Digital Transformation of Collaboration

Creative people are highly valued in all parts of the society, be it companies, government, or private life. However, organizations struggle to identify their most creative members. Is there a “magic ingredient” that sets the most creative individuals of an organization apart from the rest of the population? This paper aims to shed light on a part of this puzzle by introducing a novel method based on analyzing body language measured with sensors. We assess an individual’s creativity with the Torrance Tests of Creative Thinking, while their body signals are tracked with the sensors of a smartwatch measuring heart rate, acceleration, vector magnitude count, and loudness. These variables are complemented with external environmental features such as light level measured by the smartwatch. In addition, the smartwatch includes a custom-built app, the Happimeter, that allows users to do mood input in a two-dimensional framework consisting of pleasance and activation. Using multilevel regression, we find that people’s creativity is predictable by their body sensor readings. We thus provide preliminary evidence that the body movement as well as environmental variables have a relationship with an individual’s creativity. The results also highlight the influence of affective states on an individual’s creativity.

Collaboration and creativity are consistently among the top-ranked values across societies, industries, and educational organizations. What makes collaboration possible is social norms. Group-based norms have played a key role in the evolution and maintenance of human ability to work and create together. We are not born collaborative-beings; it is the ability for social cognition and normativity that allows us to collaborate with others. Despite social norms ubiquity and pervasiveness—and being one of the most invoked concepts in social science—it remains unclear what are the underlying mechanisms to the extent to be one of the big unsolved problems in the field. To contribute to close this gap, the authors take an enactive-ecological approach, in which social norms are dynamic and context-dependent socio-material affordances for collaborative activity. Social norms offer the agent possibilities for collaborative action with others in the form of pragmatic social cues. The novelty of this research is the application of quantitative methods using computational models and computer vision to collect and analyze data on the pragmatic social cues of social norms in creative collaboration. Researchers will benefit from those methods by having fast and reliable data collection and analysis at a high level of granularity. In the present study, we analyzed the interpersonal synchrony of physiological signals and facial expressions between participants, together with the participant’s perceived team cohesion. Despite the small size of the experiment, we could find correlations between signals and patterns that provide confidence in the feasibility of the methods employed. We conclude that the methods employed can be a powerful tool to collect and analyze data from larger groups and, therefore, shed some light on the—still not fully understood—underlying mechanisms of social normativity. The findings from the preliminary study are by no means conclusive, but serve as a proof of concept of the applicability of body signals and facial expressions to study social norms.

We describe a preliminary experiment to track the emotions of actors and audience in a theater play through machine learning and AI. During a 40-min play in Zurich, eight actors were equipped with body-sensing smartwatches. At the same time, the emotions of the audience were tracked anonymously using facial emotion tracking. In parallel, also the emotions in the voices of the actors were assessed through automatic voice emotion tracking. This paper demonstrates a first fully automated and privacy-respecting system to measure both audience and actor satisfaction during a public performance.

In this research project we predict the moral values of individuals through their body movements measured with the sensors of a smartwatch. The personal moral values are assessed using the Schwartz value theory, which proposes two dimensions of universal values (open to change versus conservative, self-enhancement versus self-transcendence). Data for all variables are gathered through the Happimeter, a smartwatch-based body-sensing system. Through multilevel mixed-effects generalized linear models, our results show that sensor and mood factors predict a person’s values. We utilized three methods to investigate the relationship between the Big Five personality traits (OCEAN: openness, conscientiousness, extraversion, agreeableness, and neuroticism) of a person and their Schwartz values. This research highlights the use of recent technological advances for studying a person’s values from an integrated perspective, combining body sensors and mood states to investigate individual behaviour and team cooperation.

We present the first steps toward building an intelligent system to measure the workload and surgical performance of minimally invasive surgeons. This pilot study was conducted during two training courses in minimally invasive suturing, one in microsurgery and one in laparoscopic surgery. During each training activity, surgeons wore a smartwatch with the Happimeter application running on it. This system recorded a set of physiological and motion parameters during the surgical execution. We found that monitoring the surgeon’s maneuvers and physiological parameters during surgical activity has the potential to play an important role in predicting the workload and surgical performance, especially regarding physical and mental demand and the level of distraction during surgery.

This work aims to explore the environmental and human factors affecting productivity of warehouse operators in material handling activities. The study was carried out in a semi-automated logistic hub and the data collection has been conducted using wearable sensors able to detect human-related variables such as heart rate and human interactions, based on a smartwatch combined with a mobile application developed by the MIT Center for Collective Intelligence. Preliminary analysis has shown that the interaction between the warehouse operators and the team leader significantly affects the productivity.

Moral beliefs are at the heart of governing a person’s behavior. In this paper, we introduce a way to automatically measure a person’s moral values through hidden “honest” signals in the person’s e-mail communication. We measured the e-mail behavior of 26 users through their e-mail interaction, calculating their seven “honest signals of collaboration” (strong leadership, balanced contribution, rotating leadership, responsiveness, honest sentiment, shared context and social capital). These honest signals—in other words, how they answered their e-mails—explained 70% of their moral values measured with the moral foundations survey. In particular, the more positive and less emotional they were in their language, the more they cared about others. We verified the results with a larger e-mail dataset of 655 employees of a services firm, where structural and temporal honest signals explained 67% of emotionality.

Migration has become an increasingly pressing topic on the national and European political agendas and in general public debate. The migratory phenomenon, as well as its humanitarian and health implications, are presented nowadays as a challenge for national and supranational governments which requires coordinated responses to ensure citizen security. During the election campaigns in the last three years, right-wing parties have largely depicted the right of freedom of movement as a risk factor, taking advantage of this issue for political propaganda. A significant part of the political debate takes place on social media, which has become the preferred platform for openly expressing political sentiment, including that considered politically incorrect. This study explores the political debate on immigration during the election campaigns of France and Italy over the last three years. More specifically, we perform Emotional Text Mining with the aim of identifying the sentiment surrounding immigration, and how immigrants are portrayed, in the online Twitter debate during the French presidential election (2017), the Italian general election (2018), and the Italian European elections (2019). Results were compared to identify the similarities and differences, and the effect on the election results that characterized two of the European Union’s founding countries.

Leveraging the power of big data represents an opportunity for brand managers to reveal patterns and trends in consumer perceptions, while monitoring positive or negative associations of the brand with desired topics. This chapter describes the functionalities of the SBS Brand Intelligence (SBS BI) app, which has been designed to assess brand importance and provide brand analytics through the analysis of (big) textual data. To better describe the SBS BI’s functionalities, we present a case study focused on the 2020 US Democratic Presidential Primaries. We downloaded 50,000 online articles from the Event Registry database, which contains both mainstream and blog news collected from around the world. These online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining.

The emotions someone associates with his or her religion and how this person talks about his or her faith have always been considered a personal topic. In this paper, the question of whether specific religions and emotions are connected is discussed. Based on Twitter data, individual networks, or so-called “tribes”, are created for four religions: Buddhism, Christianity, Islam and Judaism and four emotions: anger, fear, joy and sadness. Similarities and differences between tribes are analyzed using the content of the tweets. A network analysis is done for all tribes and the resulting data is used to create a machine learning model for each category. Using these, general patterns between emotions and religions are outlined and discussed. An analysis with further data was conducted on our model.

In this paper, we investigate the application of machine learning techniques in the context of social media. Specifically, we aim at drawing conclusions from users’ Twitter behavior and language to users’ attitudes toward the LGBT movement. By using an adjusted procedure of the Cross Industry Standard Process for Data Mining (CRISP-DM) process, we create a prediction model for investigating and identifying those attitudes. Furthermore, we formulate step-by-step instructions for its deployment. We provide the reader with a theoretical background for our research domain and describe the methods that we use. Results show that there are two groups of contrary attitudes toward the LGBT community and that the language and behavior of users in the groups, respectively, differ from each other. Also, we identify word analyses as a valuable means for prediction. We also apply our model on another dataset to investigate its interspersion with the previously identified groups and demonstrate its effectiveness for predicting attitudes of a single actor on Twitter. Finally, we critically assess our findings and propose further fields of investigation in this area.

Dominant opinion in the general public is that work automation will presumably hold negative societal implications, such as job loss, which often causes fear and misunderstanding. Contrarily to such an attitude, the approach we took in this paper is that people will experience rather positive effects of work automation, thanks to collaboration with artificial intelligence using virtual assistants. The quantitative experimental study was a business problem simulation. Participants were asked to perform tasks of a marketing manager in order to prepare a marketing campaign for a new product. Control group participants performed these tasks on their own, while experimental group participants did them in collaboration with a virtual chatbot-like assistant created specifically for this simulation. A total of 20 people participated in the study. A relevant difference in performance  was observed between the groups, n = 20, t(18) = 5.25, p < 0.001. Participants collaborating with a virtual assistant achieved a 57% higher productivity (measured by tasks done) than those working on their own. Furthermore, in a post-study survery they assessed their productivity higher and were more satisfied with their performance. Results confirmed the hypothesis, proving that human-AI collaboration increased productivity within the studied sample.

This study is the first attempt to employ Knowledge Building pedagogy and technology to integrate robotics into subjects like mathematics. Over the course of 6 weeks, 16 elementary students (Grade 5/6) engaged in engineering design process, computational thinking, and mathematical reasoning to design and program robots to collectively solve real-life issues such as creating a green and clean city. One of the knowledge building goals is to recreate schools as knowledge creation organizations. Therefore, this study employs the innovation network framework and uses social network and lexical analyses to analyze students’ collaborations in robotics against knowledge creation organizations criteria and examine the extent to which student knowledge in math improved. The results show the emergence of the innovation networks in education settings and the importance of these networks for idea improvement.

Speech Interfaces represent a new interactive phenomenon, which entails massive personal data processing. The spectrum of legal issues that arises from this interaction impacts both user privacy and social relationships. This study addresses three potential issues or ‘provocations’ relating to speech interaction that illustrate the challenges and complexity of this socio-legal domain: (i) the potential for lying; (ii) the possibility of breaching the law; (iii) the ability to interpret an order. It deploys an in-depth analysis of the related legal consequences and implications with the scope to prompt discussion around these provocative issues. It first provides an overview of the correct hermeneutical approach to frame legal paradigms, highlighting the crucial legal aspects, conceptual approaches and interpretations to be considered when addressing the whole ‘interactive artificial agents’ (IAA) phenomenon. The study adopts the classical Civil Law system’s methodology (qualitative/top-down analytical). The core of the study then focuses on the three provocations as connected by personal data processing. The goal is to provide a critical legal analysis of those interfaces that could impact the foundation of human socio-legal interrelations. By raising awareness of these controversial aspects, the work contributes to fostering further discussion about interdisciplinary privacy issues that stand at the intersection of Law, Social Sciences and HCI design, and that cross-pollinate each other.

The European General Data Protection Regulation (GDPR, EU 2016/679), adopted by the European Parliament has profoundly changed the legislative approach to the protection of personal data by the European Union. The GDPR provisions require organizations to make deep changes. Organizations have to shift from an approach based on the adoption of minimum-security measures, provided by the EU Directive of 1994, to a proactive approach based on accountability. Organizations that manage personal data of EU citizens have to adopt systems of verification and continuous improvement and adopt principles such as privacy by design and privacy by default. The rule of “privacy by design” calls for privacy to be taken into account throughout the whole engineering process. A key point is the methods for checking compliance with GDPR. This paper proposes a structured approach based on business process modelling, to support compliance with the GDPR. We have identified an approach that has to identify the most important key points for GDPR compliance.

The practice of design anthropology has continued to evolve since the publication of Design + Anthropology: Converging Pathways in Anthropology and Design in 2018. At that time, design anthropology was described as “an emerging transdisciplinary field.” ([1], [2]: 10, [3]). Working collaboratively with Ken Riopelle who provided analytical expertise in social network analysis, we approached this claim from the perspective of social network analysis “to investigate the human and nonhuman actors (i.e., people and institutions) that have contributed to design anthropological practice and theorizing.” [3]. Our initial goal was to determine if—and, if so, to what extent—design anthropology qualified as a disciplinary “field”. In our original analysis, we began by establishing a set of benchmarks that serve as indicators to identify a disciplinary field. In this paper, we revisit our initial analysis, updating it with new publications, contributors, blogs, groups, and other developments, to investigate if and how design anthropology has diffused.

Social support is considered an important factor in the recovery of individuals, who suffer from drug use disorder. Traditional drug treatment interventions have mainly focused on the individual without taking into consideration the social and environmental conditions that may support or reduce drug use. By combining a social capital framework with geospatial research methodologies, we mapped hot spots and cold spots within the 23 Boston neighborhoods and identified where social ties were stronger or weaker. The spatial correlation analysis and Geographically Weighted Regression demonstrated that in areas where social capital is low, there is a moderately high incidence of opioid deaths and sick assist calls. Our analysis shows that in neighborhoods where residents are involved in charitable organizations, where people gather around religious organizations, or where unions are more active, people help each other more and might be aware of actions to take to prevent opioid-related deaths.

The concept of “Collaborative Innovation Networks” (COINs) has been successfully applied in many projects over the past 15 years to detect COINs in given situations and to enhance the behavior of related actors in the corresponding social networks. However, what might be missing is an easily applicable tool, which helps potential initiators of an innovative endeavor in a guided process to initially constitute and further develop a COIN over several stages. In this paper, we follow the idea that reward-based crowdfunding campaigns could be such a practical tool. Therefore, we develop a conceptual framework of how reward-based crowdfunding can be applied to support the constitution and development of COINs.

In this paper, we describe an ecosystem for acquiring pattern languages from the perspectives of constructivism and collaborative way and introduce a web system called “Presen Box” that assists in pattern languages acquisition according to the ecosystem. Pattern language is a methodology that describes practical knowledge for enhancing human creativity and has been developed in various fields such as architecture, software design, education, organization, and lifestyle. In recent years, interfaces of pattern languages, pattern cards, pattern apps, and pattern objects that embed patterns in daily life, etc., have been developed in addition to reading materials such as books and papers. However, there are still things that need to be overcome in order for those who do not know of pattern languages to acquire it and promote higher quality practices. Rather than leaving pattern language acquiring to individual efforts alone, we propose an ecosystem that realizes collaborative acquisition and an “action first pattern practicing method” that supports the ecosystem. In this method, people will learn patterns through the repetition of concrete actions. The system “Presen Box” that implemented this ecosystem is a web platform that uses presentation patterns that describe presentation skills, and the users can get ideas for creating high-quality presentations. By repeating the execution of ideas, the users can acquire presentation patterns gradually.