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Structural path analysis and its applications: literature review

• Rui Xie 1 ,  ,  , 
• Yuanyuan Zhao 2 , 
• Liming Chen 2
• 1. School of Economy and Trade, Hunan University, Changsha 410079, China
• 2. College of Finance and Statistics, Hunan University, Changsha 410079, China
• Received: 15 January 2020 Accepted: 04 February 2020 Published: 07 February 2020

JEL Codes: Q56, P18, F64

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• structural path analysis ,
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Citation: Rui Xie, Yuanyuan Zhao, Liming Chen. Structural path analysis and its applications: literature review[J]. National Accounting Review, 2020, 2(1): 83-94. doi: 10.3934/NAR.2020005

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• © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0 )

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An Explanation of Path Analysis and Recommendations for Best Practice

Posted: 11 Oct 2023 Last revised: 30 Nov 2023

Clive S. Lennox

University of Southern California

Carmen Payne-Mann

University of Southern California - Leventhal School of Accounting

Date Written: September 15, 2023

Path analysis has become increasingly popular in the accounting literature with the number of papers using this methodology surging over the past decade. Yet, many scholars do not have a deep understanding of how path analysis works or the assumptions upon which it relies. In this paper, we explain the similarities and differences between path analysis, ordinary least squares (OLS), and instrumental variable estimation (IV). We then examine how path analysis is used in the accounting literature. We identify two problems with the way path analysis is used. First, path analysis is often used as a tool to disentangle direct and indirect causal effects; however, studies that use path analysis for this purpose often assume away potential endogeneity by imposing the assumption of uncorrelated errors. Second, many studies do not explicitly state their key assumptions, including the assumption of uncorrelated errors. This practice makes it difficult for a reader to determine whether endogeneity is being assumed away or necessary steps are being taken to address the problem. We conclude with several recommendations to improve the implementation of path analysis in both the archival and experimental literatures.

Keywords: Path analysis, mediation analysis

JEL Classification: C01, C36, M10, M20, M30, M40

Suggested Citation: Suggested Citation

Clive Lennox (Contact Author)

University of southern california ( email ).

2250 Alcazar Street Los Angeles, CA 90089 United States

University of Southern California - Leventhal School of Accounting ( email )

Los Angeles, CA 90089-0441 United States

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• DOI: 10.1016/j.chbah.2024.100043
• Corpus ID: 267111615

Trust in artificial intelligence: Literature review and main path analysis

• B. Henrique , Eugene Santos
• Published in Computers in Human Behavior 1 January 2024
• Computer Science, Psychology

79 References

Affective design analysis of explainable artificial intelligence (xai): a user-centric perspective, what factors contribute to the acceptance of artificial intelligence a systematic review, mitigating knowledge imbalance in ai-advised decision-making through collaborative user involvement, trust in artificial intelligence: from a foundational trust framework to emerging research opportunities, augmenting flight training with ai to efficiently train pilots, effects of explainable artificial intelligence on trust and human behavior in a high-risk decision task, trust in an ai versus a human teammate: the effects of teammate identity and performance on human-ai cooperation, how the different explanation classes impact trust calibration: the case of clinical decision support systems, the utility of explainable ai in ad hoc human-machine teaming, collective intelligence in human-ai teams: a bayesian theory of mind approach, related papers.

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Home » Literature Review – Types Writing Guide and Examples

Literature Review – Types Writing Guide and Examples

Table of Contents

Literature Review

Definition:

A literature review is a comprehensive and critical analysis of the existing literature on a particular topic or research question. It involves identifying, evaluating, and synthesizing relevant literature, including scholarly articles, books, and other sources, to provide a summary and critical assessment of what is known about the topic.

Types of Literature Review

Types of Literature Review are as follows:

• Narrative literature review : This type of review involves a comprehensive summary and critical analysis of the available literature on a particular topic or research question. It is often used as an introductory section of a research paper.
• Systematic literature review: This is a rigorous and structured review that follows a pre-defined protocol to identify, evaluate, and synthesize all relevant studies on a specific research question. It is often used in evidence-based practice and systematic reviews.
• Meta-analysis: This is a quantitative review that uses statistical methods to combine data from multiple studies to derive a summary effect size. It provides a more precise estimate of the overall effect than any individual study.
• Scoping review: This is a preliminary review that aims to map the existing literature on a broad topic area to identify research gaps and areas for further investigation.
• Critical literature review : This type of review evaluates the strengths and weaknesses of the existing literature on a particular topic or research question. It aims to provide a critical analysis of the literature and identify areas where further research is needed.
• Conceptual literature review: This review synthesizes and integrates theories and concepts from multiple sources to provide a new perspective on a particular topic. It aims to provide a theoretical framework for understanding a particular research question.
• Rapid literature review: This is a quick review that provides a snapshot of the current state of knowledge on a specific research question or topic. It is often used when time and resources are limited.
• Thematic literature review : This review identifies and analyzes common themes and patterns across a body of literature on a particular topic. It aims to provide a comprehensive overview of the literature and identify key themes and concepts.
• Realist literature review: This review is often used in social science research and aims to identify how and why certain interventions work in certain contexts. It takes into account the context and complexities of real-world situations.
• State-of-the-art literature review : This type of review provides an overview of the current state of knowledge in a particular field, highlighting the most recent and relevant research. It is often used in fields where knowledge is rapidly evolving, such as technology or medicine.
• Integrative literature review: This type of review synthesizes and integrates findings from multiple studies on a particular topic to identify patterns, themes, and gaps in the literature. It aims to provide a comprehensive understanding of the current state of knowledge on a particular topic.
• Umbrella literature review : This review is used to provide a broad overview of a large and diverse body of literature on a particular topic. It aims to identify common themes and patterns across different areas of research.
• Historical literature review: This type of review examines the historical development of research on a particular topic or research question. It aims to provide a historical context for understanding the current state of knowledge on a particular topic.
• Problem-oriented literature review : This review focuses on a specific problem or issue and examines the literature to identify potential solutions or interventions. It aims to provide practical recommendations for addressing a particular problem or issue.
• Mixed-methods literature review : This type of review combines quantitative and qualitative methods to synthesize and analyze the available literature on a particular topic. It aims to provide a more comprehensive understanding of the research question by combining different types of evidence.

Parts of Literature Review

Parts of a literature review are as follows:

Introduction

The introduction of a literature review typically provides background information on the research topic and why it is important. It outlines the objectives of the review, the research question or hypothesis, and the scope of the review.

Literature Search

This section outlines the search strategy and databases used to identify relevant literature. The search terms used, inclusion and exclusion criteria, and any limitations of the search are described.

Literature Analysis

The literature analysis is the main body of the literature review. This section summarizes and synthesizes the literature that is relevant to the research question or hypothesis. The review should be organized thematically, chronologically, or by methodology, depending on the research objectives.

Critical Evaluation

Critical evaluation involves assessing the quality and validity of the literature. This includes evaluating the reliability and validity of the studies reviewed, the methodology used, and the strength of the evidence.

The conclusion of the literature review should summarize the main findings, identify any gaps in the literature, and suggest areas for future research. It should also reiterate the importance of the research question or hypothesis and the contribution of the literature review to the overall research project.

The references list includes all the sources cited in the literature review, and follows a specific referencing style (e.g., APA, MLA, Harvard).

How to write Literature Review

Here are some steps to follow when writing a literature review:

• Define your research question or topic : Before starting your literature review, it is essential to define your research question or topic. This will help you identify relevant literature and determine the scope of your review.
• Conduct a comprehensive search: Use databases and search engines to find relevant literature. Look for peer-reviewed articles, books, and other academic sources that are relevant to your research question or topic.
• Evaluate the sources: Once you have found potential sources, evaluate them critically to determine their relevance, credibility, and quality. Look for recent publications, reputable authors, and reliable sources of data and evidence.
• Organize your sources: Group the sources by theme, method, or research question. This will help you identify similarities and differences among the literature, and provide a structure for your literature review.
• Analyze and synthesize the literature : Analyze each source in depth, identifying the key findings, methodologies, and conclusions. Then, synthesize the information from the sources, identifying patterns and themes in the literature.
• Write the literature review : Start with an introduction that provides an overview of the topic and the purpose of the literature review. Then, organize the literature according to your chosen structure, and analyze and synthesize the sources. Finally, provide a conclusion that summarizes the key findings of the literature review, identifies gaps in knowledge, and suggests areas for future research.
• Edit and proofread: Once you have written your literature review, edit and proofread it carefully to ensure that it is well-organized, clear, and concise.

Examples of Literature Review

Here’s an example of how a literature review can be conducted for a thesis on the topic of “ The Impact of Social Media on Teenagers’ Mental Health”:

• Start by identifying the key terms related to your research topic. In this case, the key terms are “social media,” “teenagers,” and “mental health.”
• Use academic databases like Google Scholar, JSTOR, or PubMed to search for relevant articles, books, and other publications. Use these keywords in your search to narrow down your results.
• Evaluate the sources you find to determine if they are relevant to your research question. You may want to consider the publication date, author’s credentials, and the journal or book publisher.
• Begin reading and taking notes on each source, paying attention to key findings, methodologies used, and any gaps in the research.
• Organize your findings into themes or categories. For example, you might categorize your sources into those that examine the impact of social media on self-esteem, those that explore the effects of cyberbullying, and those that investigate the relationship between social media use and depression.
• Synthesize your findings by summarizing the key themes and highlighting any gaps or inconsistencies in the research. Identify areas where further research is needed.
• Use your literature review to inform your research questions and hypotheses for your thesis.

For example, after conducting a literature review on the impact of social media on teenagers’ mental health, a thesis might look like this:

“Using a mixed-methods approach, this study aims to investigate the relationship between social media use and mental health outcomes in teenagers. Specifically, the study will examine the effects of cyberbullying, social comparison, and excessive social media use on self-esteem, anxiety, and depression. Through an analysis of survey data and qualitative interviews with teenagers, the study will provide insight into the complex relationship between social media use and mental health outcomes, and identify strategies for promoting positive mental health outcomes in young people.”

Reference: Smith, J., Jones, M., & Lee, S. (2019). The effects of social media use on adolescent mental health: A systematic review. Journal of Adolescent Health, 65(2), 154-165. doi:10.1016/j.jadohealth.2019.03.024

Reference Example: Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of Journal, volume number(issue number), page range. doi:0000000/000000000000 or URL

Applications of Literature Review

some applications of literature review in different fields:

• Social Sciences: In social sciences, literature reviews are used to identify gaps in existing research, to develop research questions, and to provide a theoretical framework for research. Literature reviews are commonly used in fields such as sociology, psychology, anthropology, and political science.
• Natural Sciences: In natural sciences, literature reviews are used to summarize and evaluate the current state of knowledge in a particular field or subfield. Literature reviews can help researchers identify areas where more research is needed and provide insights into the latest developments in a particular field. Fields such as biology, chemistry, and physics commonly use literature reviews.
• Health Sciences: In health sciences, literature reviews are used to evaluate the effectiveness of treatments, identify best practices, and determine areas where more research is needed. Literature reviews are commonly used in fields such as medicine, nursing, and public health.
• Humanities: In humanities, literature reviews are used to identify gaps in existing knowledge, develop new interpretations of texts or cultural artifacts, and provide a theoretical framework for research. Literature reviews are commonly used in fields such as history, literary studies, and philosophy.

Role of Literature Review in Research

Here are some applications of literature review in research:

• Identifying Research Gaps : Literature review helps researchers identify gaps in existing research and literature related to their research question. This allows them to develop new research questions and hypotheses to fill those gaps.
• Developing Theoretical Framework: Literature review helps researchers develop a theoretical framework for their research. By analyzing and synthesizing existing literature, researchers can identify the key concepts, theories, and models that are relevant to their research.
• Selecting Research Methods : Literature review helps researchers select appropriate research methods and techniques based on previous research. It also helps researchers to identify potential biases or limitations of certain methods and techniques.
• Data Collection and Analysis: Literature review helps researchers in data collection and analysis by providing a foundation for the development of data collection instruments and methods. It also helps researchers to identify relevant data sources and identify potential data analysis techniques.
• Communicating Results: Literature review helps researchers to communicate their results effectively by providing a context for their research. It also helps to justify the significance of their findings in relation to existing research and literature.

Purpose of Literature Review

Some of the specific purposes of a literature review are as follows:

• To provide context: A literature review helps to provide context for your research by situating it within the broader body of literature on the topic.
• To identify gaps and inconsistencies: A literature review helps to identify areas where further research is needed or where there are inconsistencies in the existing literature.
• To synthesize information: A literature review helps to synthesize the information from multiple sources and present a coherent and comprehensive picture of the current state of knowledge on the topic.
• To identify key concepts and theories : A literature review helps to identify key concepts and theories that are relevant to your research question and provide a theoretical framework for your study.
• To inform research design: A literature review can inform the design of your research study by identifying appropriate research methods, data sources, and research questions.

Characteristics of Literature Review

Some Characteristics of Literature Review are as follows:

• Identifying gaps in knowledge: A literature review helps to identify gaps in the existing knowledge and research on a specific topic or research question. By analyzing and synthesizing the literature, you can identify areas where further research is needed and where new insights can be gained.
• Establishing the significance of your research: A literature review helps to establish the significance of your own research by placing it in the context of existing research. By demonstrating the relevance of your research to the existing literature, you can establish its importance and value.
• Informing research design and methodology : A literature review helps to inform research design and methodology by identifying the most appropriate research methods, techniques, and instruments. By reviewing the literature, you can identify the strengths and limitations of different research methods and techniques, and select the most appropriate ones for your own research.
• Supporting arguments and claims: A literature review provides evidence to support arguments and claims made in academic writing. By citing and analyzing the literature, you can provide a solid foundation for your own arguments and claims.
• I dentifying potential collaborators and mentors: A literature review can help identify potential collaborators and mentors by identifying researchers and practitioners who are working on related topics or using similar methods. By building relationships with these individuals, you can gain valuable insights and support for your own research and practice.
• Keeping up-to-date with the latest research : A literature review helps to keep you up-to-date with the latest research on a specific topic or research question. By regularly reviewing the literature, you can stay informed about the latest findings and developments in your field.

Advantages of Literature Review

There are several advantages to conducting a literature review as part of a research project, including:

• Establishing the significance of the research : A literature review helps to establish the significance of the research by demonstrating the gap or problem in the existing literature that the study aims to address.
• Identifying key concepts and theories: A literature review can help to identify key concepts and theories that are relevant to the research question, and provide a theoretical framework for the study.
• Supporting the research methodology : A literature review can inform the research methodology by identifying appropriate research methods, data sources, and research questions.
• Providing a comprehensive overview of the literature : A literature review provides a comprehensive overview of the current state of knowledge on a topic, allowing the researcher to identify key themes, debates, and areas of agreement or disagreement.
• Identifying potential research questions: A literature review can help to identify potential research questions and areas for further investigation.
• Avoiding duplication of research: A literature review can help to avoid duplication of research by identifying what has already been done on a topic, and what remains to be done.
• Enhancing the credibility of the research : A literature review helps to enhance the credibility of the research by demonstrating the researcher’s knowledge of the existing literature and their ability to situate their research within a broader context.

Limitations of Literature Review

Limitations of Literature Review are as follows:

• Limited scope : Literature reviews can only cover the existing literature on a particular topic, which may be limited in scope or depth.
• Publication bias : Literature reviews may be influenced by publication bias, which occurs when researchers are more likely to publish positive results than negative ones. This can lead to an incomplete or biased picture of the literature.
• Quality of sources : The quality of the literature reviewed can vary widely, and not all sources may be reliable or valid.
• Time-limited: Literature reviews can become quickly outdated as new research is published, making it difficult to keep up with the latest developments in a field.
• Subjective interpretation : Literature reviews can be subjective, and the interpretation of the findings can vary depending on the researcher’s perspective or bias.
• Lack of original data : Literature reviews do not generate new data, but rather rely on the analysis of existing studies.
• Risk of plagiarism: It is important to ensure that literature reviews do not inadvertently contain plagiarism, which can occur when researchers use the work of others without proper attribution.

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• Open access
• Published: 21 August 2024

The multiple mediating effects of vision-specific factors and depression on the association between visual impairment severity and fatigue: a path analysis study

• Wouter Schakel   ORCID: orcid.org/0000-0001-7189-1451 1 , 2 ,
• Christina Bode   ORCID: orcid.org/0000-0002-1641-8028 3 ,
• Peter M. van de Ven 4 ,
• Hilde P. A. van der Aa   ORCID: orcid.org/0000-0002-1853-5674 1 , 2 , 5 , 6 ,
• Carel T. J. Hulshof   ORCID: orcid.org/0000-0002-2720-456X 7 ,
• Gerardus H. M. B. van Rens 1 , 2 &
• Ruth M. A. van Nispen   ORCID: orcid.org/0000-0003-1227-1177 1 , 2  

BMC Psychiatry volume  24 , Article number:  572 ( 2024 ) Cite this article

77 Accesses

Metrics details

Severe fatigue is a common symptom for people with visual impairment, with a detrimental effect on emotional functioning, cognition, work capacity and activities of daily living. A previous study found that depression was one of the most important determinants of fatigue, but less is known about disease-specific factors in this patient population. This study aimed to explore the association between visual impairment severity and fatigue in adults with low vision, both directly and indirectly, with vision-specific factors and depression as potential mediators.

Cross-sectional data were collected from 220 Dutch low vision service patients by telephone interviews. Fatigue was defined as a latent variable by severity and impact on daily life. Potential mediators included vision-related symptoms, adaptation to vision loss and depression. Hypothesized structural equation models were constructed in Mplus to test (in)direct effects of visual impairment severity (mild/moderate, severe, blindness) on fatigue through above mentioned variables.

The final model explained 60% of fatigue variance and revealed a significant total effect of visual impairment severity on fatigue. Patients with severe visual impairment (reference group) had significantly higher fatigue symptoms compared to those with mild/moderate visual impairment (β = -0.50, 95% bias-corrected confidence interval [BC CI] [-0.86, -0.16]) and those with blindness (β = -0.44, 95% BC CI [-0.80, -0.07]). Eye strain & light disturbance, depression and vision-related mobility mediated the fatigue difference between the severe and mild/moderate visual impairment categories. The fatigue difference between the severe visual impairment and blindness categories was solely explained by eye strain & light disturbance. Moreover, depressive symptoms (β = 0.65, p  < 0.001) and eye strain & light disturbance (β = 0.19, p  = 0.023) were directly associated with fatigue independent of visual impairment severity.

Conclusions

Our findings indicate an inverted-U shaped relationship between visual impairment severity and fatigue in patients with low vision. The complexity of this relationship is likely explained by the consequences of visual impairment, in particular by strained eyes and depressive mood, rather than by severity of the disability itself.

Peer Review reports

Fatigue in visual impairment is described by patients as a daily, uncontrollable sensation with feelings of mental and physical exhaustion [ 1 ]. Evidence from recent meta-analyses with 14 studies indicates that fatigue severity levels and the odds of fatigue are higher in adults with visual impairment compared to normally sighted controls, with small to medium effect sizes reported [ 2 ]. Other results showed that symptoms of severe fatigue were present in 57% of adults with visual impairment which is at least twice as high compared to the general Dutch population [ 3 ]. We found that consequences of severe fatigue not only affect patients’ lives, but also pose an economic burden for society at large through loss of work participation [ 3 ]. To our knowledge, in-depth analyses investigating underlying mechanisms that may explain the association between visual impairment and fatigue are lacking.

Qualitative insights indicate that multiple factors may play a role in the onset and course of fatigue in patients with visual impairment. High cognitive load due to necessary adjustments for functioning in daily life, intensity of light, negative cognitions with regard to vision loss and the effort necessary for visual perception were among the most important causes of fatigue mentioned by visually impaired adults with severe fatigue symptoms [ 1 ]. In more recent work, we developed multidimensional path models using structural equation modelling (SEM) to explore psychological and health-related factors as determinants of fatigue in adults with visual impairment and adults with normal sight. The results indicate that fatigue in visual impairment is directly associated with depressive symptoms, and to a lesser extent with perceived health, poor somatic comorbidity and flexible goal adjustment coping tendencies [ 4 ]. Depressive symptoms and perceived health were identified as mediators in the relationship between sleep disorders, self-efficacy and fatigue. Due to the nature of the comparison with healthy adults, these path models did not include vision-specific factors that have been mentioned as important causes of fatigue by persons with visual impairment [ 1 ]. Knowledge of disease-specific fatigue factors may aid healthcare professionals to develop and tailor interventions to the needs of the target population. Therefore, the first objective of the present study was to explore vision-specific factors (e.g. eye strain, light disturbance) as determinants of fatigue severity and the impact of fatigue on daily life within a sample of adults with visual impairment. The influence of depression was also examined in the present study because it is a common symptom in patients with visual impairment [ 5 ] that also shares a strong relationship with fatigue [ 6 , 7 ] and vision-specific factors [ 8 , 9 , 10 ].

Furthermore, there are some indications that poor visual acuity in adults with visual impairment may be linked to higher fatigue symptomatology. Two studies with objective measures of visual acuity found that older adults with moderate to severe visual impairment had higher fatigue levels than older adults with mild visual impairment [ 11 , 12 ]. Some other population-based studies with self-reported measures have also reported higher levels of fatigue symptomatology for older adults who rated their vision to be poorer [ 13 , 14 ]. However, this evidence is based on a small number of studies and conflicting results have been reported by Williams et al. [ 15 ], who found that persons with legal blindness in both eyes experienced less fatigue relative to persons with moderate visual impairment in the better eye. These findings suggest that vision loss may not directly influence fatigue, but may play a role through an interplay with other related factors. In the present study we therefore explored the association between visual impairment severity and fatigue and examined whether this link may be mediated by other vision-specific factors.

Design and participants

Data for the present study were collected as part of a larger cross-sectional survey on fatigue among patients with visual impairment. Two related studies on this project have been previously published, including an economic evaluation of the burden of visual impairment and comorbid fatigue [ 3 ], and a path analysis of generic factors that determine fatigue in adults with and without visual impairment [ 3 ]. A random sample of 1281 patients with visual impairment who were registered and received care at two low vision services in the Netherlands (Royal Dutch Visio and Bartiméus) at the time of the study (2015–2016) were invited by letter to participate. Patients with at least mild visual impairment according to the World Health Organization (WHO) criteria (defined as presenting visual acuity (VA) worse than 20/40 (6/12, 0.50) and/or concentric visual field impairment worse than < 45° in the better-seeing eye), sufficient mastery of the Dutch language and who were 18 years of age or older were eligible. Exclusion criteria were severe cognitive impairment, as defined by 3 or more errors on the 6-item version of the Mini Mental State Examination (MMSE) [ 16 ], and a diagnosis or receiving treatment in the last year for the following chronic conditions of which fatigue is a common symptom: cancer, multiple sclerosis, chronic fatigue syndrome and psychiatric disorders. Patients who agreed to participate completed a battery of validated questionnaires and gave information about socio-demographic and clinical characteristics through a structured telephone interview (performed by two experienced researchers with MSc and BSc in psychology).

Measures and data preparation

Socio-demographic characteristics collected include age, gender, living situation (living alone vs. living together with a partner or family), education and employment status. Somatic comorbidity was defined as having no comorbidity or being treated for one or more of seven comorbid chronic conditions: asthma or chronic obstructive pulmonary disease; osteoarthritis and rheumatoid arthritis; peripheral arterial disease; diabetes mellitus; cardiac disease; cerebrovascular accident or stroke; cancer; and other chronic somatic or psychiatric conditions.

Data on visual acuity, visual fields, ophthalmic diagnoses and other descriptions of vision loss and/or visual field impairments were obtained from patient medical records at low vision services, and were used to supplement missing values. In accordance with the WHO criteria, four categories of visual impairment were defined based on the better-seeing eye. Mild visual impairment referred to presenting VA worse than 20/40 (6/12, 0.50) and equal or better than 20/60 (6/18, 0.33), or concentric visual field impairment < 45° and ≥ 0.30. Moderate visual impairment referred to presenting VA worse than 20/60 (6/18, 0.33) and equal or better than 20/200 (6/60, 0.10), or concentric visual field impairment < 30° and ≥ 20°, or loss of upper visual field/hemianopia. Severe visual impairment referred to presenting VA worse than 20/200 (6/60, 0.10) and equal or better than 20/400 (6/120, 0.05), or concentric visual field impairment < 20°. Blindness (societal and total blindness) referred to presenting VA worse than 20/400 (6/120, 0.05) or concentric visual field impairment < 10°. Because mild visual impairment was only present in 9 patients, moderate and mild visual impairment were combined into one category for SEM analyses.

The Fatigue Assessment Scale (FAS) [ 17 ], Modified Fatigue Impact Scale (MFIS) [ 18 ], Patient Health Questionnaire (PHQ-9) [ 19 ] and Adaptation to Vision Loss questionnaire (AVL-9) [ 20 , 21 ] were analyzed with item response theory (IRT) models (i.e. the graded response model) to ensure these measures had satisfactory psychometric properties using R studio version 1.1.456, and R version 3.5.1. Questionnaires were adjusted based on their performance in these statistical models, followed by calculation of respondents’ thetas, which reflect an interval score of the underlying trait. This procedure has already been described in great detail in our previous path analysis study [ 4 ]. IRT outcomes with fit indices, questionnaire adjustments and theta parameters are available in Supplement 1. All other questionnaires used for independent, potential mediating and latent variables in SEM analyses are shown in Table 1 .

In accordance with the model of our previous path analysis study [ 4 ], a latent fatigue variable was defined by two indicators: fatigue severity (FAS) and fatigue impact (MFIS). As a measure for eye strain & light disturbance, a latent variable was created from three Low Vision Quality of Life questionnaire (LVQOL) [ 22 , 23 ] items and one item of the Dutch ICF Activity Inventory (D-AI) feeling fit subscale [ 24 ], because, to the best of our knowledge, no specific questionnaires regarding these concepts were available. LVQOL item “how much of a problem do you have: getting the right amount of light to be able to see” was removed due to poor factor loadings and strong collinearity, resulting in a three factor latent variable that was used for SEM analysis (Table 1 ).

Statistical analyses

Statistical analyses were performed using SPSS version 22.0.0.0 and Mplus version 7.4 [ 25 ]. First, descriptive statistics, Pearson and Spearman’s rho correlations were performed to examine the distribution of the data and explore statistical significance of univariate relationships between variables. Variables that were not significantly correlated with fatigue severity and fatigue impact were excluded from SEM analysis.

Second, multivariate analysis of variance (MANOVA) was used to test whether there were differences between visual impairment severity levels and vision-specific, depression and fatigue outcomes. Significant associations were followed up by univariate analyses and pairwise comparisons using the Bonferroni post-hoc test.

Third, a step-wise path model was developed within a SEM framework to investigate whether the differences among visual impairment severity on fatigue could be explained by eye strain and light disturbance, adaptation to vision loss, vision-related mobility and depressive symptoms. In contrast to our previous study [ 4 ], most psychosocial and health-related factors were omitted from path analyses because we were primarily interested in vision-related fatigue determinants that could be specific for people with visual impairment. Depression was maintained in the model, as it was associated (medium to high correlations) with all variables of interest. Furthermore, we hypothesized depressive symptoms would mediate the effect of adaptation to vision loss on fatigue [ 21 , 26 , 27 , 28 ]. Mediation was evaluated based upon the statistical significance of the estimated relative direct, indirect and total effects within each path model. The maximum likelihood estimation based on the delta method was used to calculate direct and indirect effects. This estimation method is robust to non-normality and appropriate for models with continuous and categorical variables [ 29 ]. Finally, the significance of relative indirect effects of visual impairment severity were tested with the bias-corrected bootstrapping method proposed by Preacher et al. [ 30 ]. A total of 5000 iterations was set to impute 95% bias-corrected confidence interval (BC CI) limits and standard errors for the evaluation of relative indirect effects. In this approach, the indirect effects are deemed significant if the upper and lower bound of the 95% CI does not include 0. Given that visual impairment severity was a multicategorical variable, it was represented in the model by a set of dummy variables created by indicator coding in accordance with the principles of Hayes and Preacher [ 31 ]. Severe visual impairment served as the reference category, to which all estimated direct and indirect effects for mild/moderate visual impairment and blindness were compared. These results were therefore described in terms of relative effects. In SEM analyses, a hypothesized model with assumed relationships between fatigue and potential mediating variables was initially tested and further optimized in a step-wise procedure. Model fit was improved by removal of non-significant pathways and by inclusion of additional theoretical pathways based on the modification indices. Each model was assessed using several fit criteria as advised by Wang and Wang [ 32 ]: χ 2 -goodness-of-fit, Root Mean Square Error of Approximation (RMSEA < 0.06 represents good fit), the Standardized Root Mean Residual (SRMR < 0.08 represents good fit), the Tucker-Lewis index (TLI > 0.95 represents good fit) and the Comparative Fit Index (CFI > 0.95 represents good fit).

Sample size calculation

The sample size for this study was determined using commonly accepted rule-of-thumb practices for SEM. Although there is no consensus on exact sample size requirements, previous guidelines have recommended a minimum of 200 participants [ 33 , 34 ] and a range of 5–20 observations per estimated parameter [ 35 , 36 ]. Given that our model includes up to 30 estimated parameters, a minimum sample size range of 150 to 600 participants was considered appropriate to ensure robust and reliable results.

Participants

Out of 1281 invited patients, 321 agreed to participate and gave written informed consent (response rate 25.1%). Of those, 73 were not eligible (56 were diagnosed/treated for chronic conditions and/or psychiatric disorders, 14 had no visual impairment, 3 were not fluent in Dutch), 10 could not be contacted after multiple attempts and 5 withdrew from participation. In addition, 13 had missing values on essential items for analysis, resulting in data of 220 patients that were included in the present study. The most common reasons reported by non-responders for declining participation were: too much of a burden to participate, not interested and already participating in another study. Specific information on the eye examination dates was missing for nearly half of the study sample, limiting our ability to precisely calculate the time interval between these assessments and study participation. Out of 110 participants with complete data, more than half (71%) had their eye examinations conducted within approximately one year before or after participation in our study.

Table 2 shows the sociodemographic characteristics of the study sample. Retinitis pigmentosa (26.8%) and age-related macular degeneration (24.5%) were the most common causes of visual impairment, followed by glaucoma (13.2%) and homonymous hemianopia (8.2%). The majority of participants (72.7%) reported a progressive disease course with declining visual acuity and/or increasing visual field problems.

Preliminary analysis

The descriptive statistics confirmed the assumptions of normality and multicollinearity for all study data. As shown in Table  3 , there were significant correlations between all potential mediating variables and the dependent fatigue variables. In contrast, none of the independent variables (age, education, gender, years since diagnosis and disease course) were significantly correlated with both fatigue variables, and were therefore excluded from SEM analysis. Sample characteristics and MANOVA results with visual impairment severity as the single factor and all continuous study variables as the dependent variables are presented in Table  2 . There was a statistically significant difference in potential mediating variables and dependent fatigue variables based on a patient’s severity of visual impairment, F (18, 416) = 3.02, p  < 0.001; Wilk's Λ = 0.782, partial η 2  = 0.12. Further analysis with the Bonferroni procedure (statistical significance was accepted at p  < 0.006) revealed that visual impairment severity had a statistically significant effect on fatigue severity, depressive symptoms, vision-related mobility difficulties and eye strain & light disturbance. Follow-up post-hoc tests indicated that mean fatigue and depressive symptoms were significantly higher for patients with severe visual impairment relative to those with mild/moderate visual impairment, mean levels of eye strain & light disturbance were significantly higher for patients with severe visual impairment compared to those with blindness, and mean vision-related mobility problems were significantly higher for blind patients compared to patients with mild/moderate visual impairment (Fig.  1 ).

Box plot showing thetas for fatigue, depressive symptoms and vision-specific factors by visual impairment severity category. Boxes display the median and the 25th and 75th percentiles. The plus sign within each box represents the mean. Whiskers and extreme values (dots) were plotted using the Tukey method. Asterisks indicate significant differences between visual impairment severity categories in Bonferroni corrected post-hoc tests. * Statistically significant at p  < 0.05. ** Statistically significant at p  < 0.01. VI  visual impairment

Path analysis

In the initial hypothesized model, three vision-specific potential mediators (eye strain & light disturbance, adaptation to vision loss, vision-specific mobility), one psychosocial potential mediator (depressive symptoms) and two independent dummy variables representing visual impairment severity were included in the model to evaluate their (in)direct relationships with the latent fatigue variable.

As can be seen in Table  4 , fit criteria for the initial model were acceptable in terms of CFI and TLI but RSMEA and SRMR exceeded their threshold values of 0.06 and 0.08, respectively (model 1). Because adaptation to vision loss was not significantly related to fatigue and the two dummy variables representing visual impairment severity it was excluded from further analysis. In addition, pathways from D 1 and D 2 to depressive symptoms were added to the second model, resulting in good fit across all criteria (Table  4 : model 2). In a final effort, removal of insignificant pathways failed to improve fit statistics over the previous model (Table  4 : model 3). Hence, model 2 was chosen as our final model, which explained 60% of the latent fatigue variable.

A visualization of the final model together with standardized path coefficients of all direct and indirect effects are shown in Fig.  2 and Table  5 . Eye strain & light disturbance (pathway b 1 : β = 0.19, p  = 0.023) and depressive symptoms (pathway b 2 : β = 0.65, p  < 0.001) were directly associated with fatigue. Holding visual impairment severity constant, those who experienced increased symptoms of eye strain & light disturbance and higher levels of depressive symptoms had higher levels of fatigue. Furthermore, eye strain & light disturbance (β = 0.20, 95% BC CI [0.11, 0.30]) and vision-related mobility (β = -0.16, 95% BC CI [-0.25, -0.09]) were significantly associated with the latent fatigue variable through mediation of depressive symptoms. Specifically, higher levels of eye strain and light disturbance, and more problems with vision-related mobility, were associated with higher levels of depressive symptoms (D 1 and D 2 pathways), which in turn was associated with greater fatigue.

Path analysis output for the final multicategorical SEM model ( n  = 220). Arrows represent direct effects with standardized regression coefficients (StdYX for continuous variables, StdY for categorical variables). Constructs of latent variables (diamond shapes) are shown in dotted boxes. * Statistically significant at p  < 0.05. ** Statistically significant at p  < 0.01

Path analysis revealed significant relative total effects of D 1 and D 2 on fatigue, indicating that patients with severe visual impairment had significantly higher levels of fatigue compared to those with mild/moderate visual impairment (pathway c 1 : β = -0.50, 95% BC CI [-0.86, -0.16]) and blindness (pathway c 2 : β = 0.44, 95% BC CI [0.07, 0.80]). In contrast, the direct effects of D 1 and D 2 on fatigue were non-significant when controlling for all other variables in the model. This finding indicates that the visual impairment severity-fatigue association is completely mediated by the other variables included in our model. Bias-corrected bootstrap analysis identified a significant relative total indirect effect of D 1 (β = -0.54, 95% BC CI [-0.81, -0.26]) and D 2 (β = -0.37, 95% BC CI [-0.69, -0.05]) on fatigue, accounting for 93% and 83% of their relative total effects, respectively (Table  5 ).

Eye strain & light disturbance, depressive symptoms and vision-related mobility were all identified as mediators in the link between fatigue and visual impairment severity for the D1 contrast. Relative to those with mild/moderate visual impairment, patients with severe visual impairment had significantly higher levels of depressive symptoms and symptoms of eye strain & light disturbance, and more problems with vision-related mobility, which in turn was associated with increased fatigue symptoms (see Table  5 ). In addition, the indirect effects of eye strain & light disturbance and vision-related mobility were also sequentially mediated by depression (see Table  5 ). As for the D2 contrast, the bias-corrected bootstrap analysis showed that the indirect association with fatigue was mediated by eye strain & light disturbance, and by serial mediation of eye strain & light disturbance via depressive symptoms (see Table  5 ). In other words, compared to patients with blindness, patients with severe visual impairment experienced elevated fatigue levels via higher symptoms of eye strain and light disturbances and related depression.

The present study served two purposes: to test visual impairment severity, eye strain & light disturbances, adaptation to vision loss, vision-related mobility problems as determinants of fatigue, and to examine whether the association between visual impairment severity and fatigue would be mediated by these vision-specific factors and depressive symptoms. To this end, a well-fitting SEM was developed that explained 60% of the variance in fatigue severity and impact on daily life.

With regard to our primary aim, one of the most important findings was the direct association between eye strain & light disturbance and fatigue. Since optimal lighting conditions are essential for improving visual acuity and contrast sensitivity for persons with low vision [ 37 ], light disturbances may lead to fatigue as compensatory efforts might be needed to establish visual perception. Besides, it is possible that persons with visual impairment need to invest additional mental resources to counteract focusing problems and accommodative dysfunctions of the eye, potentially leading to excessive strain and fatigue. Similar hypotheses have been formulated to explain the increased levels of fatigue frequently observed in persons with hearing impairments [ 38 ]. In these studies, fatigue is often linked to an increased cognitive load, resulting from the extra effort necessary to process degraded speech and auditory signals [ 39 , 40 , 41 ]. However, the extent to which mental effort influences fatigue remains disputed and the various mechanisms involved are not fully understood.

Another important finding from our study was the strong influence of depressive symptoms on fatigue. Depression was not only a direct determinant of fatigue with the largest effect size of all variables, it also mediated the indirect associations of vision-related mobility and eye strain & light disturbance with fatigue. These findings are consistent with previous modelling studies in multiple sclerosis [ 42 ] and rheumatoid arthritis [ 43 ], in which depression has been considered one of the most prominent determinants and mediating factors of fatigue. Psychological interventions that focus on depression, such as cognitive behavioural therapy [ 44 ], may therefore also be beneficial in fatigue management of people with visual impairment.

Contrary to expectations, adaptation to vision loss was unrelated to fatigue and was not found to be a significant mediator. However, it did have an indirect effect through depressive symptoms in the first hypothesized model, with better adaptation predicting less depression which in turn decreased fatigue. One possible explanation is that the direct effects of depression and eye strain & light disturbance on fatigue were much greater than that of adaptation to vision loss.

The second aim of the present study involved the association between visual impairment severity, fatigue and potential mediation by vision-specific factors and depression. In general, patients with severe visual impairment reported the highest levels of fatigue severity and fatigue impact on daily life, whereas fatigue levels of patients with blindness were comparable to those with mild/moderate visual impairment. Similar results have been reported by Cypel et al. [ 12 ], in which fatigue symptoms were the lowest for older adults with blindness, but seemed to increase with a greater degree of vision loss. Taken together, these findings indicate that the association between fatigue and visual impairment severity follows an inverted U-shape. Several explanations for this complex relationship seem to arise from our modelling as well.

Our results showed that the association between visual impairment severity and fatigue was fully mediated by an interplay of vision-related factors and depression. Eye strain & light disturbance was found to be an important mediator in the relationship between visual impairment severity and fatigue for both contrasts. A possible explanation for the specific impact of eye strain & light disturbance on fatigue in severe visual impairment might be that these patients rely heavily on their residual vision and therefore likely use it as much as possible. Vision-related mobility problems and depressive symptoms on the other hand, only explained variations in fatigue between patients with mild/moderate and severe visual impairment. The observation that mobility problems and depression no longer contribute to fatigue in patients with blindness suggests that some form of adjustment or coping may occur once visual decline stabilizes or cannot deteriorate any further. However, our SEM did not provide evidence for such a mediating role of adaptation to vision loss in the relationship between visual impairment severity and fatigue. All things considered, our findings indicate that the elevated levels of fatigue patients with severe visual impairment are not a direct result of decreased visual acuity and/or increased visual field problems, but could rather be explained through the consequences of these limitations. Supportive evidence for this possibility comes from previous path analysis studies in which the effect of visual impairment on mental health outcomes was largely explained by physical and psychosocial factors [ 45 , 46 , 47 ].

The findings of the present study are subject to several limitations. First of all, the cross-sectional design prevents us from inferring a causal order of the associations in our model. Although the assumptions of our model were based on a theoretical framework from previous studies among populations with visual impairment, it is important for future research to test the suggested causal pathways within a longitudinal design. A second limitation of the present study pertains the use of self-report measures only. For future studies, performance based measure such as accelerometers [ 48 ] or mobility courses [ 49 ] for vision-related mobility and the use of stray light meters as a proxy for disability glare and light disturbances, might provide some more objective insight into actual performance on vision-related measures and fatigue [ 50 ]. Fourth, the participation rate of our study was relatively low (25.1%), which may have introduced selection bias. Finally, a notable limitation is the large proportion of incomplete data regarding the time intervals between visual acuity assessments and the administration of questionnaires. This lack of precise timing data may affect the interpretation of the association between visual impairment severity and the study outcomes. However, for the 110 participants with complete data, the majority (71%) underwent their eye examination within a year before or after participating in our study. This timeframe provides some reassurance about the validity of the visual acuity measures in relation to the study outcomes. A strength of the present study is the use of visual field data in addition to visual acuity outcomes which added to the accuracy of visual impairment categorization. Another strength is the application of IRT models to optimize the psychometric properties for the majority of our outcome measures. Furthermore, the statistical advantages of SEM analysis enabled us to construct a proxy for eye strain & light disturbance in the absence of a reliable outcome measure in the scientific literature. Although this latent variable was defined by relatively few numbers of single-item indicators, it had high factor loadings and proved to be an important determinant of fatigue in patients with low vision. Another methodological strength of SEM analysis was the ability to conceptualize fatigue in terms of both severity and impact on daily functioning.

Findings from our SEM model indicate that eye strain & lighting disturbances is a vision-specific determinant of fatigue in patients with low vision regardless of the degree of visual impairment severity. Depression was also a strong direct determinant of fatigue and fully mediated the indirect effect of vision-related mobility and adaptation to vision loss on fatigue. Furthermore, our study suggests that patients with severe visual impairment may experience increased levels of fatigue compared to patients with mild/moderate visual impairment and blindness due to higher levels of eye strain & light disturbance. In contrast, the influence of vision-related mobility and depression on fatigue seems to vary by level of visual impairment.

The factors identified by our model provide key elements that can be targeted by future studies when developing treatment options for vision-related fatigue. Our findings suggest that multifaceted interventions aimed at improving underlying symptoms, such as depression and light disturbances, may support adults with visual impairment in coping with fatigue. Findings from a recent usability study showed that a newly developed vision-specific eHealth intervention based on behavioural therapy and self-management has the potential to reduce fatigue severity and fatigue impact in patients with low vision [ 51 ]. Moreover, including screening instruments of depression, (eye) fatigue and lighting disturbances during the intake and early stages of rehabilitation may identify vulnerable patients at risk of developing severe fatigue.

Availability of data and materials

The data sets used and analyzed in the current study are available from the corresponding author on reasonable request.

Abbreviations

Bias-corrected confidence interval

Structural equation modelling

World Health Organization

Visual acuity

Mini Mental State Examination

Fatigue Assessment Scale

Modified Fatigue Impact Scale

Patient Health Questionnaire

Adaptation to Vision Loss questionnaire

Item response theory

Low Vision Quality of Life questionnaire

Dutch ICF Activity Inventory

Multivariate analysis of variance

Root Mean Square Error of Approximation

Standardized Root Mean Residual

Tucker-Lewis Index

Comparative Fit Index

Schakel W, Bode C, van der Aa HPA, Hulshof CTJ, Bosmans JE, van Rens G, van Nispen RMA. Exploring the patient perspective of fatigue in adults with visual impairment: a qualitative study. BMJ Open. 2017;7(8):e015023. https://doi.org/10.1136/bmjopen-2016-015023 .

Article   PubMed   PubMed Central   Google Scholar  

Schakel W, Bode C, Elsman EBM, van der Aa HPA, de Vries R, van Rens G, van Nispen RMA. The association between visual impairment and fatigue: a systematic review and meta-analysis of observational studies. Ophthalmic Physiol Opt. 2019;39(6):399–413. https://doi.org/10.1111/opo.12647 .

Schakel W, van der Aa HPA, Bode C, Hulshof CTJ, van Rens G, van Nispen RMA. The economic burden of visual impairment and comorbid fatigue: a cost-of-illness study (from a societal perspective). Invest Ophthalmol Vis Sci. 2018;59(5):1916–23. https://doi.org/10.1167/iovs.17-23224 .

Article   PubMed   Google Scholar  

Schakel W, Bode C, van de Ven PM, van der Aa HPA, Hulshof CTJ, van Rens G, van Nispen RMA. Understanding fatigue in adults with visual impairment: a path analysis study of sociodemographic, psychological and health-related factors. PLoS One. 2019;14(10):e0224340. https://doi.org/10.1371/journal.pone.0224340 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

van der Aa HP, Comijs HC, Penninx BW, van Rens GH, van Nispen RM. Major depressive and anxiety disorders in visually impaired older adults. Invest Ophthalmol Vis Sci. 2015;56(2):849–54. https://doi.org/10.1167/iovs.14-15848 .

Corfield EC, Martin NG, Nyholt DR. Co-occurrence and symptomatology of fatigue and depression. Compr Psychiatry. 2016;71:1–10. https://doi.org/10.1016/j.comppsych.2016.08.004 .

Parravano M, Petri D, Maurutto E, Lucenteforte E, Menchini F, Lanzetta P, et al. Association between visual impairment and depression in patients attending eye clinics: a meta-analysis. JAMA Ophthalmol. 2021;139(7):753–61. https://doi.org/10.1001/jamaophthalmol.2021.1557 .

Xiang X, Freedman VA, Shah K, Hu RX, Stagg BC, Ehrlich JR. Self-reported vision impairment and subjective well-being in older adults: a longitudinal mediation analysis. J Gerontol A Biol Sci Med Sci. 2020;75(3):589–95. https://doi.org/10.1093/gerona/glz148 .

Rees G, Xie J, Holloway EE, Sturrock BA, Fenwick EK, Keeffe JE, Lamoureux E. Identifying distinct risk factors for vision-specific distress and depressive symptoms in people with vision impairment. Invest Ophthalmol Vis Sci. 2013;54(12):7431–8. https://doi.org/10.1167/iovs.13-12153 .

Nollett C, Ryan B, Bray N, Bunce C, Casten R, Edwards RT, et al. Depressive symptoms in people with vision impairment: a cross-sectional study to identify who is most at risk. BMJ Open. 2019;9(1):e026163. https://doi.org/10.1136/bmjopen-2018-026163 .

Chia EM, Wang JJ, Rochtchina E, Smith W, Cumming RR, Mitchell P. Impact of bilateral visual impairment on health-related quality of life: the Blue Mountains Eye Study. Invest Ophthalmol Vis Sci. 2004;45(1):71–6.

Cypel MC, Salomao SR, Dantas PEC, Lottenberg CL, Kasahara N, Ramos LR, Belfort R Jr. Vision status, ophthalmic assessment, and quality of life in the very old. Arq Bras Oftalmol. 2017;80(3):159–64. https://doi.org/10.5935/0004-2749.20170039 .

Mojon-Azzi SM, Sousa-Poza A, Mojon DS. Impact of low vision on well-being in 10 European countries. Ophthalmologica. 2008;222(3):205–12. https://doi.org/10.1159/000126085 .

Article   CAS   PubMed   Google Scholar  

Yamada Y, Vlachova M, Richter T, Finne-Soveri H, Gindin J, van der Roest H, et al. Prevalence and correlates of hearing and visual impairments in European nursing homes: results from the SHELTER study. J Am Med Dir Assoc. 2014;15(10):738–43. https://doi.org/10.1016/j.jamda.2014.05.012 .

Williams RA, Brody BL, Thomas RG, Kaplan RM, Brown SI. The psychosocial impact of macular degeneration. Arch Ophthalmol. 1998;116(4):514–20. https://doi.org/10.1001/archopht.116.4.514 .

Callahan CM, Unverzagt FW, Hui SL, Perkins AJ, Hendrie HC. Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med Care. 2002;40(9):771–81. https://doi.org/10.1097/01.MLR.0000024610.33213.C8 .

Michielsen HJ, De Vries J, Van Heck GL, Van de Vijver FJ, Sijtsma K. Examination of the dimensionality of fatigue: the construction of the Fatigue Assessment Scale (FAS). Eur J Psychol Assess. 2004;20(1):39–48. https://doi.org/10.1027/1015-5759.20.1.39 .

Article   Google Scholar  

Fisk JD, Pontefract A, Ritvo PG, Archibald CJ, Murray TJ. The impact of fatigue on patients with multiple sclerosis. Can J Neurol Sci. 1994;21(1):9–14.

Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaire. JAMA. 1999;282(18):1737–44. https://doi.org/10.1001/jama.282.18.1737 .

Horowitz A, Reinhardt JP, Raykov T. Development and validation of a short-form adaptation of the age-related vision loss scale: the AVL12. J Vis Impair Blind. 2007;101(3):146–59. https://doi.org/10.1177/0145482X0710100303 .

van der Aa HP, Xie J, Rees G, Fenwick E, Holloway EE, van Rens GH, van Nispen RM. Validated prediction model of depression in visually impaired older adults. Ophthalmology. 2016;123(5):1164–6. https://doi.org/10.1016/j.ophtha.2015.11.028 .

van Nispen RM, Knol DL, Neve HJ, van Rens GH. A multilevel item response theory model was investigated for longitudinal vision-related quality-of-life data. J Clin Epidemiol. 2010;63(3):321–30. https://doi.org/10.1016/j.jclinepi.2009.06.012 .

van Nispen RM, Knol DL, Langelaan M, van Rens GH. Re-evaluating a vision-related quality of life questionnaire with item response theory (IRT) and differential item functioning (DIF) analyses. BMC Med Res Methodol. 2011;11:125. https://doi.org/10.1186/1471-2288-11-125 .

Bruijning JE, van Rens G, Fick M, Knol DL, van Nispen R. Longitudinal observation, evaluation and interpretation of coping with mental (emotional) health in low vision rehabilitation using the Dutch ICF Activity Inventory. Health Qual Life Outcomes. 2014;12:182. https://doi.org/10.1186/s12955-014-0182-4 .

Muthén LK, Muthén BO. Mplus User's Guide. 7th ed. Los Angeles: Muhtén & Muthén; 2015.

Heesterbeek TJ, van der Aa HPA, van Rens G, Twisk JWR, van Nispen RMA. The incidence and predictors of depressive and anxiety symptoms in older adults with vision impairment: a longitudinal prospective cohort study. Ophthalmic Physiol Opt. 2017;37(4):385–98. https://doi.org/10.1111/opo.12388 .

Tolman J, Hill RD, Kleinschmidt JJ, Gregg CH. Psychosocial adaptation to visual impairment and its relationship to depressive affect in older adults with age-related macular degeneration. Gerontologist. 2005;45(6):747–53.

Tabrett DR, Latham K. Adjustment to vision loss in a mixed sample of adults with established visual impairment. Invest Ophthalmol Vis Sci. 2012;53(11):7227–34. https://doi.org/10.1167/iovs.12-10404 .

Mackinnon DP. Single mediator model details. In: Introduction to statistical mediation analysis. New York: Taylor & Francis Group; 2008. p. 79–103.

Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91. https://doi.org/10.3758/brm.40.3.879 .

Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014;67(3):451–70. https://doi.org/10.1111/bmsp.12028 .

Wang J, Wang X. Structural equation modeling: applications using Mplus. 2nd ed. Hoboken: John Wiley and Sons; 2012.

Book   Google Scholar  

Weston R, Gore PA. A brief guide to structural equation modeling. Couns Psychol. 2006;34(5):719–51. https://doi.org/10.1177/0011000006286345 .

Hair JF, Black WC, Babin BJ. Multivariate data analysis: a global perspective. New Jersey: Pearson Prentice Hall; 2010.

Google Scholar  

Kline RB. Principles and practice of structural equation modeling. 2nd ed. New York: Guilford Publications; 2005.

Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educ Psychol Meas. 2013;76(6):913–34. https://doi.org/10.1177/0013164413495237 .

Markowitz SN. Principles of modern low vision rehabilitation. Can J Ophthalmol. 2006;41(3):289–312. https://doi.org/10.1139/I06-027 .

Holman JA, Drummond A, Naylor G. The effect of hearing loss and hearing device fitting on fatigue in adults: A systematic review. Ear Hear. 2020;42(1):1–11. https://doi.org/10.1097/AUD.0000000000000909 .

Article   PubMed Central   Google Scholar  

Alhanbali S, Dawes P, Lloyd S, Munro KJ. Self-reported listening-related effort and fatigue in hearing-impaired adults. Ear Hear. 2017;38(1):e39–48. https://doi.org/10.1097/AUD.0000000000000361 .

Hornsby BW, Kipp AM. Subjective ratings of fatigue and vigor in adults with hearing loss are driven by perceived hearing difficulties not degree of hearing loss. Ear Hear. 2016;37(1):e1-10. https://doi.org/10.1097/AUD.0000000000000203 .

McGarrigle R, Munro KJ, Dawes P, Stewart AJ, Moore DR, Barry JG, Amitay S. Listening effort and fatigue: what exactly are we measuring? A British Society of Audiology Cognition in Hearing Special Interest Group “white paper.” Int J Audiol. 2014;53(7):433–40. https://doi.org/10.3109/14992027.2014.890296 .

Fidao A, De Livera A, Nag N, Neate S, Jelinek GA, Simpson-Yap S. Depression mediates the relationship between fatigue and mental health-related quality of life in multiple sclerosis. Mult Scler Relat Disord. 2021;47:102620. https://doi.org/10.1016/j.msard.2020.102620 .

Silva CFR, Duarte C, Ferreira RJO, Santos E, da Silva JAP. Depression, disability and sleep disturbance are the main explanatory factors of fatigue in rheumatoid arthritis: a path analysis model. Clin Exp Rheumatol. 2020;38(2):314–21.

van der Aa HP, van Rens GH, Comijs HC, Margrain TH, Gallindo-Garre F, Twisk JW, van Nispen RM. Stepped care for depression and anxiety in visually impaired older adults: multicentre randomised controlled trial. BMJ. 2015;351:h6127. https://doi.org/10.1136/bmj.h6127 .

Bookwala J, Lawson B. Poor vision, functioning, and depressive symptoms: a test of the activity restriction model. Gerontologist. 2011;51(6):798–808. https://doi.org/10.1093/geront/gnr051 .

Dong X, Ng N. Contribution of multiple pathways to the relationship between visual impairment and depression: explaining mental health inequalities among older Chinese adults. J Affect Disord. 2021;278:350–6. https://doi.org/10.1016/j.jad.2020.09.068 .

Gong X, Ni Z, Wu B. The mediating roles of functional limitations and social support on the relationship between vision impairment and depressive symptoms in older adults. Ageing Soc. 2020;40(3):465–79.

Cai Y, Schrack JA, Wang H, E JY, Wanigatunga AA, Agrawal Y, et al. Visual impairment and objectively measured physical activity in middle-aged and older adults. J Gerontol A Biol Sci Med Sci. 2021;76(12):2194–203. https://doi.org/10.1093/gerona/glab103 .

Bertaud S, Zenouda A, Lombardi M, Authie C, Brasnu E, Hamard P, et al. Glare and mobility performance in glaucoma: a pilot study. J Glaucoma. 2021;30(11):963–70. https://doi.org/10.1097/IJG.0000000000001936 .

van Rijn LJ, Nischler C, Gamer D, Franssen L, de Wit G, Kaper R, et al. Measurement of stray light and glare: comparison of Nyktotest, Mesotest, stray light meter, and computer implemented stray light meter. Br J Ophthalmol. 2005;89(3):345–51. https://doi.org/10.1136/bjo.2004.044990 .

Veldman MHJ, van der Aa HPA, Knoop H, Bode C, Hulshof CTJ, van der Ham L, et al. Usability and feasibility of E-nergEYEze: a blended vision-specific E-health based cognitive behavioral therapy and self-management intervention to reduce fatigue in adults with visual impairment. BMC Health Serv Res. 2023;23(1):1271. https://doi.org/10.1186/s12913-023-10193-4 .

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Acknowledgements

We would like to express our gratitude to all participants who agreed to be interviewed and shared their experiences in this study.

Financial support was provided by ‘ZonMw Inzicht’, the Netherlands Organizations for Health Research and Development – InSight Society [grant number 60–0063598146], Katholieke Stichting voor Blinden en Slechtzienden, Stichting tot Verbetering van het Lot der Blinden and Stichting Blindenhulp. The funders had no role in the design and conduct of the present study or in the writing of the manuscript.

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RvN, GvR, CB and WS contributed to the study conception and design. Statistical analysis was performed by WS and PvdV. RvN, CB and WS interpreted the data. The first draft of the manuscript was written by WS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Schakel, W., Bode, C., van de Ven, P.M. et al. The multiple mediating effects of vision-specific factors and depression on the association between visual impairment severity and fatigue: a path analysis study. BMC Psychiatry 24 , 572 (2024). https://doi.org/10.1186/s12888-024-06014-5

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Systematic Reviews

• Review Types
• Scoping Review Steps
• Before You Begin

P = Plan: decide on your search methods

I = identify: search for studies that match your criteria, e = evaluate: exclude or include studies, c = collect: extract and synthesize key data, e = explain: give context and rate the strength of the studies, s = summarize: write and publish your final report.

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Congratulations!

You've decided to conduct a Systematic Review! Please see the associated steps below. You can follow the  P-I-E-C-E-S = Plan, Identify, Evaluate, Collect, Explain, Summarize  system or any number of systematic review processes available  (Foster & Jewell, 2017) .

P =   Plan: decide on your search methods

Determine your Research Question 

By now you should have identified gaps in the field and have a specific question you are seeking to answer. This will likely have taken several iterations and is the most important part of the Systematic Review process. 

Identify Relevant Systematic Reviews 

Once you've finalized a research question, you should be able to locate existing systematic reviews on or similar to your topic. existing systematic reviews will be your clues to mine for keywords, sample searches in various databases, and will help your team finalize your review question and develop your  inclusion and exclusion criteria. , decide on a protocol and reporting standard, your  protocol  is essentially a project plan and data management strategy for an objective, reproducible, sound methodology for peer review. the  reporting standard or guidelines  are not a protocols, but rather a set of standards to guide the development of your systematic review. often they include checklists. it is not required, but highly recommended to follow a reporting standard. .

Protocol registry:   Reviewing existing systematic reviews and registering your protocol will increase transparency, minimize bias, and reduce the redundancy of groups working on the same topics ( PLoS Medicine Editors, 2011 ). Protocols can serve as internal or external documents. Protocols can be made public prior to the review. Some registries allow for keeping a protocol private for a set period of time.

Cochrane Database of Systematic Reviews (UGA Login)  (Health Sciences)

A collection of regularly updated, systematic reviews of the effects of health care. New reviews are added with each issue of The Cochrane Library Reviews mainly of randomized controlled trials. All reviews have protocols.

PROSPERO  (General)

This is an international register of systematic reviews and is public. 

Campbell Corporation  (Education & Social Sciences)

Topics covered include Ageing; Business and Management; Climate Solutions; Crime and Justice; Disability;  Education; International Development; Knowledge Translation and Implementation; Methods; Nutrition and Food Security; Sexual Orientation and Gender Identity; Social Welfare; and Training.

Systematic Review for Animals and Food  (Vet Med & Animal Science)

Reporting Standards:  

Campbell MECCIR Standards  (Education & Social Sciences)

Cochrane Guides & Handbooks  (Health & Medical Sciences)

Institute of Medicine of the National Academies: Finding What Works in Healthcare: Standards for Systematic Reviews  (healthcare)

• PRISMA for Systematic Review Protocols  (General)
• PRISMA Checklist  (General)
• PRISMA for Scoping Reviews  (General)

Decide on Databases and Grey Literature for Systematic Review Research

Because the purpose of a SR is to find all studies related to your research question, you will need to search multiple databases. You should be able to name the databases you are already familiar with using. Your librarian will be able to recommend additional databases, including some of the following: 

• PubMed  (Health Sciences)
• Web of Science  
• Cochrane Database  (Biomedical)
• National and Regional Databases (i.e. WHO LILACS scientific health information from Latin America and the Caribbean countries)
• CINAHL  (Health Sciences)
• PsycINFO  (Psychology)

Depending on your topic, you may want to search clinical trials and grey literature. See this  guide  for more on grey literature.

Develop Keywords and Write a Search Strategy

Go   here for help with writing your search strategy  

Translate Search Strategies

Each database you use will have different methods of searching and resulting search strings, including syntax. ideally you will create one master keyword list and translate it for each database. below are tools to assist with translating search strings. .

Includes syntax for Cochrane Library, EBSCO, ProQuest, Ovid, and POPLINE.

The IEBH SR-Accelerator is a suite of tools to assist in speeding up portions of the Systematic Review process, including the Polyglot tool which translates searches across databases. 

University of Michigan Search 101 - SR Database Cheat Sheet

Storing, Screening and Full-Text Screening of Your Citations

Because systematic review literature searches may produce thousands of citations and abstracts, the research team will be screening and systematically reviewing large amounts of results. During  screening , you will remove duplicates and remove studies that are not relevant to your topic based on a review of titles and abstracts. Of what remains, the  full-text screening  of the studies will then need to be conducted to confirm that they fit within your  inclusion/exclusion criteria.   

The results of the literature review and screening processes are best managed by various tools and software. You can also use a simple form or table to log the relevant information from each study. Consider whether you will be coding your data during the extraction process in your decision on which tool or software to use. Your librarian can consult on which of these is best suited to your research needs.

• EndNote  Guide (UGA supported citation tracking software) - great for storing, organizing, and de-duplication
• RefWorks  Guide (UGA supported citation tracking software) - great for storing, organizing, and de-duplication
• Rayyan  (free service) - great for initial title/abstract screening OR full-text screening as cannot differentiate; not ideal for de-duplication
• Covidence  (requires a subscription) - full suite of systematic review tools including meta-analysis
• Combining Software  (EndNote, Google Forms, Excel) 
• Forms such as  Qualtrics  (UGA EITS software) can note who the coder is, creates charts and tables, good when have a review of multiple types of studies

Data Extraction

Data extraction processes differ between qualitative and quantitative evidence syntheses. In both cases, you must provide the reader with a clear overview of the studies you have included, their similarities and differences, and the findings. Extraction should be done in accordance to pre-established guidelines, such as PRISMA. 

Some systematic reviews contain meta-analysis of the quantitative findings of the results. Consider including a statistician on your team to complete the analysis of all individual study results. Meta-analysis will tell you how much or what the actual results is across the studies and explains results in a measure of variance, typically called a forest plot.

Systematic review price models have changed over the years. Previously, you had to depend on departmental access to software that would cost several hundred dollars. Now that the software is cloud-based, tiered payment systems are now available. Sometimes there is a free tier level, but costs go up for functionality, number or users, or both. Depending on the organization's model, payments may be monthly, annual or per project/review.

• Always check your departmental resources before making a purchase.
• View all training videos and other resources before starting your project.
• If your access is limited to a specific amount of time, wait to purchase until the appropriate work stage

Software list

Tool created by Brown University to assist with screening for systematic reviews.

• Cochrane's RevMan

Review Manager (RevMan) is the software used for preparing and maintaining Cochrane Reviews.

Systematic review tool intended to assist with the screening and extraction process. (Requires subscription)

• Distiller SR

DistillerSR is an online application designed specifically for the screening and data extraction phases of a systematic review (Requires subscription) Student and Faculty tiers have monthly pricing with a three month minimum. Number of projects is limited by pricing.

• EPPI Reviewer  (requires subscription, free trial)

It includes features such as text mining, data clustering, classification and term extraction

Rayyan is a free web-based application that can be used to screen titles, abstracts, and full text. Allows for multiple simultaneous users.

• AHRQ's  SRDR tool  (free) which is web-based and has a training environment, tutorials, and example templates of systematic review data extraction forms

"System for the Unified Management, Assessment and Review of Information, the Joanna Briggs Institutes premier software for the systematic review of literature."

• Syras Pricing is based on both number of abstracts and number of collaborators. The free tier is limited to 300 abstracts and two collaborators. Rather than monthly pricing, the payment is one-time per project.

Evidence Synthesis or Critical Appraisal

PRISMA guidelines suggest including critical appraisal of the included studies to assess the risk of bias and to include the assessment in your final manuscripts. There are several appraisal tools available depending on your discipline and area of research.

Simple overview of risk of bias assessment, including examples of how to assess and present your conclusions.

CASP is an organization that provides resources for healthcare professionals, but their appraisal tools can be used for varying study types across disciplines.

From the Joanna Briggs Institute: "JBI’s critical appraisal tools assist in assessing the trustworthiness, relevance and results of published papers."

Johns Hopkins Evidence-Based Practice Model  (health sciences)

National Academies of Sciences, Engineering, and Medicine

Document the search; 5.1.6. Include a methods section

List  of additional critical appraisal tools from Cardiff University. 

Synthesize, Map, or Describe the Results

Prepare your process and findings in a final manuscript. Be sure to check your PRISMA checklist or other reporting standard. You will want to include the full formatted search strategy for the appendix, as well as include documentation of your search methodology. A convenient way to illustrate this process is through a  PRISMA  Flow Diagram. 

Attribution: Unless noted otherwise, this section of the guide was adapted from Texas A&M's "Systematic Reviews and Related Evidence Syntheses" 

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Since the housing reform in 1998, China’s real estate market has developed rapidly and real estate prices have been on the rise. The real estate industry has become the pillar industry of our economy. However, historical experience has repeatedly proved that if economic development relies too heavily on real estate and real estate prices continue to rise rapidly and rapidly, it will affect enterprises’ investment, production efficiency and innovation ability, which will affect the steady and healthy development of enterprises. Some studies have found that the rise of house prices has adverse effects on the development of non-real estate enterprises such as cost effect, investment transfer effect and credit effect. Therefore, it is necessary to intensify the regulation and control of house prices, vigorously develop long-term public rental housing and improve the dynamic monitoring mechanism of real estate mortgage, limit the flow of credit funds into the real estate.

Real Estate Price , Enterprise Investment , Production Efficiency , Innovation Ability

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1. Introduction

Since the house reform in 1998, China’s real estate market has undergone a revolutionary change. Real estate industry has become an important pillar of the national economy. Overall, the development of China’s real estate economy presents several characteristics: 1) Large-scale investment and rapid growth: In 2016, the proportion of real estate investment in fixed assets investment in the whole society was 17.2%, and the added value of real estate accounted for 7.3% of GDP, which were respectively 4.5% and 3.7% higher than that in 1998; 2) High prices and rapid prices rising speed: Housing prices in China’s first-tier cities far exceed the reasonable level. Since 1998, housing prices in China have been on the rise for almost 20 years. Only during the SARS in 2003 and during the international financial crisis in 2008, house prices dropped briefly. In the first half of 2017, housing prices in China’s first-tier cities are the highest in the world. According to NUMBEO, the price/income ratio of Shenzhen, Beijing, Shanghai and Guangzhou respectively ranked No. 1, No. 3, No. 4 and No. 18 in the world (“The price/income” ratio means the ratio of the average housing price to the average income of local people); 3) The profit of Real estate is very high: Accompanied by high housing prices there is high industry profit. Real estate investment can also share capital appreciation profits from higher prices beyond high industry profit. Therefore, the real estate industry’s profit margin is much higher than the real economy sector. Since the housing reform in 1998, the profit margin of the real estate industry has been increasing year by year, from 0% in 1998 to about 14% in 2010. At the same time, though the industrial profit rate has risen, the increasing rate is far less than that of the real estate industry, which remained at around 6% after 2001. 4) The development of real estate industry depends too much on bank credit. Real estate belongs to capital-intensive industry. From the land transfer, to development and construction, real estate enterprises need a lot of money. Domestic loans, self-financing and other funds are the main sources of funds for real estate enterprises in China. Other funds include deposits, advance receipts and personal mortgage loans. In addition, real estate loans grew faster than average loan growth. By the end of 2016, real estate loans from financial institutions amounted to 26.7 trillion yuan, an increase of 27.0% over the same period of last year. The loan balance of real estate accounted for 25.0% of all loans, which was 2.7 percentage points higher than the end of the previous year. In 2016, new real estate loans reached 5.7 trillion yuan, with a cumulative increase of 44.8% of the total new loans in the same period.

The rapid development of the real estate industry has played a significant role in the economic development of China. On the one hand, the investment in the real estate industry itself can promote regional economic development. On the other hand, the real estate industry has long industry chain and plays the leading role. Therefore, the development of the real estate industry can affect and prompt its upstream and downstream industries (Wang Guojun, Liu Shuixing) [1] . There is no doubt that the booming real estate industry has played a positive role on economic growth and employment expansion. However, when the real estate industry is overheated, its potential harm to the real economy can not be ignored. The rapid rise of house prices in China has attracted attention from all sectors of society. The existing research mainly focuses on the impact of rising house prices on the investment, output efficiency and innovation of enterprises. In theory, the specific impact path includes cost effect, investment crowding-out effect and credit effect, etc. Both the cost effect and the investment crowding-out effect have adverse effects on the long-term development of enterprises, and the credit effect will ease the financing constraints of enterprises to some extent. However, some studies have found that the impact of the rising house prices in China is based on cost effect and investment crowding-out effect (Liu, 2016 [2] ; Deng, 2014 [3] ; Fu, 2017 [4] ). So, it is necessary to pay enough attention to the rising house prices.

2. The Impact of Rising Housing Prices on Investment and Innovation in Business

The issue of business investment has always been a hot spot for both theorists and practitioners. Housing price fluctuations will have an impact on the company’s investment behavior, thereby affecting the investment efficiency and innovation activities of enterprises. We have combed the scientific literature for the impact of housing price fluctuations on business investment and innovation.

As early as 1904, Veloen (1904) pointed out that there was a positive relationship between asset prices and mortgage liabilities. As asset prices rise, the value of collateral owned by the business increases and the business can get more loans from banks or other financial institutions. A large number of macro-literature studied the spillover effect of rising asset prices. Samuelson (1958) [5] established a generational overlap model. In terms of financing agency cost, he studied the influence of bubble economy on economic growth and found that the higher the value of an enterprise’s asset, the smaller the cost of its financing agency. When the economic form is more optimistic, the value of corporate assets will increase and the cost of financing agents will decline, thus enterprises can obtain more loans to increase their investment. When the economic form is poor, the opposite conclusion can be reached. Therefore, the temporary impact of asset prices will be magnified and maintained by the market. At macro-economic level, Bowen (1994) [6] pointed out the positive correlation between real estate price and the scale of enterprise investment by using the macroscopic data of UK and USA. Farthi and Titole (2010) [7] assumed in the model that bubbles and external liquidity could be used to ease financing constraints. Enterprises can obtain external funds through asset securitization. When there is a bubble in securitized assets, the prices will be increased and the enterprise’s ability to obtain external funds will be enhanced, which will promote the firms’ investment. At the same time, they also pointed out that the securitization of asset bubbles will increase the systemic risk. Once the bubble burst, it is easy to induce financial risks. The above research suggests that when the price of the asset that can be used for mortgage bubbles, the value of it will be increased. It means companies can get more loans, which they call the mitigation effect. On the effect of financing mitigation enterprises promote their investment, which is helpful for economic growth and business innovation.

However, empirical studies on the financing mitigation effect of real estate price bubbles are based on the data from developed countries such as the United States and Japan. Wu et al (2013) [8] used data from 444 listed companies in 35 cities during 2003-2011. The result showed that the financing mitigation effect did not exist in our country. Miao and Wang’s (2012) [9] research based on the endogenous growth model shows that since real estate is a non-productive asset without technological spillover effect, the housing prices rising will lead to speculative bubbles and produce a crowding-out effect on productive investment. This crowding-in effect leads to investment in non-productive assets, which will produce a negative impact on activities and economic growth. The influence mechanism is that the rise of home prices makes the real estate sector returns much higher than others. Because of the capital profit chasing, the industrial capital flows to the real estate market, which seriously squeezes the space of the industrial development. Wang Wenchun and Rong Zhao (2014) [10] also showed that the high return of real estate has attracted many non-real estate enterprises to enter real estate field. The lower the profit margins, the more likely the enterprise will enter the real estate field. Particularly, state-owned enterprises can obtain scarce credit funds at lower cost against the background of soft budget constraints and financial repression. They put a lot of these credit funds into the real estate industry, and then squeeze the investment of research and development into the enterprise, which will negatively affect the innovation activities. Yu Jingwen (2015) [11] found that in the background of rapid growth in house prices and high return on real estate investment, enterprises allocated resources to the real estate sector, thus squeezing R & D investment with high investment risk and long return period. They also found that the growth rate of house prices increased by 1 percentage point, the proportion of R & D investment in total assets decreased by 0.051 percentage point. Zhang Jie et al. (2016) [12] used provincial-level panel data in China to verify that in those provinces where the faster the real estate investment growth is, the lower the growth rate of R & D investment and invention patent authorization are, which may indicate that the real estate development has a direct hindrance to the innovation activity in the Chinese situation. Second, with the rapid growth of real estate investment, China’s financial system has a further inhibitory effect on innovation activities through the biased effect of the term structure of real estate loan.

In general, rising house prices have both positive and negative impacts on the innovation and investment of non-real estate enterprises. On the one hand, the real estate bubbles raise the house prices, and increase the value of land, building and other resources owned by the enterprises, which will alleviate the financing constraints of enterprises, thus facilitating the innovation investment (credit mitigation effect) of enterprises. On the other hand, the real estate bubbles also increase the return on investment of the real estate industry, resulting an investment shift from industrial enterprises to the real estate. Because of the financing constraint, the innovation investment of industrial enterprises is restrained (investment crowding out effect). However, empirical studies in recent years found that the mitigation of financing in China was not obvious, mainly based on the effect of investment crowding out. The specific reasons for this are as follows: First, the current situation in our country is that due to the rapid increase of housing prices and the promotion of land prices and rents, the margins between different industries are great. In addition, the economic cycle is in the downward phase and the reverse inhibition has exceeded the positive promotion. The rapid development of the real estate market makes the real estate business profitable and attracts more and more capitals. But the real estate industry generally does not have the technology spillover effect, the investment direction transfer will lead the inhibition to the innovation and the entrepreneurial activity, producing a negative influence to the economic growth. Second, most important enterprises in China are state-owned enterprises. Close ties with local governments and state-owned banks make it easier to obtain capital without any financial constraints (Cull and Xu, 2005) [13] . Therefore, state-owned enterprises do not need to use the mortgage loan. Third, the collateral channel mechanism in China can’t play the role because of the financial market standards (for example, the controlling lending). Even if private companies are constrained by financing, they cannot get more loans through asset appreciation.

3. The Impact of Rising House Prices on the Productivity of Enterprises

Housing prices will not only affect the investment behavior of enterprises, but also further affect the productivity of enterprises output. Chen (2015) [14] found that the rise of housing prices is a vital factor that hinders the sustained and steady growth of economy in China. The reason is that high housing prices will lead to misallocation of resources, reduce the efficiency of resource redistribution and then reduce total factor productivity. Since 2003, as housing prices in China have risen rapidly, both the growth rate of total factor productivity and the efficiency of resource allocation have been declining. Based on the Chinese micro-industrial enterprise database from 2000 to 2007, Chen (2015) [14] found that house prices rose 1%, resource reconfiguration efficiency fell by 0. 62%, total factor production rate decreased by 0.45%. The high profit margin caused by high housing prices and the “Upside down” mechanism of total factor productivity is the important reason for the mismatch of resources and the reduction of resource allocation efficiency. Based on the database of China’s industrial enterprises, Lu Lingzhi (2016) [15] found that the rise in house prices would cause industrial enterprises to enter the real estate industry, resulting in the decline of industrial production efficiency. It verified the existence of investment crowding out effect and resource mismatch effect.

In theory, rising house prices change the price-ratio relationship between real estate and other goods and factors, increase the present value of real estate stock, expand the money supply and cause changes in the relative profit levels of the industries which have different degrees of connection with real estate. It will cause a cost effect, investment transfer effect, correlation effect, financing effect and distribution effect on industrial sectors.

3.1. Cost Effectiveness

Real estate is one of the important production factors of industrial enterprises. The rise of house price increases the cost of rent or purchase (Gao Bo, 2012) [16] , causes the price of land and building materials to rise (Liu Lin, 2013) [17] , increasing the cost of plant construction and increasing the cost of industrial enterprises. On the one hand, rising house prices raise the rent for industrial enterprises and increase the production and distribution costs of industrial products. On the other hand, rising house prices increase the cost of living in the labor force, resulting in higher wages. Rising house prices cause the monetary value of real estate to increase. In order to meet the increasing money supply for transaction demand and prevent demand, the money supply must be raised, which will cause the general price level to rise, increase the living expenses of the labor force, resulting in a further rise in labor wages and the labor cost of the industrial enterprises. Rising housing prices increase the cost of industrial enterprises from multiple dimensions and continue to erode the competitive advantage of China’s low-cost industries, inhibiting industrial output.

3.2. Investment Transfer Effect

The rise in housing prices has led to a rise in real estate investment returns, causing investment funds to real estate and closely related industries, leading to over-allocation of resources in the real estate sector, resulting in the transfer effect of industrial investment. First, the existing industrial enterprises investment funds will be affected by high returns. They will flow into the real estate industry in various forms like shareholding and borrowing, participate in the development of the real estate industry, and reduce the level of industrial investment. Second, the rapid rise in housing prices encourages industrial enterprises to hold more real estate in order to obtain high returns and squeeze industrial investment. Third, the rapid rise in house prices has changed the relative return on investment of the real estate sector and industry sector. As a result, the investment that originally flows into the industry has shifted to the real estate field. It is more and more difficult for the industrial sector to obtain external capital. The effect of investment transfer on industrial investment is unbalanced. The cycle of R&D investment is long and the impact is slow, so the effect in it is greater, Which will affect the innovation capability of industrial enterprises, make the capacity of industrial sustainable development weaken, and form a vicious circle that inhibits industrial output Continued growth.

3.3. Financing Effect

The Property prices have led to an increase in the nominal price of assets. The nominal scale of assets expands. Real estate as an important collateral for financing, its prices increasing have led to an increase in the value of collateralized corporate finance, increased the value of collateral for industrial enterprises, promoted corporate to use credit loans, given a premium on the liquidity of debt financing and reduced the financing costs, so it is helpful to alleviate the financing constraints of enterprises (Yu Wenjing, 2015; Luo Shikong, 2013) [18] . Rising home prices have led to higher profits for real estate and some closely related industries, increasing the profit opportunities by investing real estate. The real estate industry has a short cycle, quick profits and high returns. Compared with it, industrial investment lacks competitive advantage in the financing market, resulting in a raise in the financing cost of industrial investment and reduction in financing opportunities of industrial investment in the financial market. The rapid rise in housing prices will trigger the need to regulate the real estate market. The central bank will regulate the price level of the money market and the scale of credit, which will increase the cost of using capital in industrial enterprises and make it more difficult to get financing [19] .

3.4. Allocation Effect

Rising house prices have led to the expansion of asset size, increasing of money supply and higher general price levels. Since the increase speed of prices are unbalanced, the allocation effect is generated. Rising house prices are good for homeowners and real-estate holders, but bad for ordinary Working-class, especially for younger and less educated workers. Since marginal consumption of low-income earners is greater than that of high-income earners, the distribution effect reduces people’s demand for industrial products. The rise in house prices has led to an increase in the share of consumer spending. The speculative opportunities brought by the rapid rise in house prices have increased the share of spending on real estate, making consumers less able to pay for goods other than real estate and reducing demand for industrial products. Over a long period of time, housing prices in China monotonous rise. The maintenance and appreciation capacity of real estate are excellent, making it an ideal investment product. In the case of few domestic investment channels, the increase of individual investment in real estate will reduce the demand for industrial products, further strengthen the distribution effect and inhibit industrial output.

To sum up, while rising house prices can both boost the demand for industrial products and promote the growth of industrial output, they also raise the costs for industrial enterprises to use factors of production, squeeze out industrial investment and reduce the demand for industrial products. Under the influence of various forces, rising house prices generally have an inhibitory effect on industrial output.

4. Summary and Policy Recommendations

By combing the existing research at home and abroad, it is found that the rising house prices have adverse effects on the development of non-real estate enterprises such as cost effect, investment transfer effect, financing effect and distribution effect and so on. The unbalanced profit margins among industries attract capital to flow into real estate enterprises, and thus affect the innovative activities and production efficiency, resulting in a harmful effect on the stable and healthy development of enterprises. Obviously, the thought of trying to boost growth by pushing up prices is unhelpful. To build a long-term mechanism for steady and healthy development of the real estate market and to curb the trend of excessive housing price rise will be of great significance for adjusting the economic structure and promoting economic growth. In order to curb the adverse impact of rising prices on the development of enterprises, we can mainly consider and implement the policies as follows:

・ Effectively reduce the housing costs of middle-low income earners, new-employed, non-house workers and migrant workers. Government should be focus on the development of long-term public rental housing, and explore the development of housing subsidies from “subsidies in kind” to “monetary subsidies” in the form of vouchers and accelerate the promotion of common Property rights housing pilot reform.

・ Establish and perfect real estate collateral dynamic monitoring mechanism; Determine and adjust the upper limit of real estate collateral timely according to the economic cycle and risk status; Comprehensively and accurately evaluate the value of collateralized real estate, and further regulate and tighten the mortgage loan.

・ Control the inflow of credit funds into the real estate field; Set up a monitoring and early warning mechanism for real estate enterprises to invest in the real estate market; Supervise the capital status of the parent companies that have invested in the real estate industry; Prevent and stop the enterprises from using the main business loans for real estate investment.

・ Reform the fiscal and taxation system, adjust the fiscal-rights relations at the central level, and reduce the over-reliance of local governments on land transfer payments. At the same time, encourage enterprises to develop their own R & D capacity, increase the intensity of government R & D subsidies.

Conflicts of Interest

The authors declare no conflicts of interest.

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Analysing near-miss incidents in construction: a systematic literature review.

1. Introduction

• Q 1 —Are near-miss events in construction industry the subject of scientific research?
• Q 2 —What methods have been employed thus far to obtain information on near misses and systems for recording incidents in construction companies?
• Q 3 —What methods have been used to analyse the information and figures obtained?
• Q 4 —What are the key aspects of near misses in the construction industry that have been of interest to the researchers?

2. Definition of Near-Miss Events

3. research methodology, 4.1. a statistical analysis of publications, 4.2. methods used to obtain information about near misses, 4.2.1. traditional methods.

• Traditional registration forms
• Computerized systems for the recording of events
• Surveys and interviews

4.2.2. Real-Time Monitoring Systems

• Employee-tracking systems
• Video surveillance systems
• Wearable technology
• Motion sensors

4.3. Methods Used to Analyse the Information and Figures That Have Been Obtained

4.3.1. quantitative and qualitative statistical methods, 4.3.2. analysis using artificial intelligence (ai), 4.3.3. building information modelling, 4.4. key aspects of near-miss investigations in the construction industry, 4.4.1. occupational risk assessment, 4.4.2. causes of hazards in construction, 4.4.3. time series of near misses, 4.4.4. material factors of construction processes, 4.5. a comprehensive overview of the research questions and references on near misses in the construction industry, 5. discussion, 5.1. interest of researchers in near misses in construction (question 1), 5.2. methods used to obtain near-miss information (question 2), 5.3. methods used to analyse the information and data sets (question 3), 5.4. key aspects of near-miss investigations in the construction industry (question 4), 6. conclusions.

• A quantitative analysis of the Q 1 question has revealed a positive trend, namely that there is a growing interest among researchers in studying near misses in construction. The greatest interest in NM topics is observed in the United States of America, China, the United Kingdom, Australia, Hong Kong, and Germany. Additionally, there has been a recent emergence of interest in Poland. The majority of articles are mainly published in journals such as Safety Science (10), Journal of Construction Engineering and Management (8), and Automation in Construction (5);
• The analysis of question Q 2 illustrates that traditional paper-based event registration systems are currently being superseded by advanced IT systems. However, both traditional and advanced systems are subject to the disadvantage of relying on employee-reported data, which introduces a significant degree of uncertainty regarding in the quality of the information provided. A substantial proportion of the data and findings presented in the studies was obtained through surveys and interviews. The implementation of real-time monitoring systems is becoming increasingly prevalent in construction sites. The objective of such systems is to provide immediate alerts in the event of potential hazards, thereby preventing a significant number of near misses. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, and advanced IT technologies, among others;
• The analysis of acquired near-miss data is primarily conducted through the utilisation of quantitative and qualitative statistical methods, as evidenced by the examination of the Q 3 question. In recent years, research utilising artificial intelligence (AI) has made significant advances. The most commonly employed artificial intelligence techniques include text mining, machine learning, and artificial neural networks. The growing deployment of Building Information Modelling (BIM) technology has precipitated a profound transformation in the safety management of construction sites, with the advent of sophisticated tools for the identification and management of hazardous occurrences;
• In response to question Q 4 , the study of near misses in the construction industry has identified several key aspects that have attracted the attention of researchers. These include the utilisation of both quantitative and qualitative methodologies for risk assessment, the analysis of the causes of hazards, the identification of accident precursors through the creation of time series, and the examination of material factors pertaining to construction processes. Researchers are focusing on the utilisation of both databases and advanced technologies, such as real-time location tracking, for the assessment and analysis of occupational risks. Techniques such as Analytic Hierarchy Process (AHP) and clustering facilitate a comprehensive assessment and categorisation of incidents, thereby enabling the identification of patterns and susceptibility to specific types of accidents. Moreover, the impact of a company’s safety climate and organisational culture on the frequency and characteristics of near misses represents a pivotal area of investigation. The findings of this research indicate that effective safety management requires a holistic approach that integrates technology, risk management and safety culture, with the objective of reducing accidents and enhancing overall working conditions on construction sites.

7. Gaps and Future Research Directions, Limitations

• Given the diversity and variability of construction sites and the changing conditions and circumstances of work, it is essential to create homogeneous clusters of near misses and to analyse the phenomena within these clusters. The formation of such clusters may be contingent upon the direct causes of the events in question;
• Given the inherently dynamic nature of construction, it is essential to analyse time series of events that indicate trends in development and safety levels. The numerical characteristics of these trends may be used to construct predictive models for future accidents and near misses;
• The authors have identified potential avenues for future research, which could involve the development of mathematical models using techniques such as linear regression, artificial intelligence, and machine learning. The objective of these models is to predict the probable timing of occupational accidents within defined incident categories, utilising data from near misses. Moreover, efforts are being made to gain access to the hazardous incident recording systems of different construction companies, with a view to facilitating comparison of the resulting data;
• One significant limitation of near-miss research is the lack of an integrated database that encompasses a diverse range of construction sites and construction work. A data resource of this nature would be of immense value for the purpose of conducting comprehensive analyses and formulating effective risk management strategies. This issue can be attributed to two factors: firstly, the reluctance of company managers to share their databases with researchers specialising in risk assessment, and secondly, the reluctance of employees to report near-miss incidents. Such actions may result in adverse consequences for employees, including disciplinary action or negative perceptions from managers. This consequently results in the recording of only a subset of incidents, thereby distorting the true picture of safety on the site.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Occupational Risk|Safety and Health at Work EU-OSHA. Available online: https://osha.europa.eu/en/tools-and-resources/eu-osha-thesaurus/term/70194i (accessed on 28 June 2023).
• Guo, S.; Zhou, X.; Tang, B.; Gong, P. Exploring the Behavioral Risk Chains of Accidents Using Complex Network Theory in the Construction Industry. Phys. A Stat. Mech. Its Appl. 2020 , 560 , 125012. [ Google Scholar ] [ CrossRef ]
• Woźniak, Z.; Hoła, B. The Structure of near Misses and Occupational Accidents in the Polish Construction Industry. Heliyon 2024 , 10 , e26410. [ Google Scholar ] [ CrossRef ]
• Li, X.; Sun, W.; Fu, H.; Bu, Q.; Zhang, Z.; Huang, J.; Zang, D.; Sun, Y.; Ma, Y.; Wang, R.; et al. Schedule Risk Model of Water Intake Tunnel Construction Considering Mood Factors and Its Application. Sci. Rep. 2024 , 14 , 3857. [ Google Scholar ] [ CrossRef ]
• Li, X.; Huang, J.; Li, C.; Luo, N.; Lei, W.; Fan, H.; Sun, Y.; Chen, W. Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network. Period. Polytech. Civ. Eng. 2022 , 66 , 335–343. [ Google Scholar ] [ CrossRef ]
• Central Statistical Office Central Statistical Office/Thematic Areas/Labor Market/Working Conditions/Accidents at Work/Accidents at Work in the 1st Quarter of 2024. Available online: https://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/wypadki-przy-pracy-w-1-kwartale-2024-roku,3,55.html (accessed on 17 July 2024).
• Manzo, J. The $ 5 Billion Cost of Construction Fatalities in the United States: A 50 State Comparison ; The Midwest Economic Policy Institute (MEPI): Saint Paul, MN, USA, 2017. [ Google Scholar ]
• Sousa, V.; Almeida, N.M.; Dias, L.A. Risk-Based Management of Occupational Safety and Health in the Construction Industry—Part 1: Background Knowledge. Saf. Sci. 2014 , 66 , 75–86. [ Google Scholar ] [ CrossRef ]
• Amirah, N.A.; Him, N.F.N.; Rashid, A.; Rasheed, R.; Zaliha, T.N.; Afthanorhan, A. Fostering a Safety Culture in Manufacturing through Safety Behavior: A Structural Equation Modelling Approach. J. Saf. Sustain. 2024; in press . [ Google Scholar ] [ CrossRef ]
• Heinrich, H.W. Industrial Accident Prevention ; A Scientific Approach; McGraw-Hill: New York, NY, USA, 1931. [ Google Scholar ]
• Near Miss Definition Per OSHA—What Is a Near Miss? Available online: https://safetystage.com/osha-compliance/near-miss-definition-osha/ (accessed on 17 August 2024).
• Cambraia, F.B.; Saurin, T.A.; Formoso, C.T. Identification, Analysis and Dissemination of Information on near Misses: A Case Study in the Construction Industry. Saf. Sci. 2010 , 48 , 91–99. [ Google Scholar ] [ CrossRef ]
• Tan, J.; Li, M. How to Achieve Accurate Accountability under Current Administrative Accountability System for Work Safety Accidents in Chemical Industry in China: A Case Study on Major Work Safety Accidents during 2010–2020. J. Chin. Hum. Resour. Manag. 2022 , 13 , 26–40. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Gibb, A.G.F.; Li, Q. Accident Precursors and near Misses on Construction Sites: An Investigative Tool to Derive Information from Accident Databases. Saf. Sci. 2010 , 48 , 845–858. [ Google Scholar ] [ CrossRef ]
• Janicak, C.A. Fall-Related Deaths in the Construction Industry. J. Saf. Res. 1998 , 29 , 35–42. [ Google Scholar ] [ CrossRef ]
• Li, H.; Yang, X.; Wang, F.; Rose, T.; Chan, G.; Dong, S. Stochastic State Sequence Model to Predict Construction Site Safety States through Real-Time Location Systems. Saf. Sci. 2016 , 84 , 78–87. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Aria, S.; Ahn, C.R.; Stentz, T.L. Automated Detection of Near-Miss Fall Incidents in Iron Workers Using Inertial Measurement Units. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–21 May 2014; pp. 935–944. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Fishbain, B.; Shapira, A. Analyzing Risk Factors in Crane-Related near-Miss and Accident Reports. Saf. Sci. 2017 , 91 , 192–205. [ Google Scholar ] [ CrossRef ]
• Zhao, X.; Zhang, M.; Cao, T. A Study of Using Smartphone to Detect and Identify Construction Workers’ near-Miss Falls Based on ANN. In Proceedings of the Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, Denver, CO, USA, 4–8 March 2018; p. 80. [ Google Scholar ] [ CrossRef ]
• Santiago, K.; Yang, X.; Ruano-Herreria, E.C.; Chalmers, J.; Cavicchia, P.; Caban-Martinez, A.J. Characterising near Misses and Injuries in the Temporary Agency Construction Workforce: Qualitative Study Approach. Occup. Environ. Med. 2020 , 77 , 94–99. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• What Is OSHA’s Definition of a Near Miss. Available online: https://www.osha.com/blog/near-miss-definition (accessed on 4 August 2023).
• Martins, I. Investigation of Occupational Accidents and Diseases a Practical Guide for Labour Inspectors ; International Labour Office: Geneva, Switzerland, 2015. [ Google Scholar ]
• National Safety Council. Near Miss Reporting Systems ; National Safety Council: Singapore, 2013. [ Google Scholar ]
• PKN PN-ISO 45001:2018-06 ; Occupational Health and Safety Management Systems—Requirements with Guidance for Use. CRC Press: Boca Raton, FL, USA, 2019.
• PKN PN-N-18001:2004 ; Occupational Health and Safety Management Systems—Requirements. CRC Press: Boca Raton, FL, USA, 2004.
• World Health Organisation. WHO Draft GuiDelines for Adverse Event Reporting and Learning Systems ; World Health Organisation: Geneva, Switzerland, 2005. [ Google Scholar ]
• International Atomic Energy Agency IAEA Satety Glossary. Terminology Used in Nuclear Safety and Radiation Protection: 2007 Edition ; International Atomic Energy Agency: Vienna, Austria, 2007. [ Google Scholar ]
• Marks, E.; Teizer, J.; Hinze, J. Near Miss Reporting Program to Enhance Construction Worker Safety Performance. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19 May 2014; pp. 2315–2324. [ Google Scholar ] [ CrossRef ]
• Gnoni, M.G.; Saleh, J.H. Near-Miss Management Systems and Observability-in-Depth: Handling Safety Incidents and Accident Precursors in Light of Safety Principles. Saf. Sci. 2017 , 91 , 154–167. [ Google Scholar ] [ CrossRef ]
• Thoroman, B.; Goode, N.; Salmon, P. System Thinking Applied to near Misses: A Review of Industry-Wide near Miss Reporting Systems. Theor. Issues Ergon. Sci. 2018 , 19 , 712–737. [ Google Scholar ] [ CrossRef ]
• Gnoni, M.G.; Tornese, F.; Guglielmi, A.; Pellicci, M.; Campo, G.; De Merich, D. Near Miss Management Systems in the Industrial Sector: A Literature Review. Saf. Sci. 2022 , 150 , 105704. [ Google Scholar ] [ CrossRef ]
• Bird, F. Management Guide to Loss Control ; Loss Control Publications: Houston, TX, USA, 1975. [ Google Scholar ]
• Zimmermann. Bauer International Norms and Identity ; Zimmermann: Sydney, NSW, Australia, 2006; pp. 5–21. [ Google Scholar ]
• Arslan, M.; Cruz, C.; Ginhac, D. Semantic Trajectory Insights for Worker Safety in Dynamic Environments. Autom. Constr. 2019 , 106 , 102854. [ Google Scholar ] [ CrossRef ]
• Arslan, M.; Cruz, C.; Ginhac, D. Visualizing Intrusions in Dynamic Building Environments for Worker Safety. Saf. Sci. 2019 , 120 , 428–446. [ Google Scholar ] [ CrossRef ]
• Zhou, C.; Chen, R.; Jiang, S.; Zhou, Y.; Ding, L.; Skibniewski, M.J.; Lin, X. Human Dynamics in Near-Miss Accidents Resulting from Unsafe Behavior of Construction Workers. Phys. A Stat. Mech. Its Appl. 2019 , 530 , 121495. [ Google Scholar ] [ CrossRef ]
• Chen, F.; Wang, C.; Wang, J.; Zhi, Y.; Wang, Z. Risk Assessment of Chemical Process Considering Dynamic Probability of near Misses Based on Bayesian Theory and Event Tree Analysis. J. Loss Prev. Process Ind. 2020 , 68 , 104280. [ Google Scholar ] [ CrossRef ]
• Wright, L.; Van Der Schaaf, T. Accident versus near Miss Causation: A Critical Review of the Literature, an Empirical Test in the UK Railway Domain, and Their Implications for Other Sectors. J. Hazard. Mater. 2004 , 111 , 105–110. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saleh, J.H.; Saltmarsh, E.A.; Favar, F.M.; Loı¨c Brevault, L. Accident Precursors, near Misses, and Warning Signs: Critical Review and Formal Definitions within the Framework of Discrete Event Systems. Reliab. Eng. Syst. Saf. 2013 , 114 , 148–154. [ Google Scholar ] [ CrossRef ]
• Fred, A. Manuele Reviewing Heinrich. Am. Soc. Saf. Prof. 2011 , 56 , 52–61. [ Google Scholar ]
• Love, P.E.D.; Tenekedjiev, K. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression: A Comment. Reliab. Eng. Syst. Saf. 2022 , 217 , 108021. [ Google Scholar ] [ CrossRef ]
• Jan van Eck, N.; Waltman, L. VOSviewer Manual ; Universiteit Leiden: Leiden, The Netherlands, 2015. [ Google Scholar ]
• Scopus. Content Coverage Guide ; Elsevier: Amsterdam, The Netherlands, 2023; pp. 1–24. [ Google Scholar ]
• Lukic, D.; Littlejohn, A.; Margaryan, A. A Framework for Learning from Incidents in the Workplace. Saf. Sci. 2012 , 50 , 950–957. [ Google Scholar ] [ CrossRef ]
• Teizer, J.; Cheng, T. Proximity Hazard Indicator for Workers-on-Foot near Miss Interactions with Construction Equipment and Geo-Referenced Hazard Area. Autom. Constr. 2015 , 60 , 58–73. [ Google Scholar ] [ CrossRef ]
• Zong, L.; Fu, G. A Study on Designing No-Penalty Reporting System about Enterprise Staff’s near Miss. Adv. Mater. Res. 2011 , 255–260 , 3846–3851. [ Google Scholar ] [ CrossRef ]
• Golovina, O.; Teizer, J.; Pradhananga, N. Heat Map Generation for Predictive Safety Planning: Preventing Struck-by and near Miss Interactions between Workers-on-Foot and Construction Equipment. Autom. Constr. 2016 , 71 , 99–115. [ Google Scholar ] [ CrossRef ]
• Zou, P.X.W.; Lun, P.; Cipolla, D.; Mohamed, S. Cloud-Based Safety Information and Communication System in Infrastructure Construction. Saf. Sci. 2017 , 98 , 50–69. [ Google Scholar ] [ CrossRef ]
• Hinze, J.; Godfrey, R. An Evaluation of Safety Performance Measures for Construction Projects. J. Constr. Res. 2011 , 4 , 5–15. [ Google Scholar ] [ CrossRef ]
• Construction Inspection Software|IAuditor by SafetyCulture. Available online: https://safetyculture.com/construction/ (accessed on 25 August 2023).
• Incident Reporting Made Easy|Safety Compliance|Mobile EHS Solutions. Available online: https://www.safety-reports.com/lp/safety/incident/ (accessed on 25 August 2023).
• Wu, F.; Wu, T.; Yuce, M.R. An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications. Sensors 2019 , 19 , 21. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fang, W.; Luo, H.; Xu, S.; Love, P.E.D.; Lu, Z.; Ye, C. Automated Text Classification of Near-Misses from Safety Reports: An Improved Deep Learning Approach. Adv. Eng. Inform. 2020 , 44 , 101060. [ Google Scholar ] [ CrossRef ]
• Gatti, U.C.; Lin, K.-Y.; Caldera, C.; Chiang, R. Exploring the Relationship between Chronic Sleep Deprivation and Safety on Construction Sites. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–24 May 2014; pp. 1772–1781. [ Google Scholar ] [ CrossRef ]
• Hon, C.K.H.; Chan, A.P.C.; Yam, M.C.H. Relationships between Safety Climate and Safety Performance of Building Repair, Maintenance, Minor Alteration, and Addition (RMAA) Works. Saf. Sci. 2014 , 65 , 10–19. [ Google Scholar ] [ CrossRef ]
• Oni, O.; Olanrewaju, A.; Cheen, K.S. Accidents at construction sites and near-misses: A constant problem. Int. Struct. Eng. Constr. 2022 , 9 , 2022. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Yang, H.; Chew, D.A.S.; Yang, S.-H.; Gibb, A.G.F.; Li, Q. Towards an Autonomous Real-Time Tracking System of near-Miss Accidents on Construction Sites. Autom. Constr. 2010 , 19 , 134–141. [ Google Scholar ] [ CrossRef ]
• Aria, S.S.; Yang, K.; Ahn, C.R.; Vuran, M.C. Near-Miss Accident Detection for Ironworkers Using Inertial Measurement Unit Sensors. In Proceedings of the International Symposium on Automation and Robotics in Construction, ISARC 2014, Sydney, Australia, 9–11 July 2014; Volume 31, pp. 854–859. [ Google Scholar ] [ CrossRef ]
• Hasanzadeh, S.; Garza, J.M. de la Productivity-Safety Model: Debunking the Myth of the Productivity-Safety Divide through a Mixed-Reality Residential Roofing Task. J. Constr. Eng. Manag. 2020 , 146 , 04020124. [ Google Scholar ] [ CrossRef ]
• Teizer, J. Magnetic Field Proximity Detection and Alert Technology for Safe Heavy Construction Equipment Operation. In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, Oulu, Finland, 15–18 June 2015. [ Google Scholar ] [ CrossRef ]
• Mohajeri, M.; Ardeshir, A.; Banki, M.T.; Malekitabar, H. Discovering Causality Patterns of Unsafe Behavior Leading to Fall Hazards on Construction Sites. Int. J. Constr. Manag. 2022 , 22 , 3034–3044. [ Google Scholar ] [ CrossRef ]
• Kisaezehra; Farooq, M.U.; Bhutto, M.A.; Kazi, A.K. Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites. Intell. Autom. Soft Comput. 2023 , 36 , 911–927. [ Google Scholar ] [ CrossRef ]
• Li, C.; Ding, L. Falling Objects Detection for near Miss Incidents Identification on Construction Site. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 138–145. [ Google Scholar ] [ CrossRef ]
• Jeelani, I.; Ramshankar, H.; Han, K.; Albert, A.; Asadi, K. Real-Time Hazard Proximity Detection—Localization of Workers Using Visual Data. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 281–289. [ Google Scholar ] [ CrossRef ]
• Lim, T.-K.; Park, S.-M.; Lee, H.-C.; Lee, D.-E. Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace. J. Constr. Eng. Manag. 2015 , 142 , 04015065. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Jebelli, H.; Ahn, C.R.; Vuran, M.C. Threshold-Based Approach to Detect Near-Miss Falls of Iron Workers Using Inertial Measurement Units. In Proceedings of the 2015 International Workshop on Computing in Civil Engineering, Austin, TX, USA, 21–23 June 2015; 2015; 2015, pp. 148–155. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Ahn, C.R.; Vuran, M.C.; Aria, S.S. Semi-Supervised near-Miss Fall Detection for Ironworkers with a Wearable Inertial Measurement Unit. Autom. Constr. 2016 , 68 , 194–202. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Shapira, A.; Fishbain, B. AHP-Based Analysis of the Risk Potential of Safety Incidents: Case Study of Cranes in the Construction Industry. Saf. Sci. 2017 , 91 , 298–309. [ Google Scholar ] [ CrossRef ]
• Saurin, T.A.; Formoso, C.T.; Reck, R.; Beck da Silva Etges, B.M.; Ribeiro JL, D. Findings from the Analysis of Incident-Reporting Systems of Construction Companies. J. Constr. Eng. Manag. 2015 , 141 , 05015007. [ Google Scholar ] [ CrossRef ]
• Williams, E.; Sherratt, F.; Norton, E. Exploring the Value in near Miss Reporting for Construction Safety. In Proceedings of the 37th Annual Conference, Virtual Event, 6–10 December 2021; pp. 319–328. [ Google Scholar ]
• Baker, H.; Smith, S.; Masterton, G.; Hewlett, B. Data-Led Learning: Using Natural Language Processing (NLP) and Machine Learning to Learn from Construction Site Safety Failures. In Proceedings of the 36th Annual ARCOM Conference, Online, 7–8 September 2020; pp. 356–365. [ Google Scholar ]
• Jin, R.; Wang, F.; Liu, D. Dynamic Probabilistic Analysis of Accidents in Construction Projects by Combining Precursor Data and Expert Judgments. Adv. Eng. Inform. 2020 , 44 , 101062. [ Google Scholar ] [ CrossRef ]
• Zhou, Z.; Li, C.; Mi, C.; Qian, L. Exploring the Potential Use of Near-Miss Information to Improve Construction Safety Performance. Sustainability 2019 , 11 , 1264. [ Google Scholar ] [ CrossRef ]
• Boateng, E.B.; Pillay, M.; Davis, P. Predicting the Level of Safety Performance Using an Artificial Neural Network. Adv. Intell. Syst. Comput. 2019 , 876 , 705–710. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Cao, T.; Zhao, X. Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. J. Constr. Eng. Manag. 2018 , 145 , 04018120. [ Google Scholar ] [ CrossRef ]
• Gadekar, H.; Bugalia, N. Automatic Classification of Construction Safety Reports Using Semi-Supervised YAKE-Guided LDA Approach. Adv. Eng. Inform. 2023 , 56 , 101929. [ Google Scholar ] [ CrossRef ]
• Zhu, Y.; Liao, H.; Huang, D. Using Text Mining and Multilevel Association Rules to Process and Analyze Incident Reports in China. Accid. Anal. Prev. 2023 , 191 , 107224. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, M.; Lin, Q.; Jin, H. Research on Near-Miss Incidents Monitoring and Early Warning System for Building Construction Sites Based on Blockchain Technology. J. Constr. Eng. Manag. 2023 , 149 , 04023124. [ Google Scholar ] [ CrossRef ]
• Chung, W.W.S.; Tariq, S.; Mohandes, S.R.; Zayed, T. IoT-Based Application for Construction Site Safety Monitoring. Int. J. Constr. Manag. 2020 , 23 , 58–74. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Xu, F.; Zhang, Z.; Sun, K. Fall-Portent Detection for Construction Sites Based on Computer Vision and Machine Learning. Eng. Constr. Archit. Manag. 2023; ahead-of-print . [ Google Scholar ] [ CrossRef ]
• Abbasi, H.; Guerrieri, A.; Lee, J.; Yang, K. Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound. Sensors 2022 , 22 , 3482. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, F.; Li, H.; Dong, C. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression. Reliab. Eng. Syst. Saf. 2021 , 213 , 107687. [ Google Scholar ] [ CrossRef ]
• Bugalia, N.; Tarani, V.; Student, G.; Kedia, J.; Gadekar, H. Machine Learning-Based Automated Classification Of Worker-Reported Safety Reports In Construction. J. Inf. Technol. Constr. 2022 , 27 , 926–950. [ Google Scholar ] [ CrossRef ]
• Chen, S.; Xi, J.; Chen, Y.; Zhao, J. Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification. Comput. Intell. Neurosci. 2022 , 2022 , 4851615. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tang, S.; Golparvar-Fard, M.; Naphade, M.; Gopalakrishna, M.M. Video-Based Activity Forecasting for Construction Safety Monitoring Use Cases. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 204–210. [ Google Scholar ] [ CrossRef ]
• Rashid, K.M.; Behzadan, A.H. Risk Behavior-Based Trajectory Prediction for Construction Site Safety Monitoring. J. Constr. Eng. Manag. 2018 , 144 , 04017106. [ Google Scholar ] [ CrossRef ]
• Shen, X.; Marks, E. Near-Miss Information Visualization Tool in BIM for Construction Safety. J. Constr. Eng. Manag. 2016 , 142 , 04015100. [ Google Scholar ] [ CrossRef ]
• Erusta, N.E.; Sertyesilisik, B. An Investigation into Improving Occupational Health and Safety Performance of Construction Projects through Usage of BIM for Lean Management. In Communications in Computer and Information Science (CCIS) ; Springer: Berlin/Heidelberg, Germany, 2020; Volume 1188, pp. 91–100. [ Google Scholar ] [ CrossRef ]
• Coffland, M.M.; Kim, A.; Sadatsafavi, H.; Uber, M.M. Improved Data Storage for Better Safety Analysis and Decision Making in Large Construction Management Firms. Available online: https://www.researchgate.net/publication/320474383_Improved_Data_Storage_for_Better_Safety_Analysis_and_Decision_Making_in_Large_Construction_Management_Firms (accessed on 12 June 2024).
• Zhou, Z.; Li, Q.; Wu, W. Developing a Versatile Subway Construction Incident Database for Safety Management. J. Constr. Eng. Manag. 2011 , 138 , 1169–1180. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Yang, H.; Li, Q.; Chew, D. An Integrated Information Management Model for Proactive Prevention of Struck-by-Falling-Object Accidents on Construction Sites. Autom. Constr. 2013 , 34 , 67–74. [ Google Scholar ] [ CrossRef ]
• Hoła, B. Identification and Evaluation of Processes in a Construction Enterprise. Arch. Civ. Mech. Eng. 2015 , 15 , 419–426. [ Google Scholar ] [ CrossRef ]
• Zhou, C.; Ding, L.; Skibniewski, M.J.; Luo, H.; Jiang, S. Characterizing Time Series of Near-Miss Accidents in Metro Construction via Complex Network Theory. Saf. Sci. 2017 , 98 , 145–158. [ Google Scholar ] [ CrossRef ]
• Woźniak, Z.; Hoła, B. Time Series Analysis of Hazardous Events Based on Data Recorded in a Polish Construction Company. Arch. Civ. Eng. 2024; in process . [ Google Scholar ]
• Drozd, W. Characteristics of Construction Site in Terms of Occupational Safety. J. Civ. Eng. Environ. Archit. 2016 , 63 , 165–172. [ Google Scholar ]
• Meliá, J.L.; Mearns, K.; Silva, S.A.; Lima, M.L. Safety Climate Responses and the Perceived Risk of Accidents in the Construction Industry. Saf. Sci. 2008 , 46 , 949–958. [ Google Scholar ] [ CrossRef ]
• Bugalia, N.; Maemura, Y.; Ozawa, K. A System Dynamics Model for Near-Miss Reporting in Complex Systems. Saf. Sci. 2021 , 142 , 105368. [ Google Scholar ] [ CrossRef ]
• Gyi, D.E.; Gibb, A.G.F.; Haslam, R.A. The Quality of Accident and Health Data in the Construction Industry: Interviews with Senior Managers. Constr. Manag. Econ. 1999 , 17 , 197–204. [ Google Scholar ] [ CrossRef ]
• Menzies, J. Structural Safety: Learning and Warnings. Struct. Eng. 2002 , 80 , 15–16. [ Google Scholar ]
• Fullerton, C.E.; Allread, B.S.; Teizer, J. Pro-Active-Real-Time Personnel Warning System. In Proceedings of the Construction Research Congress 2009: Building a Sustainable Future, Seattle, WA, USA, 5–7 April 2009; pp. 31–40. [ Google Scholar ] [ CrossRef ]
• Marks, E.D.; Wetherford, J.E.; Teizer, J.; Yabuki, N. Potential of Leading Indicator Data Collection and Analysis for Proximity Detection and Alert Technology in Construction. In Proceedings of the 30th ISARC—International Symposium on Automation and Robotics in Construction Conference, Montreal, QC, Canada, 11–15 August 2013; pp. 1029–1036. [ Google Scholar ] [ CrossRef ]
• Martin, H.; Lewis, T.M. Pinpointing Safety Leadership Factors for Safe Construction Sites in Trinidad and Tobago. J. Constr. Eng. Manag. 2014 , 140 , 04013046. [ Google Scholar ] [ CrossRef ]
• Hobson, P.; Emery, D.; Brown, L.; Bashford, R.; Gill, J. People–Plant Interface Training: Targeting an Industry Fatal Risk. Proc. Inst. Civ. Eng. Civ. Eng. 2014 , 167 , 138–144. [ Google Scholar ] [ CrossRef ]
• Marks, E.; Mckay, B.; Awolusi, I. Using near Misses to Enhance Safety Performance in Construction. In Proceedings of the ASSE Professional Development Conference and Exposition, Dallas, TX, USA, 7–10 June 2015. [ Google Scholar ]
• Popp, J.D.; Scarborough, M.S. Investigations of near Miss Incidents—New Facility Construction and Commissioning Activities. IEEE Trans. Ind. Appl. 2016 , 53 , 615–621. [ Google Scholar ] [ CrossRef ]
• Nickel, P.; Lungfiel, A.; Trabold, R.J. Reconstruction of near Misses and Accidents for Analyses from Virtual Reality Usability Study. In Lecture Notes in Computer Science ; Springer: Berlin/Heidelberg, Germany, 2017; Volume 10700, pp. 182–191. [ Google Scholar ] [ CrossRef ]
• Gambatese, J.A.; Pestana, C.; Lee, H.W. Alignment between Lean Principles and Practices and Worker Safety Behavior. J. Constr. Eng. Manag. 2017 , 143 , 04016083. [ Google Scholar ] [ CrossRef ]
• Van Voorhis, S.; Korman, R. Reading Signs of Trouble. Eng. News-Rec. 2017 , 278 , 14–17. [ Google Scholar ]
• Doan, D.R. Investigation of a near-miss shock incident. IEEE Trans. Ind. Appl. 2016 , 52 , 560–561. [ Google Scholar ] [ CrossRef ]
• Oswald, D.; Sherratt, F.; Smith, S. Problems with safety observation reporting: A construction industry case study. Saf. Sci. 2018 , 107 , 35–45. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Shapira, A. Systematic approach to crane-related near-miss analysis in the construction industry. Int. J. Constr. Manag. 2018 , 18 , 310–320. [ Google Scholar ] [ CrossRef ]
• Whiteoak, J.; Appleby, J. Mate, that was bloody close! A case history of a nearmiss program in the Australian construction industry. J. Health Saf. Environ. 2019 , 35 , 31–43. [ Google Scholar ]
• Duryan, M.; Smyth, H.; Roberts, A.; Rowlinson, S.; Sherratt, F. Knowledge transfer for occupational health and safety: Cultivating health and safety learning culture in construction firms. Accid. Anal. Prev. 2020 , 139 , 105496. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shaikh, A.Y.; Osei-Kyei, R.; Hardie, M. A critical analysis of safety performance indicators in construction. Int. J. Build. Pathol. Adapt. 2020 , 39 , 547–580. [ Google Scholar ] [ CrossRef ]
• Martin, H.; Mohan, N.; Ellis, L.; Dunne, S. Exploring the Role of PPE Knowledge, Attitude, and Correct Practices in Safety Outcomes on Construction Sites. J. Archit. Eng. 2021 , 27 , 05021011. [ Google Scholar ] [ CrossRef ]
• Qin, Z.; Wu, S. A simulation model of engineering construction near-miss event disclosure strategy based on evolutionary game theory. In Proceedings of the 2021 4th International Conference on Information Systems and Computer Aided Education, Dalian, China, 24–26 September 2021; pp. 2572–2577. [ Google Scholar ] [ CrossRef ]
• Alamoudi, M. The Integration of NOSACQ-50 with Importance-Performance Analysis Technique to Evaluate and Analyze Safety Climate Dimensions in the Construction Sector in Saudi Arabia. Buildings 2022 , 12 , 1855. [ Google Scholar ] [ CrossRef ]
• Herrmann, A.W. Development of CROSS in the United States. In Proceedings of the Forensic Engineering 2022: Elevating Forensic Engineering—Selected Papers from the 9th Congress on Forensic Engineering, Denver, Colorado, 4–7 November 2022; Volume 2, pp. 40–43. [ Google Scholar ] [ CrossRef ]
• Al Shaaili, M.; Al Alawi, M.; Ekyalimpa, R.; Al Mawli, B.; Al-Mamun, A.; Al Shahri, M. Near-miss accidents data analysis and knowledge dissemination in water construction projects in Oman. Heliyon 2023 , 9 , e21607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Agnusdei, G.P.; Gnoni, M.G.; Tornese, F.; De Merich, D.; Guglielmi, A.; Pellicci, M. Application of Near-Miss Management Systems: An Exploratory Field Analysis in the Italian Industrial Sector. Safety 2023 , 9 , 47. [ Google Scholar ] [ CrossRef ]
• Duan, P.; Zhou, J. A science mapping approach-based review of near-miss research in construction. Eng. Constr. Archit. Manag. 2023 , 30 , 2582–2601. [ Google Scholar ] [ CrossRef ]

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Woźniak, Z.; Hoła, B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Appl. Sci. 2024 , 14 , 7260. https://doi.org/10.3390/app14167260

Woźniak Z, Hoła B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Applied Sciences . 2024; 14(16):7260. https://doi.org/10.3390/app14167260

Woźniak, Zuzanna, and Bożena Hoła. 2024. "Analysing Near-Miss Incidents in Construction: A Systematic Literature Review" Applied Sciences 14, no. 16: 7260. https://doi.org/10.3390/app14167260

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Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda

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• Published: 23 August 2024

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• Heidi Heimberger   ORCID: orcid.org/0000-0003-3390-0219 1 , 2 ,
• Djerdj Horvat   ORCID: orcid.org/0000-0003-3747-3402 1 &
• Frank Schultmann   ORCID: orcid.org/0000-0001-6405-9763 1  

Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.

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1 Introduction

The technological change resulting from deep digitisation and the increasing use of digital technologies has reached and transformed many sectors [ 1 ]. In manufacturing, the development of a new industrial age, characterized by extensive automation and digitisation of processes [ 2 ], is changing the sector’s ‘technological reality’ [ 3 ] by integrating a wide range of information and communication technologies (such as Industry 4.0-related technologies) into production processes [ 4 ].

Although the evolution of AI traces back to the year 1956 (as part of the Dartmouth Conference) [ 5 ], its development has progressed rapidly, especially since the 2010s [ 6 ]. Driven by improvements, such as the fast and low-cost development of smart hardware, the enhancement of algorithms as well as the capability to manage big data [ 7 ], there is an increasing number of AI applications available for implementation today [ 8 ]. The integration of AI into production processes promises to boost the productivity, efficiency as well as automation of processes [ 9 ], but is currently still in its infancy [ 10 ] and manufacturing firms seem to still be hesitant to adopt AI in a production-context. This appears to be driven by the high complexity of AI combined with the lack of practical knowledge about its implementation in production and several other influencing factors [ 11 , 12 ].

In the literature, many contributions analyze AI from a technological perspective, mainly addressing underlying models, algorithms, and developments of AI tools. Various authors characterise both machine learning and deep learning as key technologies of AI [ 8 , 13 ], which are often applied in combination with other AI technologies, such as natural language recognition. While promising areas for AI application already exist in various domains such as marketing [ 14 ], procurement [ 15 ], supply chain management [ 16 ] or innovation management [ 17 ], the integration of AI into production processes also provides significant performance potentials, particularly in the areas of maintenance [ 18 ], quality control [ 19 ] and production planning and management [ 20 ]. However, AI adoption requires important technological foundations, such as the provision of data and the necessary infrastructure, which must be ensured [ 11 , 12 , 21 ]. Although the state of the art literature provides important insights into possible fields of application of AI in production, the question remains: To what extent are these versatile applications already in use and what is required for their successful adoption?

Besides the technology perspective of AI, a more human-oriented field of discussion is debated in scientific literature [ 22 ]. While new technologies play an essential role in driving business growth in the digital transformation of the production industry, the increasing interaction between humans and intelligent machines (also referred to as ‘augmentation’) creates stress challenges [ 23 ] and impacts work [ 24 ], which thus creates managerial challenges in organizations [ 25 , 26 ]. One of the widely discussed topics in this context is the fear of AI threatening jobs (including production jobs), which was triggered by e.g. a study of Frey, Osborne [ 27 ]. Another issue associated to the fear of machines replacing humans is the lack of acceptance resulting from the mistrust of technologies [ 28 , 29 ]. This can also be linked to the various ethical challenges involved in working with AI [ 22 ]. This perspective, which focuses on the interplay between AI and humans [ 30 ], reveals the tension triggered by AI. Although this is discussed from different angles, the question remains how these aspects influence the adoption of AI in production.

Another thematic stream of current literature can be observed in a series of contributions on the organizational aspects of the technology. In comparison to the two research areas discussed above, the number of publications in this area seems to be smaller. This perspective focuses on issues to implement AI, such as the importance of a profound management structure [ 31 , 32 ], leadership [ 33 ], implications on the organizational culture [ 34 ] as well as the need for digital capabilities and special organizational skills [ 33 ]. Although some studies on the general adoption of AI without a sectoral focus have already been conducted (such as by Chen, Tajdini [ 35 ] or Kinkel, Baumgartner, Cherubini [ 36 ]) and hence, some initial factors influencing the adoption of AI can be derived, the contributions from this perspective are still scarce, are usually not specifically analyzed in the context of production or lack a comprehensive view on the organization in AI adoption.

While non-industry specific AI issues have been researched in recent years, the current literature misses a production-specific analysis of AI adoption, providing an understanding of the possibilities and issues related to integrating AI into the production context. Moreover, the existing literature tells us little about relevant mechanisms and factors underlying the adoption of AI in production processes, which include both technical, human-centered as well as organizational issues. As organizational understanding of AI in a business context is currently still in its early stages, it is difficult to find an aggregate view on the factors that can support companies in implementing AI initiatives in production [ 37 , 38 ]. Addressing this gap, we aim to systematise the current scientific knowledge on AI adoption, with a focus on production. By drawing on a systematic literature review (SLR), we examine existing studies on AI adoption in production and explore the main issues regarding adoption that are covered in the analyzed articles. Building on these findings, we conduct a comprehensive analysis of the existing studies with the aim of systematically investigating the key factors influencing the adoption of AI in production. This systematic approach paves the way for the formulation of a future research agenda.

Our SLR addresses three research questions (RQs). RQ1: What are the statistical characteristics of existing research on AI adoption in production? To answer this RQ, we conduct descriptive statistics of the analyzed studies and provide information on time trends, methods used in the research, and country specifications. RQ2: What factors influence the adoption of AI in production? RQ2 specifies the adoption factors and forms the core component of our analysis. By adoption factors, we mean the factors that influence the use of AI in production (both positively and negatively) and that must therefore be analyzed and taken into account. RQ3: What research topics are of importance to advance the research field of AI adoption in production? We address this RQ by using the analyzed literature as well as the key factors of AI adoption as a starting point to derive RQs that are not addressed and thus provide an outlook on the topic.

2 Methodology

In order to create a sound information base for both policy makers and practitioners on the topic of AI adoption in production, this paper follows the systematic approach of a SLR. For many fields, including management research, a SLR is an important tool to capture the diversity of existing knowledge on a specific topic for a scientific investigation [ 39 ]. The investigator often pursues multiple goals, such as capturing and assessing the existing environment and advancing the existing body of knowledge with a proprietary RQ [ 39 ] or identifying key research topics [ 40 ].

Our SLR aims to select, analyze, and synthesize findings from the existing literature on AI adoption in production over the past 24 years. In order to identify relevant data for our literature synthesis, we follow the systematic approach of the Preferred Reporting Items for Systematic reviews (PRISMA) [ 41 ]. In evaluating the findings, we draw on a mixed-methods approach, combining some quantitative analyses, especially on the descriptive aspects of the selected publications, as well as qualitative analyses aimed at evaluating and comparing the contents of the papers. Figure  1 graphically summarizes the methodological approach that guides the content of the following sub-chapters.

Methodical procedure of our SLR following PRISMA [ 41 ]

2.1 Data identification

Following the development of the specific RQs, we searched for suitable publications. To locate relevant studies, we chose to conduct a publication analysis in the databases Scopus, Web of Science and ScienceDirect as these databases primarily contain international scientific articles and provide a broad overview of the interdisciplinary research field and its findings. To align the search with the RQs [ 42 ], we applied predefined key words to search the titles, abstracts, and keywords of Scopus, Web of Science and ScienceDirect articles. Our research team conducted several pre-tests to determine the final search commands for which the test results were on target and increased the efficiency of the search [ 42 ]. Using the combination of Boolean operators, we covered the three topics of AI, production, and adoption by searching combinations of ‘Artificial Intelligence’ AND ‘production or manufacturing’ AND ‘adopt*’ in the three scientific databases. Although ‘manufacturing’ tends to stand for the whole sector and ‘production’ refers to the process, the two terms are often used to describe the same context. We also follow the view of Burbidge, Falster, Riis, Svendsen [ 43 ] and use the terms synonymously in this paper and therefore also include both terms as keywords in the study location as well as in the analysis.

AI research has been credited with a resurgence since 2010 [ 6 ], which is the reason for our choice of time horizon. Due to the increase in publications within the last years, we selected articles published online from 2010 to May 8, 2024 for our analysis. As document types, we included conference papers, articles, reviews, book chapters, conference reviews as well as books, focusing exclusively on contributions in English in the final publication stage. The result of the study location is a list of 3,833 documents whose titles, abstracts, and keywords meet the search criteria and are therefore included in the next step of the analysis.

2.2 Data analysis

For these 3,833 documents, we then conducted an abstract analysis, ‘us[ing] a set of explicit selection criteria to assess the relevance of each study found to see if it actually does address the research question’ [ 42 ]. For this step, we again conducted double-blind screenings (including a minimum of two reviewers) as pilot searches so that all reviewers have the same understanding of the decision rules and make equal decisions regarding their inclusion for further analysis.

To ensure the paper’s focus on all three topics regarded in our research (AI, production, and adoption), we followed clearly defined rules of inclusion and exclusion that all reviewers had to follow in the review process. As a first requirement for inclusion, AI must be the technology in focus that is analysed in the publication. If AI was only mentioned and not further specified, we excluded the publication. With a second requirement, we checked the papers for the context of analysis, which in our case must be production. If the core focus is beyond production, the publication was also excluded from further analysis. The third prerequisite for further consideration of the publication is the analysis of the adoption of a technology in the paper. If technology adoption is not addressed or adoption factors are not considered, we excluded the paper. An article was only selected for full-text analysis if, after analyzing the titles, abstracts, and keywords, a clear focus on all three research areas was visible and the inclusion criteria were met for all three contexts.

By using this tripartite inclusion analysis, we were able to analyse the publications in a structured way and to reduce the 3,833 selected documents in our double-blind approach to 300 articles that were chosen for the full-text analysis. In the process of finding full versions of these publications, we had to exclude three papers as we could not access them. For the rest of the 297 articles we obtained full access and thus included them for further analysis. After a thorough examination of the full texts, we again had to exclude 249 publications because they did not meet our content-related inclusion criteria mentioned above, although the abstract analysis gave indications that they did. As a result, we finally obtained 47 selected papers on which we base the literature analysis and synthesis (see Fig.  1 ).

2.3 Descriptive analysis

Figure  2 summarises the results of the descriptive analysis on the selected literature regarding AI adoption in production that we analyse in our SLR. From Fig.  2 a), which illustrates annual publication trends (2010–2024), the increase in publications on AI adoption in production over the past 5 years is evident, yet slightly declining after a peak in 2022. After a steady increase until 2022, in which 11 articles are included in the final analysis, 2023 features ten articles, followed by three articles for 2024 until the cut-off date in May 2024. Of the 47 papers identified through our search, the majority (n = 33) are peer-reviewed journal articles and the remaining thirteen contributions conference proceedings and one book chapter (see Fig.  2 b)).

Descriptive analyses of the selected articles addressing AI adoption in production

The identified contributions reveal some additional characteristics in terms of the authors country base (Fig.  2 c)) and research methods used (Fig.  2 d)). Almost four out of ten of the publications were written in collaboration with authors from several countries (n = 19). Six of the papers were published by authors from the United States, five from Germany and four from India. In terms of the applied research methods used by the researchers, a wide range of methods is used (see Fig.  2 c), with qualitative methods (n = 22) being the most frequently used.

2.4 Factor analysis

In order to derive a comprehensive list of factors that influence the use of AI in production at different levels, we follow a qualitative content analysis. It is based on inductive category development, avoiding prefabricated categories in order to allow new categories to emerge based on the content at hand [ 44 , 45 ]. To do this, we first read the entire text to gain an understanding of the content and then derive codes [ 46 ] that seem to capture key ideas [ 45 ]. The codes are subsequently sorted into distinct categories, each of which is clearly defined and establishes meaningful connections between different codes. Based on an iterative process with feedback loops, the assigned categories are continuously reviewed and updated as revisions are made [ 44 ].

Various factors at different levels are of significance to AI and influence technology adoption [ 47 , 48 ]. To identify the specific factors that are of importance for AI adoption in production, we analyze the selected contributions in terms of the factors considered, compare them with each other and consequently obtain a list of factors through a bottom-up approach. While some of the factors are based on empirical findings, others are expected factors that result from the research findings of the respective studies. Through our analysis, a list of 35 factors emerges that influence AI adoption in production which occur with varying frequency in the studies analyzed by our SLR. Table 1 visualizes each factor in the respective contributions sorted by the frequency of occurrence.

The presence of skills is considered a particularly important factor in AI adoption in the studies analyzed (n = 35). The availability of data (n = 25) as well as the need for ethical guidelines (n = 24) are also seen as key drivers of AI adoption, as data is seen as the basis for the implementation of AI and ethical issues must be addressed in handling such an advanced technology. As such, these three factors make up the accelerants of AI adoption in production that are most frequently cited in the studies analyzed.

Also of importance are issues of managerial support (n = 22), as well as performance measures and IT infrastructure (n = 20). Some factors were also mentioned, but only addressed by one study at a time: government support, industrial sector, product complexity, batch size, and R&D Intensity. These factors are often used as quantitatively measurable adoption factors, especially in empirical surveys, such the study by Kinkel, Baumgartner, Cherubini [ 36 ].

3 Factors influencing AI adoption

The 35 factors presented characteristically in Sect.  2.4 serve as the basis for our in-depth analysis and for developing a framework of influences on AI adoption in production which are grouped into supercategories. A supercategory describes a cluster of topics to which various factors of AI adoption in production can be assigned. We were able to define seven categories that influence AI adoption in production: the internal influences of ‘business and structure’, ‘organizational effectiveness’, ‘technology and system’, ‘data management’ as well as the external influences of the ‘regulatory environment’, ‘business environment’ and ‘economic environment’ (see Fig.  3 ). The factors that were mentioned most frequently (occurrence in at least half of the papers analyzed) are marked accordingly (*) in Fig.  3 .

Framework of factors influencing AI adoption in production

3.1 Internal Environment

The internal influences on AI adoption in production refer to factors that an organization carries internally and that thus also influence adoption from within. Such factors can usually be influenced and clearly controlled by the organization itself.

3.1.1 Business and structure

The supercategory ‘business and structure’ includes the various factors and characteristics that impact a company’s performance, operations, and strategic decision-making. By considering and analyzing these business variables when implementing AI in production processes, companies can develop effective strategies to optimize their performance, increase their competitiveness, and adapt to changes in the business environment.

To understand and grasp the benefits in the use of AI, quantitative performance measures for the current and potential use of AI in industrial production systems help to clarify the value and potential benefits of AI use [ 49 , 54 , 74 , 79 , 91 ]. Assessing possible risks [ 77 ] as well as the monetary expected benefits for AI (e.g. Return on Investment (ROI)) in production plays an important role for adoption decisions in market-oriented companies [ 57 , 58 , 63 , 65 , 78 ]. Due to financial constraints, managers behave cautiously in their investments [ 78 ], so they need to evaluate AI adoption as financially viable to want to make the investment [ 61 , 63 , 93 ] and also drive acceptance [ 60 ]. AI systems can significantly improve cost–benefit structures in manufacturing, thereby increasing the profitability of production systems [ 73 ] and making companies more resilient [ 75 ]. However, in most cases, the adoption of AI requires high investments and the allocation of resources (s.a. personnel or financial) for this purpose [ 50 , 51 , 57 , 80 , 94 ]. Consequently, a lack of budgets and high expected transition costs often hinder the implementation of smart concepts [ 56 , 62 , 67 , 82 , 84 , 92 ]. It is up to management to provide necessary funding for AI adoption [ 53 , 59 , 79 ], which is required, for example, for skill development of employees [ 59 , 61 , 63 ], IT adaptation [ 62 , 66 ], AI development [ 74 ] or hardware deployment [ 68 ]. In their empirical study, Kinkel, Baumgartner, Cherubini [ 36 ] confirm a positive correlation between company size and the intensity in the use of AI technologies. Large companies generally stand out with a higher propensity to adopt [ 53 ] as they have less difficulties in comparison to small firms regarding the availability of resources [ 69 ], such as know-how, budget [ 68 , 84 ] and general data organization [ 68 ]. Others argue that small companies tend to be more open to change and are characterized by faster decision-making processes [ 68 , 93 ]. Product complexity also influences a company’s propensity for AI. Companies that produce rather simple products are more likely to digitize, which in turn offers good starting points for AI adoption. On the other hand, complex product manufacturers (often characterized by small batch sizes) are often less able to standardize and automate [ 36 ]. The company’s produced batch size has a similar influence on AI adoption. Small and medium batch sizes in particular hinder the integration of intelligent technologies, as less automation often prevails here as well. Nevertheless, even small and medium lot sizes can benefit economically from AI [ 36 ]. Since a high R&D intensity indicates a high innovation capability of a company, it is assumed to have a positive influence on AI adoption, as companies with a high R&D intensity already invest heavily in and use new innovations. This in turn speaks for existing competencies, know how and structures [ 36 ].

3.1.2 Organizational effectiveness

This supercategory focuses on the broader aspects that contribute to the effectiveness, development, and success of an organization when implementing AI in a production context. As the factors are interconnected and influence each other, decision makers should consider them carefully.

Users´ trust in AI is an essential factor to enable successful AI adoption and use in production [ 52 , 68 , 78 , 79 , 88 , 90 ]. From the users´ perspective, AI often exhibits the characteristics of a black box because its inherent processes are not fully understood [ 50 , 90 ] which can lead individuals to develop a fear towards the unknown [ 71 ]. Because of this lack of understanding, successful interaction between humans and AI is not guaranteed [ 90 ], as trust is a foundation for decisions that machines are intended to make autonomously [ 52 , 91 ]. To strengthen faith in AI systems [ 76 , 80 ], AI users can be involved in AI design processes in order to understand appropriate tools [ 54 , 90 ]. In this context, trust is also discussed in close connection with transparency and regulation [ 79 ]. User resistance is considered a barrier to implementing new information technologies, as adoption requires change [ 53 , 62 , 92 ]. Ignorance, as a kind of resistance to change, is a main obstacle to successful digital transformation [ 51 , 56 , 65 ]. Some employees may resist the change brought about by AI because they fear losing their jobs [ 52 ] or have other concerns [ 78 ]. Overcoming resistance to technology adoption requires organizational change and is critical for the success of adoption [ 50 , 51 , 62 , 67 , 71 , 80 ]. Therefore, change management is important to create awareness of the importance of AI adoption and increase acceptance of the workforce [ 66 , 68 , 74 , 83 ]. Management commitment is seen as a significant driver of technology adoption [ 53 , 59 , 81 , 82 , 86 ] and a lack of commitment can negatively impact user adoption and workforce trust and lead to skepticism towards technology [ 86 ]. The top management’s understanding and support for the benefits of the adopted technology [ 53 , 56 , 67 , 78 , 93 , 94 ] enhances AI adoption, can prioritize its implementation and also affects the performance of the AI-enabled application [ 55 , 60 , 83 ]. Preparing, enabling, and thus empowering the workforce, are considered the management’s responsibility in the adoption of digital technologies [ 59 , 75 ]. This requires intelligent leadership [ 52 ] as decision makers need to integrate their workforce into decision-making processes [ 75 ]. Guidelines can support managers by providing access to best practices that help in the adoption of AI [ 50 ]. Critical measures to manage organizational change include the empowerment of visionaries or appointed AI champions leading the change and the collaborative development of digital roadmaps [ 54 , 62 ]. To demonstrate management commitment, managers can create such a dedicated role, consisting of an individual or a small group that is actively and enthusiastically committed to AI adoption in production. This body is considered the adoption manager, point of contact and internal driver of adoption [ 62 , 74 , 80 ]. AI initiatives in production do not necessarily have to be initiated by management. Although management support is essential for successful AI adoption, employees can also actively drive integration initially and thus realize pilot projects or initial trials [ 66 , 80 ]. The development of strategies as well as roadmaps is considered another enabling and necessary factor for the adoption of AI in production [ 50 , 53 , 54 , 62 , 71 , 93 ]. While many major AI strategies already exist at country level to further promote research and development of AI [ 87 ], strategy development is also important at the firm level [ 76 , 77 , 81 ]. In this context, strategies should not be delegated top-down, but be developed in a collaborative manner, i.e. by engaging the workforce [ 75 ] and be in alignment with clear visions [ 91 , 94 ]. Roadmaps are used to improve planning, support implementation, facilitate the adoption of smart technologies in manufacturing [ 93 ] and should be integrated into both business and IT strategy [ 62 , 66 ]. In practice, clear adoption roadmaps that provide approaches on how to effectively integrate AI into existing strategies and businesses are often lacking [ 56 , 87 ]. The need for AI-related skills in organizations is a widely discussed topic in AI adoption analyses [ 79 ]. In this context, the literature points both at the need for specific skills in the development and design of AI applications [ 57 , 71 , 72 , 73 , 76 , 93 ] as well as the skills in using the technology [ 53 , 65 , 73 , 74 , 75 , 84 , 93 ] which availability in the firm is not always given [ 49 ]. AI requires new digital skills [ 36 , 50 , 52 , 55 , 56 , 59 , 61 , 63 , 66 , 78 , 80 ], where e.g. advanced analytics [ 64 , 75 , 81 ], programming skills [ 68 ] and cybersecurity skills [ 78 , 93 ] gain importance. The lack of skills required for AI is seen as a major challenge of digital transformation, as a skilled workforce is considered a key resource for companies [ 51 , 54 , 56 , 60 , 62 , 67 , 69 , 70 , 82 , 93 ]. This lack of a necessary skillset hinders the adoption of AI tools in production systems [ 58 , 77 ]. Closely related to skills is the need for new training concepts, which organizations need to consider when integrating digital technologies [ 49 , 50 , 51 , 56 , 59 , 63 , 71 , 74 , 75 ]. Firms must invest in qualification in order to create necessary competences [ 73 , 78 , 80 , 81 , 92 ]. Additionally, education must target and further develop the skills required for effectively integrating intelligent technologies into manufacturing processes [ 54 , 61 , 62 , 83 ]. Regarding this issue, academic institutions must develop fitting curricula for data driven manufacturing engineering [ 64 ]. Another driving factor of AI adoption is the innovation culture of an organization, which is influenced by various drivers. For example, companies that operate in an environment with high innovation rates, facing intense competitive pressures are considered more likely to see smart technologies as a tool for strategic change [ 83 , 91 , 93 ]. These firms often invest in more expensive and advanced smart technologies as the pressure and resulting competition forces them to innovate [ 93 ]. Another way of approach this is that innovation capability can also be supported and complemented by AI, for example by intelligent systems supporting humans in innovation or even innovating on their own [ 52 ].The entrepreneurial orientation of a firm is characterized in particular by innovativeness [ 66 ], productivity [ 63 ], risk-taking [ 86 ] as well as continuous improvement [ 50 ]. Such characteristics of an innovating culture are considered essential for companies to recognise dynamic changes in the market and make adoption decisions [ 51 , 71 , 81 , 84 , 86 , 94 ]. The prevalence of a digital mindset in companies is important for technology adoption, as digital transformation affects the entire organizational culture and behavior [ 59 , 80 , 92 ] and a lack of a digital culture [ 50 , 65 ] as well as a ‘passive mindset’ [ 78 ] can hinder the digital transformation of firms. Organizations need to develop a corresponding culture [ 66 , 67 , 71 ], also referred to as ‘AI-ready-culture’ [ 54 ], that promotes development and encourages people and data through the incorporation of technology [ 71 , 75 ]. With the increasing adoption of smart technologies, a ‘new digital normal’ is emerging, characterized by hybrid work models, more human–machine interactions and an increased use of digital technologies [ 75 , 83 ].

3.1.3 Technology and System

The ‘technology and system’ supercategory focuses on the broader issues related to the technology and infrastructure that support organizational operations and provide the technical foundation for AI deployment.

By IT infrastructure we refer to issues regarding the foundational systems and IT needed for AI adoption in production. Industrial firms and their IT systems must achieve a mature technological readiness in order to enable successful AI adoption [ 51 , 60 , 67 , 69 , 83 ]. A lack of appropriate IT infrastructure [ 68 , 71 , 78 , 91 ] or small maturity of Internet of Things (IoT) technologies [ 70 ]) hinders the efficient use of data in production firms [ 56 ] which is why firms must update their foundational information systems for successful AI adoption [ 53 , 54 , 62 , 66 , 72 , 75 ]. IT and data security are fundamental for AI adoption and must be provided [ 50 , 51 , 68 , 82 ]. This requires necessary developments that can ensure security during AI implementation while complying with legal requirements [ 52 , 72 , 78 ]. Generally, security concerns are common when implementing AI innovations [ 72 , 79 , 91 , 94 ]. This fear of a lack of security can also prevent the release of (e.g. customer) data in a production environment [ 56 ]. Additionally, as industrial production systems are vulnerable to failures as well as cyberattacks, companies need to address security and cybersecurity measures [ 49 , 76 , 88 , 89 ]. Developing user-friendly AI solutions can facilitate the adoption of smart solutions by increasing user understanding and making systems easy to use by employees as well as quick to integrate [ 50 , 72 , 84 ]. When developing user-friendly solutions which satisfy user needs [ 76 ], it is particularly important to understand and integrate the user perspective in the development process [ 90 ]. If employees find technical solutions easy to use, they are more confident in its use and perceived usefulness increases [ 53 , 67 , 68 ]. The compatibility of AI with a firm and its existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 54 , 56 , 60 , 78 , 80 , 82 , 83 , 93 , 94 ], is considered an important requirement for the adoption of AI in IT systems [ 91 ]. Along with compatibility also comes connectivity, which is intended to ensure the links within the overall network and avoid silo thinking [ 59 ]. Connectivity and interoperability of AI-based processes within the company’s IT manufacturing systems must be ensured at different system levels and are considered key factors in the development of AI applications for production [ 50 , 72 , 89 ]. The design of modular AI solutions can increase system compatibility [ 84 ]. Firms deciding for AI adoption must address safety issues [ 51 , 54 , 59 , 72 , 73 , 78 ]. This includes both safety in the use and operation of AI [ 60 , 69 ]. In order to address safety concerns of integrating AI solutions in industrial systems [ 49 ], systems must secure high reliability [ 71 ]. AI can also be integrated as a safety enabler, for example, by providing technologies to monitor health and safety in the workplace to prevent fatigue and injury [ 75 ].

3.1.4 Data management

Since AI adoption in the organization is strongly data-driven, the ‘data management’ supercategory is dedicated to the comprehensive aspects related to the effective and responsible management of data within the organization.

Data privacy must be guaranteed when creating AI applications based on industrial production data [ 49 , 58 , 59 , 60 , 72 , 76 , 78 , 79 , 82 , 88 , 89 , 91 , 94 ] as ‘[M]anufacturing industries generate large volumes of unstructured and sensitive data during their daily operations’ [ 89 ]. Closely related to this is the need for anonymization and confidentiality of data [ 61 , 69 , 70 , 78 ]. The availability of large, heterogeneous data sets is essential for the digital transformation of organizations [ 52 , 59 , 78 , 80 , 88 , 89 ] and is considered one of the key drivers of AI innovation [ 62 , 68 , 72 , 86 ]. In production systems, lack of data availability is often a barrier to AI adoption [ 58 , 70 , 77 ]. In order to enable AI to establish relationships between data, the availability of large input data that is critical [ 62 , 76 , 81 ]. New AI models are trained with this data and can adapt as well as improve as they receive new data [ 59 , 62 ]. Big data can thus significantly improve the quality of AI applications [ 59 , 71 ]. As more and more data is generated in manufacturing [ 85 ], AI opens up new opportunities for companies to make use of it [ 62 ]. However, operational data are often unstructured, as they come from different sources and exist in diverse formats [ 85 , 87 ]. This challenges data processing, as data quality and origin are key factors in the management of data [ 78 , 79 , 80 , 88 , 89 , 91 ]. To make production data valuable and usable for AI, consistency of data and thus data integrity is required across manufacturing systems [ 50 , 62 , 77 , 84 ]. Another key prerequisites for AI adoption is data governance [ 56 , 59 , 67 , 68 , 71 , 78 , 88 ] which is an important asset to make use of data in production [ 50 ] and ensure the complex management of heterogenous data sets [ 89 ]. The interoperability of data and thus the foundation for the compatibility of AI with existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 56 , 84 , 93 ], is considered another important requirement for the adoption of AI in IT systems. Data interoperability in production systems can be hindered by missing data standards as different machines use different formats [ 87 ]. Data processing refers to techniques used to preparing data for analysis which is essential to obtain consistent results from data analytics in production [ 58 , 72 , 80 , 81 , 84 ]. In this process, the numerous, heterogeneous data from different sensors are processed in such a way that they can be used for further analyses [ 87 ]. The capability of production firms to process data and information is thus important to enable AI adoption [ 77 , 86 , 93 ]. With the increasing data generation in the smart and connected factory, the strategic relevance of data analytics is gaining importance [ 55 , 69 , 78 ], as it is essential for AI systems in performing advanced data analyses [ 49 , 67 , 72 , 86 , 88 ]. Using analytics, valuable insights can be gained from the production data obtained using AI systems [ 58 , 77 , 87 ]. In order to enable the processing of big data, a profound data infrastructure is necessary [ 65 , 75 , 87 ]. Facilities must be equipped with sensors, that collect data and model information, which requires investments from firms [ 72 ]. In addition, production firms must build the necessary skills, culture and capabilities for data analytics [ 54 , 75 , 87 , 93 ]. Data storage, one of the foundations and prerequisites for smart manufacturing [ 54 , 68 , 71 , 74 ], must be ensured in order to manage the larg amounts of data and thus realize the adoption of intelligent technologies in production [ 50 , 59 , 72 , 78 , 84 , 87 , 88 , 89 ].

3.2 External environment

The external drivers of AI adoption in production influence the organization through conditions and events from outside the firm and are therefore difficult to control by the organization itself.

3.2.1 Regulatory environment

This supercategory captures the broader concept of establishing rules, standards, and frameworks that guide the behavior, actions, and operations of individuals, organizations, and societies when implementing AI.

AI adoption in production faces many ethical challenges [ 70 , 72 , 79 ]. AI applications must be compliant with the requirements of organizational ethical standards and laws [ 49 , 50 , 59 , 60 , 62 , 75 ] which is why certain issues must be examined in AI adoption and AI design [ 62 , 73 , 82 , 91 ] so that fairness and justice are guaranteed [ 78 , 79 , 92 ]. Social rights, cultural values and norms must not be violated in the process [ 49 , 52 , 53 , 81 ]. In this context, the explainability and transparency of AI decisions also plays an important role [ 50 , 54 , 58 , 70 , 78 , 89 ] and can address the characteristic of AI of a black box [ 90 ]. In addition, AI applications must be compliant with legal and regulatory requirements [ 51 , 52 , 59 , 77 , 81 , 82 , 91 ] and be developed accordingly [ 49 , 76 ] in order to make organization processes using AI clear and effective [ 65 ]. At present, policies and regulation of AI are still in its infancy [ 49 ] and missing federal regulatory guidelines, standards as well as incentives hinder the adoption of AI [ 67 ] which should be expanded simultaneously to the expansion of AI technology [ 60 ]. This also includes regulations on the handling of data (e.g. anonymization of data) [ 61 , 72 ].

3.2.2 Business environment

The factors in the ‘business environment’ supercategory refer to the external conditions and influences that affect the operations, decision making, and performance of the company seeking to implement AI in a production context.

Cooperation and collaboration can influence the success of digital technology adoption [ 52 , 53 , 59 , 72 ], which is why partnerships are important for adoption [ 53 , 59 ] and can positively influence its future success [ 52 , 67 ]. Both intraorganizational and interorganizational knowledge sharing can positively influence AI adoption [ 49 ]. In collaborations, companies can use a shared knowledge base where data and process sharing [ 51 , 59 , 94 ] as well as social support systems strengthen feedback loops between departments [ 79 , 80 ]. With regard to AI adoption in firms, vendors as well as service providers need to collaborate closely to improve the compatibility and operational capability of smart technologies across different industries [ 82 , 93 ]. Without external IT support, companies can rarely integrate AI into their production processes [ 66 ], which is why thorough support from vendors can significantly facilitate the integration of AI into existing manufacturing processes [ 80 , 91 ]. Public–private collaborations can also add value and governments can target AI dissemination [ 60 , 74 ]. The support of the government also positively influences AI adoption. This includes investing in research projects and policies, building a regulatory setting as well as creating a collaborative environment [ 60 ]. Production companies are constantly exposed to changing conditions, which is why the dynamics of the environment is another factor influencing the adoption of AI [ 52 , 63 , 72 , 86 ]. Environmental dynamics influence the operational performance of firms and can favor an entrepreneurial orientation of firms [ 86 ]. In order to respond to dynamics, companies need to develop certain capabilities and resources (i.e. dynamic capabilities) [ 86 ]. This requires the development of transparency, agility, as well as resilience to unpredictable changes, which was important in the case of the COVID-19 pandemic, for example, where companies had to adapt quickly to changing environments [ 75 ]. A firm’s environment (e.g. governments, partners or customers) can also pressure companies to adopt digital technologies [ 53 , 67 , 82 , 91 ]. Companies facing intense competition are considered more likely to invest in smart technologies, as rivalry pushes them to innovate and they hope to gain competitive advantages from adoption [ 36 , 66 , 82 , 93 ].

3.2.3 Economic environment

By considering both the industrial sector and country within the subcategory ‘economic environment’, production firms can analyze the interplay between the two and understand how drivers can influence the AI adoption process in their industrial sector’s performance within a particular country.

The industrial sector of a firm influences AI adoption in production from a structural perspective, as it indicates variations in product characteristics, governmental support, the general digitalization status, the production environment as well as the use of AI technologies within the sector [ 36 ]. Another factor that influences AI adoption is the country in which a company is located. This influences not only cultural aspects, the availability of know-how and technology orientation, but also regulations, laws, standards and subsidies [ 36 ]. From another perspective, AI can also contribute to the wider socio-economic growth of economies by making new opportunities easily available and thus equipping e.g. more rural areas with advanced capabilities [ 78 ].

3.3 Future research directions

The analysis of AI adoption in production requires a comprehensive analysis of the various factors that influence the introduction of the innovation. As discussed by Kinkel, Baumgartner, Cherubini [ 36 ], our research also concludes that organizational factors have a particularly important role to play. After evaluating the individual drivers of AI adoption in production in detail in this qualitative synthesis, we draw a conclusion from the results and derive a research agenda from the analysis to serve as a basis for future research. The RQs emerged from the analyzed factors and are presented in Table  2 . We developed the questions based on the literature review and identified research gaps for every factor that was most frequently mentioned. From the factors analyzed and RQs developed, the internal environment has a strong influence on AI adoption in production, and organizational factors play a major role here.

Looking at the supercategory ‘business and environment’, performance indicators and investments are considered drivers of AI adoption in production. Indicators to measure the performance of AI innovations are necessary here so that managers can perform cost–benefit analyses and make the right decision for their company. There is a need for research here to support possible calculations and show managers a comprehensive view of the costs and benefits of technology in production. In terms of budget, it should be noted that AI adoption involves a considerable financial outlay that must be carefully weighed and some capital must be available to carry out the necessary implementation efforts (e.g., staffing costs, machine retrofits, change management, and external IT service costs). Since AI adoption is a complex process and turnkey solutions can seldom be implemented easily and quickly, but require many changes (not only technologically but also on an organizational level), it is currently difficult to estimate the necessary budgets and thus make them available. Especially the factors of the supercategory ‘organizational effectiveness’ drive AI adoption in production. Trust of the workforce is considered an important driver, which must be created in order to successfully implement AI. This requires measures that can support management in building trust. Closely related to this are the necessary change management processes that must be initiated to accompany the changes in a targeted manner. Management itself must also play a clear role in the introduction of AI and communicate its support, as this also influences the adoption. The development of clear processes and measures can help here. Developing roadmaps for AI adoption can facilitate the adoption process and promote strategic integration with existing IT and business strategy. Here, best practice roadmaps and necessary action steps can be helpful for companies. Skills are considered the most important driver for AI adoption in manufacturing. Here, there is a lack of clear approaches that support companies in identifying the range of necessary skills and, associated with this, also opportunities to further develop these skills in the existing workforce. Also, building a culture of innovation requires closer research that can help companies foster a conducive environment for AI adoption and the integration of other smart technologies. Steps for developing a positive mindset require further research that can provide approaches for necessary action steps and measures in creating a positive digital culture. With regard to ‘technology and system’, the factors of IT infrastructure and security in particular are driving AI adoption in production. Existing IT systems must reach a certain maturity to enable AI adoption on a technical level. This calls for clear requirements that visualize for companies which systems and standards are in place and where developments are needed. Security must be continuously ensured, for which certain standards and action catalogs must be developed. With regard to the supercategory ‘data management’, the availability of data is considered the basis for successful AI adoption, as no AI can be successfully deployed without data. In the production context in particular, this requires developments that support companies in the provision of data, which usually arises from very heterogeneous sources and forms. Data analytics must also be closely examined, and production companies usually need external support in doing so. The multitude of data also requires big data storage capabilities. Here, groundwork is needed to show companies options about the possibilities of different storage options (e.g., on premis vs. cloud-based).

In the ‘regulatory environment’, ethics in particular is considered a driver of AI adoption in production. Here, fundamental ethical factors and frameworks need to be developed that companies can use as a guideline to ensure ethical standards throughout the process. Cooperations and environmental dynamism drive the supercategory ‘business environment’. Collaborations are necessary to successfully implement AI adoption and action is needed to create the necessary contact facilitation bodies. In a competitive environment, companies have to make quick decisions under strong pressure, which also affects AI adoption. Here, guidelines and also best practice approaches can help to simplify decisions and quickly demonstrate the advantage of the solutions. There is a need for research in this context.

4 Conclusions

The use of AI technologies in production continues to gain momentum as managers hope to increase efficiency, productivity and reduce costs [ 9 , 13 , 20 ]. Although the benefits of AI adoption speak for themselves, implementing AI is a complex decision that requires a lot of knowledge, capital and change [ 95 ] and is influenced by various internal and external factors. Therefore, managers are still cautious about implementing the technology in a production context. Our SLR seeks to examine the emergent phenomenon of AI in production with the precise aim of understanding the factors influencing AI adoption and the key topics discussed in the literature when analyzing AI in a production context. For this purpose, we use the current state of research and examine the existing studies based on the methodology of a systematic literature analysis and respond to three RQs.

We answer RQ1 by closely analyzing the literature selected in our SLR to identify trends in current research on AI adoption in production. In this process, it becomes clear that the topic is gaining importance and that research has increased over the last few years. In the field of production, AI is being examined from various angles and current research addresses aspects from a business, human and technical perspective. In our response to RQ2 we synthesized the existing literature to derive 35 factors that influence AI adoption in production at different levels from inside or outside the organization. In doing so, we find that AI adoption in production poses particularly significant challenges to organizational effectiveness compared to other digital technologies and that the relevance of data management takes on a new dimension. Production companies often operate more traditionally and are sometimes rigid when it comes to change [ 96 , 97 ], which can pose organizational challenges when adopting AI. In addition, the existing machines and systems are typically rather heterogeneous and are subject to different digitalization standards, which in turn can hinder the availability of the necessary data for AI implementation [ 98 , 99 ]. We address RQ3 by deriving a research agenda, which lays a foundation for further scientific research and deepening the understanding of AI adoption in production. The results of our analysis can further help managers to better understand AI adoption and to pay attention to the different factors that influence the adoption of this complex technology.

4.1 Contributions

Our paper takes the first step towards analysing the current state of the research on AI adoption from a production perspective. We represent a holistic view on the topic, which is necessary to get a better understanding of AI in a production-context and build a comprehensive view on the different dimensions as well as factors influencing its adoption. To the best of our knowledge, this is the first contribution that systematises research about the adoption of AI in production. As such, it makes an important contribution to current AI and production research, which is threefold:

First, we highlight the characteristics of studies conducted in recent years on the topic of AI adoption in production, from which several features and developments can be deduced. Our results confirm the topicality of the issue and the increasing relevance of research in the field.

Having laid the foundations for understanding AI in production, we focused our research on the identification and systematization of the most relevant factors influencing AI adoption in production at different levels. This brings us to the second contribution, our comprehensive factor analysis of AI adoption in production provides a framework for further research as well as a potential basis for managers to draw upon when adopting AI. By systematizing the relevant factors influencing AI adoption in production, we derived a set of 35 researched factors associated with AI adoption in production. These factors can be clustered in two areas of analysis and seven respective supercategories. The internal environment area includes four levels of analysis: ‘business and structure’ (focusing on financial aspects and firm characteristics), ‘organizational effectiveness’ (focusing on human-centred factors), ‘technology and system’ (based on the IT infrastructure and systems) as well as ‘data management’ (including all data related factors). Three categories are assigned to the external environment: the ‘regulatory environment’ (such as ethics and the regulatory forms), the ‘business environment’ (focused on cooperation activities and dynamics in the firm environment) and the ‘economic environment’ (related to sectoral and country specifics).

Third, the developed research plan as outlined in Table  2 serves as an additional outcome of the SLR, identifying key RQs in the analyzed areas that can serve as a foundation for researchers to expand the research area of AI adoption in production. These RQs are related to the mostly cited factors analyzed in our SLR and aim to broaden the understanding on the emerging topic.

The resulting insights can serve as the basis for strategic decisions by production companies looking to integrate AI into their processes. Our findings on the factors influencing AI adoption as well as the developed research agenda enhance the practical understanding of a production-specific adoption. Hence, they can serve as the basis for strategic decisions for companies on the path to an effective AI adoption. Managers can, for example, analyse the individual factors in light of their company as well as take necessary steps to develop further aspects in a targeted manner. Researchers, on the other hand, can use the future research agenda in order to assess open RQs and can expand the state of research on AI adoption in production.

4.2 Limitations

Since a literature review must be restricted in its scope in order to make the analyses feasible, our study provides a starting point for further research. Hence, there is a need for further qualitative and quantitative empirical research on the heterogeneous nature of how firms configure their AI adoption process. Along these lines, the following aspects would be of particular interest for future research to improve and further validate the analytical power of the proposed framework.

First, the lack of research on AI adoption in production leads to a limited number of papers included in this SLR. As visualized in Fig.  2 , the number of publications related to the adoption of AI in production has been increasing since 2018 but is, to date, still at an early stage. For this reason, only 47 papers published until May 2024 addressing the production-specific adoption of AI were identified and therefore included in our analysis for in-depth investigation. This rather small number of papers included in the full-text analysis gives a limited view on AI adoption in production but allows a more detailed analysis. As the number of publications in this research field increases, there seems to be a lot of research happening in this field which is why new findings might be constantly added and developed as relevant in the future [ 39 ]. Moreover, in order to research AI adoption from a more practical perspective and thus to build up a broader, continuously updated view on AI adoption in production, future literature analyses could include other publication formats, e.g. study reports of research institutions and companies, as well discussion papers.

Second, the scope of the application areas of AI in production has been increasing rapidly. Even though our overview of the three main areas covered in the recent literature serves as a good basis for identifying the most dominant fields for AI adoption in production, a more detailed analysis could provide a better overview of possibilities for manufacturing companies. Hence, a further systematisation as well as evaluation of application areas for AI in production can provide managers with the information needed to decide where AI applications might be of interest for the specific company needs.

Third, the systematisation of the 35 factors influencing AI adoption in production serve as a good ground for identifying relevant areas influenced by and in turn influencing the adoption of AI. Further analyses should be conducted in order to extend this view and extend the framework. For example, our review could be combined with explorative research methods (such as case studies in production firms) in order to add the practical insights from firms adopting AI. This integration of practical experiences can also help exploit and monitor more AI-specific factors by observing AI adoption processes. In enriching the factors through in-depth analyses, the results of the identified AI adoption factors could also be examined in light of theoretical contributions like the technology-organization-environment (TOE) framework [ 47 ] and other adoption theories.

Fourth, in order to examine the special relevance of identified factors for AI adoption process and thus to distinguish it from the common factors influencing the adoption of more general digital technologies, there is a further need for more in-depth (ethnographic) research into their impacts on the adoption processes, particularly in the production context. Similarly, further research could use the framework introduced in this paper as a basis to develop new indicators and measurement concepts as well as to examine their impacts on production performance using quantitative methods.

Benner MJ, Waldfogel J (2020) Changing the channel: digitization and the rise of “middle tail” strategies. Strat Mgmt J 86:1–24. https://doi.org/10.1002/smj.3130

Article   Google Scholar  

Roblek V, Meško M, Krapež A (2016) A complex view of industry 4.0. SAGE Open. https://doi.org/10.1177/2158244016653987

Oliveira BG, Liboni LB, Cezarino LO et al (2020) Industry 4.0 in systems thinking: from a narrow to a broad spectrum. Syst Res Behav Sci 37:593–606. https://doi.org/10.1002/sres.2703

Li B, Hou B, Yu W et al (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers Inf Technol Electronic Eng 18:86–96. https://doi.org/10.1631/FITEE.1601885

Dhamija P, Bag S (2020) Role of artificial intelligence in operations environment: a review and bibliometric analysis. TQM 32:869–896. https://doi.org/10.1108/TQM-10-2019-0243

Collins C, Dennehy D, Conboy K et al (2021) Artificial intelligence in information systems research: a systematic literature review and research agenda. Int J Inf Manage 60:102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383

Chien C-F, Dauzère-Pérès S, Huh WT et al (2020) Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Int J Prod Res 58:2730–2731. https://doi.org/10.1080/00207543.2020.1752488

Chen H (2019) Success factors impacting artificial intelligence adoption: perspective from the telecom industry in China, Old Dominion University

Sanchez M, Exposito E, Aguilar J (2020) Autonomic computing in manufacturing process coordination in industry 4.0 context. J Industrial Inf Integr. https://doi.org/10.1016/j.jii.2020.100159

Lee J, Davari H, Singh J et al (2018) Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters 18:20–23. https://doi.org/10.1016/j.mfglet.2018.09.002

Heimberger H, Horvat D, Schultmann F (2023) Assessing AI-readiness in production—A conceptual approach. In: Huang C-Y, Dekkers R, Chiu SF et al. (eds) intelligent and transformative production in pandemic times. Springer, Cham, pp 249–257

Horvat D, Heimberger H (2023) AI Readiness: An Integrated Socio-technical Framework. In: Deschamps F, Pinheiro de Lima E, Da Gouvêa Costa SE et al. (eds) Proceedings of the 11 th international conference on production research—Americas: ICPR Americas 2022, 1 st ed. 2023. Springer Nature Switzerland; Imprint Springer, Cham, pp 548–557

Wang J, Ma Y, Zhang L et al (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156. https://doi.org/10.1016/J.JMSY.2018.01.003

Davenport T, Guha A, Grewal D et al (2020) How artificial intelligence will change the future of marketing. J Acad Mark Sci 48:24–42. https://doi.org/10.1007/s11747-019-00696-0

Cui R, Li M, Zhang S (2022) AI and procurement. Manufacturing Serv Operations Manag 24(691):706. https://doi.org/10.1287/msom.2021.0989

Pournader M, Ghaderi H, Hassanzadegan A et al (2021) Artificial intelligence applications in supply chain management. Int J Prod Econ 241:108250. https://doi.org/10.1016/j.ijpe.2021.108250

Su H, Li L, Tian S et al (2024) Innovation mechanism of AI empowering manufacturing enterprises: case study of an industrial internet platform. Inf Technol Manag. https://doi.org/10.1007/s10799-024-00423-4

Venkatesh V, Raman R, Cruz-Jesus F (2024) AI and emerging technology adoption: a research agenda for operations management. Int J Prod Res 62:5367–5377. https://doi.org/10.1080/00207543.2023.2192309

Senoner J, Netland T, Feuerriegel S (2022) Using explainable artificial intelligence to improve process quality: evidence from semiconductor manufacturing. Manage Sci 68:5704–5723. https://doi.org/10.1287/mnsc.2021.4190

Fosso Wamba S, Queiroz MM, Ngai EWT et al (2024) The interplay between artificial intelligence, production systems, and operations management resilience. Int J Prod Res 62:5361–5366. https://doi.org/10.1080/00207543.2024.2321826

Uren V, Edwards JS (2023) Technology readiness and the organizational journey towards AI adoption: an empirical study. Int J Inf Manage 68:102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588

Berente N, Gu B, Recker J (2021) Managing artificial intelligence special issue managing AI. MIS Quarterly 45:1433–1450

Google Scholar  

Scafà M, Papetti A, Brunzini A et al (2019) How to improve worker’s well-being and company performance: a method to identify effective corrective actions. Procedia CIRP 81:162–167. https://doi.org/10.1016/j.procir.2019.03.029

Wang H, Qiu F (2023) AI adoption and labor cost stickiness: based on natural language and machine learning. Inf Technol Manag. https://doi.org/10.1007/s10799-023-00408-9

Lindebaum D, Vesa M, den Hond F (2020) Insights from “the machine stops ” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Acad Manag Rev 45:247–263. https://doi.org/10.5465/amr.2018.0181

Baskerville RL, Myers MD, Yoo Y (2020) Digital first: the ontological reversal and new challenges for information systems research. MIS Quarterly 44:509–523

Frey CB, Osborne MA (2017) The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280. https://doi.org/10.1016/J.TECHFORE.2016.08.019

Jarrahi MH (2018) Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus Horiz 61:577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Fügener A, Grahl J, Gupta A et al (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quarterly 45:1527–1556

Klumpp M (2018) Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. Int J Log Res Appl 21:224–242. https://doi.org/10.1080/13675567.2017.1384451

Schrettenbrunnner MB (2020) Artificial-Intelligence-driven management. IEEE Eng Manag Rev 48:15–19. https://doi.org/10.1109/EMR.2020.2990933

Li J, Li M, Wang X et al (2021) Strategic directions for AI: the role of CIOs and boards of directors. MIS Quarterly 45:1603–1644

Brock JK-U, von Wangenheim F (2019) Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. Calif Manage Rev 61:110–134. https://doi.org/10.1177/1536504219865226

Lee J, Suh T, Roy D et al (2019) Emerging technology and business model innovation: the case of artificial intelligence. JOItmC 5:44. https://doi.org/10.3390/joitmc5030044

Chen J, Tajdini S (2024) A moderated model of artificial intelligence adoption in firms and its effects on their performance. Inf Technol Manag. https://doi.org/10.1007/s10799-024-00422-5

Kinkel S, Baumgartner M, Cherubini E (2022) Prerequisites for the adoption of AI technologies in manufacturing—evidence from a worldwide sample of manufacturing companies. Technovation 110:102375. https://doi.org/10.1016/j.technovation.2021.102375

Mikalef P, Gupta M (2021) Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Inf Manag 58:103434. https://doi.org/10.1016/j.im.2021.103434

McElheran K, Li JF, Brynjolfsson E et al (2024) AI adoption in America: Who, what, and where. Economics Manag Strategy 33:375–415. https://doi.org/10.1111/jems.12576

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222. https://doi.org/10.1111/1467-8551.00375

Cooper H, Hedges LV, Valentine JC (2009) Handbook of research synthesis and meta-analysis. Russell Sage Foundation, New York

Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71

Denyer D, Tranfield D (2011) Producing a systematic review. In: Buchanan DA, Bryman A (eds) The Sage handbook of organizational research methods. Sage Publications Inc, Thousand Oaks, CA, pp 671–689

Burbidge JL, Falster P, Riis JO et al (1987) Integration in manufacturing. Comput Ind 9:297–305. https://doi.org/10.1016/0166-3615(87)90103-5

Mayring P (2000) Qualitative content analysis. Forum qualitative Sozialforschung/Forum: Qualitative social research, Vol 1, No 2 (2000): Qualitative methods in various disciplines I: Psychology. https://doi.org/10.17169/fqs-1.2.1089

Hsieh H-F, Shannon SE (2005) Three approaches to qualitative content analysis. Qual Health Res 15:1277–1288. https://doi.org/10.1177/1049732305276687

Miles MB, Huberman AM (2009) Qualitative data analysis: An expanded sourcebook, 2nd edn. Sage, Thousand Oaks, Calif

Tornatzky LG, Fleischer M (1990) The processes of technological innovation. Issues in organization and management series. Lexington Books, Lexington, Mass.

Alsheibani S, Cheung Y, Messom C (2018) Artificial Intelligence Adoption: AI-readiness at Firm-Level: Research-in-Progress. Twenty-Second Pacific Asia Conference on Information Systems

Akinsolu MO (2023) Applied artificial intelligence in manufacturing and industrial production systems: PEST considerations for engineering managers. IEEE Eng Manag Rev 51:52–62. https://doi.org/10.1109/EMR.2022.3209891

Bettoni A, Matteri D, Montini E et al (2021) An AI adoption model for SMEs: a conceptual framework. IFAC-PapersOnLine 54:702–708. https://doi.org/10.1016/j.ifacol.2021.08.082

Boavida N, Candeias M (2021) Recent automation trends in portugal: implications on industrial productivity and employment in automotive sector. Societies 11:101. https://doi.org/10.3390/soc11030101

Botha AP (2019) A mind model for intelligent machine innovation using future thinking principles. Jnl of Manu Tech Mnagmnt 30:1250–1264. https://doi.org/10.1108/JMTM-01-2018-0021

Chatterjee S, Rana NP, Dwivedi YK et al (2021) Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol Forecast Soc Chang 170:120880. https://doi.org/10.1016/j.techfore.2021.120880

Chiang LH, Braun B, Wang Z et al (2022) Towards artificial intelligence at scale in the chemical industry. AIChE J. https://doi.org/10.1002/aic.17644

Chouchene A, Carvalho A, Lima TM et al. (2020) Artificial intelligence for product quality inspection toward smart industries: quality control of vehicle Non-conformities. In: Garengo P (ed) 2020 9th International Conference on Industrial Technology and Management: ICITM 2020 February 11–13, 2020, Oxford, United Kingdom. IEEE, pp 127–131

Corti D, Masiero S, Gladysz B (2021) Impact of Industry 4.0 on Quality Management: identification of main challenges towards a Quality 4.0 approach. In: 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, pp 1–8

Demlehner Q, Schoemer D, Laumer S (2021) How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases. Int J Inf Manage 58:102317. https://doi.org/10.1016/j.ijinfomgt.2021.102317

Dohale V, Akarte M, Gunasekaran A et al (2022) (2022) Exploring the role of artificial intelligence in building production resilience: learnings from the COVID-19 pandemic. Int J Prod Res 10(1080/00207543):2127961

Drobot AT (2020) Industrial Transformation and the Digital Revolution: A Focus on artificial intelligence, data science and data engineering. In: 2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K). IEEE, pp 1–11

Ghani EK, Ariffin N, Sukmadilaga C (2022) Factors influencing artificial intelligence adoption in publicly listed manufacturing companies: a technology, organisation, and environment approach. IJAEFA 14:108–117

Hammer A, Karmakar S (2021) Automation, AI and the future of work in India. ER 43:1327–1341. https://doi.org/10.1108/ER-12-2019-0452

Hartley JL, Sawaya WJ (2019) Tortoise, not the hare: digital transformation of supply chain business processes. Bus Horiz 62:707–715. https://doi.org/10.1016/j.bushor.2019.07.006

Kyvik Nordås H, Klügl F (2021) Drivers of automation and consequences for jobs in engineering services: an agent-based modelling approach. Front Robot AI 8:637125. https://doi.org/10.3389/frobt.2021.637125

Mubarok K, Arriaga EF (2020) Building a smart and intelligent factory of the future with industry 4.0 technologies. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1569/3/032031

Muriel-Pera YdJ, Diaz-Piraquive FN, Rodriguez-Bernal LP et al. (2018) Adoption of strategies the fourth industrial revolution by micro, small and medium enterprises in bogota D.C. In: Lozano Garzón CA (ed) 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI). IEEE, pp 1–6

Olsowski S, Schlögl S, Richter E et al. (2022) Investigating the Potential of AutoML as an Instrument for Fostering AI Adoption in SMEs. In: Uden L, Ting I-H, Feldmann B (eds) Knowledge Management in Organisations: 16th International Conference, KMO 2022, Hagen, Germany, July 11–14, 2022, Proceedings, 1st ed. 2022, vol 1593. Springer, Cham, pp 360–371

Rodríguez-Espíndola O, Chowdhury S, Dey PK et al (2022) Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technol Forecast Soc Chang 178:121562. https://doi.org/10.1016/j.techfore.2022.121562

Schkarin T, Dobhan A (2022) Prerequisites for Applying Artificial Intelligence for Scheduling in Small- and Medium-sized Enterprises. In: Proceedings of the 24 th International Conference on Enterprise Information Systems. SCITEPRESS—Science and Technology Publications, pp 529–536

Sharma P, Shah J, Patel R (2022) Artificial intelligence framework for MSME sectors with focus on design and manufacturing industries. Mater Today: Proc 62:6962–6966. https://doi.org/10.1016/j.matpr.2021.12.360

Siaterlis G, Nikolakis N, Alexopoulos K et al. (2022) Adoption of AI in EU Manufacturing. Gaps and Challenges. In: Katalinic B (ed) Proceedings of the 33 rd International DAAAM Symposium 2022, vol 1. DAAAM International Vienna, pp 547–550

Tariq MU, Poulin M, Abonamah AA (2021) Achieving operational excellence through artificial intelligence: driving forces and barriers. Front Psychol 12:686624. https://doi.org/10.3389/fpsyg.2021.686624

Trakadas P, Simoens P, Gkonis P et al (2020) An artificial intelligence-based collaboration approach in industrial IoT manufacturing: key concepts. Architectural Ext Potential Applications Sens. https://doi.org/10.3390/s20195480

Vernim S, Bauer H, Rauch E et al (2022) A value sensitive design approach for designing AI-based worker assistance systems in manufacturing. Procedia Computer Sci 200:505–516. https://doi.org/10.1016/j.procs.2022.01.248

Williams G, Meisel NA, Simpson TW et al (2022) Design for artificial intelligence: proposing a conceptual framework grounded in data wrangling. J Computing Inf Sci Eng 10(1115/1):4055854

Wuest T, Romero D, Cavuoto LA et al (2020) Empowering the workforce in Post–COVID-19 smart manufacturing systems. Smart Sustain Manuf Syst 4:20200043. https://doi.org/10.1520/SSMS20200043

Javaid M, Haleem A, Singh RP (2023) A study on ChatGPT for Industry 4.0: background, potentials, challenges, and eventualities. J Economy Technol 1:127–143. https://doi.org/10.1016/j.ject.2023.08.001

Rathore AS, Nikita S, Thakur G et al (2023) Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 41:497–510. https://doi.org/10.1016/j.tibtech.2022.08.007

Jan Z, Ahamed F, Mayer W et al (2023) Artificial intelligence for industry 4.0: systematic review of applications, challenges, and opportunities. Expert Syst Applications 216:119456

Waschull S, Emmanouilidis C (2023) Assessing human-centricity in AI enabled manufacturing systems: a socio-technical evaluation methodology. IFAC-PapersOnLine 56:1791–1796. https://doi.org/10.1016/j.ifacol.2023.10.1891

Stohr A, Ollig P, Keller R et al (2024) Generative mechanisms of AI implementation: a critical realist perspective on predictive maintenance. Inf Organ 34:100503. https://doi.org/10.1016/j.infoandorg.2024.100503

Pazhayattil AB, Konyu-Fogel G (2023) ML and AI Implementation Insights for Bio/Pharma Manufacturing. BioPharm International 36:24–29

Ronaghi MH (2023) The influence of artificial intelligence adoption on circular economy practices in manufacturing industries. Environ Dev Sustain 25:14355–14380. https://doi.org/10.1007/s10668-022-02670-3

Rath SP, Tripathy R, Jain NK (2024) Assessing the factors influencing the adoption of generative artificial intelligence (GenAI) in the manufacturing sector. In: Sharma SK, Dwivedi YK, Metri B et al (eds) Transfer, diffusion and adoption of next-generation digital technologies, vol 697. Springer Nature Switzerland, Cham

Bonnard R, Da Arantes MS, Lorbieski R et al (2021) Big data/analytics platform for Industry 4.0 implementation in advanced manufacturing context. Int J Adv Manuf Technol 117:1959–1973. https://doi.org/10.1007/s00170-021-07834-5

Confalonieri M, Barni A, Valente A et al. (2015) An AI based decision support system for preventive maintenance and production optimization in energy intensive manufacturing plants. In: 2015 IEEE international conference on engineering, technology and innovation/ international technology management conference (ICE/ITMC). IEEE, pp 1–8

Dubey R, Gunasekaran A, Childe SJ et al (2020) Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: a study of manufacturing organisations. Int J Prod Econ 226:107599. https://doi.org/10.1016/j.ijpe.2019.107599

Lee J, Singh J, Azamfar M et al (2020) Industrial AI: a systematic framework for AI in industrial applications. China Mechanical Eng 31:37–48

Turner CJ, Emmanouilidis C, Tomiyama T et al (2019) Intelligent decision support for maintenance: an overview and future trends. Int J Comput Integr Manuf 32:936–959. https://doi.org/10.1080/0951192X.2019.1667033

Agostinho C, Dikopoulou Z, Lavasa E et al (2023) Explainability as the key ingredient for AI adoption in Industry 5.0 settings. Front Artif Intell. https://doi.org/10.3389/frai.2023.1264372

Csiszar A, Hein P, Wachter M et al. (2020) Towards a user-centered development process of machine learning applications for manufacturing domain experts. In: 2020 third international conference on artificial intelligence for industries (AI4I). IEEE, pp 36–39

Merhi MI (2023) Harfouche A (2023) Enablers of artificial intelligence adoption and implementation in production systems. Int J Prod Res. https://doi.org/10.1080/00207543.2023.2167014

Demlehner Q, Laumer S (2024) How the terminator might affect the car manufacturing industry: examining the role of pre-announcement bias for AI-based IS adoptions. Inf Manag 61:103881. https://doi.org/10.1016/j.im.2023.103881

Ghobakhloo M, Ching NT (2019) Adoption of digital technologies of smart manufacturing in SMEs. J Ind Inf Integr 16:100107. https://doi.org/10.1016/j.jii.2019.100107

Binsaeed RH, Yousaf Z, Grigorescu A et al (2023) Knowledge sharing key issue for digital technology and artificial intelligence adoption. Systems 11:316. https://doi.org/10.3390/systems11070316

Papadopoulos T, Sivarajah U, Spanaki K et al (2022) Editorial: artificial Intelligence (AI) and data sharing in manufacturing, production and operations management research. Int J Prod Res 60:4361–4364. https://doi.org/10.1080/00207543.2021.2010979

Chirumalla K (2021) Building digitally-enabled process innovation in the process industries: a dynamic capabilities approach. Technovation 105:102256. https://doi.org/10.1016/j.technovation.2021.102256

Fragapane G, Ivanov D, Peron M et al (2022) Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Ann Oper Res 308:125–143. https://doi.org/10.1007/s10479-020-03526-7

Shahbazi Z, Byun Y-C (2021) Integration of Blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors (Basel). https://doi.org/10.3390/s21041467

Javaid M, Haleem A, Singh RP et al (2021) Significance of sensors for industry 4.0: roles, capabilities, and applications. Sensors Int 2:100110. https://doi.org/10.1016/j.sintl.2021.100110

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Heimberger, H., Horvat, D. & Schultmann, F. Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00436-z

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Protocol for Systematic Review and Meta-Analysis of Prehospital Large Vessel Occlusion Screening Scales

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Background Large Vessel Occlusion (LVO) is a serious condition that causes approximately 24-46% of acute ischemic strokes (AIS). LVO strokes tend to have higher mortality rates and result in more severe longterm disabilities compared to non LVO ischemic strokes. Early intervention with endovascular therapy (EVT) is recommended; however, EVT is limited to tertiary care hospitals with specialized facilities. Therefore, identifying patients with a high probability of LVO in prehospital settings and ensuring their rapid transfer to appropriate hospitals is crucial. While LVO diagnosis typically requires advanced imaging like MRI or CT scans, various scoring systems based on neurological symptoms have been developed for prehospital use. Although previous systematic reviews have addressed some of these scales, recent studies have introduced new scales and additional data on their accuracy. This systematic review and meta-analysis aim to summarize the current evidence on the diagnostic accuracy of these prehospital LVO screening scales.

Methods This systematic review and meta-analysis will be conducted in accordance with the PRISMA-DTA Statement and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. We will include observational studies and randomized controlled trials that assess the utility of LVO scales in suspected stroke patients in prehospital settings. Eligible studies must provide sufficient data to calculate sensitivity and specificity, and those lacking such data or being case reports will be excluded. The literature search will cover CENTRAL, MEDLINE, and Ichushi databases, including studies in English and Japanese. Bias will be assessed using QUADAS-2, and meta-analysis will be conducted using a random effects model, with subgroup and sensitivity analyses to explore heterogeneity.

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I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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