analysis workshops. We compared youth and researcher themes. Results: Youth saw data-driven mental health apps as useful for short-term stress relief through motivational quotes, social activity suggestions, and homework support, but unable to solve more severe issues. In the co-analysis, youth analyzed based on emotion and functions, whereas researchers employed a more technical lens. Key themes included identity-based (such as religion, gender, and age) and contextual tailoring (to school/home schedules), compassionate communication as opposed to fake support (robots), safety, and the role of social media. Conclusion: These findings highlight the need to examine how app design for young people can prioritize authentic, compassionate communication, safety–including transparency about data–tailoring to identify aspects, adapting the timing and frequency of notifications, and integrating social connections and social media. Participatory approaches are promising to better understand the needs of youth from minoritized backgrounds for digital mental health technologies, with the aim of equitable digital solutions.
Journal article
(2025)
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Vivienne Kahlmann, Astrid Dunweg, Heleen Kicken, Nick Jelicic, Johanna M. Hendriks, Richard Goossens, Marlies S. Wijsenbeek, Jiwon Jung
Introduction
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care. ...
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care. ...
Introduction
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care.
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care.
Journal article
(2025)
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Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung
Background:
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment. ...
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment. ...
Background:
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment.
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment.
“It Is a Moving Process”
Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary Medicine
Conference paper
(2024)
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Lorenzo Corti, Rembrandt Oltmans, Jiwon Jung, Agathe Balayn, Marlies Wijsenbeek, Jie Yang
Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and timeliness of their services. There are converging opinions on the need for Explainable AI (XAI) in healthcare. However, prior work considers explanations as stationary entities with no account for the temporal dynamics of patient care. In this work, we involve 16 Idiopathic Pulmonary Fibrosis (IPF) clinicians from a European university medical centre and investigate their evolving uses and purposes for explainability throughout patient care. By applying a patient journey map for IPF, we elucidate clinicians' informational needs, how human agency and patient-specific conditions can influence the interaction with XAI systems, and the content, delivery, and relevance of explanations over time. We discuss implications for integrating XAI in clinical contexts and more broadly how explainability is defined and evaluated. Furthermore, we reflect on the role of medical education in addressing epistemic challenges related to AI literacy.
...
Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and timeliness of their services. There are converging opinions on the need for Explainable AI (XAI) in healthcare. However, prior work considers explanations as stationary entities with no account for the temporal dynamics of patient care. In this work, we involve 16 Idiopathic Pulmonary Fibrosis (IPF) clinicians from a European university medical centre and investigate their evolving uses and purposes for explainability throughout patient care. By applying a patient journey map for IPF, we elucidate clinicians' informational needs, how human agency and patient-specific conditions can influence the interaction with XAI systems, and the content, delivery, and relevance of explanations over time. We discuss implications for integrating XAI in clinical contexts and more broadly how explainability is defined and evaluated. Furthermore, we reflect on the role of medical education in addressing epistemic challenges related to AI literacy.
The research question of this doctoral thesis is: What can be the future impact of design (activities) in digital health, given the rise of data collection and analysis technologies? I answered this question on three knowledge levels: design vision, knowledge-generating approach, and design tool. In Chapter 1, I envision design activities for the collective computing era (an upcoming modern computing era with complex systems of massive social interaction through various connected computing devices) that data collection and analysis technologies are a part of. Based on the literature review and informants’ interviews, I developed a design vision that demonstrates the changes posed in design activities (design tasks, processes, and the designer’s role) due to the upcoming collective computing era, and provides guidance for adopting the changes. Consequently, the vision proposes that design tasks in the collective computing era move towards designing ‘complex system(s)’ and testing these within ‘society as a lab’. The vision’s guidance states that designers can approach these tasks by addressing communities and engaging with their data. In terms of the design process, the vision claims the ‘coexploration’ of the design problem and solution spaces. To tackle such change, the guidance suggests designers: the flexible combination and analysis of mixed data, working on social forces at a system level, and developing through multiple soft launches with modular designs. Finally, the designer’s role becomes conducting an ‘accountable implementation.’ The vision recommends approaching accountable implementation by incorporating a transdisciplinary vision of the value and control of the design output....
...
The research question of this doctoral thesis is: What can be the future impact of design (activities) in digital health, given the rise of data collection and analysis technologies? I answered this question on three knowledge levels: design vision, knowledge-generating approach, and design tool. In Chapter 1, I envision design activities for the collective computing era (an upcoming modern computing era with complex systems of massive social interaction through various connected computing devices) that data collection and analysis technologies are a part of. Based on the literature review and informants’ interviews, I developed a design vision that demonstrates the changes posed in design activities (design tasks, processes, and the designer’s role) due to the upcoming collective computing era, and provides guidance for adopting the changes. Consequently, the vision proposes that design tasks in the collective computing era move towards designing ‘complex system(s)’ and testing these within ‘society as a lab’. The vision’s guidance states that designers can approach these tasks by addressing communities and engaging with their data. In terms of the design process, the vision claims the ‘coexploration’ of the design problem and solution spaces. To tackle such change, the guidance suggests designers: the flexible combination and analysis of mixed data, working on social forces at a system level, and developing through multiple soft launches with modular designs. Finally, the designer’s role becomes conducting an ‘accountable implementation.’ The vision recommends approaching accountable implementation by incorporating a transdisciplinary vision of the value and control of the design output....
Advancing Design Approaches through Data-Driven Techniques
Patient Community Journey Mapping Using Online Stories and Machine Learning
Designers are increasingly collaborating with data scientists to apply smart data technologies to understand large-scale user behavior during their design research. This is useful in specific impact domains with vulnerable users and unfamiliar contexts, such as healthcare design. Patient journey mapping is the most common design tool for developing and communicating patient-centred perspectives in healthcare design. However, creating a traditional patient journey map is labor intensive. Consequently, they often represent the experiences of a limited number of patients and, therefore, have limitations in including an extensive group patient experience. To overcome these challenges, we present a new data-driven and hybrid intelligent design approach that utilizes tens of thousands of online patient stories and machine-learning techniques through collaboration with data scientists. We set up two studies in the field of oncology and demonstrate that combining the two machine-learning techniques allows for quantifying the experiences of a wide range of patients, detecting relationships between co-occurring experiences within the journey, and detecting new design opportunities/directions. In these studies, designers gained a large-scale, yet qualitative and inspiring, understanding of a complex context in healthcare with reduced time and cost investments
...
Designers are increasingly collaborating with data scientists to apply smart data technologies to understand large-scale user behavior during their design research. This is useful in specific impact domains with vulnerable users and unfamiliar contexts, such as healthcare design. Patient journey mapping is the most common design tool for developing and communicating patient-centred perspectives in healthcare design. However, creating a traditional patient journey map is labor intensive. Consequently, they often represent the experiences of a limited number of patients and, therefore, have limitations in including an extensive group patient experience. To overcome these challenges, we present a new data-driven and hybrid intelligent design approach that utilizes tens of thousands of online patient stories and machine-learning techniques through collaboration with data scientists. We set up two studies in the field of oncology and demonstrate that combining the two machine-learning techniques allows for quantifying the experiences of a wide range of patients, detecting relationships between co-occurring experiences within the journey, and detecting new design opportunities/directions. In these studies, designers gained a large-scale, yet qualitative and inspiring, understanding of a complex context in healthcare with reduced time and cost investments
In this study, we envision engineering design activities for collective computing, an upcoming era of complex systems of massive social interaction through a wide variety of connected computing devices. A literature review reveals how collective computing, compared to the previous eras of personal and ubiquitous computing, may lead to new design tasks and design processes, as well as new roles for designers. Based on this review, new design activities for the collective computing era are envisioned, and further revised in an interview study with 24 informants. The result is a vision for design in the collective computing era, with actionable guidance for designers in terms of a coherent set of new design activities proposed in relation to advances in computing.
...
In this study, we envision engineering design activities for collective computing, an upcoming era of complex systems of massive social interaction through a wide variety of connected computing devices. A literature review reveals how collective computing, compared to the previous eras of personal and ubiquitous computing, may lead to new design tasks and design processes, as well as new roles for designers. Based on this review, new design activities for the collective computing era are envisioned, and further revised in an interview study with 24 informants. The result is a vision for design in the collective computing era, with actionable guidance for designers in terms of a coherent set of new design activities proposed in relation to advances in computing.
Reviewing Design Movement Towards the Collective Computing Era
How will Future Design Activities Differ from Those in Current and Past Eras of Modern Computing?
This paper aims to envision design activities –design task, design process, and designer–for the collective computing era, an upcomingdominance of applications where many people interact with each other through many computing devices(Abowd, 2016). To achieve this aim, thetypical design activities over the last two computing eras, personal and ubiquitous, are studied through a literature review. Based on the activities shift discovered there, new design activities for collective computing are envisioned. This study urgesdesigners to change the way to work with user-data: from referring the small but in-depth ethnographical user-centeredliving-lab data (during the ubiquitous computing era), to create an understanding about both the individual user and the society or crowd as a whole(collective computing era). These different types of data provide designers with a rich understanding on the usersin their contexts, both as individual and community levels. Also, the design process that authors envision is a process of co-exploration (merge of problem exploration and solution delivery), and co-evolution (the constant reflection back and forth with exchange of information) of design problem and solution spaces. Moreover, the designersin collective computing erahave to take ethical and privacyissues into account. They have to create plans for dealing with the uncontrollability of the data generated and uncertainty about the social impact of the design that come with it. This research cantrigger the designers to innovate and remodel their workat the forefront of new computing technologies.
...
This paper aims to envision design activities –design task, design process, and designer–for the collective computing era, an upcomingdominance of applications where many people interact with each other through many computing devices(Abowd, 2016). To achieve this aim, thetypical design activities over the last two computing eras, personal and ubiquitous, are studied through a literature review. Based on the activities shift discovered there, new design activities for collective computing are envisioned. This study urgesdesigners to change the way to work with user-data: from referring the small but in-depth ethnographical user-centeredliving-lab data (during the ubiquitous computing era), to create an understanding about both the individual user and the society or crowd as a whole(collective computing era). These different types of data provide designers with a rich understanding on the usersin their contexts, both as individual and community levels. Also, the design process that authors envision is a process of co-exploration (merge of problem exploration and solution delivery), and co-evolution (the constant reflection back and forth with exchange of information) of design problem and solution spaces. Moreover, the designersin collective computing erahave to take ethical and privacyissues into account. They have to create plans for dealing with the uncontrollability of the data generated and uncertainty about the social impact of the design that come with it. This research cantrigger the designers to innovate and remodel their workat the forefront of new computing technologies.