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While previous research established that culture plays an important role in technology adoption, there is only limited work on the role of cultural appropriateness in user interface design for users from a specific background. In this study, we focus on the case of avatar design as a user interface element for facilitating positive user experience. Building on the theoretical lenses of social response theory and the “Computers Are Social Actors” paradigm, we develop a research model to investigate how cultural appropriateness of avatar design is a vital driver for users’ trust. We evaluate our research model by means of an online experiment ( n = 313) in the context of online health advice for users from Saudi Arabia. The avatars differed in appearance (Arab, non-Arab), gender (male, female), and clothing (athletic, medical, everyday). Our results show that Arab avatars exhibited significantly higher cultural appropriateness than non-Arab avatars. Furthermore, participants were more inclined to select an Arab avatar (88.2%) that matched their gender (77.3%). Confirming the critical role of cultural appropriateness, our study demonstrates the importance of carefully considering the target audience in designing user interfaces.

UNSTRUCTURED Background: Nutrition in pregnancy is pivotal to optimising infant growth and maternal wellbeing. The factors affecting Indigenous people’s food and nutrition intake are complex, with a history of colonisation impacting the disproportionate effect of social determinants to this day. Literature regarding the dietary intake or priorities of Indigenous women in Australia is scarce with supportive, culturally appropriate resources developing for and with this group rare. Research suggests mHealth tools are effective in supporting health knowledge of Indigenous people and positive health behaviour changes when designed and developed with the expertise of Indigenous communities. Objectives: This study seeks to build the body of knowledge related to nutrition needs and priorities for Indigenous women in Australia during pregnancy and co-design an mHealth digital tool to support these nutrition needs. Methods: The Mums and Bubs Deadly Diets study recruits Indigenous women and healthcare professionals who support Indigenous women during pregnancy into two phases. Phase One (pre-design) utilises a mixed methods convergent design utilising a biographical questionnaire and social/focus groups to inform Phase Two (generative). Phase Two will use a participatory action research process during co-design workshops to iteratively develop the digital tool, the exact actions within a workshop will evolve according to the participant group decisions. Conclusions: This study is an iterative and adaptive research program that endeavours to develop real world, impactful resources to support the nutrition needs and priorities of pregnant Indigenous women in Australia. This comprehensive project requires a combination of methods and methodologies to ensure Indigenous voices are heard at each stage and in all aspects of research output. The development of an mHealth resource for this cohort will provide a necessary bridge where there is often a gap in access to nutrition resources for women in pregnancy in Indigenous communities.

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.

Nutrition interventions to support young adults are needed due to low diet quality. The aims were to explore the (1) circumstances and (2) barriers regarding dietary habits of the young adult users of the No Money No Time (NMNT) healthy eating website with the lowest diet quality scores. An online cross-sectional survey was conducted from August–September 2022 with a sample of NMNT users aged 18–35 years with low diet quality (defined as Healthy Eating Quiz score 0–38/73). The survey included demographics (e.g., gender), circumstances (6-item US Food Security Survey, Cooking and Food Skills Confidence Measures), and challenges and resources used in relation to healthy eating (open-responses). Theoretical thematic analysis was used to analyse open-response questions and derive main themes. The study sample (n = 108; 71.3% female, median age 28; 28.7% food insecure) had a mean (standard deviation) Cooking Skills score 70.2 (17.5)/98, and median (interquartile range) Food Skills score 96.0 (83.5–107.5)/133. The main challenges regarding healthy eating were (1) time and (2) cost, and the main resources to support healthy eating were (1) online resources (e.g., websites, Google) and (2) recipes. Findings identify possible targets for future interventions to support healthy eating in this vulnerable group (e.g., supporting cooking and food skills).

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 devel...

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

Competition and competitive contexts inherently contain elements that generate arousal (sympathetic activation of the autonomic system). In addition to the arousal experienced from competing against another person(s), other elements in competitive contexts, such as time pressure, audience effects, competing for scarce and/or valued items, etc., can create arousal. This chapter elaborates on how competition and competitive contexts can generate arousal, which then affects decision making. Simultaneously, the authors discuss how arousal that is independent of the competitive context can have similar decision-making effects. On one hand, arousal can facilitate and improve decision making. On the other hand, it can impair sound and rational decision making. This chapter gives an overview of the concept of competitive arousal by providing a review of what is known about the nature of competitive arousal, the factors that influence its emergence, moderators, and its effects on decision making. The authors conclude with several open questions as well as a general discussion of the theoretical and practical implications of competitive arousal in different decision making contexts.

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...

Time pressure, a common phenomenon in everyday workplace environments, is an important driver for non-secure cybersecurity behaviour in organisations. Under time pressure, users are more likely to rely upon fast, affect-driven decision making, increasing their susceptibility to make mistakes and justify non-secure workarounds. This contributes to the role of human error in cybersecurity and counteracts cybersecurity measures (CSMs) designed to protect organisations from threats and vulnerabilities. In this study, we report results from an online survey (N = 207), investigating how users perceive the effectiveness of CSMs for facilitating secure behaviour under time pressure. Understanding how users perceive the effectiveness of CSMs is important to inform the design and implementation of such measures in practice. We find that perceived CSM effectiveness differs greatly across measures. Thereby, users’ appreciation of incident severity and the general level of time pressure in their daily lives emerge as important antecedents. We discuss theoretical and practical implications for the design and implementation of CSMs.

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 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.

Online user representation (UR) is a cornerstone of platform-mediated interactions within the sharing economy. While the general usefulness of UR artifacts for facilitating online and offline interactions is widely acknowledged and understood, the underlying mechanisms and operating principles often require a more detailed analysis. In this chapter, we thus introduce a systematic framework grounded in signaling and social presence theory for analyzing UR artifacts for online platforms in general—and the sharing economy in particular. We apply our framework as a structural lens in a case study on user profiles on Airbnb, unveiling structural similarities and differences between the opposing market sides. We discuss our findings against the backdrop of emerging information systems research directions and suggest paths for future work on the sharing economy.

At this stage, empirical studies in the NeuroIS field have been conducted primarily in laboratory environments. However, the continuing advances in sensor technologies and software interfaces have created novel opportunities to explore the potential of NeuroIS not only in highly controlled lab environments but also in the wild. In this exploratory study, we focus particularly on the potential of conducting NeuroIS studies in remote home environments (NeuroIS@Home) by physically sending equipment (e.g., sensors) to the participant’s location and/or utilizing existing equipment in the participants’ environment (e.g., cameras, input devices). To explore the potential of NeuroIS@Home, we conducted an online expert survey with 16 respondents. We identify higher external/ecological validity of experimental results and the potential of scalability as the most promising opportunities, whereas the lack of control over environmental factors and data quality turned out to be the most severe challenges.

In a Dutch auction, an item is offered for sale at a set maximum price. The price is then gradually lowered over a fixed interval of time until a bid is made, securing the item for the bidder at the current price. Bidders must trade-off between certainty and price: bid early to secure the item and you pay a premium; bid later at a lower price but risk losing to another bidder. These properties of Dutch auctions provide new opportunities to study competitive decision-making in a group setting. We developed a novel computerised Dutch auction platform and conducted a set of experiments manipulating volatility (fixed vs varied number of items for sale) and price reduction interval rate (step-rate). Triplets of participants ($$N=66$$ N = 66 ) competed with hypothetical funds against each other. We report null effects of step-rate and volatility on bidding behaviour. We developed a novel adaptation of prospect theory to account for group bidding behaviour by balancing certainty and subjective expected utility. We show the model is sensitive to variation in auction starting price and can predict the associated changes in group bid prices that were observed in our data.

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.

Whilst filmmakers intuitively believe that the movie experience must be emotionally engaging, audience testing continues to follow traditional approaches involving questionnaires and focus groups w...

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.

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.

The information systems (IS) field contains a rich body of knowledge on approaches, methods, and frameworks that supports researchers in conducting design science research (DSR). It also contains some consensus about the key elements of DSR projects—such as problem identification, design, implementation, evaluation, and abstraction of design knowledge. Still, we lack any commonly accepted tools that address the needs of DSR scholars who seek to structure, manage, and present their projects. Indeed, DSR endeavors, which are often complex and multi-faceted in nature and involve various stakeholders (e.g., researchers, developers, practitioners, and others), require the support that such tools provide. Thus, to investigate the tools that DSR scholars actually need to effectively and efficiently perform their work, we conducted an open workshop with DSR scholars at the 2017 DESRIST conference in Karlsruhe, Germany, to debate 1) the general requirement categories of DSR tool support and 2) the more specific requirements. This paper reports on the results from this workshop. Specifically, we identify nine categories of requirements that fall into the three broad phases (pre-design, design, and post design) and that contribute to a software ecosystem for supporting DSR endeavors.

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.

False information such as “fake news” is widely believed to influence the opinions of individuals. So far, information systems (IS) literature is lacking a theoretical understanding of how users react and respond to fake news. In this study, we analyze drivers of cognitive and affective processing in terms of linguistic cues. For this purpose, we performed a NeuroIS experiment that involved N = 42 subjects with both eye tracking and heart rate measurements. We find that users spend more cognitive effort (more eye fixations) in assessing the veracity of fake news when it is characterized by better readability and less affective words. In addition, we find that fake news is more likely to trigger affective responses (lower heart rate variability) when it is characterized by a higher degree of analytic writing. Our findings contribute to IS theory by disentangling linguistic cues that help to explain how fake news is processed. The insights can aid researchers and practitioners in designing IS to better counter fake news.

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.

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.

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.

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.

Cybersecurity is a growing concern for private individuals and professional entities. Thereby, 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 insecure HCS behavior. However, as our review reveals, studies on the role of time pressure in HCS are scant and there is no framework that can inform researchers and practitioners on this matter. In this paper, we present a conceptual framework consisting of contexts, psychological constructs, and boundary conditions pertaining to the role time pressure plays on HCS behavior. The framework is also validated and extended by findings from semi-structured interviews of different stakeholder groups comprising of cybersecurity experts, professionals, and general users. The framework will serve as a guideline for future studies exploring different aspects of time pressure in cybersecurity contexts and also to identify potential countermeasures for the detrimental impact of time pressure on HCS behavior.

The application of neuroscience theories, methods, and tools holds great potential for the development of novel decision support systems. In this paper, we develop a theoretical framework for how NeuroIS may support the test screening process of filmmakers where decisions are made about what narrative material is shown to the audience, what sequence it is to be ordered, and what emotional value it must carry. While current methods for audience test screenings commonly rely on standardized questionnaires and focus groups, decision support systems may employ neuroscience tools as built-in functions to provide the filmmaker with novel insights into how their movie is ultimately perceived by the audience. Thereby, a key focus lies on the coherence between the emotional experience intended by the filmmaker and the emotional experience exhibited by the audience. Further, NeuroIS allows an evaluation of how the emotional experience to specific cinematic moments affects overall movie satisfaction.

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.