Mario Passalacqua

The rising integration of human-AI collaboration is transforming the manufacturing sector, altering human work and training requirements. High automation can boost performance but may impair human operators' ability to detect defects or respond quickly when they need to take manual control. As AI and human collaboration becomes more common, addressing these performance issues is essential. Effective training that enhances skills, cognitive abilities, and affective outcomes, alongside fostering… Voir plus

The rising integration of human-AI collaboration is transforming the manufacturing sector, altering human work and training requirements. High automation can boost performance but may impair human operators' ability to detect defects or respond quickly when they need to take manual control. As AI and human collaboration becomes more common, addressing these performance issues is essential. Effective training that enhances skills, cognitive abilities, and affective outcomes, alongside fostering motivation and engagement, is key to mitigating these challenges. Yet, most manufacturing training research has focused on technology's effectiveness rather than how training design impacts motivation and engagement, crucial for long-term training success. Our study investigated how AI training affects worker motivation, engagement, and skill development. We varied the automation level in training 102 participants for a quality control task and observed that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. In contrast, partial automation boosted motivation and engagement, preparing participants better for AI failures by building necessary skills. This suggests that including workers in decision-making, using AI to assist rather than replace human decision-making, leads to better outcomes. This strategy balances technological advances with human skill, motivation, and engagement enhancement. Implementing such training approaches in real-world manufacturing could improve technical, methodological, and personal skill development, though companies might struggle with the costs of training redesign and the need for continuous updates to match technological progress. Voir moins

The fourth industrial revolution (Industry 4.0) is characterized by strategies aimed towards process, product, and service improvement through technology interconnectivity, decision-making speed, and automation capacity. These strategies, in which artificial intelligence (AI) plays a central role, emphasize technological advancement to drastically improve performance-related factors in operational environments. However, their development follows a technocentric approach neglecting their… Voir plus

The fourth industrial revolution (Industry 4.0) is characterized by strategies aimed towards process, product, and service improvement through technology interconnectivity, decision-making speed, and automation capacity. These strategies, in which artificial intelligence (AI) plays a central role, emphasize technological advancement to drastically improve performance-related factors in operational environments. However, their development follows a technocentric approach neglecting their profound impacts on human work. For the anticipated benefits of technological advancement to materialize, it is necessary to consider social and organizational factors (e.g., trust calibration, work redesign) in addition to technical factors. In this chapter, we present HUMan-centred AI for Industry 5.0 (HUMAI5.0) as an evolution of the current industrial revolution. It is an innovative 6-step framework to support the development of AI systems that promote human long-term well-being, engagement, and system performance within complex socio-technical environments. HUMAI5.0 builds upon established human factors and ergonomics practices, organizational psychology, and our own experiences working with AI-based technologies. We applied this framework to two use cases in industrial context: error detection in manufacturing and quality control in aircraft maintenance. We were able to develop future work environments that leveraged the best abilities of both parties (human and AI) in a way respectful of the workers. Artifacts of this framework were also helpful to offer a common ground to various stakeholders to understand the potential impacts AI has in their workplace and how best to address it. Voir moins

The integration of Artificial Intelligence (AI) in manufacturing is shifting the focus of operators from manual labor to cognitive supervision roles. While this transition demands more engagement from operators, the less stimulating nature of monitoring tasks has, paradoxically, reduced operator involvement, consequently presenting new challenges in performance maintenance. Addressing this issue, our research adopted an iterative design science methodology to create a biocybernetic system that… Voir plus

The integration of Artificial Intelligence (AI) in manufacturing is shifting the focus of operators from manual labor to cognitive supervision roles. While this transition demands more engagement from operators, the less stimulating nature of monitoring tasks has, paradoxically, reduced operator involvement, consequently presenting new challenges in performance maintenance. Addressing this issue, our research adopted an iterative design science methodology to create a biocybernetic system that aims to enhance operator engagement in their evolving workplace. This system leverages physiological signals to intuitively display how much an operator’s engagement level deviates from an ideal state, ensuring operators stay aware of their psychophysiological state of engagement and can quickly adjust to any decreases in engagement. In this paper, we detail the 4-step process that led to the development of the first version of the system. Capitalizing on the physiological differences observed in manufacturing operators during “high” and “low” engagement scenarios, we defined a task-specific Optimal State Deviation Index (OSDI) formula. This formula enabled us to predict participants' engagement states with an 80.95 % success rate in our testing dataset. Voir moins

Eye-trackers provide continuous information on individuals’ gaze behavior. Due to the increasing popularity of eye- tracking in the information systems (IS) field, we reviewed how past research has used eye-tracking to inform future research. Accordingly, we conducted a literature review to describe the use of eye-tracking in IS research based on a sample of 113 empirical papers published since 2008 in IS journals and conference proceedings. Specifically, we examined the methodologies and… Voir plus

Eye-trackers provide continuous information on individuals’ gaze behavior. Due to the increasing popularity of eye- tracking in the information systems (IS) field, we reviewed how past research has used eye-tracking to inform future research. Accordingly, we conducted a literature review to describe the use of eye-tracking in IS research based on a sample of 113 empirical papers published since 2008 in IS journals and conference proceedings. Specifically, we examined the methodologies and experimental settings used in eye-tracking IS research and how eye-tracking can be used to inform the IS field. We found that IS research that used eye-tracking varies in its methodological and theoretical complexity. Research on pattern analysis shows promise since such research develops a broader range of analysis methodologies. The potential of eye-tracking remains unfulfilled in the IS field since past research has mostly focused on attention-related constructs and used fixation count metrics on desktop computers. We call for researchers to utilize eye-tracking more broadly in IS research by extending the type of metrics they use, the analyses they perform, and the constructs they investigate. Voir moins

Seuls 21 % des employés se considèrent engagés au travail. Le désengagement, générant une variété de résultats négatifs dont l'absentéisme et la perte de productivité, est encore plus problématique lorsque le travail est de nature répétitive. La gamification, c'est-à-dire l'intégration d'éléments de jeu dans les systèmes de travail, est une solution possible permettant d'accroître l'engagement et la motivation. Dans la présente étude, nous nous concentrons sur un de ses leviers, soit la… Voir plus

Seuls 21 % des employés se considèrent engagés au travail. Le désengagement, générant une variété de résultats négatifs dont l'absentéisme et la perte de productivité, est encore plus problématique lorsque le travail est de nature répétitive. La gamification, c'est-à-dire l'intégration d'éléments de jeu dans les systèmes de travail, est une solution possible permettant d'accroître l'engagement et la motivation. Dans la présente étude, nous nous concentrons sur un de ses leviers, soit la fixation d'objectifs. Nous soutenons que les objectifs assignés produisent une motivation extrinsèque qui n'améliore l'engagement et la performance qu'à court terme. Nous postulons aussi que les objectifs autodéfinis conduisent à une motivation autodéterminée, et donc, à un engagement et une performance accrue à long terme. Ainsi, un cadre expérimental impliquant cent deux participants effectuant une tâche répétitive dans une de trois conditions (objectif assigné, autodéfini, aucun objectif) a été réalisé. Les résultats ont montré que l'autonomie perçue et la performance étaient meilleures lorsque les objectifs étaient autodéfinis. Cependant, l'engagement demeure égal lorsque les objectifs sont autodéfinis et assignés. Ces résultats suggèrent que les objectifs autodéfinis ont un plus grand potentiel de générer des résultats positifs à long terme. Voir moins

L'objectif affiché de la transformation numérique opérée dans le cadre de l’Industrie 4.0 est de créer une entreprise apprenante, agile et capable de s'adapter à des conditions changeantes en utilisant de nouvelles technologies. Cependant, après une décennie de développements, les résultats restent très mitigés notamment en raison d’une approche trop technocentrée. Par opposition, l'Industrie 5.0 se définit aujourd’hui comme une approche centrée sur l'humain, incluant des considérations… Voir plus

L'objectif affiché de la transformation numérique opérée dans le cadre de l’Industrie 4.0 est de créer une entreprise apprenante, agile et capable de s'adapter à des conditions changeantes en utilisant de nouvelles technologies. Cependant, après une décennie de développements, les résultats restent très mitigés notamment en raison d’une approche trop technocentrée. Par opposition, l'Industrie 5.0 se définit aujourd’hui comme une approche centrée sur l'humain, incluant des considérations sociales, sociétales et environnementales. L’évolution vers de nouveaux modèles d’organisations agiles implique notamment une autonomie renforcée des équipes s’appuyant sur des prises de décision améliorées et accélérées. Cependant, la question de l’influence des technologies 4.0 sur les performances, la motivation, l'engagement et la charge cognitive des employés en production reste globalement en suspens. Cet article présente le cadre expérimental développé pour combler cette lacune via l’utilisation d’une Intelligence Artificielle couplée à de la Réalité Augmentée. Les choix, les orientations le protocole expérimental et la méthodologie retenus sont discutés avant les phases de tests/mise en œuvre opérationnelle du dispositif développé. Les résultats obtenus permettront de déterminer les principaux facteurs clés de succès/échec pour la mise en œuvre de systèmes couplant IA et RA permettant d’obtenir l’adhésion et l’engagement des équipes pour une autonomie renforcée. Voir moins

Research within AI-based Industry 4.0 (I4.0) work systems has predominantly focused on technical and process performance, while human and psychosocial factors are rarely examined. These factors must be considered to design human-centred systems that cultivate sustainable human-AI interaction, i.e., human-AI interaction that promotes long-term well-being, engagement, and performance. The European Commission has brought forward a new vision of I4.0 called Industry 5.0, where well-being and… Voir plus

Research within AI-based Industry 4.0 (I4.0) work systems has predominantly focused on technical and process performance, while human and psychosocial factors are rarely examined. These factors must be considered to design human-centred systems that cultivate sustainable human-AI interaction, i.e., human-AI interaction that promotes long-term well-being, engagement, and performance. The European Commission has brought forward a new vision of I4.0 called Industry 5.0, where well-being and technological advancement are jointly considered, thus overcoming the weaknesses of I4.0. To move forward with Industry 5.0, it is necessary to consolidate our knowledge of human-technology interaction within I4.0. This systematic review aims to uncover the antecedents and consequences of human and psychosocial factors within AI-based I4.0 systems, with an end goal of providing guidelines for the sustainable design, implementation, and use of these systems. This protocol presents the background and the methodology behind our review, as well as preliminary results and expected contributions. Voir moins

Information system (IS) gamification has been successful in many contexts. Yet, research has shown gamification’s success to vary between individuals. In this paper, we compare personalized versus non-personalized gamification in a warehouse management setting. We devised a 26-participant within-subject experiment in which we programmed goal setting and feedback gamification elements into a wearable warehouse management system to evaluate the effectiveness of personalized gamification in terms… Voir plus

Information system (IS) gamification has been successful in many contexts. Yet, research has shown gamification’s success to vary between individuals. In this paper, we compare personalized versus non-personalized gamification in a warehouse management setting. We devised a 26-participant within-subject experiment in which we programmed goal setting and feedback gamification elements into a wearable warehouse management system to evaluate the effectiveness of personalized gamification in terms of user performance. We examined the extent to which personalized gamification succeeded by categorizing participants into one of six user types through the HEXAD scale and then evaluating their performance time and errors across user types and conditions. We found that personalized gamification is more effective than non-personalized gamification. We present and discuss the motivational mechanisms through which personalized gamification can be more effective. Voir moins

The gamification of information systems has seen success in a variety of contexts. However, research has shown that the degree to which gamification is successful varies between individuals. The current paper evaluates the effectiveness of personalized gamification in a warehouse management context. Additionally, this paper explores why personalized gamification can be more successful than non-personalized gamification. Twenty-six subjects participated in a within-subject laboratory experiment… Voir plus

The gamification of information systems has seen success in a variety of contexts. However, research has shown that the degree to which gamification is successful varies between individuals. The current paper evaluates the effectiveness of personalized gamification in a warehouse management context. Additionally, this paper explores why personalized gamification can be more successful than non-personalized gamification. Twenty-six subjects participated in a within-subject laboratory experiment in which goal setting and feedback game elements were integrated into a wearable management information system to examine their effect on user performance in a warehouse picking task. The effectiveness of personalized gamification was evaluated by categorizing participants into user types using the HEXAD model and examining performance across conditions and user types. Results show that user type significantly affects the relationship between game elements and user performance. This paper takes a step forward in exploring the motivational mechanisms that explain the efficacy of personalized gamification. Voir moins

Purpose
In a warehouse setting, where hourly workers performing manual tasks account for more than half of total warehouse expenditure, a lack of employee engagement has been directly linked to company performance. In this article, the authors present a laboratory experiment in which two gamification elements, goal setting and feedback, are implemented in a wearable warehouse management system (WMS) interface to examine their effect on user engagement and performance in an item picking task.… Voir plus

Purpose
In a warehouse setting, where hourly workers performing manual tasks account for more than half of total warehouse expenditure, a lack of employee engagement has been directly linked to company performance. In this article, the authors present a laboratory experiment in which two gamification elements, goal setting and feedback, are implemented in a wearable warehouse management system (WMS) interface to examine their effect on user engagement and performance in an item picking task. Both implicit (neurophysiological) and explicit (self-reported) measures of engagement are used, allowing for a richer understanding of the user's perceived and physiological state.

Design/methodology/approach
This experiment uses a within-subject design. Two experimental factors, goals and feedback, are manipulated, leading to three conditions: no gamification condition, self-set goals and feedback and assigned goals and feedback. Twenty-one subjects participated (mean age = 24.2, SD = 2.2).

Findings
This article demonstrates that gamification can successfully increase employee engagement, at least in the short-term. The integration of self-set goals and feedback game elements has the greatest potential to generate long-term intrinsic motivation and meaningful engagement, leading to greater employee engagement and performance.

Originality/value
This article explores the underlying effects of gamification through two of the most prominent motivational theories (self-determination theory [SDT] and goal-setting theory) and one of the leading employee engagement models (job demands-resource model [JD-R] model). This provides a theory-rich interpretation of the data, which allows to uncover the motivational pathways by which gamification affects engagement and performance. Voir moins

The purpose of video game tutorials is to help players easily understand new game mechanics and thereby facilitate chances of early engagement with the main contents of one’s game. The mobile game market (i.e., phones and tablets) faces important retention issues caused by a high number of players who abandon games permanently within 24 h of downloading them. A laboratory experiment with 40 players tested how tutorial presence and player expertise impact on users’ psychophysiological states and… Voir plus

The purpose of video game tutorials is to help players easily understand new game mechanics and thereby facilitate chances of early engagement with the main contents of one’s game. The mobile game market (i.e., phones and tablets) faces important retention issues caused by a high number of players who abandon games permanently within 24 h of downloading them. A laboratory experiment with 40 players tested how tutorial presence and player expertise impact on users’ psychophysiological states and continuous-use intentions (CUIs). The results suggest that in a simple game context, tutorials have a positive impact on non-expert players’ perceived state of flow and have no effect on expert players’ perceived flow. The results also suggest that flow has a positive impact on CUIs for both experts and non-experts. The theoretical contributions and managerial implications of these results are discussed Voir moins

Engagement, or rather lack thereof has become a major issue because of its negative impact on productivity. Recently, gamification has successfully been implemented into corporate technological interfaces to increase the engagement of employees. This paper proposes a theory-driven experiment that examines the impact a gamified interface has on engagement and performance of workers in a warehouse-management task. Specifically, the experiment proposed in this paper compares how the integration of… Voir plus

Engagement, or rather lack thereof has become a major issue because of its negative impact on productivity. Recently, gamification has successfully been implemented into corporate technological interfaces to increase the engagement of employees. This paper proposes a theory-driven experiment that examines the impact a gamified interface has on engagement and performance of workers in a warehouse-management task. Specifically, the experiment proposed in this paper compares how the integration of two different types of goal-setting (self-set goals or assigned goals) into a warehouse-employee interface will affect engagement and performance. Voir moins