Data Analysis Approach Research Articles

Understanding tourists’ ambassador role of their origin places from perspective of residents’ attitudes towards tourists

Recognizing the pivotal role of tourists as ambassadors for their hometown is imperative, as it relates to the dynamic host-guest relationship, thereby significantly influencing the sustainable tourism progress in both their originating locations and the visited destinations. To delve deeper into this ambassadorial role, this study employed the tripartite model of resident attitude, which bridges the intricate interplay between residents’ cognitive, affective, and conative attitudes towards tourists. 204 U.S. people have been enrolled through Amazon’s Mechanical Turk to finish semi-structured questionnaire about their attitudes towards Chinese outbound tourists. Leximancer, a computer-assisted qualitative data analysis approach was applied. Results demonstrated that neutral or positive cognitive-attitudes U.S. residents held towards Chinese tourists exceeded negative cognitive attitudes. Further analysis inferred that it was the interpretation or attribution of cognitive attitudes that would induce residents’ different emotional feelings and behavioral intention. Positive interpretation would lead to positive feeling (i.e., admiration) and positive intention to visit China. Negative interpretation would lead to negative feeling (i.e., contempt) and negative intention to visit China. This research provided implications for destination management organization of tourists’ origin place to incorporate outbound tourists as their place ambassadors.

A Qualitative Assessment of Changes in Occupational Exposures Among Healthcare Facility Workers.

Adult-onset asthma, notably prevalent among healthcare professionals, especially nurses, is often attributed to occupational factors such as exposure to cleaning agents. Studies consistently underscore the substantial role of such exposure in work-related asthma among hospital staff. We aimed to (a) identify and characterize current practices in cleaning and aerosolized medication administration; (b) assess changes in practices since a similar 2003 study of Texas healthcare workers; and (c) identify factors contributing to diverse exposures within healthcare job categories. We conducted focus groups with 38 participants in 6 healthcare settings, analyzing current practices, changes since 2003, and factors contributing to exposure diversity. We used a three-step approach for data analysis, including sociodemographic characterization, a scissor-and-sort technique for exposure description, and qualitative content analysis. Participants were primarily healthcare providers (76%) and housekeepers/cleaners (11%) who reported exposure to aerosolized medications, cleaning products, adhesives, and solvents. Participants reported transitioning from cleaning practices to new formulas with reduced odors and shifting from spray cleansers to wipes. Personal protective equipment (PPE) used during cleaning tasks varied, with training differing among job categories. Aerosolized medication administration varied among facilities, with reported medication types and protocol changes over time. The results emphasized the significance of maintaining uniform protection, disseminating knowledge, and consistently adhering to PPE protocols in the healthcare environment. Addressing the identified gaps in comprehension and potential sources of exposure variability requires additional focus on occupational health and safety initiatives.

Analysis of Spatial Vitality Characteristics and Influencing Factors of Old Neighborhoods: A Case Study of Ya’an Xicheng Neighborhood

Amidst the pursuit of sustainable urban development, bolstering the spatial vitality of historic neighborhoods emerges as a pivotal endeavor. This study, centering on the Xicheng neighborhood in Ya’an City, employs a multifaceted data analysis approach to unravel the intricate spatial vitality patterns. By adopting a dual lens focusing on both “people” and “space”, we delve into the intricate interplay between spatial vitality, spatial perception, and various influencing factors, such as the spatial environment, business format layout, and place atmosphere. Leveraging spatial correlation analysis, we meticulously examine the extent to which these multifarious factors shape spatial vitality and perception. Our findings reveal a profound connection between the emotional dimension of perception and the distribution of spatial vitality. Notably, aspects of spatial character—green rating, accessibility, and interface richness—exert the most pronounced influence on vitality. Furthermore, the creation of a distinctive atmosphere in a place is more likely to resonate with a person’s spatial perception than the physical environment itself.

Maximization of Hydrogen Production via Methane Steam Reforming in a Wavy Microreactor by Optimization of Catalyst Coating: A Combined Computational and Data Analytics Approach

Maximization of Hydrogen Production via Methane Steam Reforming in a Wavy Microreactor by Optimization of Catalyst Coating: A Combined Computational and Data Analytics Approach

School Principal Leadership In Improving Teacher Performance In SMPN 1 Banda Aceh And SMPN 8 Banda Aceh

The leadership style of the principal affects how well the school performs as a whole. The general pattern or course of action that the principle takes, which is seen as a tactic for raising teacher professionalism in order to achieve the next objective, which will build a pattern and an integral unity, is known as the principal's leadership. The purpose of this study is to determine how performance is enhanced by the principal's leadership. instructor at Banda Aceh SMPN 1 and Banda Aceh SMPN 8. This study employs a qualitative methodology using a descriptive research design. Techniques for gathering data include documentation, interviews, and observation. Using methods for data reduction, data presentation, and conclusion-making, qualitative descriptive analysis is the data analysis approach applied. According to research findings, it was discovered that: (1) programs designed and implemented to enhance the performance of teachers, such as those that involve training or improving soft skills for teachers, educational seminars, workshops for teachers, MGMP, KKG, awarding excellence in teaching, adding incentives for teachers, and giving them the chance to use IT devices to solve problems that arise during teaching and learning activities; (2) The principal's plan to raise teacher performance includes: offering incentives, recognizing exceptional teachers with prizes, introducing teacher incentives, educational seminars, MGMP, and teacher workshops; (3) The following are barriers to better teacher performance that school administrators must overcome: insufficient funds, time, or motivation; moreover, they lack the assistance of professionals who can conduct workshops or offer training in a language that is easily understood by teachers. The provision of suitable infrastructure that facilitates engaging, successful, and efficient teaching and learning activities is one of the supporting variables in the interim.

ANALYSIS OF THE IMPACT OF SOCIAL MEDIA MARKETING, SERVICE QUALITY, AND SALES ACTIVITIES ON THE INCREASE IN SALES OF PROBLEMATIC FINANCING GUARANTEES (DEFAULT) THROUGH AUCTION: CASE STUDY AT ABC ISLAMIC BANK

The study's purpose is to assess and disseminate the effect of the interaction between Social Media Marketing components, Quality, and Sales Activities on increasing the sales of customer guarantees for problem financing (losses) through auctions at ABC Service Sharia Bank. This study employs quantitative methodologies with a population of all workers of PT Bank Syariah ABC and a random sample of 30 respondents from Bank Syariah ABC employees in the Surabaya region. Data collection methods use questionnaires, case studies and interviews. The data analysis approach includes performing “Data Quality Tests”, “Classic Assumption Tests”, “Multiple Linear Regression Tests”, and “Hypothesis Testing”. Based on this study, the components of Social Media Marketing, Service Quality and Sales Activity, both on their own and in combination, have a notable influence on increasing sales of collateral for non-performing financing customers through auctions: Case Study at ABC Sharia Bank. The managerial implications for PT Bank Syariah ABC resulting from this research include optimizing the implementation or running of the three components above so that sales of customer collateral through auctions can increase significantly so that the rate of return or recovery of problematic/defaulting customers is reduced.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

A Nonparametric Global Win Probability Approach to the Analysis and Sizing of Randomized Controlled Trials With Multiple Endpoints of Different Scales and Missing Data: Beyond O'Brien-Wei-Lachin.

Multiple primary endpoints are commonly used in randomized controlled trials to assess treatment effects. When the endpoints are measured on different scales, the O'Brien rank-sum test or the Wei-Lachin test for stochastic ordering may be used for hypothesis testing. However, the O'Brien-Wei-Lachin (OWL) approach is unable to handle missing data and adjust for baseline measurements. We present a nonparametric approach for data analysis that encompasses the OWL approach as a special case. Our approach is based on quantifying an endpoint-specific treatment effect using the probability that a participant in the treatment group has a better score than (or a win over) a participant in the control group. The average of the endpoint-specific win probabilities (WinPs), termed the global win probability (gWinP), is used to quantify the global treatment effect, with the null hypothesis gWinP = 0.50. Our approach involves converting the data for each endpoint to endpoint-specific win fractions, and modeling the win fractions using multivariate linear mixed models to obtain estimates of the endpoint-specific WinPs and the associated variance-covariance matrix. Focusing on confidence interval estimation for the gWinP, we derive sample size formulas for clinical trial design. Simulation results demonstrate that our approach performed well in terms of bias, interval coverage percentage, and assurance of achieving a pre-specified precision for the gWinP. Illustrative code for implementing the methods using SAS PROC RANK and PROC MIXED is provided.

Evaluation of Lifestyle for Women with Breast Cancer

Introduction: Breast cancer is a significant global health concern, affecting millions of women worldwide. While advancements in diagnosis and treatment have improved survival rates, the impact of this disease extends beyond physical health. It also significantly influences a woman's lifestyle and overall well-being. Objectives: The current study intends to analyze the lifestyle of breast cancer patients who are receiving therapy or are being followed up at the Oncology Teaching Hospital in Medical City, Baghdad, Iraq. Method: The present study uses a descriptive design with an application of an evaluation approach. A convenience sample of 100 women with breast cancer was selected from the Teaching Oncology Hospital at the Medical City in Baghdad City, Iraq. The study's objectives are served by a questionnaire. Physical, role, emotional, cognitive, and social functioning are its five functional domains. These domains are evaluated as low, moderate and high. Each study participant received a questionnaire explaining how to complete it, along with a form requesting their approval to remain anonymous. They also had the opportunity to leave the study at any time without being provided an explanation. A panel of (10) experts determines the questionnaire's content validity, and its internal consistency reliability is calculated via the split-half method with a Cronbach’s alpha correlation coefficient of (r=0.92). Data are collected using the study questionnaire, and they are then analyzed via a descriptive statistical data analysis approach on the basis of frequency and percentage. Results: The study revealed that the majority of these women experienced sleep issues, but most of them experienced problems with body weight gain (70%), social and community activity (70%), sexuality (60%), stress (60%), diet (56%), physical activity (50%) and smoking (45%), and some of them took birth control pills and used hormone replacement therapy (43%). Conclusion: This study revealed that women with breast cancer have experienced a variety of lifestyle domain-associated problems. These problems are clearly manifested in the results of this study.

Emergence of psycho-cognitive and socio-political perspectives during the dashboard appropriation process: The case of industrial SMEs in Morocco

The study aims to explore how the dashboard, initially designed to provide a rational view of organizational complexity, is appropriated by organizational actors in Moroccan Small and Medium Enterprises (SMEs). The process of appropriation is iterative and complex, involving modifications to the artifact dimension of the tool and adaptations by user-actors in their cognitive, behavioral, and social dynamics. The research highlights the psycho-cognitive and socio-political factors influencing the use of the dashboard as a key management control tool. The motivation behind this study is to understand why such a tool can lead to disruptions, resistance to change, contextualization issues, and adoption problems within organizations, rather than fulfilling its initial purpose of rationalizing processes and standardizing behaviors. The research adopts a qualitative approach using the case study method, specifically multiple embedded case studies. The sample consists of three Moroccan SMEs within the industrial food sector. We deemed it appropriate to use a textual data analysis approach, selecting two methods for this purpose: lexical analysis and thematic analysis. The qualitative data analysis was conducted using NVivo 12 software. The findings indicate that the Pre-Appropriation phase creates a context that balances technical and political factors to reduce resistance among stakeholders. The subsequent Original Appropriation Phase involves a collective decision to adopt the dashboard, which can lead to either compliant integration or deviations that enhance user adaptation. Lastly, the Reappropriation phase challenges established routines, potentially resulting in either meaningful transformations or superficial engagement with the dashboard's features. This study demonstrates that the iterative process of dashboard appropriation in Moroccan SMEs, influenced by psychocognitive and sociopolitical factors, can result in resistance, adoption challenges, and unanticipated organizational adaptations and transformations.

Differences Between Serum and Plasma: An Indirect Approach to Derive a Combined Reference Interval for Total Protein, Albumin, and Globulin

Abstract The objective of our study is to derive reference intervals (RI) for total protein (TP), albumin and globulin that are appropriate for both plasma and serum samples by applying statistical analyses to retrospective patient data. Both plasma and serum are acceptable matrices for these tests at our institution, with a single RI that applies to all specimens. The widespread shortage of lithium heparin (LH) tubes that occurred as a consequence of the pandemic prompted our lab to switch the primary tube type for comprehensive metabolic panels (CMPs) from LH to serum separator tubes (SST) in October 2021. The primary tube type reverted to LH in October 2023, and no adjustments were made to RIs at either time. To determine how well our RIs align with the accepted matrices, we used R Statistical Programing version 4.3.1 to apply an indirect, nonparametric statistical approach to data extracted from the laboratory information system (LIS) between July 2018 and December 2023. All orders for TP, albumin and globulin were queried to include the following components in the analysis: TP, albumin, globulin, serum TP, serum albumin and serum globulin. The records were divided into 3 epochs based on the primary tube type for CMP on the order date, with epochs 1 and 3 reflecting LH and epoch 2 reflecting SST. We attempted to identify a subset of serum and plasma samples in healthy individuals by applying exclusion criteria based on inpatient status, age, and draw station at which the specimen was collected. For each component, a resampling bootstrap method was used to calculate the RIs and corresponding 95% confidence intervals by epoch. Our results show that the RI of TP, albumin and globulin changed significantly between the epochs, reflecting the difference in primary matrix. The components serum TP, serum albumin, and serum globulin did not see a significant change in values over the epochs. A verification study looking at plasma and serum samples in 20 healthy individuals support these results. Based on these findings, our reference intervals were updated from 6.6-8.7 g/dL to 5.9-8.3 g/dL for TP, from 3.6-4.9 g/dL to 3.6-5.1 g/dL for albumin and from 2.3-3.5 g/dL to 1.5-3.9 g/dL for globulin. We show that there are statistically and potentially clinically significant differences in the reference intervals of TP, albumin and globulin in serum compared to plasma. This study demonstrates a big data analytics approach to derivation of RIs and emphasizes the importance of RI assessment and adjustment for patient population and acceptable specimen type.

STOPS and multivariate hierarchical aglomerative clustering: segmentation of the public regarding children’s vaccinaton communication in Slovenia

PurposeFrom a public health perspective, vaccination programmes significantly add to long-term, safe co-existence. However, because there is no social consensus about their benefits and risks, the promotion of vaccinations is difficult. Based on Kim and Grunig’s situational theory of problem solving (STOPS), including communicative action in problem solving (CAPS), this paper both proposes a model for identifying the involvement of mothers of young children in communication regarding vaccination and advocates for a novel approach to STOPS and CAPS data analyses.Design/methodology/approachThe methodological design develops empirical analyses of the data yielded by the STOPS model. Two approaches to determining associations between situational-motivational variables and communicative-action variables in random-sample survey data obtained in Slovenia in 2016 (N = 1704) are implemented – i.e. visual methods and multivariate agglomerative clustering algorithm.FindingsThe STOPS model has been confirmed and both data-analyses approaches have shown potential by clearly demonstrating associations and patterns in the data. Based on these findings, we conclude that they have the potential to be the norm in analysing STOPS models.Research limitations/implicationsLimitations of the study, which are still to be overcome, involve drawing on one sample in one country and testing only one set of indicators.Practical implicationsFrom an academic point of view, confirmation of both the model and the analytical power of the pragmatic data-analyses methods significantly add to communication studies. From practical and social points of view, relationships among attitudes and communication behaviour, as outlined in the exposed segments of the public, enable the improvement of every step in strategic-communication planning and implementation.Originality/valueThis paper fulfils an identified need to establish a theoretical framework and methodology of segmentation in vaccination-communication studies.

Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.

A stableness of resistance model for nonresponse adjustment with callback data

Abstract Nonresponse arises frequently in surveys, and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing, and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such data have not been widely used in statistical analysis until recently. We propose a stableness of resistance assumption for nonresponse adjustment with callback data. We establish the identification and the semiparametric efficiency theory under this assumption, and propose a suite of semiparametric estimation methods including doubly robust estimators, which generalize existing parametric approaches for callback data analysis. We apply the approach to a Consumer Expenditure Survey dataset. The results suggest an association between nonresponse and high housing expenditures.

Resonance Testing Data Evaluation Approaches for Scaling Onset Detection in Pipelines

Most industries dealing with pipelines face problems resulting from the buildup of deposits therein, such as reduced efficiency, downtime and increased maintenance costs. Although solutions to this issue have been sought for decades, no widely employed technique for monitoring growth of inorganic deposits (or ‘scaling’) in pipelines exists. In this research, a means of detecting the onset of scaling growth, by processing resonance testing data, was sought. For the resonance testing measurements the pipeline segment of interest is equipped with acceleration sensors which record signals generated by impacting the pipeline with a steel tip. The signals are Fourier transformed and analysed in the frequency domain, where a clear shift in frequency peak positions can be observed as the scaling thickness changes. How best to extract quantitative information from the generated frequency data is an open question. In this research, two data analysis approaches for scaling thickness prediction are compared: a supervised (binary classification) machine learning approach as well as a comparison-based change detection approach using cross-correlation. The supervised machine learning approach yields generalizable results for different acceleration sensors and impactor diameters whilst the change detection approach is sensitive from a scaling thickness of 0.5 mm. Whilst this research is specific to the pipe–scaling geometry—and type used in the experiments conducted, resonance testing can be applied to any pipe–scaling combination. The robustness of the data processing approaches presented in this work, when applied to other pipe–scaling materials and geometries, is the next point of research.

Pengaruh Pembinaan Disiplin dan Motivasi Kerja terhadap Kepuasan Kerja Karyawan Bagian Rajut CV. Berdikari Textile

The industrial era 4.0 encourages textile and apparel companies to increase efficiency. CV. Berdikari Textile experienced high levels of absenteeism in its knitting department, reflecting low job satisfaction. Building discipline and work motivation is a concern, especially with rules that are often violated, such as prohibitions on smoking and the use of cell phones. Data on employee absenteeism and tardiness indicates problems that need to be addressed. The purpose of this research is to determine the effect of developing discipline and work motivation on the level of job satisfaction of CV Knitting Department employees. Autonomous Fabric. This research uses a quantitative research approach. The common form of data collection is by distributing questionnaires and conducting direct interviews in the field. The data analysis approach uses several tests, including validity tests, reliability tests, classical assumption tests, multiple linear regression tests, and hypothesis tests. Testing was carried out by analyzing the output table produced by the Statistical Package for Social (SPSS) software program, namely version 26. The results of the research showed that disciplinary development partially influenced job satisfaction and work motivation partially influenced job satisfaction. Furthermore, fostering discipline and work motivation simultaneously influence the job satisfaction of CV Knitting Department employees. Berdikari Textile. This shows that the more the company provides good work discipline guidance and the higher the work motivation, the more it can strengthen the job satisfaction of employees in the Knitting Section at CV. Berdikari Textile.

Public Sector Governance and Service Delivery in Foreign Ministries: An Assessment of the Ugandan Ministry of Foreign Affairs

The paper presents findings of a study carried out to examine the effects of public sector governance on service delivery in the Ministry of Foreign Affairs- MoFA, Uganda. The primary objective of the study were to establish how public accountability and transparency mechanisms influence service delivery in Foreign Service. A cross-sectional research design, adopting both qualitative and quantitative approaches of data collection and analysis was used. The findings revealed that public accountability and transparency are positively correlated with service delivery in the Foreign Service. The other finding is that the social and financial accountability systems in MoFA are weak. The paper concludes that there is urgent need to improve effective and sustainable service delivery in the Foreign Service. Accordingly, the paper recommends that MoFA should ensure that every Foreign Service officer is fully accountable for their actions at work; management should be just in all its actions regarding its officers; and MoFA should modernize its communication system. The paper identifies some areas of future investigation.

What uses of digital technology support and enhance the learning and the engagement of diverse learners in high school classes?

Digital technologies may play an important role in helping students from all walks of life to overcome barriers and benefit from teaching and learning. However, due to a lack of knowledge or skills related to educational technologies, K-12 teachers struggle to harness the full potential of digitalisation to support student learning and engagement, causing further marginalization and exclusion that put diverse group of learners at risk of being left behind in education (Chevalère et al., 2021). In response to these research gaps, the aim of this paper is to explore which high school uses of digital technology cater for learners’ needs and preferences for the sake of a more digitally inclusive environment that supports engagement and equitable learning opportunities for all students (Stenman & Pettersson, 2020). We adopted a qualitative approach to attend to the richness and depth in various contexts. We have conducted individual semi-structured interviews with 17 high school teachers, aiming to obtain in-depth qualitative data through open-ended questions. Then, a general inductive data analysis approach was followed to generate preliminary results on the research objectives, which are presented in this study.

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

Psychosocial factors related to the increasing automation of work processes: A systematic review

Purpose: To identify which psychosocial factors can be related to the increasing automation of work processes, determining practical implications relevant to the evaluation of psychosocial risk factors at work within organizations before the imminent transition towards industry 4.0 Design/methodology/approach: A systematic review of the literature was carried out. The review structure was based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach for the studies selection and the Noblit and Hare's meta-ethnographic approach for data analysis and synthesis. Findings: Thirty-five studies were selected which passed all the selection stages. Six psychosocial risk factors were detected whose behaviors may be influenced by the increasing automation of work. Evidence suggests that the factors of development possibility, change management, mental load, routine content, and job insecurity may increase their exposure due to job modifications owing to new automation technologies. On the other hand, social relationships at work have the ability to positively influence the successful implementation of new automated processes. Originality/value: The results obtained represent excellent indications of an overview of psychosocial risk factors that may increase their danger due to the increasing automation of work processes and Industry 4.0.