Hasil untuk "Industrial psychology"

M. D. Dunnette

Michael Muthukrishna, A. Bell, J. Henrich et al.

In this article, we present a tool and a method for measuring the psychological and cultural distance between societies and creating a distance scale with any population as the point of comparison. Because psychological data are dominated by samples drawn from Western, educated, industrialized, rich, and democratic (WEIRD) nations, and overwhelmingly, the United States, we focused on distance from the United States. We also present distance from China, the country with the largest population and second largest economy, which is a common cultural comparison. We applied the fixation index (FST), a meaningful statistic in evolutionary theory, to the World Values Survey of cultural beliefs and behaviors. As the extreme WEIRDness of the literature begins to dissolve, our tool will become more useful for designing, planning, and justifying a wide range of comparative psychological projects. Our code and accompanying online application allow for comparisons between any two countries. Analyses of regional diversity reveal the relative homogeneity of the United States. Cultural distance predicts various psychological outcomes.

C. Rudolph, B. Allan, Malissa A. Clark et al.

Abstract Pandemics have historically shaped the world of work in various ways. With COVID-19 presenting as a global pandemic, there is much speculation about the implications of this crisis for the future of work and for people working in organizations. In this article, we discuss 10 of the most relevant research and practice topics in the field of industrial and organizational psychology that will likely be strongly influenced by COVID-19. For each of these topics, the pandemic crisis is creating new work-related challenges, but it is also presenting various opportunities. The topics discussed herein include occupational health and safety, work–family issues, telecommuting, virtual teamwork, job insecurity, precarious work, leadership, human resources policy, the aging workforce, and careers. This article sets the stage for further discussion of various ways in which I-O psychology research and practice can address the issues that COVID-19 creates for work and organizational processes that are affecting workers now and will shape the future of work and organizations in both the short and long term. This article concludes by inviting I-O psychology researchers and practitioners to address the challenges and opportunities of COVID-19 head-on by proactively adapting the work that we do in support of workers, organizations, and society as a whole.

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Clara Kühner, J. Hüffmeier, Hannes Zacher

Abstract Humanity faces an unprecedented challenge in the necessity to rapidly change behaviors across various life domains to address multiple environmental crises, such as climate change, pollution, and biodiversity loss. This includes the behavior of individuals at work and within organizations. Industrial and organizational (I-O) psychology is uniquely positioned to provide evidence-based recommendations for changing organizational decision-making and behavior toward greater environmental sustainability. Although a substantial body of research on this topic has emerged over the past decade, the discipline has yet to realize its full potential because the topic is currently not prioritized and the practical and societal impact of previous research is limited. This article aims to propel research on environmental sustainability at work forward. To do so, it (a) outlines the interconnections between organizations and environmental sustainability; (b) portrays previous research efforts on environmental sustainability at work, resulting in an integrative conceptual framework across micro, meso, macro, and magno levels; and (c) provides actionable recommendations for high-impact future I-O psychology research and practice related to environmental sustainability. Following an “impact-first” rationale, we identified 10 areas for future research across the four levels of the conceptual framework. For each area, we present relevant theoretical perspectives, methodological approaches, and connections to related disciplines. Finally, we provide suggestions for effective science–practice transfer. Overall, the article seeks to spark discussion on this crucial topic within the community and to inspire I-O psychology researchers and practitioners to contribute to environmental sustainability.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Sergi Blanco-Cuaresma

This study explores the use of Large Language Models (LLMs) to analyze text comments from Reddit users, aiming to achieve two primary objectives: firstly, to pinpoint critical excerpts that support a predefined psychological assessment of suicidal risk; and secondly, to summarize the material to substantiate the preassigned suicidal risk level. The work is circumscribed to the use of "open-source" LLMs that can be run locally, thereby enhancing data privacy. Furthermore, it prioritizes models with low computational requirements, making it accessible to both individuals and institutions operating on limited computing budgets. The implemented strategy only relies on a carefully crafted prompt and a grammar to guide the LLM's text completion. Despite its simplicity, the evaluation metrics show outstanding results, making it a valuable privacy-focused and cost-effective approach. This work is part of the Computational Linguistics and Clinical Psychology (CLPsych) 2024 shared task.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Narendra Javadekar, Archana Javadekar, Deepa Thakur

Mental illnesses have a significant impact on the lives of people not only because of their morbidity but also because of their noticeable impact on economic wellbeing. Out-of-pocket expenditure for mental healthcare services is significant in India and may even lead to impoverishment of the families. The present paper states that Health Technology Assessment (HTA) is necessary for mental healthcare primarily because of its rising cost and competing interests in government decisions and prioritization. HTA does a systematic evaluation of the consequences of using health technology. HTA will provide information to decision makers to develop and implement safer, cost-effective, and efficient policies at the individual and government levels. Appropriate guidance regarding the cost-effectiveness of mental health interventions will help to serve the purpose of providing transparent reports in the context of limited budgets.

Ying Shi, I-Jun Chen, Mengping Yang et al.

An individual’s social adaptation is affected by their early maladaptive schemas. Previous studies have shown that early maladaptive schemas may be intergenerationally transmitted in families. It is important to explore the intergenerational effect of early maladaptive schemas on adolescents’ social adaptation, as they are in a critical period of growth and development. In this study, a cross-sectional design and questionnaire survey were used to collect data to explore the intergenerational influence of early maladaptive schemas in families and their relationship with adolescents’ social adaptation. The participants were 201 adolescents aged 12 to 16 years and their primary caregivers (father or mother), of whom 125 (62.2%) were boys and 76 (37.8%) were girls. There were 70 fathers (34.8%) and 131 mothers (65.2%). Chinese adolescents and their primary caregivers were surveyed using paired questionnaires, and the Young Schema Questionnaire (short form) and Adolescent Social Adaptation Scale were completed. The results show that adolescents’ early maladaptive schema plays an intermediary role between parents’ early maladaptive schema and adolescents’ social adaptation. Parental mistrust/abuse and insufficient self-control schemas affected adolescents’ social adaptation through the mediating effect of their corresponding schemas. Our results reveal the negative impact path of parents’ early maladaptive schemas on adolescents’ social adaptation and provide a new direction for the clinical practice of adolescent family therapy.

Ma C, Zhang W, Da S et al.

Chenlu Ma,1 Wen Zhang,1 Shu Da,2 Huan Zhang,3 Xichao Zhang1 1Faculty of Psychology, Beijing Normal University, Beijing, 100875, People’s Republic of China; 2School of Psychology, Nanjing Normal University, Nanjing, 210024, People’s Republic of China; 3Academy of Global Innovation & Governance, University of International Business and Economics, Beijing, 100029, People’s Republic of ChinaCorrespondence: Xichao Zhang, Faculty of Psychology, Beijing Normal University, No. 19 XinJieKouWai Street, HaiDian District, Beijing, 100875, People’s Republic of China, Email xchzhang@bnu.edu.cnPurpose: Environmental uncertainty has reached unprecedented levels in recent years. While there is substantial knowledge about the connection between environmental uncertainty and organizational outcomes, limited attention has been devoted to investigating its impact on employees’ depression and anxiety symptoms. Grounded in job demands-resources theory, this study aims to explore the relationship between environmental uncertainty and employees’ depression and anxiety symptoms, and it further investigates the mediating role of work pressure and the moderating role of union practices.Methods: In September 2022, we undertook a cross-sectional survey study, gathering data from 1081 employees across various enterprises situated in Liaoning, China. Throughout this timeframe, notable global occurrences heightened the awareness of environmental uncertainty. Following the exclusion of participants who did not provide information on the main variables, the final valid sample comprised 940 employees. To test all hypotheses, a series of confirmatory factor analyses and path-analytic procedures were conducted using Mplus 7.0.Results: Our results confirm that environmental uncertainty, as a high job demand, increases employees’ work pressure, thereby elevating rates of anxiety and depression; the indirect relationship between environmental uncertainty and employees’ anxiety and depression through work pressure is stronger when union practices are lower.Conclusion: Our findings indicate the detrimental impact of environmental uncertainty on employees’ mental health, and highlight the roles of work pressure and union practices. In light of this, organizations should take steps to mitigate employees’ perceptions of environmental uncertainty and establish mental health programs, in cooperation with union practices, to protect employees’ mental well-being.Keywords: environmental uncertainty, anxiety, depression, union practices, work pressure

Markanday Sharma, Prateek Yadav, Srikrishna P. Panda

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.

F. Oswald, Tara S. Behrend, Dan J. Putka et al.

Big data and artificial intelligence (AI) have become quite compelling—and relevant, ideally—to organizations and the consulting services that help manage them. Researchers and practitioners in ind...

L. Watts, J. Lefkowitz, Manuel F. Gonzalez et al.

Abstract Opinions have been divided regarding the relevance of the APA Ethics Code to non-mental health specialties and even whether the code should attempt to encompass all psychology specializations. However, these opinions have crystallized without the benefit of any appreciable empirical data, until now. This study investigates the applicability of the ethical principles and standards of the code to 398 first-person narratives of ethical incidents reported by industrial-organizational (I-O) psychologists. On average, 2.8 (of the 5) principles enumerated in the code were deemed applicable to each incident, and each principle was applicable to more than half the incidents provided. Of the code’s 89 ethical standards, 75 (84.3%) were applicable to at least one incident. Among the 10 categories of standards, resolving ethical issues and human relations were the most frequently applicable, whereas therapy standards were virtually never applicable. However, for 42.7% of the incidents, trained judges identified a substantive deficiency or ambiguity for I-O psychologists in the code. These deficiencies were subsequently grouped into seven higher order categories (assessments in organizations; research practices; data management; professional interactions; business practices; student ethics; and proactive ethical behavior). Recommendations are offered for improving those putative deficiencies, and implications are discussed for I-O psychologists, the APA’s Ethics Code Task Force (ECTF), and other nonclinical domains of psychology.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Guilherme F. C. F. Almeida, José Luiz Nunes, Neele Engelmann et al.

Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains. Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models reason about moral and legal issues. In this paper, we employ the methods of experimental psychology to probe into this question. We replicate eight studies from the experimental literature with instances of Google's Gemini Pro, Anthropic's Claude 2.1, OpenAI's GPT-4, and Meta's Llama 2 Chat 70b. We find that alignment with human responses shifts from one experiment to another, and that models differ amongst themselves as to their overall alignment, with GPT-4 taking a clear lead over all other models we tested. Nonetheless, even when LLM-generated responses are highly correlated to human responses, there are still systematic differences, with a tendency for models to exaggerate effects that are present among humans, in part by reducing variance. This recommends caution with regards to proposals of replacing human participants with current state-of-the-art LLMs in psychological research and highlights the need for further research about the distinctive aspects of machine psychology.

Sabirin Sabirin, Aulia Azimi, Harry Wahyudi

Tujuan penelitian ini adalah untuk mengetahui pengaruh etika profesi auditor dan fee audit terhadap kualitas audit. Desain / metodologi / pendekatan: dalam penelitian ini dilakukan analisis statistik deskriptif dengan pendekatan kuantitatif yang menggunakan teknik analisis regresi linear berganda dengan alat analisis SPSS 24. Temuan Penelitian: Hasil dari penelitian ini menunjukkan bahwa etika profesi dan fee audit memiliki pengaruh terhadap kualitas audit. Kontribusi Teoretis / Orisinalitas: Perbedaan penelitian ini dengan penelitian sebelumnya adalah pada teknik analisis yang digunakan, selain itu objek penelitian juga berbeda, pada penelitian ini yang menjadi objek penelitian adalah Kantor Akuntan Publik yang berada di Kota Pontianak dan Bandung dan struktur bisnis yang kompleks sehingga menjadikan penelitian layak untuk diteruskan. Berdasarkan permasalahan di atas, dan melihat pentingnya etika profesi serta sangat sensitifnya fee audit penulis tertarik untuk meneliti kembali dengan fokus KAP di Pontianak Bandung sebagai responden. Keterbatasan dan implikasi penelitian: Peneliti menyadari keterbatasan dalam penelitian ini yang tentunya memerlukan perbaikan dan pengembangan untuk penelitian selanjutnya. Keterbatasan dalam penelitian ini adalah Variabel independen dalam penelitian belum memberikan kontribusi yang baik terhadap variabel dependen. Hal tersebut terlihat dari analisis koefisien determinasi dimana nilai R2 sebesar 66,6%. Sisanya sebesar 33.4% dipengaruhi oleh variabel lain diluar model ini sehingga disarankan bagi peneliti selanjutnya untuk menambahkan variabel-variabel independen yang secara teoritis dapat berpengaruh lebih besar terhadap kualitas audit. Selain itu data yang dikumpulkan untuk diteliti dan dianalisis berdasarkan pada persepsi masing-masing responden terhadap item-item instrumen penelitian sehingga dapat memungkinkan terjadinya bias atau miss perseption.