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Social media platforms can be used as a tool to expand awareness and the consideration of cultural heritage organizations and their activities in the digital world. These platforms produce daily behavioral analytical data that could be exploited by the administrators of libraries, archives and museums (LAMs) to improve users’ engagement with the provided published content. There are multiple papers regarding social media utilization for improving LAMs’ visibility of their activities on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’ engagement with the content that LAMs post to social network platforms. In this paper, we propose a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, (b) examining a more extended set of LAMs social media pages compared to other prior investigations with limited samples as case studies, and (c) understanding which are the administrators’ actions that increase the engagement of users. The results of this study constitute a solid stepping-stone both for practitioners and researchers, as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs services on the Social Web.
Content may be subject to copyright.
Citation: Drivas, I.C.; Kouis, D.;
Kyriaki-Manessi, D.;
Giannakopoulou, F. Social Media
Analytics and Metrics for Improving
Users Engagement. Knowledge 2022,2,
225–242. https://doi.org/10.3390/
knowledge2020014
Academic Editor: Gautam Srivastava
Received: 16 March 2022
Accepted: 10 May 2022
Published: 12 May 2022
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4.0/).
Article
Social Media Analytics and Metrics for Improving
Users Engagement
Ioannis C. Drivas * , Dimitrios Kouis , Daphne Kyriaki-Manessi and Fani Giannakopoulou
Information Management Research Lab, Department of Archival, Library and Information Studies University of
West Attica, 12243 Egaleo, Greece; dkouis@uniwa.gr (D.K.); dkmanessi@uniwa.gr (D.K.-M.);
fgiannakopoulou@uniwa.gr (F.G.)
*Correspondence: idrivas@uniwa.gr
Abstract:
Social media platforms can be used as a tool to expand awareness and the consideration
of cultural heritage organizations and their activities in the digital world. These platforms produce
daily behavioral analytical data that could be exploited by the administrators of libraries, archives
and museums (LAMs) to improve users’ engagement with the provided published content. There are
multiple papers regarding social media utilization for improving LAMs’ visibility of their activities
on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’
engagement with the content that LAMs post to social network platforms. In this paper, we propose
a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding
LAMs Facebook performance page that involves several variables, (b) examining a more extended
set of LAMs social media pages compared to other prior investigations with limited samples as case
studies, and (c) understanding which are the administrators’ actions that increase the engagement of
users. The results of this study constitute a solid stepping-stone both for practitioners and researchers,
as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs
services on the Social Web.
Keywords:
social media platforms; Facebook; social media networks; social media data; analytics;
metrics; libraries; archives; museums; users’ engagement
1. Introduction
Social Web or Web 2.0 refers to a set of social relations that interconnects people,
helping them to communicate through the World Wide Web [
1
]. Over the last decade, Social
Media Platforms (SMPs) such as Facebook, Twitter, YouTube and Instagram have become
vital neurons of the social web ecosystem, becoming increasingly popular for individuals
and various organizations [
2
]. Social media platforms allow people to communicate,
contribute to content creation as well as engage and interact with the published content.
When it comes to the usage of SMPs in libraries, archives and museums (LAMs), the purpose
is not articulated just to cover users’ information needs by LAMs administrators. Taking one
step further, SMPs constitute a powerful tool to improve brand awareness and consideration
of LAMs [
3
]. They also work as a fast-spreading vehicle to promote services and, thus,
expand their web visibility and, most importantly, allow stakeholders to engage with the
published content of such organizations through reactions, shares, and comments [4].
It is common knowledge that several studies focus on the SMPs’ importance in LAMs
context [
5
9
]. On the one hand, this fact positively pushes the limits to established well-
organized research frameworks with the purpose to understand users’ interaction in SMPs
of specific LAMs as cases [
3
,
10
]. On the other hand, wider contexts of research efforts
through funding projects in the European Union are examined, aiming to establish policies
for efficient social media data encapsulation within the LAMs sector (e.g., PLUGGY: Plug-
gable Social Platform for Heritage Awareness and Participation—https://cordis.europa.eu/
Knowledge 2022,2, 225–242. https://doi.org/10.3390/knowledge2020014 https://www.mdpi.com/journal/knowledge
Knowledge 2022,2226
project/id/726765 (accessed on 11 March 2022), UNCHARTED: Understanding, Capturing
and Fostering the Societal Value of Culture—https://cordis.europa.eu/project/id/870793
(accessed on 11 March 2022)). LAMs administrators need to aptly examine users’ engage-
ment with the published content through SMPs and understand what types of content
improve engagement levels [
11
]. Social media analytics (SMAs) and metrics can measure
users’ engagement with the published content of LAMs by using different platforms [
12
].
Social media analytics and metrics are set under the strategic framework of integrating
information technology tools to harvest, pre-process, analyze and summarize the produced
social media data to accomplish specific organizational goals.
Nevertheless, research efforts around the utilization of SMAs and metrics for LAMs
indicate several difficulties. First, after examining their empirical findings, most of the stud-
ies indicate LAM staff difficulties, realizing the value that SMAs produce in order to utilize
them in LAMs context. Second, several studies examine SMAs in specific organizational
contexts as cases, without including multiple different LAMs within their data sample.
This fact limits the holistic overview and knowledge that administrators should have about
users’ engagement with the provided published content of LAMs. Moreover, very few
approaches proposed an assessment schema to measure users’ engagement within LAMs
context. However, these approaches did not consider the issue of the statistical reliability
that could allow administrators to replicate the proposed methodologies to measure users’
engagement in SMPs of the LAMs they manage. Beyond these drawbacks, it is also noted
that, to the best of our knowledge, there are no published research efforts that indicate
which of the administrators’ actions (such as types of posts or post frequency) impact
users’ engagement (reactions, comments and shares) with the published content of LAMs
through SMPs.
Against this background, in this study, we propose a three-stage data-driven method-
ology to capture, analyze and interpret SMAs to identify the administrators’ actions that
increase users’ engagement with the published content through SMPs. More specifically,
at the initial stage, social media data coming from Facebook for 220 libraries, archives
and museums worldwide were collected. Subsequently, the study verifies the statistical
reliability and the internal consistency of the collected SMAs, aiming to provide potential
researchers and administrators with an instrument for measuring users’ engagement in
Facebook. At the third stage, the paper proceeds into the development of predictive re-
gression models, further examining which are the administrators’ actions that bring upon
greater users’ engagement with the published content of the examined LAMs.
To this end, the study is organized into five sections. First, an overall conceptualization
regarding social media analytics and users’ engagement research topics is provided. This
helps the readers of the study to understand the importance of these topics and how
they could be utilized to develop new social media strategies or optimize existing ones.
Thereafter, a scientometrics analysis is provided, highlighting the importance of SMPs and
SMAs within the research context of LAMs. Hereupon, the related prior research efforts are
presented while also designating the research gaps. In Section 3, the proposed data-driven
methodology is described in detail. After that, in Section 4, the results are presented. Lastly,
Section 5is devoted to discussing the results, the study’s practical contribution and the
future implications.
2. Related Background
2.1. Conceptualizing Social Media Analytics and Metrics—An Overall Point of View
As big data analytics characterized more scientifically matured than the previous
decade’s beginnings [
13
], the vision expressed by Kaplan and Haenlein [
14
] for efficient so-
cial media analytics methods starts to flourish. Social media platforms produce voluminus
and volatile data that could be monitored, measured and analyzed by the organizations’
administrators for improving the online social presence and visibility of their services,
products and activities. To achieve this goal, social media analytics techniques and strate-
gies are used. From the academic perspective, according to [
15
], SMAs entail “the process
Knowledge 2022,2227
of developing and evaluating informatics tools to measure the activities of users within
social media platforms. Social data are derived from conversations, users’ engagement with
posts, sentiment, influence, and other attributes that can be collected, monitored, analyzed
and visualized”. According to Zeng et al. [
16
], SMAs are defined as “an interdisciplinary
research field that aims to combine, extend and adapt methods to analyze social media
data”. From practitioners’ perspective, SMA is an evolving business topic that encapsulates
and analyzes online conversation (industry, competitive, prospect and user/customer) and
social activity articulated by organizations through social network platforms [
17
]. More
specifically, Awareness Inc. states that “Social analytics enable organizations to act on
the derived intelligence for business results, improving brand awareness and reputation,
marketing and sales effectiveness, and customer satisfaction and advocacy”.
By conducting a detailed scientometrics analysis, Misirlis and Vlachopoulou [
18
]
mapped that most of the papers around the topic of SMAs and metrics related to users’ en-
gagement and behaviour (38.4% of the total papers examined), while the rest focus on other
disciplines (awareness and branding: 13.4%; predictive marketing research: 9.6%; social
capital, value and ROI: 15.4%). The increased interest in the sub-topic of users’ engagement
and behaviour in SMPs could be justified by prior research approaches. Measuring social
media users’ engagement and reactions provides organisations the capabilities to predict
users’ loyalty to products, services and activities [
19
]. It also allows understanding what
type of content users are more engaged in [
20
], which can potentially improve their trust
and affinity with the organizations’ online presence [21].
Social media engagement constitutes a multidimensional and polysemic research
field [
22
]. By examining the systematic literature review of Trunfio and Rossi, research
around social media engagement is framed into four areas. Research around users’ be-
haviour, platforms and tools for harvesting social media engagement data; the derived
metrics; and theoretical efforts for greater conceptualization of the topic. Hallock et al. [
23
]
proposed a theoretical definition of social media engagement, defined as “what occurs
as a user builds relationships with other users and brands. It is more than merely lik-
ing, commenting or posting within a social network. Instead, it reveals a longer-term
relationship among users”. From a more practical point of view, Le [
24
] described social
media engagement as the capability of measuring users’ online behaviour via the so-called
engagement metrics of actions. These include the number of users, click-through rates,
page views, content likes, comments or reactions depending on the SMP that the organi-
zations use. Therefore, based on the practical point of view, analysts and administrators
of organizations should focus more on the behavioural interactions associated with likes,
shares and comments when trying to encapsulate quantitatively social media engagement
per platform [25].
Libraries, archives and museums—as organizations that foster and expand users’
interactions with the cultural content on the Social Web—could not be an exception in
terms of SMAs, metrics and the methods used to estimate users’ engagement. In Section 2.2,
a scientometrics analysis reveals the increased research activity around the topic. Subse-
quently, in Section 2.3, the related research efforts around SMAs and metrics utilization for
measuring users’ engagement in LAMs organizations are unfolded.
2.2. Importance of Social Media Platforms and Analytics for LAMs—A Scientometrics Analysis
While SMPs started their initial acquaintances with web users almost 18 years ago,
mainly through the Facebook platform, research efforts regarding their utilization within
the LAMs context have not started before 2009.
More specifically, by accessing the Scopus citation index with the following query
string in the advanced search field “TITLE (“social media” OR “social media analytics”
OR “social media metrics”) AND TITLE (“libraries” OR “archives” OR “museums”) AND
PUBYEAR < 2022”, 310 published documents were returned from 2009 up to 2021 (Figure 1).
Based on the extracted results, it is noted that there has been a tremendous increase
in document publication activity over the last four years (2018: 32 documents; 2021:
Knowledge 2022,2228
42 documents). Without a doubt, this entails a recent overall increase in related researchers’
efforts to use SMPs and SMA for improving LAMs services on the Web. A fact that
designates the importance for LAMs and how the related research activity pays attention
to this specific topic.
Knowledge 2022, 2, FOR PEER REVIEW 4
OR “social media metrics”) AND TITLE (“libraries” OR “archives” OR “museums”) AND
PUBYEAR < 2022”, 310 published documents were returned from 2009 up to 2021 (Figure
1). Based on the extracted results, it is noted that there has been a tremendous increase in
document publication activity over the last four years (2018: 32 documents; 2021: 42 doc-
uments). Without a doubt, this entails a recent overall increase in related researchers’ ef-
forts to use SMPs and SMA for improving LAMs services on the Web. A fact that desig-
nates the importance for LAMs and how the related research activity pays attention to this
specific topic.
Figure 1. Publication activity around social media and LAMs between 2009 and 2021. The horizontal
axis indicates the years, while the vertical axis indicates the number of documents per year.
Furthermore, the following figure (Figure 2) depicts the publication type for the doc-
uments retrieved through the Scopus citation index. Most of the documents have been
published in journals as articles (220 documents), while books and book chapters come in
second place (42 documents).
Figure 2. Document types extracted after conducting the search query in the Scopus search engine.
Further analyzing Scopus results, in Figure 3, we present the research activity per
country around the topics of SMPs and SMAs, and their utilization on LAMs. We
20
11 13 15
34
31
23
33 32
36 38
42
0
10
20
30
40
50
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Number of Documents
Years
220
42
28 20
0
25
50
75
100
125
150
175
200
225
Articles Books / Book Chapters Conference papers Other
Number of Documents
Types of Documents
Figure 1.
Publication activity around social media and LAMs between 2009 and 2021. The horizontal
axis indicates the years, while the vertical axis indicates the number of documents per year.
Furthermore, the following figure (Figure 2) depicts the publication type for the
documents retrieved through the Scopus citation index. Most of the documents have been
published in journals as articles (220 documents), while books and book chapters come in
second place (42 documents).
Knowledge 2022, 2, FOR PEER REVIEW 4
OR “social media metrics”) AND TITLE (“libraries” OR “archives” OR “museums”) AND
PUBYEAR < 2022”, 310 published documents were returned from 2009 up to 2021 (Figure
1). Based on the extracted results, it is noted that there has been a tremendous increase in
document publication activity over the last four years (2018: 32 documents; 2021: 42 doc-
uments). Without a doubt, this entails a recent overall increase in related researchers’ ef-
forts to use SMPs and SMA for improving LAMs services on the Web. A fact that desig-
nates the importance for LAMs and how the related research activity pays attention to this
specific topic.
Figure 1. Publication activity around social media and LAMs between 2009 and 2021. The horizontal
axis indicates the years, while the vertical axis indicates the number of documents per year.
Furthermore, the following figure (Figure 2) depicts the publication type for the doc-
uments retrieved through the Scopus citation index. Most of the documents have been
published in journals as articles (220 documents), while books and book chapters come in
second place (42 documents).
Figure 2. Document types extracted after conducting the search query in the Scopus search engine.
Further analyzing Scopus results, in Figure 3, we present the research activity per
country around the topics of SMPs and SMAs, and their utilization on LAMs. We
20
11 13 15
34
31
23
33 32
36 38
42
0
10
20
30
40
50
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Number of Documents
Years
220
42
28 20
0
25
50
75
100
125
150
175
200
225
Articles Books / Book Chapters Conference papers Other
Number of Documents
Types of Documents
Figure 2. Document types extracted after conducting the search query in the Scopus search engine.
Further analyzing Scopus results, in Figure 3, we present the research activity per
country around the topics of SMPs and SMAs, and their utilization on LAMs. We considered
the authors’ affiliation origin to identify the country per document. A world map is
illustrated on the left side of the figure. The more publications there are per country,
the more intense the color tone on the map. On the right side, the bar chart presents the
number of documents per country.
Knowledge 2022,2229
Knowledge 2022, 2, FOR PEER REVIEW 5
considered the authors’ affiliation origin to identify the country per document. A world
map is illustrated on the left side of the figure. The more publications there are per coun-
try, the more intense the color tone on the map. On the right side, the bar chart presents
the number of documents per country.
(a) (b)
Figure 3. Research activity per country around SMPs/SMAs and their utilization on LAMs: (a) world
map; (b) number of documents per country.
As it can be seen, the researchers from the United States published most of the docu-
ments on the SMPs and SMAs topics in LAMs (76 documents). What is noteworthy is the
publication activity in Nigeria, reaching up to 36 published documents. Lastly, there is
sporadic publication activity in other countries, reaching 53 published documents with 3
or fewer documents per country.
Based on the vigorous research activity, the prior related efforts and research gaps
are discussed in the next section.
2.3. Prior Efforts and Research Gaps
In the era of Big Data and the robust and voluminous social data production, Cervone
[26] focused on LAMs social media analytics exploitation and highlighted why it is im-
portant to understand the relation between users’ engagement with the published content
by providing five fundamental reasons: (i) tracking the growth of presence, (ii) under-
standing how content performs and resonates among users, (iii) understanding audience
characteristics, (iv) observing upcoming trends, and lastly, (v) tracking progression to-
wards pre-defined key point indicators.
Within this context, Mensah et al. [10] performed an in-depth investigation about
social media utilization in academic libraries’ context. They developed a quantitative sur-
vey to examine library staff’s willingness to utilize SMPs to promote library services.
However, although their results indicate that staff agreed to develop social media strategy
as the most appropriate tool to increase users’ awareness about libraries’ services, they
did not possess the expected skills to understand users’ engagement with the provided
content. The same conclusions were also stated in a prior investigation by Jones and Har-
vey and published in the Journal of Academic Librarianship [27]. Cheng and colleagues
[3] conducted a comparative study between the users’ and library staff perspectives re-
garding the effectiveness of Facebook as a marketing tool in the Hong Kong University
libraries. Their findings indicated no satisfactory reactions and engagement from users
regarding the published library content. Their research also proved that the interactions
of the current users affect the future visitors’ attitudes toward accepting the library’s Fa-
cebook page as a social media tool to promote content and services.
76
36
18 17 14 13 11 11 10 10 854444444
53
0
10
20
30
40
50
60
70
80
Number of Documents
Figure 3.
Research activity per country around SMPs/SMAs and their utilization on LAMs: (
a
) world
map; (b) number of documents per country.
As it can be seen, the researchers from the United States published most of the doc-
uments on the SMPs and SMAs topics in LAMs (76 documents). What is noteworthy is
the publication activity in Nigeria, reaching up to 36 published documents. Lastly, there is
sporadic publication activity in other countries, reaching 53 published documents with 3 or
fewer documents per country.
Based on the vigorous research activity, the prior related efforts and research gaps are
discussed in the next section.
2.3. Prior Efforts and Research Gaps
In the era of Big Data and the robust and voluminous social data production,
Cervone [
26
] focused on LAMs social media analytics exploitation and highlighted why
it is important to understand the relation between users’ engagement with the published
content by providing five fundamental reasons: (i) tracking the growth of presence,
(ii) understanding how content performs and resonates among users, (iii) understanding au-
dience characteristics, (iv) observing upcoming trends, and lastly, (v) tracking progression
towards pre-defined key point indicators.
Within this context, Mensah et al. [
10
] performed an in-depth investigation about
social media utilization in academic libraries’ context. They developed a quantitative
survey to examine library staff’s willingness to utilize SMPs to promote library services.
However, although their results indicate that staff agreed to develop social media strategy
as the most appropriate tool to increase users’ awareness about libraries’ services, they did
not possess the expected skills to understand users’ engagement with the provided content.
The same conclusions were also stated in a prior investigation by Jones and Harvey and
published in the Journal of Academic Librarianship [
27
]. Cheng and colleagues [
3
] con-
ducted a comparative study between the users’ and library staff perspectives regarding
the effectiveness of Facebook as a marketing tool in the Hong Kong University libraries.
Their findings indicated no satisfactory reactions and engagement from users regarding the
published library content. Their research also proved that the interactions of the current
users affect the future visitors’ attitudes toward accepting the library’s Facebook page as a
social media tool to promote content and services.
Understanding users’ engagement with the provided content through the utilization
of SMAs constitutes a well-informed approach to improve and support the LAMs social
media strategy. For example, Pozas [
28
] exploited the produced SMAs from the National
Library of Spain. A strong relationship between social media campaigns and a significant
increase in digital collections usage was discovered. The research of Pozas [
28
] relied on a
prior proposed framework by González-Fernández-Villavicencio [
29
], who suggested six
categories of social media metrics: (i) Reach (popularity, size and visibility), (ii) Engagement
Knowledge 2022,2230
(comments, shares, views, downloads, etc.), (iii) Loyalty (website traffic coming from social
media), (iv) Influence (users’ brand perception: mentions, sentiment, reputation index),
(v) Activity frequency, (number of posts, uploads, etc.) and (vi) Conversion (number of
downloads of digital collections, downloads of tutorials, number of loans, etc.).
Another study utilizes SMAs for understanding what type of Twitter content “inspires”
museum users. The primary purpose was to adopt similar content development and
distribution approaches for future users [
30
]. The proposed method for encapsulating
users’ inspiration through Twitter data resulted in up to 67% f-measure score of effective
retrievals. The results of [
30
] also support the assumption that SMAs could be used to
understand users’ engagement with content and, hence, produce material according to their
preferences. In a similar vein, Ref. [
31
] proposed a framework to utilize Twitter analytics to
understand the most appropriate metrics that could be used to quantify users’ engagement
with the Tate Modern Museum of Art content. The appropriate metrics were identified by
using the Balance Scorecard theory: the Number of tweets sent by Tate, the Impressions,
the Retweets, the Favourites, the Replies and the Other Interactions.
Another effort that utilizes the SMAs provided by Instagram to increase awareness of
specific collections in the museum sector is the Metropolitan Museum of Art [
32
]. The au-
thor suggests several data-driven actions to attract more followers based on five metrics
categories which collect information about users’ profiles and engagement with the pro-
vided content, which includes Users actions (profile views and website clicks), Discovery
(reach of posts and impressions), Followers (demographics, time that followers are most
active and top locations), Media and Promotions (metrics related to the performance of the
published posts) and Stories (metrics related with users’ engagement with stories such as
taps forward, taps back, replies, swipes away, etc.).
Social media analytics contribution for promoting LAMs services to relevant stake-
holders has also been stated by Boulton [
33
]. The author examined the utilization of
SMPs for the institutional repository of Griffith University and pointed out their contri-
bution through two pillars: first, to measure users’ interaction with the provided content
and estimate the success of the deployed strategies for promoting repositories services;
secondly, how management staff could use SMAs to overhaul communication and relation-
ships among different teams within the university (librarians, marketing team, students,
researchers, etc.).
Furthermore, in the archival sector, Magier [
34
] investigated how SMAs
could be used in the State Archives in Siedlce to inform users and keep the same level of
engagement with them during the covid era.
Without a doubt, all the studies mentioned above highlight the contribution of SMPs
and the derived SMAs to understand users’ engagement with the published content and
set guidelines for increasing the awareness of the services provided by LAMs. Most of the
studies proceed into the examination of a particular case of LAM and how it utilizes social
media platforms and analytics to accomplish specific organizational goals [
28
,
30
34
]. Other
studies used structured questionnaires to investigate users’ interaction with the LAMs’
social media platforms and measure their engagement with the published content [3,10].
Nevertheless, very few studies [
3
,
10
] involved a large number of LAMs cases and their
SMAs in providing a holistic approach and consequently a generalization framework on
how the latter could be utilized to increase users’ engagement and expand the awareness
of LAMs organizations.
In our paper, we intend to provide concrete evidence that the utilization of social media
analytics and metrics could be a reliable alternative for understanding users’ engagement
with the published content in an SMP by a LAM organization. Our efforts focus on
providing an additional framework for measuring users’ engagement through SMAs while
simultaneously collecting a more representative number of LAMs as use cases. This
framework could support and complement previously established efforts that collected
users’ engagement data within an SMP by using a questionnaire as a research instrument.
Moreover, assessing the proposed framework’s validity, reliability and internal consistency
could result in future studies adopting it in the LAMs and other domains, as the same level
Knowledge 2022,2231
of reliability is expected [
35
] (p. 32) [
36
]. The following table (Table 1) summarizes the
research context issues and the contributions of the present study.
Table 1. Research Context Issues and Contributions of the present study.
Research Context Issues Contributions
There is a need to improve the social media skills of
staff for understanding users’ engagement with the
uploaded content [310].
Understanding social media analytics and metrics and the possible
intercorrelations between them will improve staff skills in providing
content that results in higher levels of users’ engagement.
The majority of the current studies proceed into
individual examinations of how a LAM utilizes SMPs
to understand and measure users’ engagement with
the published content [28,3034].
Further research is needed to provide a holistic approach and
consequently a generalization framework on how SMAs could be utilized
to increase users’ engagement and expand the awareness of LAMs
organizations. This could also work as a benchmarking process for the
administrators of the LAMs.
Lack of a SMAs methodological framework that
exhibits validity, reliability and internal consistency in
terms of the included variables that measure LAMs
users’ engagement with the published content [35,36].
Suggest an assessment schema that expresses statistical reliability in its
nature. This schema will quantitatively measure users’ engagement
within an SMP of a LAM.
In the following section, the research methodology is unfolded to cover the research
gaps that have been discussed previously.
3. Methodology
3.1. Data Collection and Sample
Based on prior research efforts for collecting information about LAMs and their
presence on the Web [
37
], a dataset consisting of 341 domains was developed. Data about
the available social media pages were gathered from each LAM’s website. In most cases,
this information was located on the footer of the websites or on the contact pages as social
media icons, including a link that redirected users to the social media page. The social
media pages from various SMPs for 220 LAMs organizations were collected at the end of
this process. Several cases were excluded from the initial dataset as they did not have a
social media page or had no activity for the last six months.
Fanpage Karma (https://www.fanpagekarma.com/ (accessed on 17 March 2022))
was used to gather data from the social media pages in our case, as in similar research
efforts [
38
,
39
]. Fanpage Karma tool provides several metrics for each SMP, including
Facebook, Twitter, YouTube, Instagram and LinkedIn. We extracted metrics for each SMP
regarding users’ behaviour and administrators’ actions (such as types of posts or posts’
frequency) in a time range of 30 days for all the 220 LAMs organizations.
Only the Facebook social media platform and its retrieved metrics were finally chosen
to be analyzed in this paper. Based on the literature review, there are multiple papers
over the last ten years that highlight the contribution of Facebook as an SMP for LAMs.
In libraries context, Cassidy et al. [
40
] reported that more than 70% of students selected
Facebook as a communication tool to provide library services in the Sam Houston State
University library. In a similar approach, Okoroma [
41
] indicated that Facebook constitutes
the preferable SMP for reference services to students, among others. Mensah et al. [
10
]
study articulated up to 81% Facebook preference for staff and 56% for library patrons.
Furthermore, in the museums’ context, Fissi et al. [
42
] and Camarero et al. [
43
] high-
lighted Facebook’s prominence among other SMP usage for cultural heritage institutions
through their research. Mukwevho and Ngoepe [
44
] identified Facebook as the most com-
mon SMP to promote archival material to society in the archival organizations’ context.
In the same way, Magier ’s data analysis [
34
] proved that Facebook activity increased during
the COVID-19 pandemic among other social networks, both among the staff and patrons at
the State Archives in Siedlce. Similar approaches have been followed by Tkacováet al. [
45
].
Another reason for choosing Facebook SMP is based on preliminary research efforts
regarding SMP and their contribution to website traffic in LAMs. Through the Similar
Knowledge 2022,2232
Web API, the leading social networks that drove traffic to each one of the cases in our
dataset were identified, confirming the popularity of Facebook (for more details, see in the
Data Availability Statement the Dataset Traffic Acquisition to LAMs Websites). Specifically,
the average incoming website traffic for a month monitoring period from social networks
was approximately 2.68% (social traffic). The analysis of the results indicated that Facebook
was responsible for 46.1% of social traffic. The following graph (Figure 4) depicts the SMPs
responsible for creating incoming traffic and their percentage share. Therefore, both the
findings of the related literature review and the results of our preliminary data harvesting
process support choosing Facebook as the most appropriate SMP compared to others.
Knowledge 2022, 2, FOR PEER REVIEW 8
Facebook as a communication tool to provide library services in the Sam Houston State
University library. In a similar approach, Okoroma [41] indicated that Facebook consti-
tutes the preferable SMP for reference services to students, among others. Mensah et al.
[10] study articulated up to 81% Facebook preference for staff and 56% for library patrons.
Furthermore, in the museums’ context, Fissi et al. [42] and Camarero et al. [43] high-
lighted Facebook’s prominence among other SMP usage for cultural heritage institutions
through their research. Mukwevho and Ngoepe [44] identified Facebook as the most com-
mon SMP to promote archival material to society in the archival organizations’ context. In
the same way, Magier’s data analysis [34] proved that Facebook activity increased during
the COVID-19 pandemic among other social networks, both among the staff and patrons
at the State Archives in Siedlce. Similar approaches have been followed by Tkacová et al.
[45].
Another reason for choosing Facebook SMP is based on preliminary research efforts
regarding SMP and their contribution to website traffic in LAMs. Through the Similar
Web API, the leading social networks that drove traffic to each one of the cases in our
dataset were identified, confirming the popularity of Facebook (for more details, see in
the Data Availability Statement the Dataset Traffic Acquisition to LAMs Websites). Spe-
cifically, the average incoming website traffic for a month monitoring period from social
networks was approximately 2.68% (social traffic). The analysis of the results indicated
that Facebook was responsible for 46.1% of social traffic. The following graph (Figure 4)
depicts the SMPs responsible for creating incoming traffic and their percentage share.
Therefore, both the findings of the related literature review and the results of our prelim-
inary data harvesting process support choosing Facebook as the most appropriate SMP
compared to others.
Figure 4. Average incoming social media traffic share per SMP.
The following table (Table 2) explains Facebook metrics, allowing readers to under-
stand the possible causal relationship.
Table 2. Description of Facebook metrics involved in this study.
Metric Name Metric Description
Number of Posts Number of posts that have been published in a specific period.
Link posts It is the number of posts in URL format that have been published in a specific
period.
Picture posts It is the number of posts in picture format that have been published in a specific
period.
Video Posts Number of posts in video format that have been published in a specific period.
Figure 4. Average incoming social media traffic share per SMP.
The following table (Table 2) explains Facebook metrics, allowing readers to under-
stand the possible causal relationship.
Table 2. Description of Facebook metrics involved in this study.
Metric Name Metric Description
Number of Posts Number of posts that have been published in a specific period.
Link posts It is the number of posts in URL format that have been published in a specific period.
Picture posts
It is the number of posts in picture format that have been published in a specific period.
Video Posts Number of posts in video format that have been published in a specific period.
Comments per post The average number of comments on posts in a specific period.
Number of reactions
The total number of (like, love, haha, thankful, wow, sad, angry) on posts that have been
published in a specific period.
Reactions per Post
The average number of reactions on posts that have been published in a specific period.
Number of Comments (total) It refers to the total number of comments on posts. This includes answers to these
comments that have been published in a specific period.
Total Reactions, Comments, Shares
It expresses the number of reactions of any type (like, love, haha, thankful, wow, sad
and angry), comments and shares on posts that the LAM organization has published in
a specific period.
For the 220 LAMs cases, the Facebook metrics were calculated for a 30-day harvesting
period. In the following table (Table 3), a sample of the overall dataset is presented.
The first column depicts the name of LAM, while the rest provide data for the metrics.
The sample presented allows readers to understand the upcoming steps of the proposed
methodology, namely the validity and reliability of the metrics and the development of
predictive regression models.
Knowledge 2022,2233
Table 3. Sample of the dataset regarding a random selection of three different LAMs.
Name of LAM Number
of Posts
Link
Posts
Picture
Posts
Video
Posts
Comments
per Post
Number of
Reactions
Reactions
per Post
Number of
Comments
(Total)
Total Reactions,
Comments, Shares
Denver Art Museum 29 3 17 3 2.52 1799 78.21 58 2105
National Library of Spain 19 1 9 5 7.25 3940 246.25 116 5512
National Archives
of Georgia 34 3 22 6 1.22 1847 59.58 38 2242
3.2. Validity and Reliability Assessment
After harvesting the social media analytics data for Facebook, a statistical analysis
was performed to validate their reliability and consistency. Before the reliability analysis,
a preliminary examination of the first 25% of the dataset was conducted. The aim was
to ensure that the retrieved dataset expressed normality [
46
], which was necessary to
construct a valid predictive model through linear regressions [
47
]. We used Shapiro–
Wilk as the most potent normality indicator among Kolmogorov–Smirnov, Lilliefors and
Anderson–Darling tests [48].
In addition, by using the descriptive statistics approach, we also measured skewness
to understand, in a practical way, the initial situation of the Facebook page performance
of a LAM. Skewness measures the tendency of a variable between the minimum and the
maximum values contained [
49
]. Negative skewness indicates that most of the variables’
values tend to be closer to the maximum value. In contrast, a positive value indicates
that most values tend to be closer to the minimum value. This helps in understanding the
overall tendency of the proposed Facebook metrics and if each of the examined LAM tends
to be closer to the minimum or maximum values.
Facebook metrics were divided into two factors. The first factor contains the metrics
that indicate administrators’ actions on a LAM Facebook page: Number of Posts, Link
posts, Picture posts and Video posts. The second factor contains the metrics that express
followers’ engagement on a LAM Facebook page: Comments per post, Number of reactions,
Reactions per post, Number of comments (total) and Total reactions, comments and shares.
This categorization will allow administrators to understand which of their actions impact,
at a lower/higher level, the followers’ engagement with the posts. In other words, some
of the administrators’ actions may impact specific metrics of engagement more than oth-
ers. For example, posting pictures probably resulted in more total reactions, comments
and shares by followers than the link posts or video posts. An exploratory factor anal-
ysis (EFA) was also conducted to ensure the statistical significance of the two factors
of metrics. By using EFA, Kaiser–Meyer–Olkin (KMO), Bartlett’s test of Sphericity and
χ2
tests were performed to test the goodness of fit of each variable relative to the two
proposed factors [50].
One step further, the study aims to provide a reliable assessment model regarding
LAMs Facebook performance page through the involvement of several variables that
express validity, reliability and cohesion. As stated before, if model reliability tests are
successful, then there is a high probability that this could also apply in other domains [
35
]
(p. 32). On this basis, to testify to the reliability of the proposed model, we deploy four
different statistical tests: McDonald’s
ω
, Cronbach’s a, Guttman’s
λ
-2 and
λ
-6, respectively.
McDonald’s
ω
estimates the strength association among the involved variables within a
factor [
51
]. The greater the association among the variables, the closer the value to 1, while
the lower the association, the closer the value to 0. Cronbach’s
α
estimates the acceptance
level of the two proposed factors, while Guttman’s
λ
-2 reinforces the Cronbach’s results by
measuring the variance trustworthiness among the selected variables in each factor [
52
,
53
].
In the effort to construct linear predictive models that estimate the potential impact
of administrators’ actions on users’ engagement, Guttman’s
λ
-6 test was also conducted.
The latter calculates the variance in each variable involved within the linear regression
proposed models [
54
]. Lastly, by deploying Variance Inflation Factor (VIF), it is ensured
Knowledge 2022,2234
that the proposed constructs (users’ engagement and administrators’ actions) do not face
multicollinearity issues [55].
3.3. Predictive Regression Models
After categorizing metrics into two factors, namely, administrators’ actions and users’
engagement, we developed linear predictive regression models to understand the cause-
and-effect relationship among the involved metrics. More specifically, it will be helpful
to understand the predicted value of change in users’ engagement metrics if each of the
administrators’ actions metrics increases by one unit. For example, if picture posts are
increased by one unit, there will be an increase in the metric of total reactions, shares and
comments. The results of the predictive regression models will help LAM administrators
identify which of their post strategies bring higher levels of users’ engagement with content.
In the following figure (Figure 5), we present the proposed data-driven methodology,
helping the readers of the study wrap up all the actions that have been made in each stage,
while in the next section, the results are presented.
Knowledge 2022, 2, FOR PEER REVIEW 11
Figure 5. Three stages of the proposed data-driven methodology in a sequential way.
4. Results
4.1. Validation of the Proposed Factors
Following the proposed methodology, the exploratory factor analysis (EFA) and
reliability analysis results are presented first. Table 4 reports the values for each one of the
factors: the variable loadings for each metric and the goodness of fit tests (KMO, Bartlett’s
test of Sphericity and χ2). In addition, KMO extracted higher values for the two factors
(0.634 for Administrators Actions and 0.708 for Users Engagement) than the
recommended tolerance of exclusion (<0.50).
Table 4. Exploratory factor analysis results and loading per factor.
Administrators Actions Users Engagement
Variables Variable Loading Variable Variable Loading
Number of posts 0.767 Comments per post 0.706
Link posts 0.519 Number of reactions 0.727
Picture posts 0.667 Reactions per post 0.690
Video posts 0.624 Number of comments
(total) 0.655
Total reactions, comments,
shares 0.751
0.634 * | < 0.001 ** | < 0.001 *** 0.708 * | < 0.001 ** | < 0.001 ***
* KMO, ** p-value of Bartlett’s test of Sphericity, *** p-value of χ2.
The following table (Table 5) presents the proposed factors’ reliability and internal
consistency results. As observed, Administrators Actions designated high reliability with
values ranging from 0.748 (Cronbach’s α) up to 0.967 (McDonald’s ω). In a similar vein,
Users Engagement indicated sufficient reliability starting from 0.648 (Cronbach’s α) up to
0.934 (Guttman’s λ-6). Moreover, the Administrators actions’ VIF values and Users’
Data collection process Reliability and validity assessment
Descriptive statistics for initial
performance estimations
Development of predictive
regression models
Search and identification of
LAMs social media
platforms on their official
websites
Identification of Facebook
page for 220 LAMs around
the globe
FanPage Karma API
deployment for collecting
social media analytics and
metrics for
Facebook platform in a time
period of 30 days.
Preliminary analysis on the first 25% of
the dataset to test the normal
distribution of the social media metrics
Separation of the gathered social media
metrics into two factors and
examination of internal consistency of
the involved variables within the factors
Exploratory factor analysis with KMO,
Bartlett’s Test and χ
2
indicators for factor
loadings extraction
Examination of discriminant validity
between the metrics of each factor
through the deployment of McDonald’s
ω, Cronbach’s a, Guttman’s λ-2,
Guttman’s λ-6 and Variance Inflation
Factor
Initial per formance estimations for th e
total 220 LAMs and the extracted
Facebook analytics and metrics
Descriptive statistics indicators: Mean,
Std. Deviation, Skewness and Shapiro-
Wilk, Minimum, Maximum
Construction of linear
predictive regression models
Identification of different
predicted changes in the
values of Users' Engagement
metrics for every unit increase
in Administrators Actions
metrics.
Stage 1 Stage 2 Stage 3
Figure 5. Three stages of the proposed data-driven methodology in a sequential way.
4. Results
4.1. Validation of the Proposed Factors
Following the proposed methodology, the exploratory factor analysis (EFA) and
reliability analysis results are presented first. Table 4reports the values for each one of the
factors: the variable loadings for each metric and the goodness of fit tests (KMO, Bartlett’s
test of Sphericity and
χ2
). In addition, KMO extracted higher values for the two factors
(0.634 for Administrators Actions and 0.708 for Users Engagement) than the recommended
tolerance of exclusion (<0.50).
Knowledge 2022,2235
Table 4. Exploratory factor analysis results and loading per factor.
Administrators Actions Users Engagement
Variables Variable Loading Variable Variable Loading
Number of posts 0.767 Comments per post 0.706
Link posts 0.519 Number of reactions 0.727
Picture posts 0.667 Reactions per post 0.690
Video posts 0.624 Number of comments (total) 0.655
Total reactions, comments, shares
0.751
0.634 * | < 0.001 ** | < 0.001 *** 0.708 * | < 0.001 ** | < 0.001 ***
* KMO, ** p-value of Bartlett’s test of Sphericity, *** p-value of χ2.
The following table (Table 5) presents the proposed factors’ reliability and internal
consistency results. As observed, Administrators Actions designated high reliability with
values ranging from 0.748 (Cronbach’s
α
) up to 0.967 (McDonald’s
ω
). In a similar vein,
Users Engagement indicated sufficient reliability starting from 0.648 (Cronbach’s
α
) up
to 0.934 (Guttman’s
λ
-6). Moreover, the Administrators actions’ VIF values and Users’
engagement were below the tolerance of 3.3, as Diamantopoulos and Siguaw [
56
] suggest;
thus, no multicollinearity issue was observed.
Table 5. Reliability analysis and internal consistency of the two factors.
Factors McDonald’s ωCronbach’s αGuttman’s λ-2 Guttman’s λ-6
Administrators Actions 0.967 0.748 0.847 0.917
Followers Engagement 0.915 0.648 0.889 0.934
Based on the above results, there is a high probability that the proposed factors
and their metrics can extract similar reliability and internal consistency in a different
dataset of Facebook pages of other domains rather than LAMs [
35
,
36
]. If future research
approaches adopt the proposed methodology to measure Facebook pages’ performance of
other organizations apart from LAMs, it is expected to have similar reliability and internal
consistency results.
The second stage of the proposed methodology presents the results of the descriptive
statistics for the two factors and their metrics.
4.2. Descriptive Data Summarization for Initial Performance Estimations
The upcoming tables (Tables 6and 7) contain the descriptive results of the two fac-
tors and their social media metrics. The following descriptive statistics represent the
administrative actions (Table 6) and users’ engagement (Table 7) for a 30 days period.
Table 6.
Descriptive statistics results for the variables included within Administrators Actions factor
(30 days period).
Number of Posts Link-Posts Picture Posts Video-Posts
Mean 27.591 3.695 19.447 4.448
Std. Deviation 23.498 6.069 17.448 8.498
Skewness 2.304 4.882 2.206 4.814
Shapiro-Wilk 0.811 0.563 0.816 0.539
Minimum 1.000 1.000 1.000 1.000
Maximum 158.000 56.000 114.000 75.000
N = 220 | p-value of Shapiro–Wilk 0.001
Knowledge 2022,2236
Table 7.
Descriptive statistics results for the variables included within the Users Engagement factor.
Comments
per Post
Number of
Reactions Reactions per Post Number of
Comments (Total)
Total Reactions,
Comments, Shares
Mean 3.562 3148.467 101.424 121.029 3890.619
Std. Deviation 6.278 5706.804 159.020 262.374 7155.607
Skewness 3.506 3.127 2.964 4.143 3.147
Shapiro-Wilk 0.574 0.576 0.613 0.487 0.565
Minimum 1.000 1.000 1.000 1.000 1.000
Maximum 44.429 35,424.000 871.000 2109.000 40,991.000
N = 220 | p-value of Shapiro–Wilk 0.001
In terms of the administrators’ actions metrics, all of them extracted significant Shapiro–
Wilk p-values (<0.001), indicating statistical normality. Moreover, all metrics extracted
positive skewness, resulting in their values tending more to the minimum than the maxi-
mum points. Descriptive statistics indicate that Picture Posts are the most frequent type of
publication (Mean: 19.447) for LAMs Facebook pages compared to Link Posts (Mean: 3.695)
and Video Posts (Mean: 4.448). It is also noted that the mean value of the Number of posts
reaches up to 27.591. This practically means that the examined LAMs post on Facebook
almost every day at least one type of post (27.59/30 = 0.91 posts per day).
The Users Engagement metrics are presented in the following table (Table 7). Regard-
ing their normal distribution, all the metrics resulted in sufficient Shapiro–Wilk values
ranging from 0.487 (Number of Comments (total)) up to 0.613 (Reactions per post) with
statistically significant pvalues at <0.001.
In the same line with Administrators’ Actions metrics, all metrics in this factor ex-
tracted positive skewness values ranging from 2.964 up to 4.143, indicating their tendency
closely to minimum points. In addition, as shown in Table 7, the examined LAMs receive
on average 3.56 comments per post and a total mean value of 121.02 comments within
the period of 30 days. Finally, the Total Reactions, Comments and Shares of the examined
220 LAMs extracted a mean value of 3890.61.
4.3. Predictive Regressions Results
Tables 810 present the regression equation outputs. The scope is to indicate the
potential predicted change in the metrics within the Users Engagement factor if specific
administrators’ actions are performed. In most cases, the extracted regression predictive
models indicated high statistical significance with p< 0.001. F value is also included.
The results support the assumption that the proposed predictive models can reject the null
hypothesis, which is that the regression coefficients are equal to zero values; thus, the model
lags behind predictive discriminant capability [
57
]. The only exception appears for the
Link Posts metric. The results confirm the statistically non-significant interdependence and
correlation between Link Posts and the Users’ Engagement metrics (results for Link Posts
metrics are depicted in Tables 810 with a strikethrough line).
Knowledge 2022,2237
Table 8.
Regression equation output of the Number of Total Reactions, Comments and Shares and
their potential predicted change in each of the proposed Administrator Actions.
Variable Coefficient R2Fp-Value
Constant (Number of Total Reactions,
Comments, Shares)
Number of Posts
785.88
122.02 0.154 38.44 <0.001
Constant
Link Posts
4097.08
12.19 0.000 0.007 <0.931
Constant
Picture posts
806.75
168.01 0.155 38.25 <0.001
Constant
Video Posts
2802.88
454.38 0.122 19.16 <0.001
Table 9.
Regression equation output of the Number of Comments (total) and their potential predicted
change in each of the proposed Administrators’ Actions.
Variable Coefficient R2Fp-value
Constant (Number of Comments Total)
Number of Posts
9.723
4.46 0.171 35.47 <0.001
Constant
Link Posts
133.23
2.28 0.002 0.227 <0.635
Constant
Picture posts
19.548
6.55 0.108 21.61 <0.001
Constant
Video Posts
70.64
16.68 0.102 22.18 <0.001
Table 10.
Regression equation output of the Number of Reactions and their potential predicted
change in each proposed Administrators Action.
Variable Coefficient R2Fp-Value
Constant (Number of Reactions)
Number of Posts
78.87
127.26 0.163 40.66 <0.001
Constant
Link Posts
3232.98
4.80 0.000 0.002 <0.935
Constant
Picture posts
170.83
164.79 0.164 38.96 <0.001
Constant
Video Posts
2490.82
322.38 0.107 15.78 <0.001
In Table 8, the potential predicted changes of the metric Number of Total Reactions,
Comments and Shares are presented if Administrators Actions metrics are increased by
one. To begin with, a high statistical significance was observed with p< 0.001 and R
2
of 0.154 between the Number of Total Reactions, Comments, Posts and the Number of
Posts. More specifically, for each new post on the Facebook page of the examined LAMs,
the Number of Total Reactions, Comments and Shares is expected to increase by 122.02.
Picture posts (R
2
0.155 and p< 0.001) resulted in an increase in the Number of Total
Reactions, Comments and Shares by 168.01. The highest engagement is observed for
video posts. A significant regression equation was observed with p< 0.001 and R
2
of
0.122 between the Number of Total Reactions, Comments and Shares and Video Posts.
In more detail, for every new video post on the Facebook page of the examined LAMs,
the Number of Total Reactions, Comments and Shares could be increased by up to 454.38.
Knowledge 2022,2238
Continuing the presentation of the regression predictive models and their results,
Table 9depicts the potential predicted change of the metric Number of Comments (total) if
Administrators Actions metrics are increased by one. More specifically, for each new post
on the Facebook page of the examined LAMs, the Number of Comments (total) is expected
to increase by 4.46. Picture Posts indicated that they could result in a higher number
of comments, as for each new Picture Post, the Number of Comments increases by 6.55
(R
2
0.108 and p< 0.001). In the same line with the results of Table 8, Video Posts extracted
the highest predicted change. More specifically, for each new video post, the Number of
Comments within a period of 30 days could be increased by up to 16.68 (R
2
0.102 and
p< 0.001).
Lastly, in Table 10, the potential predicted change of the dependent variable Number
of Reactions (like, love, haha, thankful, wow, sad and angry) is presented. In line with
previous results (Tables 8and 9), Video Posts extracted the highest impact on the Number
of Posts metric. More specifically, for each new Video Post, the Number of Reactions could
be increased by 322.38 (R
2
0.107 and p< 0.001). Moreover, for every new Picture Post
published on the LAMs Facebook page, the Number of Reactions could be increased by
164.79 (R20.164 and p< 0.001).
The Number of Posts impacts the Number of Reactions (R
2
0.163 and p< 0.001): That
is, for each new post, the Number of Reactions could be increased by 127.26. This result
could bring contradictory perspectives regarding the posts’ publication frequency and the
expected Number of Reactions. Other efforts pointed out that the Number of reactions
does not impact users’ engagement with the published content [
11
]. In any case, further
research is needed to understand which types of reactions are probably correlated with the
types of users’ engagement on a LAMs’ Facebook, such as user retention as a follower or
even user willingness to unfollow the page.
5. Discussion
5.1. Practical-Managerial Implications
Social media platforms constitute a cost-efficient tool for LAMs to promote their con-
tent and services to the society they serve and belong to [
12
,
58
]. This same principle is also
applied to other domains [
59
] and not only cultural heritage-related institutions. At the
same time, over the last three years, reports in the European context indicate that the need
for access to cultural information has increased significantly [
60
]. Controversially, govern-
mental expenditures for the cultural heritage domain are low, including funding for web
presence [
61
]. In this context, the present study and its proposed methodology can reinforce
the cost-efficient use of SMPs to promote LAMs services on the Social Web and, therefore,
expand their visibility and awareness. More specifically, through a data-driven approach,
a methodological schema has been developed to understand (a) what social media metrics
should be included to measure LAMs’ Facebook performance, (b) what the current perfor-
mance of a LAM is through the utilization of descriptive statistics and (c) which are the
Administrators actions that bring greater engagement between users and posts. Therefore,
the current study could support prior efforts that relied on the SMPs utilization, especially
Facebook, as a cost-effective marketing strategy for
LAMs [10,12,34]
. That is, understand-
ing stakeholders’ needs by conducting quantitative survey development [
3
,
10
] and then
deploying the proposed data-driven methodology to articulate helpful information about
users’ engagement with the published content through Facebook analytics and metrics.
Apart from verifying SMPs as a cost-efficient strategy for marketing LAMs, the paper
also contributes to the administrators’ knowledge reinforcement on how to effectively
promote services and actions. More precisely, prior efforts indicated the non-sufficient skills
of LAMs administrators to use SMPs, even if they believe that these tools are appropriate
to promote organizations’ services [
10
,
27
]. In this sense, the current study contributes two
main pillars to LAMs administrators on their knowledge and skills: first, to improve overall
web analytics competency while understanding the meaning of the involved metrics and the
possible intercorrelations among them [
12
,
62
64
]. The study results enable administrators
Knowledge 2022,2239
to develop social media analytics projects within a LAM and, hence, encapsulating and
realizing the added value created by the organization’s awareness and consideration on
the Web [
65
]. Second, LAMs exhibit a high level of multidisciplinarity, as they bring
together scientists and professionals from both humanities and information technologies
sectors [
66
,
67
]. The study stands as a bridge and supports the efforts to connect the
different scientific and professional backgrounds within the LAMs sector. On the one hand,
the study overhauls the knowledge of LAMs administrators to use SMPs to improve users’
engagement. On the other hand, cultural analytics scientists could adopt the proposed
methodology for replication purposes in other organizations apart from the 220 cases.
Lastly, this study enriches benchmarking efforts in LAMs domain through the data
gathering method. More specifically, other research efforts utilize benchmarking theoretical
lens for improving overall managerial performance [
68
], remote information services [
69
]
or the quality of the provided services among stakeholders [
70
]. However, to the best of
our knowledge, no prior study provides a benchmarking method to gather a vast amount
of LAMs Facebook pages (220 cases) and their performance in terms of administrators’
actions and users’ engagement. In this respect, administrators could compare their Face-
book analytics with data from other LAMs, understand cutting-edge strategies in SMPs
management and adopt the best strategies within their organizational context.
5.2. Theoretical Implications
One of the study’s goals was to provide LAMs with a statistically reliable and validated
assessment schema capable of quantifying users’ engagement in the Facebook platform
through social media analytics and metrics. To implement perform this, we involved
several metrics that were categorized into two factors, namely, Administrators Actions
and Users’ Engagement. This helped practically to understand the cause-and-effect re-
lationship between them. In other words, the study helps social media administrators
in understanding which types of posts bring upon greater engagement compared to the
others. For example, examining the current dataset of LAMs, video posts returned a higher
engagement in all the Users’ Engagement metrics compared to the picture or link posts.
Furthermore, different tests took place to prove the statistical reliability and internal
consistency of the metrics in each factor. In this way, the study contributes practically to
developing a reliable assessment schema that could be utilized in LAMs—or other domains
apart from LAMs—while expecting the same reliability level [
51
] (p. 32) [
52
,
71
]. If other
researchers repeat experiments in the future to understand how administrators’ actions on
a Facebook page impact users’ engagement, they could adopt this model and its involved
metrics as they express statistical reliability and internal cohesion and consistency.
5.3. Limitations and Future Work
Developing a statistically significant model to measure users’ engagement on SMPs
and especially on Facebook opens new research paths to investigate even more LAMs cases
compared with the current study. In this sense, we have already started to expand the data
sample, including more libraries, archives, museums and their Facebook page analytics
and metrics worldwide.
Furthermore, we intend to include other SMPs, apart from Facebook, and hence
develop reliable assessment schemes for other platforms. More specifically, based on
our research findings (see Figure 4—Average incoming social media traffic share per
SMP), we aim to harvest the social media analytics from LAMs on Twitter, YouTube and
Instagram. In line with the current methodology, we aim to understand the cause-and-
effect relationship between each SMP metrics and provide LAMs administrators with
practical suggestions. The ultimate goal is to construct an overall framework of social
media analytics and metrics deriving from multiple platforms and how LAMs could benefit
from this information.
In conclusion, we encourage related prior studies to integrate the proposed data-
driven methodology as a supportive tool in the already established quantitative efforts
Knowledge 2022,2240
by using questionnaires and/or interviews [
3
,
18
,
27
,
72
]. In this way, LAMs could benefit
from the combined use of already established research instruments, while at the same
time, valuable conclusions could be drawn about the technological acceptance level of the
proposed methodology by the administrators.
Author Contributions:
Conceptualization, I.C.D., D.K., D.K.-M. and F.G.; methodology, I.C.D., D.K.,
D.K.-M. and F.G.; formal analysis, I.C.D., D.K., D.K.-M. and F.G.; data curation, I.C.D., D.K., D.K.-M.
and F.G.; writing—original draft preparation, I.C.D., D.K., D.K.-M. and F.G.; writing—review and
editing I.C.D. and D.K. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The datasets used and presented in this study are openly available in
Zenodo: Dataset Social Media Analytics and Metrics of Facebook Performance of Libraries, Archives
and Museums in https://doi.org/10.5281/zenodo.6361774 (accessed on 11 May 2022) and Dataset
Traffic Acquisition to LAMs Websites in https://doi.org/10.5281/zenodo.6505277 (accessed on
11 May 2022).
Conflicts of Interest: The authors declare no conflict of interest.
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... Some crucial elements that need to be taken under consideration are the users' behavioral factors on the corporate website, such as "average time on site" and "pages per visit", as well as the behavioral factors of the users on social media, such as "post interaction" and "number of likes". The analysis of those factors can provide a holistic view of the user engagement and corporate digital brand name [111]. ...
... A previous study has revealed that the total reactions to social media posts have a positive impact on user engagement and on social media marketing in general [111]. Hence, the second hypothesis attempts to examine the social media interactivity of tourismenterprises. ...
... Since the (H1) is rejected from the statistical analysis, the investments on paid traffic through digital advertisements produce imperceptible results to social media traffic, which is in contradiction with previous research [111,141]. The amount of money and the placement gaps are different from other industries, and this has to do with the seasonality of the tourism industry [154,155]. ...
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