Towards Routines Mining – Designing and Implementing the Argos Miner, a Design Science Artifact for Studying Routine Dynamics with Process Mining

Digital artifacts increasingly support actors in carrying out organizational routines. These artifacts leave digital trace data, that is, time-stamped data about what actions actors performed. While extant research on routines largely builds on qualitative methods, the increasing ubiquitousness and prevalence of trace data enable novel methodological opportunities. However, several challenges currently hinder the adoption of trace data in empirical research on routines in general and their dynamics in particular. Promising approaches such as process mining are neither designed for nor sensitive to the concept of routines. In this paper, we follow a design science research approach to develop the first iteration of an artifact, which we coin Argos Miner. This artifact is based on process mining algorithms and overcomes challenges inherent in adopting process mining in routine dynamics research. It enables scholars to capture reality in flight by analyzing routine dynamics using a computational, mixed-methods approach

Supporting Self-Regulation of Children with ADHD Using Wearables: Tensions and Design Challenges

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch in various contexts. These results indicate key considerations—from both the child and caregiver viewpoints—for designing technological interventions supporting children with ADHD

Routines Miner – Design Requirements for a Design Science Artifact to Study Organizational Routines with Process Mining

Digital artifacts increasingly support actors in carrying out organizational routines. These artifacts leave digital trace data, that is time-stamped information about what actions actors perform. While extant research on routines largely builds on qualitative methods, the prevalence of trace data opens up novel methodological opportunities. However, several challenges currently hinder the adoption of trace data in empirical routine dynamics research. Promising approaches, such as process mining, are per-se neither designed for nor sensitive to the concept of organizational routines. In this paper, we follow a design science research approach to propose design requirements for a design science artifact, which we coin Routines Miner. This artifact is based on process mining algorithms and aims to overcome current challenges. This will enable scholars to capture reality in flight by analyzing routine dynamics as they unfold using a computational, mixed methods research approach

Measuring Ethical Values with AI for Better Teamwork

Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task

Dynamically Adapting the Environment for Elderly People Through Smartwatch-based Mood Detection

none5siThe ageing population and age-related diseases are some of the most urgent challenges in healthcare. This leads to an increasing demand in innovative solutions to afford a healthy and safe lifestyle to the elderly. Towards this goal, the City4Age project, funded by the Horizon 2020 Programme of the European Commission, focuses on IoT-based personal data capture, supporting smart cities to empower social/health services. This paper describes the combination of the smartwatch-based Happimeter with City4Age data capture technology. Through measuring the mood of the wearer of the smartwatch, a signal is transmitted to the Philips Hue platform, enabling mood controlled lighting. Philips Hue allows the wireless remote control of energy-efficient LED light bulbs. Thus, measuring the mood through the Happimeter, the living environment for elderly people can be dynamically adapted. In our experiments, by changing colors and brightness of light bulbs using the Philips Hue platform, their quality of life can be improved. A validation test will be done in the context of the City4Age project, involving 31 elderly people living in a Southern Italian city.restrictedCapodieci, A.; Budner, P.; Eirich, J.; Gloor, P.; Mainetti, L.Capodieci, Antonio; Budner, P.; Eirich, J.; Gloor, P.; Mainetti, Luc

Information System Continuance Intention in the Context of Network Effects and Freemium Business Models: A Replication Study of Cloud Services in Germany

This paper reexamines the reasons for continued usage of information systems (IS), methodologically replicating a study by Bhattacherjee (2001) that investigates IS continuance by means of the expectation-confirmation model. For this purpose, the original research model was adapted and examined in a different context: cloud service usage in Germany, focusing on Dropbox. The conditions in a cloud service context differ fundamentally from those in the original study (online banking), since use is free of charge (freemium business models), customers have a wide choice of providers with low switching costs, and positive network effects are presumably in effect. The empirical analysis of 321 responses from a cross-sectional study based on the research model of Bhattacherjee (2001) confirmed his results for a different sample group and in a different context: confirmation was a predictor of perceived usefulness, satisfaction was significantly influenced by confirmation and perceived usefulness, and satisfaction and perceived usefulness predicted continuance intention. Nevertheless, the path coefficients of satisfaction and perceived usefulness on continuance intention were measurably lower in our results than in the original study. The findings imply that although the model is generally confirmed, additional factors are likely to influence the intention to continue IS usage in this specific context

Aristotle Said Happiness is a State of Activity Predicting Mood Through Body Sensing with Smartwatches

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people's geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies