Several reviews have been critical of the degree to which scales in industrial/organizational psychology and organizational behavior adequately reflect the content of their construct. One potential reason for that circumstance is a tendency for scholars to focus less on content validation than on other validation methods (e.g., establishing reliability, performing convergent, discriminant, and criterion-related validation, and examining factor structure). We provide clear evaluation criteria for 2 commonly used content validation approaches: Anderson and Gerbing (1991) and Hinkin and Tracey (1999). To create those guidelines, we gathered all new scales introduced in Journal of Applied Psychology, Academy of Management Journal, Personnel Psychology, and Organizational Behavior and Human Decision Processes from 2010 to 2016. We then subjected those 112 scales to Anderson and Gerbing's (1991) and Hinkin and Tracey's (1999) approaches using 6,240 participants from Amazon's Mechanical Turk with detailed, transparent, and replicable instructions. For both approaches, our results provide evaluation criteria for definitional correspondence-the degree to which a scale's items correspond to the construct's definition-and definitional distinctiveness-the degree to which a scale's items correspond more to the construct's definition than to the definitions of other orbiting constructs. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
With the vision of Industry 4.0 and cobots, working conditions in industrial settings are starting to change. We review related literature from the fields of human–robot interaction, work and organizational psychology, and sociology of work, as well as an exemplary project case study, and identify research gaps regarding the implications of cobots for work environments. We argue that we are in a transition phase from automation to actual collaboration with robots in manufacturing, and that this will open up a new problem space for investigations, in which a practice lens will be crucial. Based on this, we propose a research agenda for social practice and workplace studies to explore the sociotechnical environment of Industry 4.0 involving cobots at the individual, team, and organizational levels.
Flavien Armangeon, Thibaud Ehret, Enric Meinhardt-Llopis
et al.
Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.
Cyber-Physical Systems (CPS) play a critical role in modern industrial domains, including manufacturing, energy, transportation, and healthcare, where they enable automation, optimization, and real-time decision-making. Ensuring the robustness of these systems is paramount, as failures can have significant economic, operational, and safety consequences. This paper present findings from an industrial survey conducted in Wallonia, covering a wide range of sectors, to assess the current state of practice in CPS robustness. It investigates robustness from how it is understood and applied in relationship with requirements engineering, system design, test execution, failure modes, and available tools. It identifies key challenges and gaps between industry practices and state-of-the-art methodologies. Additionally, it compares our findings with similar industrial surveys from the literature.
Daiana Colledani, Claudio Barbaranelli, Egidio Robusto
et al.
As technology becomes ever-present in daily routines, the impact of technostress (TS) extends beyond the workplace, calling for a broader investigation of the construct. This study was aimed at developing a scale to measure TS in everyday life. A 68-item pool capturing key TS dimensions (TS creators) identified through literature review was administered to a large sample of respondents from the general population (N=602). A cross-validation approach employing exploratory and confirmatory factor analyses yielded a final 20-item scale assessing four main factors: techno-overload/invasion (T-OI), techno-complexity/uncertainty (T-CU), techno-insecurity/privacy (T-IP), and techno-relationships/isolation (T-RI). The scale, called Everyday Life TS Scale (ELTS), exhibited strong psychometric properties, including high reliability, convergent and discriminant validity, and invariance across gender and two age groups. Interestingly, latent mean differences emerged, with younger individuals and females reporting higher levels of T-OI, while older individuals and females reported higher levels of T-CU. Older respondents also showed higher levels of T-IP. Consistent with previous research, TS dimensions correlated positively with neuroticism and negatively with life satisfaction and well-being (psychological, social, and emotional). The ELTS uniquely contributes to the field by addressing TS in everyday contexts, beyond traditional workplace-focused measures. The ELTS offers a valuable tool for assessing TS in the general population beyond the workplace. Moreover, it can be a useful resource for facilitating a smoother integration of technology into daily life and promoting well-being.
Diana Domenichini, Sebastian Strauß, Sebastian Gombert
et al.
Abstract Science education aims to foster knowledge-in-use, which is supported by the integration of scientific ideas. To study knowledge integration effectively, network analysis provides a valuable tool for visualizing and understanding how ideas are connected. Successful knowledge integration requires following a learning progression that leads to increasingly sophisticated connections between ideas. However, traditional learning progression models have limitations, as they often fail to account for the nonlinear and individualized nature of learning. This study explores the potential of digital learning environments and AI techniques to address these limitations by enabling frequent, high-resolution data collection and analysis in order to uncover individual students’ learning trajectories at a high resolution. We analyze a case study of middle school students’ learning about energy to investigate patterns and variations in their learning trajectories. Additionally, we explore how different learning trajectories influence the development of knowledge-in-use, leading to either productive or unproductive learning outcomes. Our findings aim to guide instruction for teachers and instructional designers, providing insights on how to develop more effectively adaptive learning environments that support diverse student learning trajectories.
Xiaoyan Xie,1 Ting Sun,2 Yumei Wu,1 Liping Dong2 1Department of Hematology, Northern Jiangsu People’s Hospital, Yangzhou, 225001, People’s Republic of China; 2Department of Clinical Psychology, Northern Jiangsu People’s Hospital, Yangzhou, 225001, People’s Republic of ChinaCorrespondence: Yumei Wu, Department of hematology, Northern Jiangsu People’s Hospital, No. 98, Nantong West Road, Yangzhou, 225001, People’s Republic of China, Email 18952578625@163.com Liping Dong, Department of Clinical Psychology, Northern Jiangsu People’s Hospital, No. 98, Nantong West Road, Yangzhou, 225001, People’s Republic of China, Email Lipingdong001@163.comBackground: Psychological distress is common among young and middle-aged lymphoma patients. Psychological resilience and emotion regulation are key factors in coping with cancer, but their interrelationships remain unclear. Clinical psychologists play a crucial role in addressing psychological resilience and distress by providing therapeutic interventions that enhance coping mechanisms and emotion regulation. This study examines the mediating role of difficulties in emotion regulation between psychological resilience and psychological distress.Methods: A cross-sectional study was conducted among lymphoma patients at Northern Jiangsu People’s Hospital from January to December 2023. Psychological resilience, distress, and emotion regulation were assessed using standardized scales. SPSS 26.0 was used for descriptive statistics and spearman correlation analysis. PROCESS 4.0 was used to calculate the significance of the mediating effects of the variables.Results: Patients had a psychological resilience score of 5.72 ± 2.31, a difficulties in emotion regulation score of 95.22 ± 8.86, and a psychological distress score of 5.72 ± 2.31. Psychological resilience was negatively correlated with both difficulties in emotion regulation (r = − 0.28, P < 0.01) and psychological distress (r = − 0.31, P < 0.01), while difficulties in emotion regulation were positively correlated with distress (r = 0.29, P < 0.01). Mediation analysis confirmed that difficulties in emotion regulation fully mediated the link between resilience and distress (effect size = − 0.310, 95% CI: − 1.195, − 0.136).Conclusion: Difficulties in emotion regulation partially mediate the relationship between psychological resilience and distress. Enhancing resilience and improving emotion regulation may help alleviate distress, emphasizing the need for targeted psychological interventions in young and middle-aged lymphoma patients.Keywords: lymphoma, psychological resilience, psychological distress, emotion regulation, young and middle-aged
Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.
Annika Härenstam, Erik Berntson, Lisa Björk
et al.
The study aims to develop and psychometrically test an instrument to assess trust in public sector organizations. Although trust-based management is of interest in many municipalities in the Nordic countries, an instrument to assess trust adapted for this context is lacking. The present study complies with Luhmann’s system theory and commonly used instruments in this tradition. Data from 240 first-line managers (RR 75%) and 1,871 employees (RR 47%) from all departments in one Swedish municipality were analyzed with a multi-level approach. Out of 16 initial items presented to first-line managers and employees, 8 were retained after the psychometrical testing in both samples. Factor loadings at both within and between levels, and the multilevel homology when exploring convergent and criterion-related validity, showed a similar pattern of two distinct factors. Similar to other previously developed instruments, Supervisory Trust was one dimension. The second construct Organizational Trust included items on trust in politicians and administrative specialists, in addition to trust in top-level managers. The Public Sector Organizational Trust Inventory can be recommended for use in future research on trust at individual as well as group levels.
Xin-Yu Duan,1 Tao Sun,2 Feng Lu,1 Xiao-Jing Yang,1 Hong-Yan Yin,3 De-Pin Cao,1,* Shu-E Zhang1,* 1Department of Health Management, School of Health Management, Harbin Medical University, Harbin, People’s Republic of China; 2Department of Health Policy and Management, School of Public Health, Hang Zhou Normal University, Harbin, People’s Republic of China; 3Department of Humanities and Social Sciences, Harbin Medical University (Daqing), Daqing, Heilongjiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: De-Pin Cao; Shu-E Zhang, Department of Health Management, School of Health Management, Harbin Medical University, Harbin, 150081, People’s Republic of China, Tel +86 13351113008; +86 15104694354, Email caodp211@126.com; hydzhangshue@163.comBackground: Depressive symptoms and multimorbidity are global public health concerns, the relationship between the two variables remains unclear. This study was an intervention attempt through the lens of regional relational culture to identify and reduce adverse consequences of this relationship. We aimed to explore the prevalence of multimorbidity and depressive symptoms among older Chinese adults, the association between the two variables, and the underlying moderating mechanism.Methods: This study extracted data from the China Health Retirement Longitudinal Study (CHARLS) of 8356 older adults aged 60 years and older and analyzed the correlation between multimorbidity and depressive symptoms in this population using Stata 16.0. Moreover, the correlation between multimorbidity and depressive symptoms was verified using logistic regression analysis, and a hierarchical multiple regression analysis was used to test the existence of moderating effects between the two variables.Results: The prevalence of multimorbidity and depressive symptoms among seniors aged 60 years and older was 66.16 and 36.85%, respectively. Multimorbidity was positively associated with depressive symptoms (p< 0.001), and relationship satisfaction, social activity, and information isolation moderated this association (p < 0.05).Conclusion: Older adults with multimorbidity are more likely to develop depressive symptoms, and regional relational culture can play a moderating role between them. The government, as well as aging-related sectors, can reduce the risk of depressive symptoms by improving relationship satisfaction, increasing social activity, and decreasing information isolation among older adults.Keywords: depressive symptoms, multimorbidity, older adults, relationship satisfaction, social activity, information isolation
Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors