Associate Professor Marc Adam

Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees¿ flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task¿an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, self-reported flow was positively associated with both perceived and objective task performance, while PNS activation was not related to either performance measure. Lastly, we found no relationship between PNS activation (measured by HF-HRV) and self-reported flow, contributing to an important debate in the NeuroIS literature on whether physiological evidence constitutes an alternative or a complement to self-reports. Overall, our findings indicate that frequent interruptions are not harmful per se. Rather, considering content relevance is critical for a more comprehensive understanding of the effects on self-reported flow, its dimensions, and the underlying physiology.

Purpose: A growing body of research has identified time pressure as a key driver of cybersecurity (CS) risks and vulnerabilities. To strengthen CS, organizations use CS documents (e.g. best practices, guidelines and policies) intended to strengthen CS. The purpose of this paper is to provide an overview of how specifically time pressure is addressed by CS documents. Design/methodology/approach: The authors conducted a systematic search for CS documents followed by a content analysis of the identified documents. First, the authors carried out a systematic Web search and identified 92 formal and informal CS documents (e.g. security policies, procedures, guidelines, manuals and best practices). Second, they systematically analyzed the resulting documents (n = 92), using a structured approach of data familiarization and low-/high-level coding for the identification and interpretation of themes. Based on this analysis, the authors formulated a conceptual framework that captures the sources and effects of time pressure along the themes of industry, operations and users. Findings: The authors developed a conceptual framework that outlines the role of time pressure for the CS industry, threats and operations. This provides a shared frame of reference for researchers and practitioners to understand the antecedents and consequences of time pressure in the organizational CS context. Research limitations/implications: While the analyzed documents acknowledge time pressure as an important factor for CS, the documents provide limited information on how to respond to these concerns. Future research could, hence, consult with CS experts and policymakers to inform the development of effective guidelines and policies on how to address time pressure in the identified areas. A dedicated analysis within each area will allow to investigate the corresponding aspects of time pressure in-depth along with a consideration for targeted guidelines and policies. Last, note that a differentiation between CS document types (e.g. formal vs informal and global vs regional) was beyond the scope of this paper and may be investigated by future work. Originality/value: This study makes three main contributions to the CS literature. First, this study broadens the understanding of the role of time pressure in CS to consider the organizational perspective along the themes of industry, threats and operations. Second, this study provides the first comprehensive assessment of how organizations address time pressure through CS documents, and how this compares to existing research in academic literature. Third, by developing a conceptual framework, this study provides a shared frame of reference for researchers and practitioners to further develop CS documents that consider time pressure¿s role in secure behavior.

The expansion of technostress research in the organisational and private IS usage contexts has generated substantial theoretical and empirical insights into the relationship between technostress creators and psychological and behavioural outcomes. However, we observe empirical inconsistencies in terms of effect sizes and conceptual inconsistencies regarding the aggregated and disaggregated treatment of technostress creators. Against this background, we argue that a fine-grained estimation and comparison of effect size strengths of technostress creators on outcomes can provide clarity on these essential matters. Using the Hunter and Schmidt method, we integrated and synthesised empirical data from 102 articles, encompassing 113 independent studies with a total of 49,955 observations. Our analysis offers four important contributions to the technostress literature. First, it confirms that technostress is meaningful in terms of its detrimental impact on both psychological and behavioural outcomes. Second, the results provide accurate effect size estimates for technostress creators on different outcomes in organisational and private usage contexts. Third, the results reveal that psychological outcomes are more immediate than behavioural outcomes. Fourth, the findings suggest that in certain contexts, a disaggregated account of technostress creators can reveal meaningful empirical information.

2D images can be used to capture food intake data in nutrition studies. Estimates of food volume from these images are required for nutrient analysis. Although 3D image capture is possible, it is not commonplace. Additionally, nutrition studies often require multiple food images taken by non-expert users, typically collected using mobile phones, due to their convenience. Current 2D image to 3D volume approaches are restricted by the need for prescribed camera placement, image metadata analysis and/or significant computational resources. A new method is presented combining 2D image capture and automated 3D scene projection with manual placement and resizing of wire mesh objects. 2D images, with a reference object, are taken on low specification mobile phones. 3D scene projection is calculated by twinning a cuboid in 3D space to the reference object in the 2D image. A manually selected 3D wire mesh object is then positioned over the target food item and manually transformed to improve accuracy. The virtual wire mesh object is then projected into the 3D scene and the volume of the target food item calculated. The whole process is computationally light and runs in real-time as an app on a standard Apple iPad. Based on a user study with 60 participants, experimental evaluations of volume estimates over regular shape and ground truth food items demonstrate that this approach provides acceptable accuracy. We demonstrate that the accuracy of estimates can be increased by combining multiple independent estimates.

User interfaces often utilise imagery of pristine natural environments, even if the system¿s purpose and context are unrelated to nature. In this paper, we build on evolutionary psychology to develop a theoretical model for the influence of nature imagery on user perceptions of trust, visual aesthetics, and purchase intentions in a corporate sales setting. We evaluate our model by means of an online experiment (n = 408) using a website with different configurations of nature imagery. The results provide support for our theoretical model and hence confirm a positive influence of nature presence, that is, the extent to which the website allows a user to experience the natural environment as being present, on trust, visual aesthetics, and purchase intentions. Thereby, user perceptions of nature presence are specifically linked to nature imagery depicting water as well as vegetation. This study furthers our understanding of how the environmental context of on-site imagery can have subtle information processing benefits for users. For practitioners this study offers insight to the types of imagery that could be utilised more effectively in corporate interface designs.

Due to global advances in technology, image-based food record methods have emerged as an alternative to traditional assessment methods. The use of image-based food records in low and lower-middle income countries such as Tanzania is limited, with countries still using traditional methods. The current study aimed to determine the feasibility of using a new voice and image-based dietary assessment system (VISIDA) in Dar es Salaam, Tanzania. This mixed-method study recruited 18 nutritionists as participants who collected image-based records of food and drinks they consumed using the VISIDA smartphone app. Participants viewed an online demonstration of the VISIDA web platform and the analysis process for intake data collected using the VISIDA app. Then, participants completed an online survey and were interviewed about the VISIDA app and web platform for food and nutrient intake analysis. The method was reported as being acceptable and was found to be easy to use, although technical challenges were experienced by some participants. Most participants indicated a willingness to use the VISIDA app again for one week or longer and were interested in using the VISIDA system in their current role. Participants acknowledged that the VISIDA web platform would simplify some aspects of their current job. Image-based food records could potentially be used in Tanzania to improve the assessment of dietary intake by nutritionists in urban areas. Participants recommended adding sound-on notifications, using the VISIDA app in both Apple and Android phones, enabling installation from the app store, and improving the quality of the fiducial markers.

Diet quality is influenced by demographics and can change over time. This study aimed to (1) compare diet quality among adolescents/adults who completed the online Healthy Eating Quiz (HEQ) by demographic characteristics, and (2) to evaluate change in score over time for repeat completers. HEQ data collected between July 2016 and May 2022 were analysed, including demographics (age, gender, vegetarian status, socio-economic status, number of people main meals are shared with, country), and diet quality calculated using the Australian Recommended Food Score (ARFS) (range 0¿73) for respondents aged = 16 years. Differences in ARFS by demographic characteristics and change in score over time, adjusted for age, gender and vegetarian status, were tested by linear regression. The participants (n = 176,075) were predominantly female (70.4%), Australian (62.8%), and aged 18¿24 years (27.7%), with 4.0% (n = 7087) repeat completers. Mean ± SD ARFS was 33.9 ± 9.4/73. Results indicate that ARFS was significantly lower among males and significantly higher with increasing age group, higher socio-economic status, in vegetarians, those who shared main meals with others, and those living in Australia (p-values < 0.001). Mean change in ARFS over time (2.3 ± 6.9) was significantly higher for those with lower baseline scores (p < 0.001). Publicly available, brief dietary assessment tools have the potential to improve diet quality at the population level.

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.

Recent research has employed deep learning to detect intake gestures from inertial sensor and video camera data. However, the fusion of these modalities has not been attempted. The present research explores the potential of fusing the outputs of two individual deep learning inertial and video intake gesture detection models (i.e., score-level and decision-level fusion) using the test sets from two publicly available multimodal datasets: (1) OREBA-DIS recorded from 100 participants while consuming food served in discrete portions and (2) OREBA-SHA recorded from 102 participants while consuming a communal dish. We first assess the potential of fusion by contrasting the performance of the individual models in intake gesture detection. The assessment shows that fusing the outputs of individual models is more promising on the OREBA-DIS dataset. Subsequently, we conduct experiments using different score-level and decision-level fusion approaches. Our results from fusion show that the score-level fusion approach of max score model performs best of all considered fusion approaches. On the OREBA-DIS dataset, the max score fusion approach (F1 = 0.871) outperforms both individual video (F1 = 0.855) and inertial (F1 = 0.806) models. However, on the OREBA-SHA dataset, the max score fusion approach (F1 = 0.873) fails to outperform the individual inertial model (F1 = 0.895). Pairwise comparisons using bootstrapped samples confirm the statistical significance of these differences in model performance (p&#x003C;.001).

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.

Experiments in the field of behavioral economics often require repeated matching of participants to groups over multiple periods. Perfect stranger matching requires that no two participants interact more than once during the experiment. Computing a sequence of perfect stranger matches is an NP-hard problem that has received little attention in experimental economics literature beyond brute-force approaches. This work provides a problem definition and an algorithm for perfect stranger matching that outperforms existing approaches in the field of experimental economics in terms of problem size and number of found matches.

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.

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.

© 2013 IEEE. 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 2.5 s prior to and 0.3 s 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.

Consumers interact with reputation signals such as star ratings in many online environments. Reputation portability has been proposed as an emerging application to leverage said signals across platform boundaries. While first research demonstrates the general trust-building potential of portable reputation, the cognitive and economic effects of simultaneously available signals from different sources - especially when the information is conflicting - have thus far received only little research attention. In this study, we address the gap, using a multi-method approach. First, we assess the impact of rating discrepancy on consumer purchase intentions by means of an online experiment. We find that imported ratings are a driver of purchase intentions on digital platforms, but, also that a higher rating discrepancy is detrimental. Second, we propose a study design to analyze the underlying cognitive processes that may contribute to explain consumers' decision making in the presence of discrepant signals using eye-tracking.

User experience designers often employ natural landscapes as background imagery in e-commerce user interfaces (UI). However, at this stage, there is only limited work on how nature imagery in UI design affects user perception and behaviour. In this paper, we present a qualitative study involving semi-structured interviews into the use of nature imagery in UI design. Our study builds on theories in environmental psychology and seeks to develop a theoretical framework for the role of nature imagery in user perception and behaviour. Further, building on the expertise of user experience practitioners and end users, we seek to develop theoretically-grounded design guidelines for the capture, selection, and integration of nature imagery in e-commerce UI designs.

Management decisions are taken by human beings, not by robots. Consequently, management decisions, and of course also the respective managers, are affected by emotions. Thus, they rely on accurate emotional processing. Research on decision making has shown that individuals with high emotion regulation capabilities perform better in taking effective decisions. Managers perpetually have to take rapid decisions in fast-paced environments, between the poles of diverse interests and motives of colleagues, customers, partners, and rivals. Sophisticated management is the key to any business. Therefore, we argue that IS research should build on the advances in cognitive neuroscience and harness the potential of NeuroIS tools in the field of management support. In this paper, we propose a conceptual framework and taxonomy for how NeuroIS tools may support managers in taking effective decisions by firstly improving their emotion regulation capabilities and, secondly, providing them with real-time feedback and decision support based on physiological measurements. Based on the framework, we outline a specific application for how emotions can affect decision making in the dynamic process of negotiations and for how NeuroIS research can contribute to a better understanding of the underlying visceral processes.

Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? 'Learning by doing' sounds like a straightforward approach. But how can one perform 'learning by doing' when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner's heart rate data. More specifically, depending on the learner's ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels.

Information systems have revolutionized the nature of markets. Traditionally, markets inherently comprised the strategic interaction of human traders only. Nowadays, however, automated trading agents are responsible for at least 60% of the US trading volume on financial stock markets. In this respect, financial markets of the 21st century are different to markets of previous centuries. Fuelled by discussions on their possible risks, there is a need for research on the effects of automated trading agents on market efficiency and on human traders. In order to systematically investigate these issues, we introduce a market framework for human-computer interaction. This framework is then applied in a case study on a financial market scenario. In particular, we plan to conduct a NeuroIS experiment in which we analyze overall market efficiency as well as the trading behavior and emotional responses of human traders when they interact with computerized trading agents.

Although the importance of emotions in economic decision-making is being widely accepted, it is still unclear how and to what extent emotional processing affects economic behavior. In this paper, we propose a new methodological framework for behavioral IS studies that we call physioeconomics, by which it is possible to measure psychophysiological correlates of human emotions during controlled laboratory experiments. In contrast to brain imaging technologies as in NeuroIS, the analysis of autonomic physiological parameters can be achieved with a comparatively low amount of measurement overhead and, thus, allows collecting empirical data for much larger sample sizes. Therefore, this approach is particularly qualified for the study of emotions in electronic markets. In order to show that physioeconomics is able to provide insightful results we apply this method in an exemplary auction experiment in which we compare bidders' arousal throughout the bidding process and in response to winning or losing.