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The focal role of flow in promoting desirable outcomes in companies, such as increased employees' well-being and performance, led scholars to study flow in the context of work. However, current measurement approaches which assess flow via self-report scales after task execution are limited due to obtrusiveness and a lack of real-time support. Hence, new measurement approaches must be created to overcome these limitations. In this paper, we use cardiac features (heart rate variability; HRV) and a Random Forest classifier to distinguish high and low flow. Our results from a large-scale lab experiment with 158 participants and a field study with nine participants reveal, that with HRV features alone, flow-classifiers can be built with an accuracy of 68.5% (lab) and 70.6% (field). Our research contributes to the challenge of developing a less obtrusive, real-time measurement method of flow based on physiological features and to investigate flow from a physiological perspective. Our findings may serve as foundation for future work aiming to build physio-adaptive systems which can improve employee's performance. For instance, these systems could ensure that no notifications are forwarded to employees while the system is 'sensing' flow.

Observing brain activity in real life offers exciting possibilities like the support of physical health, mental well-being, and thought-controlled interaction modalities. The development of such applications is, however, strongly impeded by low accessibility to research-grade neural data and a lack of easy-to-use and comfortable sensors. To overcome these challenges, we present the cost-effective adaptation of concealed around-the-ear EEG electrodes (cEEGrids) to the open-source OpenBCI EEG signal acquisition platform. The system combines publicly available electronic components with 3D-printed parts, forming an easily replicable recording system for prolonged use and flexible application development. To demonstrate the system’s feasibility, experimentally induced changes in visual stimulation and mental workload are replicated. Lastly, a novel application area is investigated, the observation of flow experiences through observation of temporal Alpha power changes. Support for a link between temporal Alpha power and flow is found, indicating an efficient engagement of verbal-analytic reasoning with intensified flow levels.

Fueled by the pervasion of tools like Slack or Microsoft Teams, the usage of text-based communication in distributed teams has grown massively in organizations. This brings distributed teams many advantages, however, a critical shortcoming in these setups is the decreased ability of perceiving, understanding and regulating emotions. This is problematic because better team members? abilities of emotion management positively impact team-level outcomes like team cohesion and team performance, while poor abilities diminish communication flow and well-being. Leveraging chatbot technology in distributed teams has been recognized as a promising approach to reintroduce and improve upon these abilities. In this article we present three chatbot designs for emotion management for distributed teams. In order to develop these designs, we conducted three participatory design workshops which resulted in 153 sketches. Subsequently, we evaluated the designs following an exploratory evaluation with 27 participants. Results show general stimulating effects on emotion awareness and communication efficiency. Further, they report emotion regulation and increased compromise facilitation through social and interactive design features, but also perceived threats like loss of control. With some design features adversely impacting emotion management, we highlight design implications and discuss chatbot design recommendations for enhancing emotion management in teams.

Predicting flow intensities from unobtrusively collected sensor data is considered an important yet challenging endeavor for NeuroIS scholars aiming to understand and support flow during IS use. In this direction, a limitation has been the focus on cross-subject models built on data collected in controlled laboratory settings. We investigate the potential of predicting flow in the field through personalized models by collecting report and ECG data from a clerical worker over the course of two weeks. Results indicate that a lack of variation in flow experiences during this time likely diminished these potentials. Through pre-training feature selection methods, model accuracies could be achieved that nonetheless approach related cross-subject flow prediction work. Novel recommendations are developed that could introduce more flow variation in future flow field studies to further investigate the within-subject predictability of flow based on wearable physiological sensor data.

NeuroIS scholars increasingly rely on more extensive and diverse sensor data to improve the understanding of information system (IS) use and to develop adaptive IS that foster individual and organizational productivity, growth, and well-being. Collecting such data often requires multiple recording devices, which leads to inflated study cost and decreased external validity due to greater intrusion in natural behavior. To overcome this problem, we investigated the potential of using an around-the-ear electrode array capable of capturing neural and cardiac activity for detecting an additional set of variables, namely facial muscle activity. We find that reading, speaking, chewing, jaw clenching, and six posed emotion expressions can be differentiated well by a Random Forest classifier. The results are complemented by the presentation of an open-source signal acquisition system. Thereby, an economical approach for naturalistic NeuroIS research and artefact development is provided.

Information systems (IS) are nowadays at the core of many personal and institutional activities and influence daily life more than ever before. To understand, evaluate and envision the forms of how we interact with IS, interdisciplinary and multifaceted research efforts are required. At the Information and Market Engineering chair at the Karlsruhe Institute of Technology, this task is taken head-on via research that stretches from user experiences to system design. In this review, the present research foci at the department are outlined, together with a brief description of its origins and the global developments that underly the necessity of conducting these particular IS studies.

With the advances in sensor technology and real-time processing of neurophysiological data, a growing body of academic literature has begun to explore how live biofeedback can be integrated into information systems for everyday use. While researchers have traditionally studied live biofeedback in the clinical domain, the proliferation of affordable mobile sensor technology enables researchers and practitioners to consider live biofeedback as a user interface element in contexts such as decision support, education, and gaming. In order to establish the current state of research on live biofeedback, we conducted a literature review on studies that examine self and foreign live biofeedback based on neurophysiological data for healthy subjects in an information systems context. By integrating a body of highly fragmented work from computer science, engineering and technology, information systems, medical science, and psychology, this paper synthesizes results from existing research, identifies knowledge gaps, and suggests directions for future research. In this vein, this review can serve as a reference guide for researchers and practitioners on how to integrate self and foreign live biofeedback into information systems for everyday use.

Virtual reality (VR) enables people to engage in experiences that reach far beyond physical reality. This has inspired humanitarian organizations (among others the United Nations) to use VR technology to raise the awareness of humanitarian crises by virtually transporting people to the regions affected. As a consequence, these immersive experiences may lead to a change in the readiness to donate. As scientific evidence for this effect is still rare we propose an experimental design which aims at investigating how immersion affects donation behavior. In particular, neurophysiological measurement (EEG) shall shed light on the influence of immersion on emotional and motivational processes. First results from a convenient sample of young men indicate that donation behavior is linked to the dynamics of frontal alpha asymmetry changes.

Neuro-Information-Systems (NeuroIS) research has become an established approach in the information systems (IS) discipline for investigating and understanding user behavior. Our outlined package with the name brownieR is a freely-available open source R-package for analyzing NeuroIS data (i.e. the combination of physiological and behavioral data). The central purpose of this work is to instruct researchers how brownieR can be used in IS research by providing a practical guide on how to conduct the analysis of bio-physiological data combined with behavioral data (e.g., from the web, experimental tasks, or log files). Further, the article provides an analysis framework and covers the different stages involved in analyzing physiological data.

We report on a first exploration of a new paradigm to study flow physiology in knowledge work that we call controlled experience sampling (cESM) in order to build a bridge for flow physiology research to more unstructured tasks. Results show that the approach elicits a consistent flow experience with intensities as least as high as in an established difficulty-manipulated math task. Yet, significantly lower stress perceptions and heart rate variability (HRV) responses are found in the cESM approach which highlights gaps and consequences for the diagnostic potential of HRV features for the understanding of flow physiology and automated flow observation in bio-adaptive systems.

As information technologies (IT) are both, drivers of highly engaging experiences and sources of disruptions at work, the phenomenon of flow - defined as "the holistic sensation that people feel when they act with total involvement" [5, p. 36] - has been suggested as promising vehicle to understand and enhance user behavior. Despite the growing relevance of flow at work, contemporary measurement approaches of flow are of subjective and retrospective nature, limiting our possibilities to investigate and support flow in a reliable and timely manner. Hence, we require objective and real-time classification of flow. To address this issue, this article combines recent theoretical considerations from psychology and experimental research on the physiology of flow with machine learning (ML). The overall aim is to build classifiers to distinguish flow states (i.e., low and high flow). Our results indicate that flow-classifiers can be derived from physiological signals. Cardiac features seem to play an important role in this process resulting in an accuracy of 72.3%. Hereby, our findings may serve as foundation for future work aiming to build flow-aware IT-systems.

As information systems (IS) are increasingly able to induce highly engaging and interactive experiences, the phenomenon of flow is considered a promising vehicle to understand IS user behavior and to ultimately inform the design of flow-fostering IS. However, despite growing interest of researchers in the phenomenon, knowledge about how to continuously assess flow during IS usage is limited. Hereby, recent developments in NeuroIS and psychophysiology propose novel possibilities to overcome this limitation. This article presents the results of a systematic literature review (SLR) on peripheral nervous system indicators of flow. The findings revealed that currently four major approaches exist towards physiological measurement. Propositions for simple and unobtrusive measurement in IS research are derived in conclusion.