The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human–AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators’ technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology.

This study, introduces and evaluates different countermeasures using real-time eye-tracking data. The countermeasures detect when driver gaze deviates from the road for longer than a predetermined threshold and then redirect the driver's attention back to the road. The countermeasures include bimodal and trimodal alerts using combinations of auditory, tactile, and visual modalities. These countermeasures showcase the utility of adopting eye-tracking technologies in the context of driver monitoring and advanced driver's assistance systems. They enhance safety as a safeguard for the increased use of devices such as in-vehicle infotainment systems. Results show that countermeasures effectively redirect drivers’ attention to the road, with higher on-road gaze time. Additionally, bimodal alerts that include the visual modality are less effective at redirecting participants’ gaze on-road and result in poorer driving performance.

Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.

With the emergent focus on human-centered artificial intelligence (HCAI), research is required to understand the humanistic aspects of AI design, identify the mechanisms through which user concerns may be alleviated, thereby positively influencing AI adoption. To fill this void, we introduce “Situation Awareness” (SA) as a conceptual framework for considering human-AI interaction (HAII). We argue that SA is an appropriate and valuable theoretical lens through which to decompose and view HAII as hierarchical layers that allow for closer inquiry and discovery. Furthermore, we illustrate why the SA perspective is particularly relevant to the current need to understand HCAI by identifying three tensions inherent in AI design and explaining how an SA-oriented approach may help alleviate these tensions. We posit that users’ enactment of SA will mitigate some negative impacts of AI systems on user experience, improve human agency during AI system use, and promote more efficient and effective in-situ decision-making.

Design thinking is an iterative, human-centered approach to innovation. Its success rests on collaboration within a multidisciplinary project team going through cycles of divergent and convergent ideations. In these teams, nondesigners risk diminishing the divergent reach because they are generally reluctant to sketch, thus missing out on theambiguous, imprecise early conceptual divergent phases. We hypothesized that LEGO® could advantageously be a substitute to sketching. In this comparative study, 44 nondesigners randomly paired in 22 dyads did two conceptual ideations of healthcare landing pages, one using pen/paper (spontaneously writing words on sticky notes) and the other using LEGO, assessed through Torrance and Guilford frameworks for divergent thinking. Results show that LEGO interfaces gathered significantly higher divergent thinking scores because their concepts were significantly more elaborated. Furthermore, when using LEGO, teams who generated more elements were likely to also generate more ideas, more categories of ideas and more original ideas.

On the 10th anniversary of the NeuroIS field, we reflect on accomplishments but, more importantly, on the future of the field. This commentary presents our thoughts on a future NeuroIS research agenda with the potential for high impact societal contributions. Four key areas for future information systems (IS) research are: (1) IS design, (2) IS use, (3) emotion research, and (4) neuro-adaptive systems. We reflect on the challenges of each area and provide specific research questions that serve as important directions for advancing the NeuroIS field. The research agenda supports fellow researchers in planning, conducting, publishing, and reviewing high impact studies that leverage the potential of neuroscience knowledge and tools to further information systems research.

Practitioners in many fields of human-computer interaction are now using physiological data to measure different aspects of user experience. The dynamic nature of physiological data offers a continuous window to the users and allows a better understanding of their experience while interacting with a system. However, in order to be truly informative, physiological signals need to be closely linked to users’ behaviors and interaction states. This paper presents an analysis method that provides a direct visual interpretation of users’ physiological signals when interacting with an interface. The proposed physiological heatmap tool uses eyetracking data along with physiological signals to identify regions where users are experiencing different emotional and cognitive states with a higher frequency. The method was evaluated in an experiment with 44 participants. Results show that physiological heatmaps are able to identify emotionally significant regions within an interface better than standard gaze heatmaps. Applications of the method to different fields of HCI research are also discussed.

Information seeking activities have been broadly categorized into two types: exploratory and directed search. Likewise, product-related information is processed either by alternative or by attribute. The current literature does not specifically describe the effect of online information-seeking activities on the type of information processing employed by a consumer. The present study seeks to investigate this relationship, as well as the effect of repeat visits to a website as an antecedent of information seeking. An eye-tracking experiment was performed to test these relationships. Results indicate that participants with a greater proportion of time spent looking at directed-search elements had longer visual sweeps across the attributes of a song, indicating a greater degree of alternative-based processing. Additionally, repeat website visits resulted in a marginally higher proportion of time spent performing directed search. The implication of the findings in relation to website customization according to users’ information searching behavior is discussed.

In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners.

This research studies the influence of individual knowledge mastery of competency task performance of Enterprise Resource Planning (ERP) learners. The research design involved the assessment of participants' ERP competency, each of whom participated in four games of a computer-based simulation, ERPsim. ERP knowledge was assessed using a validated questionnaire, which included questions with different complexity levels. Results indicate that although reported student grade point average is not a predictor of ERP competency, ERP knowledge mastery (particularly complex knowledge) does predict ERP competence. While mastering basic ERP knowledge does not predict the competency of the participants, these results can provide useful guidelines with respect to teaching and assessment practices, as well as the development of ERP curricula. To effectively prepare learners to be able to perform in authentic learning contexts, instructors could emphasize the mastery of complex knowledge and consequently use complex knowledge test questions as a component of the instruction.

Design science has evolved as a major research paradigm in the information systems (IS) discipline, which aims to design innovative and useful IT artifacts, such as conceptual models and software systems. Despite the increasing attention paid to the cognitive and emotional mechanisms that underlie the perception of such artifacts, research that explores the neurobiological determinants of these mechanisms has only recently begun to emerge. The primary argument for the use of neurobiological approaches in IS design science research is that IT artifact design — and, ultimately, human-computer interaction in general — may significantly benefit from neuroscience theories, concepts, methods, and data. In particular, the consideration of neuroscience may improve IT artifacts' alignment with users' perceptual and information processing mechanisms, particularly the brain. Against this background, this article presents a taxonomy of application strategies for neuroscience in IS design science research. It describes three major areas of application and explains that conducting research in an area comes with a specific set of requirements (e.g., applicability, costs, accessibility, and knowledge relevant to planning and conducting a research project). Therefore, if an IS design science scholar decides to draw upon neuroscience, the taxonomy transparently explains possible working areas and corresponding requirements. The taxonomy is described based on example studies published in the IS literature and on contributions that appeared in outlets pertaining to related disciplines, such as affective computing and neuroergonomics. The article concludes that, if neuroscience is considered a valuable complement to the more traditional approaches, it has the potential to become a major reference discipline for IS design science research.

A survey involving 1,200 Canadian corporations engaged in e-commerce was carried out to analyze the influence of the company’s size, the type of offering and their e-commerce practices on purchasing and sales activities in international markets. The results indicate that not only does the use of electronic commerce explain much more of the variance in the level of international business than the size of the organization; but that certain e-commerce practices have no significant explanatory value and that larger firms do not appear to be able to better exploit e-commerce techniques than smaller firms.

Observations in industrial sectors indicate that companies that evolve in an industry in which a specific ERP system has been adopted by a number of members are more likely to adopt the same software. In this paper, we investigate two main effects influencing this diffusion pattern: the exposure of a firm in the network to its neighbours and the power of a firm within the network. To perform this analysis, we propose two models: a direct model that characterized the influence of immediate related ties, as well as an indirect model that characterized the influence of ties of ties. Network ties are here defined by interlock between board directorates. The statistical analysis of Canadian firm's data suggests that network ties, especially indirect exposure, influence the diffusion of ERP systems. The influence of direct and indirect exposure and firm power also appear to differ significantly from one ERP system to another.