Search

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications., Comment: To be published in IEEE Transactions on Affective Computing. 20 pages, 7 figures, 6 tables

Co-usage sharing involves social interactions between providers and consumers. Previous research established that individuals’ motivation to engage in such transactions are not only driven by econo...

Complementors accumulate reputation on an ever-increasing number of online platforms. While the effects of reputation within individual platforms are well-understood, its potential effectiveness across platform boundaries has received much less attention. This research note considers complementors’ ability to increase their trustworthiness in the eyes of prospective consumers by importing reputational data from another platform. The study evaluates this potential lever by means of an online experiment, during which specific combinations of on-site and imported rating scores are tested. Results reveal that importing reputation can be advantageous – but also detrimental, depending on ratings’ values. Implications for complementors, platform operators, and regulatory bodies concerned with online reputation are considered.

Wrist-worn inertial measurement units have emerged as a promising technology to passively capture dietary intake data. State-of-the-art approaches use deep neural networks to process the collected inertial data and detect characteristic hand movements associated with intake gestures. In order to clarify the effects of data preprocessing, sensor modalities, and sensor positions, we collected and labeled inertial data from wrist-worn accelerometers and gyroscopes on both hands of 100 participants in a semi-controlled setting. The method included data preprocessing and data segmentation, followed by a two-stage approach. In Stage 1, we estimated the probability of each inertial data frame being intake or non-intake, benchmarking different deep learning models and architectures. Based on the probabilities estimated in Stage 1, we detected the intake gestures in Stage 2 and calculated the F1 score for each model. Results indicate that top model performance was achieved by a CNN-LSTM with earliest sensor data fusion through a dedicated CNN layer and a target matching technique (F1 = .778). As for data preprocessing, results show that applying a consecutive combination of mirroring, removing gravity effect, and standardization was beneficial for model performance, while smoothing had adverse effects. We further investigate the effectiveness of using different combinations of sensor modalities (i.e., accelerometer and/or gyroscope) and sensor positions (i.e., dominant intake hand and/or non-dominant intake hand).

With the ever-growing popularity of sharing economy platforms, complementors increasingly face the challenge to manage their reputation on different platforms. The paper reports the results from an experimental online survey to investigate how and under which conditions online reputation is effective to engender trust across platform boundaries. It shows that (1) cross-platform signaling is in fact a viable strategy to engender trust and that (2) its effectiveness crucially depends on source–target fit. Implications for three stakeholders are discussed. First, platform complementors may benefit from importing reputation, especially when they have just started on a new platform and have not earned on-site reputation yet. The results also show, however, that importing reputation (even if it is excellent) may be detrimental if there occurs a mismatch between source and target and that, hence, fit is of utmost importance. Second, regulatory authorities may consider reputation portability as a means to make platform boundaries more permeable and hence to tackle lock-in effects. Third, platform operators may employ cross-platform signaling as a competitive lever.

Background: Reaching and engaging individuals, especially young adults, in web-based prevention programs is challenging. ‘No Money No Time’ (NMNT) is a purpose built, healthy eating website with content and a social marketing strategy designed to reach and engage a young adult (18–34 year olds) target group. The aim of the current study was to conduct a process evaluation of the 12-month social marketing strategy to acquire and engage NMNT users, particularly young adults. Methods: a process evaluation framework for complex interventions was applied to investigate the implementation of the social marketing strategy component, mechanisms of impact and contextual factors. Google Analytics data for the first 12 months of operation (17 July 2019 to 17 July 2020) was evaluated. Results: in year one, 42,413 users from 150+ countries accessed NMNT, with 47.6% aged 18–34 years. The most successful channel for acquiring total users, young adults and return users was via organic search, demonstrating success of our marketing strategies that included a Search Engine Optimisation audit, a content strategy, a backlink strategy and regular promotional activities. For engagement, there was a mean of 4.46 pages viewed per session and mean session duration of 3 min, 35 s. Users clicked a ‘call-to-action’ button to commence the embedded diet quality tool in 25.1% of sessions. The most common device used to access NMNT (63.9%) was smartphone/mobile. Engagement with ‘quick, cheap and healthy recipes’ had the highest page views. Conclusions: findings can inform online nutrition programs, particularly for young adults, and can apply to other digital health programs.

Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of the labeled sensor data to automatically learn how to make detections. One characteristic, especially for deep learning models, is the need for large datasets. To meet this need, we collected the Objectively Recognizing Eating Behavior and Associated Intake (OREBA) dataset. The OREBA dataset aims to provide comprehensive multi-sensor data recorded during the course of communal meals for researchers interested in intake gesture detection. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands. We report the details of data collection and annotation, as well as details of sensor processing. The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research. Specifically, the best baseline models achieve performances of $F_1$ = 0.853 for the discrete dish using video and $F_1$ = 0.852 for the shared dish using inertial data., To be published in IEEE Access

In today's fast-paced society, users of information technology increasingly operate under high time pressure. Loaded with multiple tasks and racing against deadlines, users experience considerable ...

Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.

Background The proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners emphasize that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and the lack of integration into existing system landscapes. Co-design is an approach used to address these pitfalls. By recognizing users as experts of their own experience, co-design directly involves users in the design process and provides them an active role in knowledge development, idea generation, and concept development. Objective Despite the existence of a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mobile health (mHealth) systems. This study aims to contextualize an existing co-design framework for mHealth applications and construct guidelines to address common challenges of co-designing mHealth systems. Methods Tapping into the knowledge and experience of experts in co-design and mHealth systems development, we conducted an exploratory qualitative study consisting of 16 semistructured interviews. Thereby, a constructivist ontological position was adopted while acknowledging the socially constructed nature of reality in mHealth system development. Purposive sampling across web-based platforms (eg, Google Scholar and ResearchGate) and publications by authors with co-design experience in mHealth were used to recruit co-design method experts (n=8) and mHealth system developers (n=8). Data were analyzed using thematic analysis along with our objectives of contextualizing the co-design framework and constructing guidelines for applying co-design to mHealth systems development. Results The contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases, and an emphasis on immersion in real-world contexts. In addition, 7 guidelines were constructed that directly pertain to mHealth: understanding stakeholder vulnerabilities and diversity, health behavior change, co-design facilitators, immersion in the mHealth ecosystem, postdesign advocates, health-specific evaluation criteria, and usage data and contextual research to understand impact. Conclusions System designers encounter unique challenges when engaging in mHealth systems development. The contextualized co-design framework and constructed guidelines have the potential to serve as a shared frame of reference to guide the co-design of mHealth systems and facilitate interdisciplinary collaboration at the nexus of information technology and health research.

The Dutch auction, also known as the descending-price auction or reverse clock auction, has a long-standing history in practice and in academic literature. In practice, the Dutch auction is commonly used to rapidly sell large quantities of homogeneous goods, such as cut flowers, fish, or tobacco. However, most e-commerce auction sites focus on other auction mechanisms, and overall research on human behavior in Dutch auctions is scant. To facilitate research on Dutch auctions and their applications in electronic commerce, we conduct a structured literature review of experimental studies and establish the current state of research on bidding behavior in single-unit and multi-unit Dutch auctions. The findings are based on an analysis of twenty-nine articles published in the fields of economics, information management, marketing, and operations research and management science between 1970 and 2016. This review reveals (1) the characteristics that make the Dutch auction unique compared to other auction...

Aims: Cardiac autonomic reflex tests (CARTs) are time consuming and require patient cooperation for detecting cardiac autonomic neuropathy (CAN). Heart rate variability (HRV) analysis requires less patient cooperation and is quicker to complete. However the reliability of HRV results as a clinical tool, with respect to length of recording and accuracy of diagnosis is inconclusive. The current study investigated the reproducibility associated with varying length of recording for early CAN (eCAN) assessment. Methods: Participants were 68 males, 72 females with average age of 55 for controls and 63 for early CAN. Inclusion criteria were that participants were medication free and presented with no comorbidities. ECGs of control and eCAN were recorded and heart rate changes analyzed with the fast Fourier transform (FFT) and Lomb-Scargle periodogram (LSP). Ten-second to 5-minute recordings were extracted from a 15-minute lead-II ECG and accuracy in assessment of eCAN determined. Results: The eCAN group was older ( P < .001) and systolic blood pressure was higher ( P < .01). HDL-cholesterol was also higher in the eCAN group ( P < .05). HRV analysis showed that both FFT and LSP results were significantly different between eCAN and control down to a 10-second ECG length for low frequency (LSP: P = .013, FFT: P = .024) and high frequency (HF-LSP: P = .002, FFT: P = .002) power. eCAN assessment was optimal down to 90-second recordings with a sensitivity of 100% and specificity of 29.49%. Conclusion: HRV is suitable for clinical practice from ECG recordings of more than 90 seconds with high accuracy and repeatability within a session for each participant.

Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) frame-level intake probabilities are learned from the sensor data using a deep neural network, and then (ii) sparse intake events are detected by finding the maxima of the frame-level probabilities. In this study, we propose a single-stage approach which directly decodes the probabilities learned from sensor data into sparse intake detections. This is achieved by weakly supervised training using Connectionist Temporal Classification (CTC) loss, and decoding using a novel extended prefix beam search decoding algorithm. Benefits of this approach include (i) end-to-end training for detections, (ii) simplified timing requirements for intake gesture labels, and (iii) improved detection performance compared to existing approaches. Across two separate datasets, we achieve relative $F_1$ score improvements between 1.9% and 6.2% over the two-stage approach for intake detection and eating/drinking detection tasks, for both video and inertial sensors.

Fake news undermines individuals’ ability to make informed decisions. However, the theoretical understanding of how users assess online news as real or fake has thus far remained incomplete. In particular, previous research cannot explain why users fall for fake news inadvertently and despite careful thinking. In this work, we study the role of affect when users assess online news as real or fake. We employ NeuroIS measurements as a complementary approach beyond self-reports, which allows us to capture affective responses in situ, i.e., directly in the moment they occur. We draw upon cognitive dissonance theory, which suggests that users experiencing affective responses avoid unpleasant information to reduce psychological discomfort. In our NeuroIS experiment, we measured affective responses based on electrocardiography and eye tracking. We find that lower heart rate variability and shorter mean fixation duration are associated with greater perceived fakeness and a higher probability of incorrect assessments, thus providing evidence of affective information processing. These findings imply that users may fall for fake news automatically and without even noticing. This has direct implications for information systems (IS) research and practice as effective countermeasures against fake news must account for affective information processing.

Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely been attempted. In this study, we address this gap and show that deep learning architectures can successfully be applied to the problem of video-based detection of intake gestures. For this purpose, we collect and label video data of eating occasions using 360-degree video of 102 participants. Applying state-of-the-art approaches from video action recognition, our results show that (1) the best model achieves an $F_1$ score of 0.858, (2) appearance features contribute more than motion features, and (3) temporal context in form of multiple video frames is essential for top model performance., To be published in IEEE Journal of Biomedical and Health Informatics

Purpose Human lab experiments have become an established method in information systems research for investigating user behavior, perception and even neurophysiology. The purpose of this paper is to facilitate experimental research by providing a practical guide on how to implement and conduct lab experiments in the freely available experimental platform Brownie. Design/methodology/approach Laying the groundwork of the tutorial, the paper first provides a brief overview of common design considerations for lab experiments and a generic session framework. Building on the use case of the widely used trust game, the paper then covers the different stages involved in running an experimental session and maps the conceptual elements of the study design to the implementation of the experimental software. Findings The paper generates findings on how computerized lab experiments can be designed and implemented. Furthermore, it maps out the design considerations an experimenter may take into account when implementing an experiment and organizing it along a session structure (e.g. participant instructions, individual and group interaction, state and trait questionnaires). Originality/value The paper reduces barriers for researchers to engage in experiment implementation and replication by providing a step-by-step tutorial for the design and implementation of human lab experiments.

Internet auction sites frequently employ images as design elements on their websites in order to either induce a sense of community or competition among the bidders. In this paper, we investigate the impact of such affective images on bidding behavior in a controlled laboratory experiment during which participants' emotional processes are assessed through psychophysiological measurements. Immediately before placing a bid in a first-price sealed-bid auction, bidders are presented a) pictures of competitive sports scenes, b) pictures of families or children, or c) a blank screen. Participants place significantly lower bids when they were exposed to pictures that induce competition emotions as opposed to pictures that induce community emotions. This relationship is moderated by the bidders' emotion regulation strategy. In particular, we find that the more participants try to suppress their emotional responses to the presented images, the more they are affected in their bidding behavior. Our results entail valuable insights about the coherence of emotional stimuli on Internet auction marketplaces and customers' decisions. They also question recent marketing strategies by the market leader.

Cybersecurity is a growing concern for private individuals and professional entities. Reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind non-secure HCS behavior. However, there is limited conceptual work to guide researchers and practitioners in this regard. Against this backdrop, we investigate how the impact of time pressure on HCS behavior can be conceptualized within an integrative framework and which countermeasures can be used to reduce its negative impact. Altogether, we conducted 35 interviews with cybersecurity experts, non-security professionals, and private users. The results of our study shed light on the theoretical pathways through which time pressure can affect different types of security behaviors and identify a range of operational, human, technical, and physical countermeasures with important implications for research and practice.

While avatars are frequently employed as a user interface (UI) element for improving user experience in human-computer interaction, the current design of avatars is primarily dominated by non-Arabian cultures. To the best of our knowledge, no previous research or guidelines (based on empirical evidence) can be found for avatar design in the context of Arabian culture. Aiming to address this gap, we conducted an exploratory study to investigate how avatars can be designed for Arab users. Following a hybrid approach of deductive and inductive reasoning, we reviewed the literature on UI design for Arabian culture, (non-Arabian) avatar design in human-computer interaction research, and social response theory. Subsequently, we conducted 32 semi-structured interviews with Arabian culture experts, psychologists, and potential users. Based on thematic analysis of the interviews, we developed a set of six general guidelines for the design of avatars for users from Arabian culture. These six guidelines are expected to provide system or UI designers the ability to design and employ appropriate avatars that can promote Arab users’ experience and engagement.

In many platform-mediated services, people share resources in a way that involves social value beyond mere economic considerations. Considering peer-to-peer accommodation sharing, this paper links booking intentions to hosts’ user representation (UR). We consider how the most common UR artifacts facilitate sharing through social and economic value. Specifically, we differentiate artifacts that (1) convey personal information (e.g., self-descriptions), (2) are provided by exogenous sources (e.g., star ratings), and (3) conflate both of these informational properties (e.g., text reviews). We investigate these factors by means of an online experiment. Our results illustrate the dual property of text reviews and that booking intentions in co-usage sharing are driven by expected social and economic value to about equal extents.

Stock market prediction plays an important role in financial decision-making for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unlike previous work along this line of research, which typically applies bag-of-words to extract tens of thousands of features to build a prediction model, we propose a sentiment analysis-based approach for financial market prediction using news disclosures. Specifically, sentiment analysis is carried out in the pre-processing phase to extract sentiment-related features from financial news. Historical stock market data from the perspective of time series analysis is also included as an input feature. With the extracted features, we use a support vector machine (SVM) to build the prediction model, with its parameters optimised through particle swarm optimisation (PSO). Experimental results show that our proposed SVM and PSO-based model is able to obtain better results than a deep learning model in terms of time and accuracy. The results presented here are to date the best in the literature based on the financial news dataset tested. This excellent performance is attributed to the sentiment analysis done during the pre-processing stage, as it reduces the feature dimensions significantly.

Auction sites on the Internet frequently put bidders under time pressure or highlight the social competition that is inherent to auctions. Both aspects are believed to elicit an exciting shopping experience, which may culminate in auction fever. In two laboratory experiments, we investigate the process of auction fever in retail auctions and demonstrate when and how auction fever affects bidding behavior. In contrast to previous studies, we employ physiological measurements as an objective and continuous assessment of bidders’ arousal in addition to a subjective assessment of bidders’ emotions through psychometric scales. Moreover, we explicitly study the interaction of time pressure and social competition on arousal and bids. We find that bidders’ arousal is increased in high time pressure auctions and that this leads to higher bids in ascending auctions—but only when bidders compete with human opponents. Thus, social competition is the actual driver underlying the auction fever phenomenon. Furthermore, we show that the “joy of winning” is significantly stronger than the “frustration of losing” in ascending auctions. Finally, we discuss the implications of our findings for the design of retail auctions.

Emotion elicitation and classification have been performed on standardized stimuli sets, such as international affective picture systems and international affective digital sound. However, the literature which elicits and classifies emotions in a financial decision making context is scarce. In this paper, we present an evaluation to detect emotions of private investors through a controlled trading experiment. Subjects reported their level of rejoice and regret based on trading outcomes, and physiological measurements of skin conductance response and heart rate were obtained. To detect emotions, three labeling methods, namely binary, tri-, and tetrastate blended models were compared by means of C4.5, CART, and random forest algorithms, across different window lengths for heart rate. Taking moving window lengths of 2.5s prior to and 0.3s postevent (parasympathetic phase) led to the highest accuracies. Comparing labeling methods, accuracies were 67 for binary rejoice, 44 for a tristate, and 45 for a tetrastate blended emotion models. The CART yielded the highest accuracies.

With the proliferation of information technology and artificial intelligence in society, human users have started to engage in social interactions with computer agents. In this study, we conducted a laboratory experiment in which neurophysiological measurements were used to investigate the effect of computer agents on the affective processes and behavior of human negotiators. Participants engaged in alternating-offer bargaining over the partition of a pie with either human or computer counterparts and under different levels of urgency to reach an agreement. Overall, our data show that the subjects claimed significantly higher proportions for themselves when they made opening offers to computer agents than when bargaining with human counterparts, regardless of the degree of urgency in the negotiation. However, when the subjects responded to computer-issued offers the picture was more complex. Whereas under high-level urgency, the subjects were more likely to accept offers made by computer agents than by human counterparts, we observed the opposite effect for low-level urgency, where they were less likely to accept the offers of computer agents. In combination, these behavioral patterns lead to the use of computer agents yielding an increase in economic efficiency. Further, the subjects exhibited less emotionally charged behavior when facing computer agents than when facing human counterparts, as the intensity of affective processes was lower and the relationship between arousal and offer acceptance was observable only when the counterparts were human. The results of our study shed light on the potential benefits and intricacies of employing computer agents in electronic negotiations.

In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Through systematic simulation experiments, five different scenarios with varying proportions and types of providers and consumers were considered. Our results show that each type of players influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Our findings may have important implications for understanding the emergence of trust in the context of sharing economy transactions.

Traders and investors are aware that emotional processes can have material consequences on their financial decision performance. However, typical learning approaches for debiasing fail to overcome emotionally driven financial dispositions, mostly because of subjects' limited capacity for self-monitoring. Our research aims at improving decision makers' performance by (1) boosting their awareness to their emotional state and (2) improving their skills for effective emotion regulation. To that end, we designed and implemented a serious game-based NeuroIS tool that continuously displays the player's individual emotional state, via biofeedback, and adapts the difficulty of the decision environment to this emotional state. The design artifact was then evaluated in two laboratory experiments. Taken together, our study demonstrates how information systems design science research can contribute to improving financial decision making by integrating physiological data into information technology artifacts. Moreover,...

Stress is a major problem in the human society, impairing the well-being, health, performance, and productivity of many people worldwide. Most notably, people increasingly experience stress during human-computer interactions because of the ubiquity of and permanent connection to information and communication technologies. This phenomenon is referred to as technostress. Enterprise systems, designed to improve the productivity of organizations, frequently contribute to this technostress and thereby counteract their objective. Based on theoretical foundations and input from exploratory interviews and focus group discussions, the paper presents a design blueprint for stress-sensitive adaptive enterprise systems (SSAESes). A major characteristic of SSAESes is that bio-signals (e.g., heart rate or skin conductance) are integrated as real-time stress measures, with the goal that systems automatically adapt to the users’ stress levels, thereby improving human-computer interactions. Various design interventions on the individual, technological, and organizational levels promise to directly affect stressors or moderate the impact of stressors on important negative effects (e.g., health or performance). However, designing and deploying SSAESes pose significant challenges with respect to technical feasibility, social and ethical acceptability, as well as adoption and use. Considering these challenges, the paper proposes a 4-stage step-by-step implementation approach. With this Research Note on technostress in organizations, the authors seek to stimulate the discussion about a timely and important phenomenon, particularly from a design science research perspective.

Affective processes play an important role in determining human behavior in auctions. While previous research has shown that physiological measurements provide insights into these processes, it remains unclear which of the many features that can be computed from physiological data are particularly useful in predicting human behavior. Identifying these features is important for gaining a better understanding of affective processes in electronic auctions and for building biofeedback systems. In this study, we propose a new approach to identify physiological features for predicting auction behavior. We apply an Evolutionary Algorithm in combination with either the Multiple Linear Regression or Artificial Neural Network models to select physiological features and assess their predictive power. To test the approach, we use a unique dataset of participants' auction decisions and their synchronously recorded electrocardiography data. Our results show that the approach is able to identify subsets of physiological features that consistently outperform other physiological features.

This chapter describes cluster evaluation, description, and interpretation for evaluating player profiles based on log files available from a game server. Calculated variables were extracted from these logs in order to characterize players. Using circular statistics, we show how measures can be extracted that enable players to be characterized by the mean and standard deviation of the time that they interacted with the server. Feature selection was accomplished using a correlation study of variables extracted from the log data. This process favored a small number of the features, as judged by the results of clustering. The techniques are demonstrated based on a log file data set of the popular online game Minecraft. Automated clustering was able to suggest groups that Minecraft players fall into. Cluster evaluation, description, and interpretation techniques were applied to provide further insight into distinct behavioral characteristics, leading to a determination of the quality of clusters, using the Silhouette Width measure. We conclude by discussing how the techniques presented in this chapter can be applied in different areas of serious games analytics.

Advances in sensor technology and real-time analysis of neurophysiological data have enabled the use of live biofeedback in information systems and the development of neuro-adaptive information systems. In this article, we transfer this notion to the use of foreign neurophysiological data. We sketch out an experimental approach and research model for investigating the impact of such foreign data in a trust scenario. We argue that foreign live biofeedback may be a powerful means to establish social presence and thus trust among the parties. Moreover, we discuss controversies such technology is likely to raise and sketch out potential strategies for IS service providers in this regard.

The objective of this paper is to examine the possibilities and limitations of heart rate variability (HRV) as an indicator of emotional arousal for mobile applications which require online biofeedback. In contrast to offline classification, feature extraction for online applications sets other requirements to the window size in which data is analyzed as the delay between a change of a person's arousal level and the reaction of an application should be as short as possible. For this purpose we compare various HRV features in order to evaluate how far window size can be decreased to enable online arousal recognition. Using data from a study where high and low arousal were induced in a game scenario, HRV features are analyzed for their discriminatory power depending on the window size using Fisher's discriminant analysis. Moreover, we use these features to train an SVM based classifier. Results indicate that for some features it is possible to use ultra-short term window sizes, i.e. window sizes shorter than the 5 minute window which has traditionally been used for short term HRV analysis.

Compared to traditional channels, Internet transactions are intrinsically untrustworthy in nature. We investigate the impact of social identity and reciprocity on trusting and cooperative behavior in dynamic gift giving networks by means of an online laboratory experiment, with a main focus on value transfers among the users individually and directed towards the group. In this study, we display profile pictures and full names of the experiment participants in order to abrogate anonymity. Moreover, we provide the possibility for private peer-to-peer interaction, in contrast to mere contributions to the entire, undifferentiated group. We find indications for the efficacy of both dimensions as well as for an interaction effect. Our study has implications for the design of information systems where mutual trust between private users forms the basis for market interaction (e.g. ride-, car or apartment sharing platforms).