README.Rmd

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# bibliometrix

## An R-tool for comprehensive science mapping analysis

[![bibliometrix: An R-tool for comprehensive science mapping
analysis.](https://www.bibliometrix.org/JOI-badge.svg)](https://doi.org/10.1016/j.joi.2017.08.007)
[![Project Status: Active - The project has reached a stable, usable
state and is being actively
developed.](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![R-CMD-check](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml)
[![cran
version](http://www.r-pkg.org/badges/version/bibliometrix)](https://cran.r-project.org/package=bibliometrix)
[![rstudio mirror
downloads](https://cranlogs.r-pkg.org/badges/bibliometrix)](https://github.com/r-hub/cranlogs.app)
`r badger::badge_cran_download("bibliometrix", "grand-total")`

<p align="center">

<img src="https://raw.githubusercontent.com/massimoaria/bibliometrix/master/inst/biblioshiny/www/logoAI.jpg" width="400"/>

</p>

## Overview

**bibliometrix** provides a comprehensive set of tools for quantitative
research in bibliometrics and scientometrics.

Bibliometrics applies quantitative analysis and statistics to scientific
publications and their citation patterns. It has become essential across
all scientific fields for evaluating growth, maturity, leading authors,
conceptual and intellectual maps, and emerging trends within research
communities.

Today, bibliometrics is widely used in research performance evaluation
by universities, government laboratories, policymakers, research
directors, information specialists, librarians, and scholars themselves.

**bibliometrix** supports scholars in three key phases of analysis:

-   **Data importing and conversion** to R format from major
    bibliographic databases;

-   **Bibliometric analysis** of publication datasets, including
    descriptive statistics, author productivity, and source impact;

-   **Building and visualizing matrices** for co-citation, coupling,
    collaboration, and co-word analysis. These matrices serve as input
    for network analysis, multiple correspondence analysis, and other
    data reduction techniques.

For an in-depth guide to science mapping with bibliometrix, see the
companion book [Science Mapping Analysis: A Primer with
Biblioshiny](#the-book).

## The SAAS Workflow

**bibliometrix** and **biblioshiny** are designed around the **SAAS
workflow** — a four-stage methodological framework for conducting
rigorous bibliometric research:

<p align="center">
<img src="man/figures/c02_SAAS_WF1.png" width="700"/>
</p>

The SAAS workflow aligns the analytical pipeline with the structure of
a scientific article:

-   **Search** — Define the research question, select bibliographic
    databases (Web of Science, Scopus, OpenAlex, PubMed, Lens.org,
    Cochrane, Dimensions), formulate search queries, and collect data
    via web export or API.

-   **Appraisal** — Assess and refine the dataset using the PRISMA
    flow diagram, apply inclusion/exclusion filters (timespan, language,
    document type, impact metrics), and ensure data quality through
    pre-processing and citation matching.

-   **Analysis** — Perform descriptive analysis at three levels
    (Sources, Authors, Documents) and explore the knowledge structures:
    conceptual (co-word analysis, thematic mapping), intellectual
    (co-citation, historiograph), and social (collaboration networks).

-   **Synthesis** — Generate visualizations (networks, maps, matrices,
    Sankey diagrams) and interpret results with the support of
    **Biblio AI**, the integrated AI assistant.

This framework ensures transparency and reproducibility, mapping
each analytical step to a specific section of the resulting
research paper.

## The Book

<p align="center">
<img src="man/figures/book_cover.png" width="800"/>
</p>

**Science Mapping Analysis: A Primer with Biblioshiny** by Massimo
Aria and Corrado Cuccurullo (McGraw-Hill) is the definitive guide to
bibliometric research using bibliometrix and biblioshiny.

The book covers the full SAAS workflow, from research design and data
collection to advanced analyses including thematic evolution, content
analysis, and AI-assisted interpretation. Each chapter provides
step-by-step instructions with practical examples using biblioshiny.

## biblioshiny

**bibliometrix** includes **biblioshiny: bibliometrix for no-coders**

**biblioshiny** is a *shiny web application providing an intuitive
interface for bibliometrix*.

It enables scholars to easily access the main features of bibliometrix
through an interactive workflow organized around the SAAS model.

<p align="center">
<img src="man/figures/c02_biblioshiny_interface.png" width="700"/>
</p>

### Data Management

-   **Import and convert** data from multiple bibliographic databases
    (Web of Science, Scopus, PubMed, OpenAlex, Cochrane CDSR, Lens.org,
    Dimensions)

-   **API Integration** for direct data retrieval from OpenAlex and
    PubMed

-   **Filter data** by various criteria including publication years,
    journals, countries, citation counts, and custom journal rankings

-   **Merge collections** from different databases

-   **Citation Matching**: Intelligent algorithm to match and reconcile
    citations across different databases

-   **PRISMA Flow Diagram**: Automatically generate a PRISMA-compliant
    flow diagram documenting the data selection process — from
    identification through screening, eligibility, and final inclusion.

<p align="center">
<img src="man/figures/c03_prisma.png" width="500"/>
</p>

### Analytics and Visualization

-   **Main Information**: A comprehensive dashboard summarizing the
    key bibliometric indicators of the collection.

-   **Three-Field Plot**: A Sankey diagram connecting three metadata
    fields (e.g., cited references, authors, and keywords) to reveal
    relational patterns at a glance.

<p align="center">
<img src="man/figures/c04_threefieldplot.png" width="700"/>
</p>

-   **Three-level metrics** for comprehensive analysis:

    -   **Sources**: journal performance, impact metrics, Bradford's
        law, source production over time

    -   **Authors**: productivity analysis, h-index, Lotka's law,
        collaboration patterns, author profiles with biographical
        information from OpenAlex

    -   **Documents**: citation analysis, most relevant papers,
        reference publication year spectroscopy (RPYS), trend topics

-   **Life Cycle Analysis**: Fit a logistic growth model to annual
    publication counts to identify the developmental stage of a
    research field (emergence, rapid growth, maturity, or saturation)
    and forecast future trends.

<p align="center">
<img src="man/figures/c04_lifecycle.png" width="700"/>
</p>

### Knowledge Structure Analysis

-   **Conceptual Structure**: Analyze the topics and themes through
    co-word networks, thematic mapping (strategic diagrams), and
    thematic evolution over time.

<p align="center">
<img src="man/figures/c08_thematicMap.png" width="500"/>
</p>

<p align="center">
<img src="man/figures/c08_TE_lineages.png" width="700"/>
</p>

-   **Intellectual Structure**: Examine the citation networks through
    co-citation analysis (at document, author, and source level),
    historiograph, and bibliographic coupling.

<p align="center">
<img src="man/figures/c09_co-citation_papers_network.png" width="500"/>
</p>

<p align="center">
<img src="man/figures/c09_historiograph_temporal_network.png" width="700"/>
</p>

-   **Social Structure**: Explore collaboration patterns through
    co-authorship networks at author, institution, and country levels,
    including an interactive collaboration world map.

<p align="center">
<img src="man/figures/c10_collaboration_map.png" width="700"/>
</p>

### Content Analysis

**Content Analysis** goes beyond metadata to examine the full text of
key scientific publications. It includes:

-   Citation function analysis (background, method, comparison, critique)
-   In-context citation analysis with citation windows
-   Keyword and concept extraction (TF-IDF, RAKE, YAKE)
-   Word frequency trends and structural analysis (IMRaD)
-   AI-powered summaries via Biblio AI

<p align="center">
<img src="man/figures/c11_CA_menu.png" width="400"/>
</p>

### Biblio AI

**Biblio AI** is an integrated AI assistant that helps scholars
interpret bibliometric results, generate insights, and provide
context-aware recommendations. It supports the synthesis and
interpretation phases of the SAAS workflow by translating quantitative
outputs into actionable research narratives.

### Additional Features

-   **Animated Networks**: Dynamic visualization of diachronic networks
    showing temporal evolution

-   **Interactive Reports**: Generate comprehensive Excel reports
    combining multiple analyses

### How to use biblioshiny

To launch the application, simply run:

```{r eval=FALSE}
library(bibliometrix)
biblioshiny()
```

For detailed tutorials and guides, visit the bibliometrix website:
<https://www.bibliometrix.org/>

## How to cite bibliometrix

If you use this package for your research, please cite it as:

Aria, M. & Cuccurullo, C. (2017) **bibliometrix: An R-tool for
comprehensive science mapping analysis**, *Journal of Informetrics*,
11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007

## Community

Official website: <https://www.bibliometrix.org>

CRAN page: <https://cran.r-project.org/package=bibliometrix>

GitHub repository: <https://github.com/massimoaria/bibliometrix>

### Tutorials

Introduction to bibliometrix:
<https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html>

Data importing and converting:
<https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html>

## Installation

Install the stable version from CRAN:

```{r eval=FALSE}
install.packages("bibliometrix")
```

Or install the development version from GitHub:

```{r eval=FALSE}
if (!require("pak", quietly=TRUE)) install.packages("pak")
pak::pkg_install("massimoaria/bibliometrix")
```

Load `bibliometrix`:

```{r echo=TRUE}
library(bibliometrix)
```

## Data loading and converting

Export files from bibliographic databases can be imported into R using
the function **convert2df**:

**convert2df**(*file*, *dbsource*, *format*)

The argument *file* is a character vector containing the names of export
files downloaded from SCOPUS, Clarivate Analytics WoS, OpenAlex, Digital
Science Dimensions, PubMed, Lens.org, or Cochrane CDSR. The *file*
argument can also contain JSON/XML objects downloaded using OpenAlex,
Digital Science Dimensions, or PubMed APIs (through the packages
*openalexR*, *dimensionsR*, and *pubmedR*).

Example:

```{r eval=FALSE}
file <- c("file1.txt", "file2.txt", ...)
```

```{r Data-loading-and-Converting}
## An example from bibliometrix vignettes

file <- c("https://www.bibliometrix.org/datasets/management1.txt","https://www.bibliometrix.org/datasets/management2.txt")

M <- convert2df(file = file, dbsource = "wos", format = "plaintext")

data(management, package = "bibliometrixData")
M <- management
```

The **convert2df** function creates a bibliographic data frame where
rows correspond to manuscripts and columns to metadata fields.

Each manuscript contains multiple elements including authors' names,
title, keywords, abstract, and other bibliographic information. All
these elements constitute the metadata of a document.

Data frame columns are named using the standard Clarivate Analytics WoS
Field Tag codes: [(Field Tags
documentation)](https://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf)

## Checking metadata completeness

After importing a bibliographic data frame, you can assess the
completeness of metadata using **missingData()**:

**missingData**(*M*)

The argument *M* is a bibliographic data frame obtained by the
**convert2df** function.

```{r Completeness-of-metadata}
## An example from bibliometrix vignettes

com <- missingData(M)

com$mandatoryTags
```

## Bibliometric Analysis

The function **biblioAnalysis** performs a comprehensive bibliometric
analysis of a dataset, calculating main bibliometric measures.

**biblioAnalysis**(*M*, *sep = ";"*)

The argument *sep* indicates the field separator character used in the
data frame.

```{r biblioAnalysis}
results <- biblioAnalysis(M, sep = ";")
```

The **biblioAnalysis** function returns an object of class
"bibliometrix" containing:

-   Annual scientific production
-   Most productive authors
-   Most cited manuscripts
-   Most productive countries
-   Total citations per country\
-   Most relevant sources (journals)
-   Most frequent keywords

Use the generic function **summary** to display main results:

```{r summary-generic-function}
S <- summary(object = results, k = 10, pause = FALSE)
```

The *k* parameter specifies the number of rows to display in each table
(top k authors, sources, etc.). The *pause* parameter controls whether
to pause between tables.

Basic visualizations can be generated using the **plot** function:

```{r plot-generic-function, fig.width=7}
plot(x = results, k = 10, pause = FALSE)
```

## Author Bio Cards

The **AuthorBio** function retrieves and displays biographical
information about authors using OpenAlex data:

```{r eval=TRUE}
# Example: Get biographical information for an author
authorInfo <- authorBio(author_position = 1, 
                        doi = "10.1016/j.joi.2017.08.007")

dplyr::glimpse(authorInfo)
```

This function provides comprehensive author profiles including:

-   Institutional affiliations
-   Research areas and topics
-   Publication history
-   Citation metrics
-   Co-author networks

## Citation Matching

The **applyCitationMatching** function implements an intelligent
algorithm to match and reconcile citations across different databases:

```{r eval=FALSE}
# Apply citation matching to improve reference consistency
results <- applyCitationMatching(management, threshold = 0.85)
```

This feature is particularly useful when:

-   Merging datasets from multiple databases
-   Identifying duplicate or variant citations
-   Building accurate citation networks
-   Conducting historiographic analysis

## Life Cycle Analysis

The **lifeCycle** function analyzes the temporal evolution of research
topics, identifying different phases in their development:

```{r eval=TRUE}
# Perform life cycle analysis
data <- M %>% group_by(PY) %>% count()
LC <- lifeCycle(data, forecast_years = 20, plot = TRUE, verbose = FALSE)

print(LC$parameters)

print(LC$metrics)
```

The \*\*Life Cycle of Scientific Production\* function implements a
logistic growth model to analyze the temporal dynamics of research
topics. This approach, grounded in the theory of scientific paradigms
and innovation diffusion, allows researchers to identify the current
developmental stage of a field, predict future trends, and estimate when
a topic will reach maturity or saturation.

By fitting a logistic curve to the annual publication counts in your
collection, this analysis reveals whether a research area is in its
emergence phase, rapid growth phase, maturity phase, or decline phase.

## Bibliographic network matrices

Manuscript attributes are interconnected through the publications
themselves: authors link to journals, keywords to publication dates, and
references create citation networks.

These connections form bipartite networks that can be represented as
rectangular matrices (Manuscripts × Attributes).

Additionally, scientific publications regularly cite other works,
generating co-citation and coupling networks that reveal the
intellectual structure of research fields.

### biblioNetwork function

The **biblioNetwork** function calculates the most frequently used
bibliometric networks from a bibliographic data frame:

**Analysis types:** - **Coupling**: Documents sharing references -
**Co-citation**: References cited together - **Co-occurrences**:
Keywords or terms appearing together\
- **Collaboration**: Co-authorship patterns

**Network levels:** - Authors - References - Sources (journals) -
Countries - Universities - Keywords (Author keywords or Keywords Plus) -
Titles - Abstracts

Example - classical co-citation network:

```{r eval=FALSE}
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
```

## Visualizing bibliographic networks

Bibliographic networks can be visualized and analyzed using the
**networkPlot** function, which offers multiple layout algorithms and
customization options.

The main argument *type* specifies the network layout: circle,
kamada-kawai, fruchterman-reingold, mds, etc.

### Country Scientific Collaboration

```{r Country-collaboration, fig.height=7, fig.width=7, warning=FALSE}
# Create a country collaboration network

M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")

# Plot the network
net <- networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", 
                   type = "circle", size = TRUE, remove.multiple = FALSE, labelsize = 0.8)
```

This visualization reveals international research collaborations,
highlighting countries with strong scientific partnerships.

### Co-Citation Network

```{r Co-citation-network, fig.height=7, fig.width=7, warning=FALSE}
# Create a co-citation network

NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", n = 30, sep = ";")

# Plot the network
net <- networkPlot(NetMatrix, Title = "Co-Citation Network", type = "fruchterman", 
                   size = TRUE, remove.multiple = FALSE, labelsize = 0.7, edgesize = 5)
```

Co-citation analysis identifies the intellectual foundations of a
research field by revealing which references are frequently cited
together.

### Keyword Co-occurrences

```{r Keyword-co-occurrences, fig.height=7, fig.width=7, warning=FALSE}
# Create keyword co-occurrences network

NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")

# Plot the network
net <- networkPlot(NetMatrix, normalize = "association", weighted = TRUE, n = 30, 
                   Title = "Keyword Co-occurrences", type = "fruchterman", 
                   size = TRUE, edgesize = 5, labelsize = 0.7)
```

Keyword co-occurrence networks reveal the conceptual structure of a
research field, identifying main themes and their relationships.

### Thematic Map

The thematicMap function creates a **strategic diagram** based on
co-word network analysis and clustering. It plots themes in a
two-dimensional space according to their centrality (measure of
importance) and density (measure of development).

This visualization helps identify:

\- Motor Themes (well-developed and central),

\- Niche Themes (well-developed but peripheral),

\- Emerging or Declining Themes (weakly developed), and

\- Basic Themes (important but not well-developed).

The methodology is based on Cobo et al. (2011) co-word analysis
approach.

```{r fig.height=7, fig.width=7}
# Create a Thematic Map
thematicMapResults <- thematicMap(M, field = "DE", n = 250, minfreq = 5, 
                                  stemming = FALSE, size = 0.3, n.labels = 3, 
                                  repel = TRUE, cluster="louvain")
plot(thematicMapResults$map)
plot(thematicMapResults$net$graph)
```

### Thematic Evolution

The thematicEvolution function analyzes how themes evolve over time by
dividing the collection into multiple time periods and tracking thematic
changes across them. It performs a thematic map analysis for each period
and measures the conceptual relationships between themes in consecutive
periods using inclusion indexes and stability measures. This
longitudinal analysis reveals emerging topics, declining themes, stable
research areas, and thematic transformations.

The function produces an interactive Sankey-like diagram showing
thematic flows between periods, along with strategic maps for each time
slice.

```{r fig.height=7, fig.width=9}
# Thematic Evolution
TEResults <- thematicEvolution(M, field = "DE", n = 250, 
                                              minFreq = 5, stemming = FALSE, 
                                              size = 0.3, n.labels = 1, 
                                              repel = TRUE, cluster="louvain", 
                                              years = c(2004, 2008, 2015))
plotThematicEvolution(TEResults$Nodes, TEResults$Edges, measure="weighted")

```

#### 1985-2004

```{r fig.height=9, fig.width=9}
# 1985-2004
plot(TEResults$TM[[1]]$map)

```

#### 2005-2008

```{r fig.height=9, fig.width=9}

# 2005-2008
plot(TEResults$TM[[2]]$map)

```

#### 2009-2015

```{r fig.height=9, fig.width=9}


# 2009-2015
plot(TEResults$TM[[3]]$map)

```

#### 2016-2020

```{r fig.height=9, fig.width=9}

# 2016-2020
plot(TEResults$TM[[4]]$map)
```

## Co-Word Analysis: The conceptual structure of a field

Co-word analysis maps the conceptual structure of a research domain by
examining word co-occurrences in a bibliographic collection.

The analysis employs dimensionality reduction techniques including: -
**Multiple Correspondence Analysis (MCA)** - **Correspondence Analysis
(CA)** - **Multidimensional Scaling (MDS)**

The **conceptualStructure** function performs CA or MCA to visualize the
conceptual structure and uses K-means clustering to identify document
clusters sharing common themes. Results are displayed on two-dimensional
maps.

The function includes natural language processing (NLP) routines (see
**termExtraction**) to extract terms from titles and abstracts, and
implements Porter's stemming algorithm to reduce words to their root
form.

```{r Co-Word-Analysis, fig.height=9, fig.width=9, warning=FALSE}
# Conceptual Structure using keywords (method="MCA")

CS <- conceptualStructure(M, field = "ID", method = "MCA", minDegree = 10, 
                          clust = 5, stemming = FALSE, labelsize = 15, 
                          documents = 20, graph = FALSE)

plot(CS$graph_terms)
plot(CS$graph_dendogram)
```

This analysis helps identify: - Main research themes and sub-themes -
Relationships between concepts - Evolution of research focus - Emerging
topics and declining areas

## Referenced Publication Years Spectroscopy (RPYS)

RPYS analysis examines the age distribution of cited references to
identify seminal works and breakthrough moments in a research field. The
**rpys** function provides advanced capabilities for detecting:

```{r eval=TRUE}
# Perform RPYS analysis
rpysResults <- rpys(M, sep = ";", timespan = NULL, graph = TRUE)

print(rpysResults$Sequences %>% filter(Class!="") %>% group_by(Class) %>% slice_max(order_by=Freq,n=3, with_ties = FALSE) %>% arrange(desc(RPY),.by_group = TRUE), n=30)
```

The analysis identifies four types of influential references:

-   **Hot Papers**: Recently published works receiving immediate,
    intense attention
-   **Constant Performers**: Works consistently cited over extended
    periods
-   **Life Cycles**: Publications showing typical rise and fall patterns
    in citation frequency
-   **Sleeping Beauties**: Works initially overlooked but later
    recognized as significant contributions

RPYS helps researchers: - Identify foundational works in a field -
Detect breakthrough moments and paradigm shifts - Understand citation
patterns over time - Discover underappreciated but important
contributions

## Historical Direct Citation Network

The historiograph, proposed by Eugene Garfield, represents a
chronological network of the most relevant direct citations in a
bibliographic collection.

The **histNetwork** function generates a chronological direct citation
network matrix that can be visualized using **histPlot**:

```{r Historical-Co-citation-network, fig.height=9, fig.width=9, warning=FALSE}
# Create a historical citation network

histResults <- histNetwork(M, sep = ";")

# Plot a historical citation network
net <- histPlot(histResults, n = 20, size = FALSE, label = "short")
```

The historiograph reveals: - The chronological development of ideas in a
field - Key publications and their influence over time - Citation paths
showing knowledge flow - Temporal relationships between foundational
works

This visualization is particularly valuable for understanding how
scientific knowledge evolves and builds upon previous research.

## Main Authors' References

### The Book

Aria, M. & Cuccurullo, C. (2026). **Science Mapping Analysis: A
Primer with Biblioshiny**, *McGraw-Hill Education*. ISBN: 978-88-386-2297-7.

### Core bibliometrix publication

Aria, M. & Cuccurullo, C. (2017). **bibliometrix: An R-tool for
comprehensive science mapping analysis**, *Journal of Informetrics*,
11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007
(<https://doi.org/10.1016/j.joi.2017.08.007>)

### Recent methodological advances

M. Aria, C. Cuccurullo, L. D'Aniello, M. Misuraca, M. Spano (2024).
**Comparative science mapping: a novel conceptual structure analysis
with metadata**, *Scientometrics*.
(<https://doi.org/10.1007/s11192-024-05161-6>)

Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J. (2024).
**openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlex**.
*The R Journal*, [DOI:
10.32614/RJ-2023-089](https://doi.org/10.32614/RJ-2023-089).

### Applications in various domains

Aria, M., D’Aniello, L., Grassia, M. G., Marino, M., Mazza, R., &
Stavolo, A. (2024). **Mapping the evolution of gender dysphoria
research: a comprehensive bibliometric study**. *Quality & Quantity*,
58(6), 5351-5375.

Aria, M., Cuccurullo, C., D'Aniello, L., Misuraca, M., & Spano, M.
(2022). **Thematic Analysis as a New Culturomic Tool: The Social Media
Coverage on COVID-19 Pandemic in Italy**. *Sustainability*, 14(6), 3643,
(<https://doi.org/10.3390/su14063643>)

Aria M., Misuraca M., Spano M. (2020) **Mapping the evolution of social
research and data science on 30 years of Social Indicators Research**,
*Social Indicators Research*.
(<https://doi.org/10.1007/s11205-020-02281-3>)

Aria M., Alterisio A., Scandurra A, Pinelli C., D'Aniello B, (2021)
**The scholar's best friend: research trends in dog cognitive and
behavioural studies**, *Animal Cognition*.
(<https://doi.org/10.1007/s10071-020-01448-2>)

Angelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., & Aria, M.
(2025). **Conceptual structure and thematic evolution in partial least
squares structural equation modeling research**. *Quality & Quantity*,
1-46.

Belfiore, A., Cuccurullo, C., & Aria, M. (2022). **IoT in healthcare: A
scientometric analysis**. *Technological Forecasting and Social Change*,
184, 122001. (<https://doi.org/10.1016/j.techfore.2022.122001>)

Ciavolino, E., Aria, M., Cheah, J. H., & Roldán, J. L. (2022). **A tale
of PLS structural equation modelling: episode I—a bibliometrix citation
analysis**. *Social Indicators Research*, 164(3), 1323-1348
(<https://doi.org/10.1007/s11205-022-02994-7>).

D'Aniello, L., Spano, M., Cuccurullo, C., & Aria, M. (2022). **Academic
Health Centers' configurations, scientific productivity, and impact:
insights from the Italian setting**. *Health Policy*.
(<https://doi.org/10.1016/j.healthpol.2022.09.007>)

Sarto, F., Cuccurullo, C., & Aria, M. (2014). **Exploring healthcare
governance literature: systematic review and paths for future
research**. *Mecosan*
(<https://www.francoangeli.it/Riviste/Scheda_Rivista.aspx?IDarticolo=52780&lingua=en>)

Scarano, A., Aria, M., Mauriello, F., Riccardi, M. R., & Montella, A.
(2023). **Systematic literature review of 10 years of cyclist safety
research**. *Accident Analysis & Prevention*, 184, 106996
(<https://doi.org/10.1016/j.aap.2023.106996>).