International Review of Applied Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cira20
The impact of social media adoption on innovative
SMEs’ performance
Filippo Domma & Lucia Errico
To cite this article: Filippo Domma & Lucia Errico (2023) The impact of social media adoption
on innovative SMEs’ performance, International Review of Applied Economics, 37:3, 324-356,
DOI: 10.1080/02692171.2023.2205108
To link to this article: https://doi.org/10.1080/02692171.2023.2205108
Published online: 04 May 2023.
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INTERNATIONAL REVIEW OF APPLIED ECONOMICS
2023, VOL. 37, NO. 3, 324–356
https://doi.org/10.1080/02692171.2023.2205108
The impact of social media adoption on innovative SMEs’
performance
Filippo Domma
and Lucia Errico
Department o Economics, Statistics and Finance ‘Giovanni Anania’, University o Calabria, Rende, Italy
ABSTRACT
This paper empirically investigates Social Media adoption as a
driver o rms’ perormance. Specically, we ocus on the relationship between the embracing o Twitter and Italian Innovative Small
and Medium Enterprises (SMEs) protability over 2011–19.
Although Twitter is perceived as a low-cost and eective communication channel, the main results show that Innovative SMEs
adopting Social Media appear to have lower protability than
those without social network implementation. An interpretation
o this evidence can rely on the difculty o innovative SMEs in
overcoming barriers to Twitter adoption related to the human
capital required to maintain relationships with the online community o consumers in the Italian context.
ARTICLE HISTORY
Received 22 February 2023
Accepted 12 April 2023
KEYWORDS
Innovative SMEs; frm
perormance; social media;
Twitter
JEL CLASSIFICATION
O30; L20; C33
1. Introduction
Social media (SM) is a double-edged sword for business. Without a doubt, it is a powerful
and accessible tool for any business that, in a sense, replaced traditional business
conferences and meetings to display the company’s product. Any firm can adopt SM
without additional resources with an Internet connection. Due to its low cost and
minimal technical requirements, Small and Medium Enterprises (SMEs) can quickly
implement this digital technology (Ferrer et al. 2013). Thus, there is a growing usage of
SM among businesses, and nowadays, it plays a crucial role in business management
(Trainor et al. 2014). As a result, the effective use of social media – a relatively inexpensive innovation helping businesses reach customers easily – assumes a particular relevance for these kinds of firms (Ahmad, Bakar, and Ahmad 2018; Xiong, Nelson, and
Bodle 2018).
Recently, enterprises have embraced SM technology for various purposes. For
instance, it is used as an advertising and marketing tool for interacting and establishing
long-lasting relationships with customers and partners, for monitoring, active listening,
and capturing trends in customers’ needs and preferences (Mohammadian and
Mohammadreza 2012; Chirumalla 2013; He 2014; Sigala and Chalkiti 2014; PérezGonzález, 2017). Thus, SM technology represents a powerful means for supporting
business functions, such as sales, research and development, marketing, and capturing
external knowledge and competitive information (Scuotto, Del Giudice, and Carayannis
CONTACT Lucia Errico
lucia.errico@unical.it
© 2023 Inorma UK Limited, trading as Taylor & Francis Group
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
325
2017). When used successfully, many benefits are associated with adopting and using SM
for firms’ activities and performance.1 With specific regard to SMEs, several contributions evidence a significant impact of SM adoption on their sales performance (Wong
2012; Kwok and Yu 2013; Rodriguez, Peterson, and Ajjan 2015); relationships, visibility
and cost reduction (Parveen, Jaafar, and Ainin 2015; Tajudeen, Jaafar, and Ainin 2018);
interactivity, reputation, and customer service (Qalati et al. 2022); and non-financial and
financial outcomes (Ainin et al. 2015; Franco, Haase, and Pereira 2016; Fan et al. 2021;
Tajvidi and Karami 2021).
SMEs often show scepticism in embracing information technology (Ramdani,
Chevers, and Williams 2013) and tend to adopt and use SM technology with caution
(Zolkepli and Kamarulzaman 2015). The main reason for this may be the uncertainty
regarding profitability when undertaking changes in business structure (Abu Bakar,
Ahmad, and Ahmad 2019). Moreover, due to their opacity, the limited access to external
sources of finance makes SMEs reluctant to invest in major projects (McCann and
Barlow 2015), and inexpert in addressing unforeseen problems (Ahmad, Bakar, and
Ahmad 2018). Thus, SMEs have limited resources to hire external support for marketing
activities (Manelli, Pace, and Leone 2022), perceiving the adoption of new technologies
for facilitating innovation and marketing processes as expensive, risky, and complex
(Dong and Yang 2020). Finally, the adoption and use of SM include some barriers,
involving the potential regulatory risk related to monitoring the content of SM accounts
and the human capital required to maintain relationships with the online community of
consumers in the context in which they operate (Xiong, Nelson, and Bodle 2018; Ndiege
2019). Indeed, SMEs should engage in platform maintenance, frequent updates of
information and be interactive and reactive with their customers (Toker et al. 2016;
Ahmed et al. 2019; Foltean, Trif, and Tuleu 2019). Thus, hiring a dedicated social media
team or training existing employees should be fundamental for analysing data generated
on the platform that grows to an uncontrollable size at an increasing rate. Typically,
unlike large firms, SMEs are more likely to be strictly controlled and do not tend to
employ specialists (Thong 2001). The scarcity of knowledge of information technology
combined with the lack of technical and specialised expertise implies SMEs do not reap
the benefits of SM adoption (DeLone 1988). Lastly, SMEs can also fail to take advantage
of social media because of the lack of strategic planning (McCann and Barlow 2015;
Ahmad, Bakar, and Ahmad 2018).
Such features introduced for SMEs became even more accentuated for the so-called
Innovative SMEs, which have been identified – consequently to the conceptualisation
and measurement of innovation – in the Oslo Manual of OECD/Eurostat (2018).
Consistently, ‘the innovation status of a firm is defined on the basis of its engagement
in innovation activities and its introduction of one or more innovations over the
observation period of a data collection exercise’. (OECD/Eurostat (2018), p. 248).2
These kinds of firms play a crucial role in developing modern industrial economies as
their dynamism and performance strongly influence the advanced capacity and competitiveness of the economic system (Nadotti 2014). However, innovative SMEs suffer from
limited financial backing as their activities are typically based on intangible and highly
firm-specific assets. Innovative SMEs are characterised, in fact, by a high degree of
information asymmetries and costs related to the evaluation of creditworthiness, and
they are often incapable of offering suitable collateral for loans. Thus, these features
326
F. DOMMA AND L. ERRICO
represent significant constraints limiting access to traditional sources of financing for
innovative companies and, therefore, compromising their structure and performance.
Even if it is possible to obtain funding, the costs could be too onerous compared to the
company’s profitability (Vannoni 2019).
As SMEs represent around 99% of firms in Italy (ISTAT, 2019) and play a crucial role
in the country, the Italian Government has introduced into the legal system a welldefined entity (i.e. Innovative SME), which receive specific advantages to overcome the
difficulties reported above. In more detail, expanding the interventions applied for
innovative start-ups with the ‘Decree Growth 2.0’ of 2012, the ‘Investment Compact’ of
2015 introduces benefits for a broader range of Innovative SMEs. The latter can be all
firms operating in the field of technological innovation, regardless of the date of
incorporation and the formulation of the corporate purpose. These aid measures are
for companies that employ fewer than 250 people and whose annual turnover does not
exceed 50 million euros or whose balance sheet total does not exceed 43 million euros,
which meet the following requirements: they are set up as a limited company/corporation, also in a cooperative form; they have their headquarters in Italy or another EU
country, provided that there is a production site or a branch in Italy; have the certification
of the latest financial statements and any consolidated financial statements prepared by
an auditor or by an auditing company registered in the register of auditors (therefore
newly established companies are excluded); their shares are not listed on a regulated
market; they are not listed in the section of the Business Register dedicated to innovative
start-ups and certified incubators. The fulfilment of at least two of the following three
criteria identifies the innovative nature: (1) at least 3% of the firm’s expenses are ascribed
to R&D activities; (2) at least 1/5 of the workforce are PhD students, PhDs or researchers
or 1/3 of the workforce have a master’s degree; (3) the firm is the holder/licensee of a
patent.
This paper considers whether, for Italian Innovative SMEs, there is a relationship
between adopting specific social media tools (i.e. Twitter), and profitability. To the best of
our knowledge, Innovative SMEs have not been investigated regarding social media
adoption. Given their importance in the economic structure of Italy, it is interesting to
verify whether their performance is affected by digitalisation strategies, often limited by
constraints associated with access to credit and tangible and intangible infrastructures.
The sample of firms used in this work refers to those defined as Innovative SMEs by
the Italian Chambers of Commerce in 2018. Based on this sample, we retrieve balance
sheet data on firms from the database ORBIS for 2011–19. Then, we merge these data
with information about Twitter adoption obtained using the Twitter application programming interface (API) for Academic Research. There are two reasons to focus on
Twitter. First, Twitter is considered a means of communication where new ideas and
quick information are launched. As a result, innovative SMEs may prefer to sponsor/
affirm their product/brand for the first time (Xiong, Nelson, and Bodle 2018). Secondly,
innovative SMEs need to target a specific clientele, so they tend to favour a definite
channel more than other companies. The selective track for these firms leads to the
adoption of Twitter, whose audience is mainly high – in the age group over 25 –
compared to other social networks (Dixon 2022).
Our econometric results suggest that Innovative SMEs adopting SM appear to have
lower profitability than those without SM implementation. Such a finding leads us to
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
327
conclude that Innovative SMEs do not seem wholly settled to reap the benefits derived
from the adoption of internet technology. In terms of time and effort, the resources
devoted to SM adoption may be more remunerative if employed for other parts of the
production process (e.g. capital and labour) or strategically planned activities. In other
words, it seems that for Innovative SMEs, the charge of adopting SM goes behind simply
the sign-in to a social media platform, being relatively accessible. Indeed, due to high
barriers to technology adoption, the cost of implementing valuable social media analytics
technologies and successfully leveraging data on customers or products to unlock new
insights appears to overcome the potential profitable benefits.
The remainder of this paper is organised as follows. The next section offers a review of
the literature. Sections 3 and 4 report the data used and the empirical model. Section 5
discusses the results obtained. Section 6 concludes.
2. Related literature
2.1. Social media characteristics
The Internet allows access to a virtually unlimited amount of information. Specifically,
social media technology provides a disruptive opportunity for information-sharing and
relationship-building practices as the consumers’ information, previously passive, can be
transformed into powerful creators, transmitters, and discussants of data (Lee, Oh, and
Kim 2013). SM presents distinguishing features. First, SM is an open, fast and affordable
multi-way communication tool in which building and maintaining relationships is
essential. The dialogic interaction benefits firms and stakeholders as SM adoption can
be considered ethical activities (Taylor, Kent, and White 2001). Second, SM is far beyond
the control of any single entity as the information flow is multidirectional, interconnected, and difficult to predict (Friedman 2006). Third, social media has a ripple effect
affecting inferior companies and products as dissatisfied consumers can easily share their
experiences (Ward and Ostrom 2006). Therefore, the association between these characteristics and the increased availability of information to stakeholders reduces asymmetric information on the market. An informed stakeholder group – ready to act against
any sign of disinformation or manipulation – sets various challenges to different firms
depending on the likelihood of a public backlash, which may lead to a loss of the firm’s
reputation.
In detail, SM is defined as ‘a group of Internet-based applications that build on the
ideological and technological foundations of Web 2.0, and allow the creation and
exchange of user-generated content’ (Kaplan and Haenlein 2010, 61). SM is related to
internet-based applications such as, for instance, social networks, blogs and content and
media sharing platforms (Solis 2010; Ainin et al. 2015; Tajudeen, Jaafar, and Ainin 2018;
Fan et al. 2021). According to Eurostat (2020), in 2019, 53% of EU enterprises used at
least one type of social media, with more than 80% of these businesses using social media
to build their image and market products. Indeed, between 2013 and 2019, SM use
increased most for marketing purposes (22% − 45% of enterprises) and for recruiting
employees (9% − 28% of firms). Also, social networks (51%) were the most used form of
social media in 2019; compared to 2013, their use increased by 23% points. Before3
328
F. DOMMA AND L. ERRICO
discussing how the adoption of SM influences firms’ performance, a review of Twitter,
the representative social media, is in order.
2.1.1. Twitter
Twitter was launched in 2006 and has become one of the most popular social media
platforms. On Twitter, users write and share 140-character text messages and subscribe
to each other with simple clicks. These characteristics allow Twitter to combine social
networking and news media features within a single structure. Messages can be retweeted
(i.e. Retweet): others’ tweets can be forwarded to one’s followers. Thus, the number of
retweets denotes a user’s ability to generate content with a pass-along value. Also, a user’s
profile can be mentioned referring to either replying or simply mentioning other users in
the message. The number of mentions (Reply and Mention) represents the ability to
engage others in conversation (Cha et al. 2010). The way to get connected on Twitter is a
bidirectional and unidirectional relationship. In the first case, as in Facebook, people
share interests while, simultaneously, following the contents of another profile account
(e.g. News, blogs). Hence, the influence of any individual user is often estimated by the
number of followers (Kwak et al. 2010). The latter offers an overview of potential
influence; the number of retweets and mentions represents a metric of actual influence
(Cha et al. 2010; Kwak et al. 2010). According to Cha et al. (2010), Twitter activities can
be grouped into three categories: (1) degree-influence (i.e. number of followers showing
the extent of the audience), (2) retweet-influence (i.e. number of retweets representing
the ability to generate contents with pass-along value), and (3) mention-influence (i.e.
number of tweets that reply to or comment on others’ tweets).
2.2. Social media and rm performance
As part of digital technologies, SM has become a common practice in the workplace.
Firms exploit its functionalities to increase their presence on the Internet, interact with
customers and partners, facilitate collaboration and knowledge-sharing within the enterprise and improve business opportunities (e.g. marketing) (Nisar and Whitehead 2016;
Alalwan et al. 2017; Misirlis and Vlachopoulou 2018; Qalati et al. 2022). However, given
the lack of unified metrics for capturing varied SM platforms (Durkin, McGowan, and
McKeown 2013; McCann and Barlow 2015), measuring the impact of SM adoption on
firms’ performance is quite tricky (Fernandes, Belo, and Castela 2016). Some studies
account for this effect by employing self-reported measures (e.g. Parveen, Jaafar, and
Ainin 2016; Charoensukmongkol and Sasatanun 2017). Moreover, a further examination
of SM adoption and SMEs’ performance is claimed by several authors. In particular,
Olanrewaju et al. (2020) recommend employing a longitudinal approach in exploring this
relation for a holistic interpretation of the phenomenon. As a result, a puzzling scenario
emerges in the literature investigating the relationship between SM adoption (and usage)
and firms’ performance.4
Several studies find a significant positive connection between SM adoption and
firm performance (Cette, Nevoux, and Py 2021). According to the literature, SM
adoption is common in nearly every sort and size of business (Zhang et al. 2017)
as it should improve communication, boost collaboration, and intensify interactions among enterprises and their partners, yielding to the enhancement of firm
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
329
performance (Schniederjans, Cao, and Schniederjans 2013). 5Rialp-Criado et al.
(Rialp-Criado and Rialp-Criado 2018) argue that more intensive use of SM (e.g.
direct involvement of the owner/manager) yields greater financial and non-financial performance. Moreover, Dong and Yang (2020) find that the interaction of
social media dissimilarity and big data analytics positively affects market performance – more significantly for SMEs than large firms. By using the Compustat
North America database, Majumdar and Bose (2019) analyse the impact of Twitter
adoption on Tobin’s Q by employing a PSM and DID research design. Authors
evidence that the adoption of Twitter increases the value of the firm postadoption.
As far as SMEs are concerned, several contributions evidence a significant impact of
SM adoption on their performance. For instance, social media platform adoption (e.g.
Facebook) positively affects SMEs’ sales performance (Wong 2012; Kwok and Yu 2013).
Similarly, Rodriguez, Peterson, and Ajjan (2015) find that SM use positively impacts sales
performance through customer- facing activities. Moreover, Parveen, Jaafar, and Ainin
(2015) argue that SMEs in developing countries benefit from SM adoption in terms of
relationships, visibility and cost reduction as it is possible to overcome the lack of
resources and customers’ information to increase competition and reduce marketing
costs. In line with these arguments, by using a survey questionnaire from SMEs in
Malaysia, Ainin et al. (2015) find that the effect of Facebook usage on both non-financial
and financial performance is positive. Also, a positive link between social media adoption
and the success of firms emerges (Cervellon and Galipienzo 2015; Garrido-Moreno and
Lockett 2016). In analysing the role of social networks in Portuguese SMEs’ performance,
Franco, Haase, and Pereira (2016) suggest that to assess their performance, SME leaders
should use a combination of financial (sales volume, level of growth) and non-financial
indicators (customer satisfaction, reputation). Hence, SM usage significantly influences
SMEs’ performance by increasing marketing activities and customer relationships
(Tajudeen, Jaafar, and Ainin 2018). According to Fan et al. (2021), SMEs can adopt
SM to improve their performance, enabling firms to create and disseminate user-generated content. Tajvidi and Karami (2021) highlight the positive effect of SM use and UK
firm performance in the hotel industry. Similarly, Qalati et al. (2022) evidence a positive
link between SM adoption and interactivity, reputation, relationships, visibility, and
customer service of the SMEs operating in emerging countries.
On the other hand, empirical findings on the connection between the adoption/usage
of social media platforms and SMEs’ performance are not necessarily significant. Indeed,
Al Tenaiji and Cader (2010) examined the adoption of social media in business-tobusiness marketing and found that the adoption of SM in business-to-business marketing
has an insignificant impact on business performance. Analysing the travel and tourism
sector across six Middle Eastern countries, Al- Bakri (2017) finds no significant relationship between SM adoption and SMEs’ competitive advantage. In addition, small business
revenue performance across the USA is not influenced by social network platform
adoption (Gavino et al. 2018). Using survey data on SMEs operating in the United
Arab Emirates, Ahmad, Bakar, and Ahmad (2018) found a similar outcome. According
to such authors, social media adoption does not affect performance, arguing that
enterprises adopt SM due to bandwagon pressure rather than as a planned strategy in
line with business objectives.
330
F. DOMMA AND L. ERRICO
Finally, the relationship between SM and firms’ performance could be negative, as
evidenced by Grimmer, Grimmer, and Mortimer (2018). The authors use a longitudinal
dataset to explore the relationship between informational (web) resources (e.g. sales via
store website, social networking, and web presence) and performance in small retail firms
operating in Tasmania. Yet, these findings can be attributed to the relatively young age of
the businesses (Grimmer, Grimmer, and Mortimer 2018) and a smaller investment in
social media (Gavino et al. 2018).
2.2.1. Firms proftability literature
One of the most relevant financial performance measures of firms relates to profitability,
which is the measure of how the firm can create profits as a consequence of the efficient
use of resources and proper management (Fonseca, Guedes, and da Conceição Gonçalves
2022). Theoretical and empirical studies on the determinants of firm profitability are
found in several research fields, such as economics, management, accounting and finance
(Nunes, Serrasqueiro, and Sequeira 2009). These studies can be classified into two major
groups. Studies in one strand mainly focus on external determinants, i.e. factors that
reflect the market, business, and economic environment in which the firms operate
(Bowman and Helfat 2001; McGahan and Porter 2002; Hawawini, Subramanian, and
Verdin 2003). Macro-level determinants typically introduced in the literature are Gross
Domestic Product (GDP), unemployment rate and financial market returns. These
variables account for the status of the economy and are likely to affect firm profitability
through their impact on the aggregated demand and supply (Pattitoni, Petracci, and
Spisni 2014).
Industry-specific effects could affect profitability because of concentration levels,
product differentiation, and market entry barriers. However, firm characteristics have a
boundless explanatory power for profitability concerning industry affiliation (Hamel and
Prahalad 1990). In this respect, the other strand of work concentrates on internal
determinants, namely factors affected by management decisions (e.g. Rumelt 1991;
Mahoney and Pandian 1992; McGahan and Porter 1997). Typical micro-level variables
are firm size, age, growth, leverage, liquidity (Capon, Farly, and Hoenig 1990; Jensen and
Murphy 1990; Yazdanfar 2013; Pattitoni, Petracci, and Spisni 2014), and the lagged
profitability to account for dynamic adjustments (e.g. Goddard and Wilson 1999).
A limited number of works consider the adoption of SM as an internal firm’s
determinant of profitability. For instance, Mohammadian and Mohammadreza
(2012) explain how using social media for marketing helps firms establish long-lasting
relationships and powerful interactions with their customers. These communications
allow firms to satisfy consumers’ needs and improve their reputation, sales and profitability. In addition, reaching many customers globally should aid in internationalisation and lead to high sales volume and, hence, higher profitability (Seth, 2012).
González-Fernández-Villavicencio (2014) examines the connection between the use
of social media and the profitability of Spanish libraries, suggesting that the latter can
achieve a higher return on investment if a social media marketing plan or a digital
marketing plan is implemented. Similarly, social media can increase company sharing
and knowledge and, as a result, improve worker productivity and firm profitability
(Nisar, Prabhakar, and Strakova 2019).
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
331
While some insight into the relationship between SM and firms’ profitability is present
in the economic literature, it is quite scant concerning innovative SMEs. However, as
Troise et al. (2022) point out, digital technologies play a central role in Italian Innovative
SMEs’ success. Indeed, the ability to anticipate or adapt to external changes, characterised
by technological advancements and digitalisation, is essential for the competition and
survival of these kinds of firms. Therefore, the following econometric analysis aims to
shed light on this relationship by focusing on a sample of innovative Italian SMEs.
3. Data and descriptive statistics
3.1. Data
The sample of firms was drawn from the Italian Chambers of Commerce (Camera di
Commercio), which offers a complete list of Innovative SMEs registered in a special
section of the Business Register, operating in Italy in 2018. Based on this sample, we
retrieve balance sheet data on firms – useful for measuring profitability and other firm
characteristics – from the database ORBIS held by Bureau van Dijk for 2011–19. By
visiting the website address of each firm or by exploiting the Google browser, all
Innovative SMEs using social network (i.e. Twitter, Facebook, LinkedIn) were identified,
and their Twitter username collected. The number of Innovative SMEs in 2018 was 918,
of which 359 had a Twitter account. We6 focus on a single SM platform (Twitter) as,
according to Olanrewaju et al. (2020), Facebook was the dominant social media platform
investigated, with Twitter and Instagram receiving less attention.
By creating a Twitter Developer Account Academic and using the Twitter application
programming interface (API) for Academic Research, we downloaded information on
each firm’s tweets from 1st January 2011 to 31st December 2019. This7 was processed to
match information from the database ORBIS, also retrieved for the same period. Hence,
the final dataset counts 8262 observations related to 559 and 359 Innovative SMEs,
which, respectively, do not have and do have a Twitter account, observed over nine
years. We consider the 2011–19 period to exploit all data available in the ORBIS database
and to rule out the consequences of the COVID-19 pandemic. Finally, information on
provincial demographic and socio-economic characteristics employed as control variables are drawn from the Italian National Institute of Statistics and Eurostat, except for
data on loans provided by the Bank of Italy.
3.2. Descriptive statistics
Table 1 reports a description and the main summary statistics of the variables used in the
econometric analysis, while Table A1, in the APPENDIX, shows the correlation matrix of
variables used in the analysis for 2018. According8 to Table 2, offering the total number of
Innovative SMEs in 2018 by Italian regions, Lombardy hosts the majority of Innovative
SMEs (251), followed by Piedmont (87) and Lazio (85). The regions with the lowest
number of Innovative SMEs are Molise (1), Aosta Valley (3) and Basilicata (4). The table
reports the number of Innovative SMEs with and without a Twitter account, the latter
being larger in all regions except Liguria, Molise and Sardinia.
Roe1
Roce1
Twitter adoption
beore 2011
Twitter adoption ater
2011
Facebook adoption
LinkedIn adoption
Multiple SM adoption
Roa1
Age
Working capital
Firm Growth
Leverage
Debt Sustainability
2016
2017
2018
GDP per capita
Financial Development
Education
Social Capital
Variable
Roa
Roe
Roce
Twitter adoption
0.62
0.7
0.3
−0.01
Dummy = 1 i frm has a Facebook account
Dummy = 1 i frm has a LinkedIn account
Dummy = 1 i frm has at the same time a Twitter, Facebook and LinkedIn
account
Proft or loss beore taxes over total assets (previous period)
Proft or loss beore taxes over stakeholders equity (previous period)
Proft or loss beore taxes over (shareholders unds + reserves + long-term
loans) (previous period)
−0.06
0
9.03
14.47
0.15
65.1
1.35
0.26
0.36
0.29
33474.13
79861.62
61.1
0.68
0.06
0.33
Mean/
Relative requency
−0.01
−0.07
0
0.39
Description
Proft or loss beore taxes over total assets
Proft or loss beore taxes over stakeholders equity
Proft or loss beore taxes over (shareholders unds + reserves + long-term
loans)
Dummy = 1 i frm has a Twitter account
Current year minus frm’s year o establishment
(Currents assets minus current liabilities) to total assets
(current period sales minus previous period sales) divided previous period sales
(Current plus non-current liabilities) to total assets
Interests paid to total debts
Registration in the section o innovative SMEs in 2016
Registration in the section o innovative SMEs in 2017
Registration in the section o innovative SMEs in 2018
Provincial real gross domestic product per capita
Provincial amount in euros o BCCs’ loans over banks branches
Provincial share o population (20–24 years) with high school diploma
Proxy o social capital – item Voice and Accountability rom IQI proposed by
Nio and Vecchione (2014, 2015)
Dummy = 1 i frm has a Twitter account beore 2011
Dummy = 1 i frm has a Twitter account ater 2011
Table 1. Summary Statistics.
0.96
0.58
0.46
0.46
0.21
0.49
0.24
0.47
9.95
24.93
0.42
25.07
1.36
0.43
0.48
0.45
11322.63
52680.93
6.05
0.23
StdD
0.21
0.95
0.57
0.49
−5.21
−3.12
0
0
−0.81
0
0
0
0
−58.94
−3.2
3.52
0
0
0
0
14538.37
22658.33
46.3
0
Min
−0.81
−5.21
−3.12
0
4.62
2.47
1
1
0.6
1
1
1
90
77.14
3.89
98.21
7.01
1
1
1
54759.35
232068.9
71.3
1
Max
0.6
4.62
2.47
1
6009
5548
8262
8262
6192
8262
8262
8262
8262
7052
6190
7105
6425
8262
8262
8262
8253
8262
8262
8262
Obs
6993
6775
6295
8262
332
F. DOMMA AND L. ERRICO
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
333
Table 2. Total number o innovative SMEs in 2018 by Italian region.
Region
Abruzzo
Apulia
Aosta Valley
Basilicata
Calabria
Campania
Emilia-romagna
Friuli-Venezia Giulia
Lazio
Liguria
Lombardy
Molise
Piedmont
Sardinia
Sicily
The Marches
Tuscany
Trentino-Alto Adige
Umbria
Veneto
Total
Innovative SMEs
19
55
3
4
14
57
72
25
85
28
251
1
87
11
26
38
39
21
10
72
918
Innovative SMEs with
Twitter Account
4
22
1
0
4
21
31
7
35
16
98
1
32
9
7
17
19
10
1
24
359
Innovative SMEs without
Twitter Account
15
33
2
4
10
36
41
18
50
12
153
0
55
2
19
21
20
11
9
48
559
Authors’ elaboration.
In 2018, the firms analysed belong to various industries – most of them in the
Information and communication sector, as reported in Table 3. Out of 320 Innovative
SMEs, about one-half are Twitter users. Then, the Manufacturing industry registers
239 Innovative SMEs, of which about 80 have a social network account. The presence
of these firms is also relevant in the Professional, scientific and technical activities
sector, with 220 Innovative SMEs over a total of 918. In this industry, the majority of
them do not have a Twitter account (153). By contrast, SMEs do not embrace
innovative or social media activities in sectors such as Agriculture, forestry and
fishing, water supply and waste management, and Public administration and defence.
In the latter, the only existing Innovative SMEs are not Twitter users.
Figure 1, allowing an immediate understanding, displays the percentage of innovative
SMEs using Twitter in 2018 by Italian regions. Molise and Basilicata are in the two most
extreme conditions, recording 100% use, and no use.
Instead, a more heterogeneous use emerges among the Central and Northern
regions. Indeed, in some areas – such as Trentino-Alto Adige, Emilia-Romagna and
The Marches – more than 50% of innovative SMEs use Twitter. In the Southern
regions and islands, the percentage of Innovative SMEs using Twitter is less than 40%,
except for Sardinia.
Figure 2 illustrates the location of Innovative SMEs in Italian provinces, with over 100
Innovative SMEs in Milan, and between 50 and 100 in Turin and Rome. Overall, the
North of Italy registers a higher presence of Innovative SMEs than the South. However,
334
F. DOMMA AND L. ERRICO
Table 3. Total number o innovative SMEs in 2018 by sector.
NACE Rev. 2 classifcation – Main sectors
A – Agriculture, orestry and fshing
C – Manuacturing
D – Electricity, gas, steam and air conditioning
supply
E – Water supply and waste management
F – Construction
G – Wholesale and retail trade
H – Transportation and storage
I – Accommodation and ood service activities
J – Inormation and communication
K – Financial and insurance activities
L – Real estate activities
M – Proessional, scientifc and technical activities
N – Administrative and support service activities
O – Public administration and deence
P – Education
Q – Human health and social work activities
R – Arts, entertainment and recreation
S – Other service activities
Total
Innovative
SMEs
1
239
7
Innovative SMEs
with
Twitter Account
0
82
0
Innovative SMEs
without
Twitter Account
1
157
7
1
20
57
3
3
320
13
3
223
14
1
4
5
3
1
918
0
4
23
1
0
162
5
1
70
7
1
2
0
1
0
359
1
16
34
2
3
158
8
2
153
7
0
2
5
2
1
559
Authors’ elaboration.
Figure 1. Percentage o Innovative SMEs using Twitter in 2018 by Italian regions.
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
335
Figure 2. Number o Innovative SMEs using Twitter in 2018 by Italian provinces.
Figure 3. Total amount o Innovative SMEs having a Twitter account in 2018 by Industry.
no remarkable differences emerge among Italian provinces, focusing on the percentage of
Twitter users.
Finally, recalling numbers shown in Table 3, Figure 3 reports the number of innovative SMEs having a Twitter profile in 2018 by industry. The highest presence is recorded
in sector J, followed by C, M and G. For all other industries, the number of Innovative
SMEs that use Twitter is almost constant and less than 10.
336
F. DOMMA AND L. ERRICO
4. Empirical model
The estimated equation is specified as follows:
where the indices i and t refer to individuals and time periods, respectively. Profitability
is the dependent variable and, in detail, is alternatively considered as the Return on
Assets (ROA), Return on Equity (ROE), and Return on Capital Employed (ROCE) are
obtained using profit or loss before taxes. We9 adopt accounting measures of profitability to evaluate firms’ performance following other studies (e.g. Montoriol Garriga
2006; Fasano, Sánchez-Vidal, and La Rocca 2022; Fonseca, Guedes, and da Conceição
Gonçalves 2022). Focusing on the right side of Equation (1), the key regressor is the
Twitter adoption of the i-th firm, which is a binary variable taking value equal to one if a
firm has a Twitter account. Xit represents a vector of control factors that include firm
characteristics. Selecting those suggested by previous studies and for which data are
available, variables comprised are: firm age; a measure of internal funds, namely
working capital; firm growth based on sales value; the levels of leverage scaled by
total assets; and debt sustainability. We control for the year of registration in the list of
Innovative SMEs by inserting in the model time dummies (i.e. 2016, 2017 and 2018)
and for unobserved heterogeneity at the industry level by including sectorial dummies
(INDis). Finally, Εit = µi + vit with µi being an individual-specific time-invariant
component and vit is a stochastic noise.
Equation (1) is estimated by applying the Random Effect (RE) estimator to control for
unobserved time-invariant heterogeneity at the firm level, exploiting the panel data
structure of the dataset. We10 then use the Ordinary Least Square (OLS) estimator to
corroborate the results. We also apply the Lewbel (2012) IV approach to control for
potential endogeneity problems due to omitted variables bias. For instance, the entrepreneur’s past experiences and abilities, which we cannot control for in the analysis, may
influence the firm profitability. The latter could even be determined by unobservable
features affecting both the context and performance, such as cultural and historical
determinants, causing omitted variables concerns. The Lewbel (2012) method is justified
when finding valid (external) instruments is difficult or impossible. This approach allows
for identifying structural parameters in regression models with endogenous variables by
exploiting the heteroscedasticity of the error term. In other words, it enables to obtain
instruments based on information about the heteroscedasticity of the error term. In a
nutshell, the greater the degree of heteroscedasticity in the structural equation error
process, the higher the correlation between the generated instruments and the endogenous variables will be.11
As a first robustness check, we add to the vector of control variables a set of proxies of
local (at the provincial level) socio-economic features, namely the real gross domestic
product per capita, the amount of BCCs’ loans over banks branches, the share of the
population with a high school diploma, and social capital. Then, we change the model
specification by accounting for SM adoption in several ways. Finally, we repeat the
analysis using alternative profitability indicators as dependent variables.
R-squared overall model
R-squared within model
R-squared between model
F statistic
Observations
Wald chi2 test
2018
2017
2016
Debt Sustainability
Leverage
Firm Growth
Working capital
Age
Twitter adoption
0.091
0.057
0.097
ROA
(1)
−0.0230**
(0.011)
0.0016***
(0.000)
0.1128***
(0.012)
0.0583***
(0.005)
−0.1067***
(0.012)
−0.6003***
(0.182)
0.0488**
(0.022)
0.0462**
(0.021)
0.0552**
(0.022)
5580
380.95
0.000
0.066
0.074
0.067
ROE
(2)
−0.0813*
(0.049)
0.0069***
(0.002)
0.5065***
(0.059)
0.2821***
(0.025)
−0.8324***
(0.061)
−2.4196***
(0.939)
0.2738***
(0.092)
0.1651*
(0.090)
0.3206***
(0.092)
5425
418.49
0.000
0.072
0.068
0.077
ROCE
(3)
−0.0776***
(0.030)
0.0015
(0.001)
0.3145***
(0.034)
0.1863***
(0.014)
−0.3743***
(0.035)
0.1125
(0.539)
0.1363**
(0.057)
0.1062*
(0.055)
0.1778***
(0.057)
5555
414.16
0.000
RANDOM EFFECT ESTIMATOR
Table 4. Results or innovative SMEs proftability.
32.67
0.000
ROA
(4)
−0.0257***
(0.005)
0.0023***
(0.000)
0.1863***
(0.011)
0.0439***
(0.006)
−0.0781***
(0.011)
−1.0244***
(0.193)
0.0520***
(0.010)
0.0563***
(0.010)
0.0569***
(0.010)
5580
24.40
0.000
ROE
(5)
−0.0552**
(0.025)
0.0104***
(0.001)
0.7251***
(0.053)
0.2131***
(0.030)
−0.4096***
(0.053)
−3.3559***
(0.916)
0.2945***
(0.046)
0.2287***
(0.045)
0.3612***
(0.046)
5425
27.66
0.000
ROCE
(6)
−0.0648***
(0.015)
0.0049***
(0.001)
0.4892***
(0.032)
0.1477***
(0.018)
−0.1823***
(0.032)
−0.4571
(0.551)
0.1502***
(0.028)
0.1461***
(0.027)
0.1980***
(0.028)
5555
POOLED OLS ESTIMATOR
18.54
0.000
5580
ROA
(7)
−0.3304**
(0.150)
0.0022***
(0.001)
0.1885***
(0.024)
0.0672***
(0.014)
−0.1038***
(0.032)
−0.8010*
(0.414)
18.90
0.000
5425
ROE
(8)
−1.0769*
(0.551)
0.0100***
(0.003)
0.7396***
(0.100)
0.2889***
(0.056)
−0.5121***
(0.123)
−2.7350
(1.782)
LEWBEL IV ESTIMATOR
(Continued)
21.50
0.000
5555
ROCE
(9)
−0.6192*
(0.346)
0.0049***
(0.002)
0.4982***
(0.065)
0.1898***
(0.032)
−0.2186***
(0.069)
−0.0685
(1.064)
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
337
2735.22
0.000
ROA
(1)
1424.67
0.000
ROE
(2)
ROCE
(3)
2390.96
0.000
RANDOM EFFECT ESTIMATOR
131.86
0.000
ROA
(4)
0.105
1533.03
0.000
ROE
(5)
0.083
POOLED OLS ESTIMATOR
1282.56
0.000
ROCE
(6)
0.091
12.89
0.535
ROA
(7)
0.473
16.85
0.264
ROE
(8)
0.224
LEWBEL IV ESTIMATOR
18.72
0.176
ROCE
(9)
0.163
***p < 0.01; **p < 0.05; *p < 0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using proft or loss beore taxes. Sectorial dummies
always included but not reported.
Breusch-Pagan/
Cook-Weisberg test
Hansen test
R-squared
Breusch-Pagan LM test
Table 4. (Continued).
338
F. DOMMA AND L. ERRICO
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
339
5. Estimation results
Table 4 shows the results on benchmark models when considering ROA, ROE and ROCE
– based on profit or loss before tax – as dependent variables for 2011–19. Columns 1 to 3
contain findings obtained with the Random Effect estimator; then other columns show
those related to the use of the Pooled OLS estimator (columns 4 to 6) and Lewbel IV
approach (columns 7 to 9).
Firstly, as the diagnostics show (bottom of Table 4), the Breusch and Pagan
Lagrangian multiplier test for random effects, checking for group-wise effect, supports
the evidence of a random effect in our panel dataset. Moreover, performed after the
POLS estimator, the Breusch-Pagan/Cook-Weisberg test statistics are sufficiently large
to reject the null hypothesis of homoscedasticity, justifying the use of the Lewbel (2012)
approach. In particular, the Hansen J statistic for the instrument’s validity is satisfied
when controlling for omitted variable concerns. Twitter12 adoption significantly negatively impacts profitability, suggesting that Innovative SMEs embracing SM experience
a fall in their financial performance compared to those without a Twitter account. The
estimated effect of control variables is quite informative. According to Table 4, when
Innovative SMEs’ age increases, their profitability increase. Similarly, internal funds
indicators (Working capital) and Firm Growth positively influence Innovative SMEs’
profitability: firms with additional liquidity may invest more in R&D and adopt new
technologies, consequently boosting their financial performance. Contrariwise, the
leverage level and debt sustainability negatively affect profitability, in line with work
finding a negative relation between these micro-level variables and firms’ profitability
(e.g. Jensen and Murphy 1990). Finally, controlling for the year of switching to
Innovative SMEs status, it seems that subscribing in 2016, 2017 or 2018 increased
profitability more than joining in 2015. The findings are robust across the estimation
methods, i.e. Random Effect (columns 1–3), Pooled OLS (columns 4–6) and Lewbel IV
approach (columns 7–9).
5.1. Robustness checks
A battery of checks were performed to verify the robustness of our findings. We control
macro-level determinants by adding socio-economic context factors to the benchmark
models (Table 5). Results related to the main regressor – Twitter adoption – are
confirmed, as well as those regarding firm characteristics control variables. A non-linear
relation emerges between Innovative SMEs’ profitability and the provincial GDP per
capita: when this latter increases, the profitability rises and then reduces following an
inverted U shape. Finally, the local social capital endowment increases firms’ profitability. The application of different estimation methods corroborates the main results.
Moreover, among Innovative SMEs adopting Twitter, we distinguish the adoption of
the SM platform before and after 2011 to control for any potential difference in the effect
on profitability by replacing the variable Twitter adoption with two alternative binary
variables. According to Table 6 (columns 1–3), the overall negative impact of embracing
Twitter seems to be mainly driven by its adoption during the period we consider. This
finding emphasises the importance of analysing the link under scrutiny more deeply.
Columns 4 to 6 report additional variables among regressors: Facebook and LinkedIn
Education
Financial Development
GDP per capita squared
GDP per capita
2018
2017
2016
Debt Sustainability
Leverage
Firm Growth
Working capital
Age
Twitter adoption
ROA
(1)
−0.0226**
(0.011)
0.0020***
(0.001)
0.1104***
(0.011)
0.0589***
(0.005)
−0.1059***
(0.012)
−0.6376***
(0.182)
0.0547***
(0.021)
0.0548***
(0.021)
0.0653***
(0.021)
0.1901*
(0.101)
−0.1376***
(0.044)
−0.0010
(0.001)
0.0002
(0.001)
ROE
(2)
−0.0788
(0.048)
0.0088***
(0.002)
0.4929***
(0.059)
0.2868***
(0.025)
−0.8371***
(0.061)
−2.5957***
(0.938)
0.2916***
(0.091)
0.1939**
(0.089)
0.3616***
(0.091)
0.6287
(0.466)
−0.4717**
(0.201)
−0.0019
(0.006)
−0.0048
(0.005)
ROCE
(3)
−0.0760***
(0.029)
0.0031**
(0.001)
0.3059***
(0.034)
0.1889***
(0.014)
−0.3740***
(0.035)
−0.0087
(0.537)
0.1532***
(0.055)
0.1318**
(0.054)
0.2100***
(0.055)
0.7093**
(0.278)
−0.4512***
(0.120)
−0.0012
(0.004)
−0.0038
(0.003)
RANDOM EFFECT ESTIMATOR
ROA
(4)
−0.0238***
(0.005)
0.0021***
(0.000)
0.1839***
(0.011)
0.0496***
(0.006)
−0.0720***
(0.011)
−1.0403***
(0.191)
0.0556***
(0.010)
0.0587***
(0.009)
0.0623***
(0.010)
0.0340
(0.060)
−0.0681***
(0.026)
−0.0003
(0.001)
0.0026***
(0.001)
ROE
(5)
−0.0493**
(0.025)
0.0099***
(0.001)
0.7196***
(0.053)
0.2348***
(0.030)
−0.3971***
(0.053)
−3.4785***
(0.911)
0.3055***
(0.045)
0.2355***
(0.045)
0.3835***
(0.046)
0.0242
(0.286)
−0.2292*
(0.122)
−0.0001
(0.004)
0.0060*
(0.003)
ROCE
(6)
−0.0602***
(0.015)
0.0044***
(0.001)
0.4805***
(0.032)
0.1643***
(0.018)
−0.1707***
(0.031)
−0.4933
(0.545)
0.1606***
(0.027)
0.1533***
(0.027)
0.2149***
(0.028)
0.1909
(0.172)
−0.2309***
(0.073)
0.0001
(0.002)
0.0045**
(0.002)
POOLED OLS ESTIMATOR
Table 5. Results or innovative SMEs proftability. Robustness check: adding context actors.
0.1265
(0.159)
−0.1011
(0.067)
0.0001
(0.002)
0.0011
(0.002)
ROA
(7)
−0.3175**
(0.143)
0.0021***
(0.001)
0.1848***
(0.024)
0.0712***
(0.013)
−0.0966***
(0.031)
−0.8433**
(0.399)
0.2673
(0.528)
−0.3220
(0.232)
0.0013
(0.007)
0.0022
(0.006)
ROE
(8)
−0.9326*
(0.503)
0.0098***
(0.002)
0.7304***
(0.096)
0.2985***
(0.051)
−0.4827***
(0.117)
−2.9672*
(1.680)
LEWBEL IV ESTIMATOR
(Continued)
0.3206
(0.318)
−0.2800**
(0.139)
0.0009
(0.004)
0.0020
(0.004)
ROCE
(9)
−0.5189*
(0.314)
0.0046***
(0.001)
0.4882***
(0.062)
0.1991***
(0.029)
−0.1972***
(0.065)
−0.1745
(0.995)
340
F. DOMMA AND L. ERRICO
0.082
0.078
0.094
1424.67
0.000
2735.22
0.000
ROE
(2)
0.2546**
(0.105)
5418
464.67
0.000
0.116
0.060
0.153
ROA
(1)
0.0375*
(0.021)
5573
454.98
0.000
2390.96
0.000
0.099
0.073
0.126
ROCE
(3)
0.1243**
(0.060)
5548
492.71
0.000
RANDOM EFFECT ESTIMATOR
201.18
0.000
0.132
33.61
0.000
ROA
(4)
0.0183
(0.020)
5573
1654.86
0.000
0.098
23.46
0.000
ROE
(5)
0.2136**
(0.093)
5418
POOLED OLS ESTIMATOR
1443.05
0.000
0.116
29.11
0.000
ROCE
(6)
0.0571
(0.056)
5548
12.09
0.882
0.405
12.37
0.000
ROA
(7)
0.0355
(0.047)
5573
18.01
0.522
0.134
12.86
0.000
ROE
(8)
0.2810*
(0.158)
5418
LEWBEL IV ESTIMATOR
19.44
0.429
0.063
14.83
0.000
ROCE
(9)
0.0917
(0.090)
5548
***p < 0.01; **p < 0.05; *p < 0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using proft or 31 loss beore taxes. Sectorial
dummies always included but not reported.
Breusch-Pagan/
Cook-Weisberg test
Hansen test
R-squared overall model
R-squared within model
R-squared between model
R-squared
Breusch-Pagan LM test
F statistic
Observations
Wald chi2 test
Social Capital
Table 5. (Continued).
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
341
(0.005)
Leverage
(0.012)
(0.012)
(0.000)
Roce 1
(0.012)
Roe 1
Multiple SM adoption
(0.012)
Roa 1
LinkedIn adoption
(1)
ROA
Twitter adoption
(0.013)
Twitter adoption beore
2011
(0.024)
Twitter adoption ater
2011
(0.012)
Facebook adoption
(0.063)
−0.0721**
(0.031)
(0.103)
−0.0840*
(0.051)
−0.0200*
0.0012
(0.013)
0.0016
(0.014)
(4)
ROA
−0.0251*
0.0065
(0.055)
−0.0031
(0.058)
(5)
ROE
−0.0889
(0.055)
−0.0178
(0.034)
−0.0070
(0.036)
(6)
ROCE
−0.0725**
(0.034)
−0.0223*
(7)
ROA
−0.0780
(0.052)
(8)
ROE
−0.0799**
(0.032)
(9)
ROCE
0.5988***
(0.011)
(10)
ROA
−0.0124***
(0.004)
0.0016*** 0.0068***
0.0015
0.0015*** 0.0064***
0.0012
0.0016*** 0.0067***
0.0014
0.0011***
(0.002)
(0.001)
(0.001)
(0.002)
(0.001)
(0.000)
(0.002)
(0.001)
(0.000)
(0.01)
0.1129*** 0.5081*** 0.3146*** 0.1116*** 0.5049*** 0.3144*** 0.1131*** 0.5098*** 0.3158*** 0.0878***
(0.059)
(0.034)
(0.012)
(0.059)
(0.034)
(0.012)
(0.059)
(0.034)
(0.009)
(0.050)
0.0583*** 0.2820*** 0.1864*** 0.0580*** 0.2819*** 0.1860*** 0.0582*** 0.2816*** 0.1861*** 0.0848***
(0.025)
(0.014)
(0.005)
(0.025)
(0.014)
(0.005)
(0.025)
(0.014)
(0.005)
(0.024)
−0.1069*** −0.8310*** −0.3747*** −0.1060*** −0.8308*** −0.3777*** −0.1063*** −0.8286*** −0.3728*** −0.0222**
(0.061)
(0.035)
(0.012)
(0.062)
(0.035)
(0.012)
(0.061)
(0.035)
(0.009)
−0.1098*
−0.0757
(3)
ROCE
−0.0410*
(2)
ROE
Table 6. Results or innovative SMEs proftability. Robustness check: changing model specifcation.
0.0070***
(0.01)
0.5115***
(0.029)
0.2952***
(0.014)
−0.4891***
(0.051)
0.3374***
(0.012)
(11)
ROE
−0.0377
(0.028)
(Continued)
−0.2302***
(0.029)
0.2078***
0.3018***
0.0027***
0.4143***
(12)
ROCE
−0.0363**
(0.016)
342
F. DOMMA AND L. ERRICO
0.092
0.057
0.098
2731.15
0.0547**
(0.092)
5580
381.63
(0.539)
0.0491**
(0.092)
0.0463**
−2.4090**
(0.183)
0.2737***
(0.057)
0.1671*
(0.090)
0.3207***
(0.057)
5425
417.57
0.000
0.066
0.074
0.066
1424.71
0.000
(2)
ROE
0.1140
(0.944)
0.1367**
(0.022)
0.1064*
(0.055)
0.1770***
(0.022)
5555
414.44
0.000
0.072
0.068
0.077
2390.58
0.000
(0.542)
0.0467**
(0.092)
0.0463**
(0.021)
0.0544**
(0.092)
5542
372.51
0.000
0.090
0.057
0.096
2691.36
0.000
(3)
(4)
ROCE
ROA
−0.5980*** −2.4293**
(0.182)
0.2677***
(0.057)
0.1629*
(0.090)
0.3197***
(0.057)
5388
409.99
0.000
0.065
0.073
0.066
1403.11
0.000
(5)
ROE
0.0991
(0.939)
0.1310**
(0.022)
0.1031*
(0.056)
0.1768***
(0.022)
5517
411.33
0.000
0.071
0.068
0.077
2369.17
0.000
(0.539)
0.0480**
(0.092)
0.0442**
(0.021)
0.0545**
(0.092)
5580
380.21
0.000
0.090
0.057
0.097
2736.44
0.000
(6)
(7)
ROCE
ROA
−0.6025*** −2.4202***
(0.151)
0.2710***
(0.057)
0.1598*
(0.090)
0.3181***
(0.057)
5425
416.95
0.000
0.065
0.074
0.066
1423.89
0.000
(8)
ROE
0.1034
(0.834)
0.1335**
(0.008)
0.0993*
(0.055)
0.1759***
(0.008)
5555
413.70
0.000
0.071
0.068
0.077
2386.41
0.000
(0.478)
0.0200**
(0.053)
0.0205***
(0.008)
0.0197**
(0.053)
5527
4224.16
0.000
0.462
0.075
0.844
13.77
0.000
(9)
(10)
ROCE
ROA
−0.4308*** −2.3122***
0.1940***
(0.030)
0.1322**
(0.052)
0.2074***
(0.030)
5294
1253.50
0.000
0.281
0.049
0.594
48.01
0.000
(11)
ROE
−0.1167
(0.182)
5176
1816.86
0.000
0.376
0.065
0.697
50.95
0.000
0.0728**
(0.030)
0.1021***
0.0871***
(12)
ROCE
(0.939)
***p < 0.01; **p < 0.05; *p < 0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using proft or loss beore taxes. Sectorial dummies
always included but not reported.
(0.021)
2018
(0.022)
Observations
Wald chi2 test
0.000
R-squared overall model
R-squared within model
R-squared between model
Breusch-Pagan LM test
0.000
(0.022)
Debt Sustainability
(1)
ROA
−0.5995***
Table 6. (Continued).
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
343
344
F. DOMMA AND L. ERRICO
adoption to account for the adoption of other SM. The main result of our key variable is
confirmed. Also, we replace the critical variable Twitter adoption with the multiple SM
adoption (columns 7 to 9), which is a dummy variable equal to one if a firm has all the
considered SM accounts. The results corroborate the negative influence of SM adoption
on Innovative SMEs’ profitability. Finally, we add to the benchmark model the previous
outcomes of Innovative SMEs’ profitability, namely Roa_1, Roe_1 and Roce_1 (columns
10–12). Findings obtained by amending our benchmark equation estimated with
Random Effect estimator are in line with those discussed above.
To conclude, our findings are confirmed even when we repeat the econometric
analysis by considering ROA, ROE, and ROCE, obtained using net income, as dependent
variables (Tables A2 to A4). Specifically, in line with the previous results, the Twitter
adoption dummy is negative and statistically significant in all the models specified,
corroborating the adverse effect of SM embracing on Innovative SMEs’ profitability.
6. Concluding remarks
This work investigates whether SM adoption (i.e. Twitter) affected Italian Innovative SMEs’
profitability during 2011–19. The investigation aims to fill a gap in the literature by analysing
the role of Internet technology implementation, a state-of-the-art communication tool, for a
sample of firms representing a significant part of the Italian entrepreneurial fabric. According
to our evidence, robust to several estimation specifications and methods, SM adoption plays a
negative role in Innovative SMEs’ performance, probably due to the lack of ability of SMEs to
adapt, integrate and exploit the advanced way of interaction through social platforms. The
disadvantages of adopting Twitter appear to outweigh its benefit, creating higher costs in
terms of time, implementation, maintenance, and acquisition of specialised human capital,
which, in turn, is reflected in profitability reduction.
The study has some limitations that can suggest directions for future research. First,
the investigation is carried out by using a binary variable on a single platform. Given the
lack of unified metrics for capturing varied SM platforms, a challenge would be to repeat
the analysis using other SM adoption measures. Second, this research examines the
specific kind of firms that contribute significantly to Italy’s economy, namely
Innovative SMEs. Future work could be carried out to assess whether this generalises
to other types of enterprises. Finally, the sample of Italian Innovative SMEs could be
updated to investigate the role of the COVID-19 pandemic on the relevance of SM
adoption for Innovative SMEs.
Notes
1. See Olanrewaju et al. (2020) for further details.
2. The OECD/Eurostat (2018) divides innovative activities into four classes: product innovation (i.e. a product/service that is new or significantly improved), process innovation (i.e. a
new or significantly improved production/delivery method), marketing innovation (i.e. a
new marketing method involving substantial changes in product design/packaging, product
positioning, product promotion or pricing), organizational innovation (i.e. a new corporate
scheme in business practices, workplace organization or external industry relationships).
3. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Social_media_-_statis
tics_on_the_use_by_enterprises#Use_of_social_media_by_enterprises
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
345
4. Some authors put emphasis on the social media’s profitability. In making decisions on which
strategic marketing initiatives to undertake, firms operate pursuing profit maximization. Thus,
if social media initiatives provide a sufficient return on investment (ROI), firms adopt this
strategy. Stated differently, if a definite investment threshold is cleared and social media’s longterm benefits overcome the costs, firms use social media marketing (Lenskold, 2003).
5. Another stand of literature explores factors affecting SM adoption. For instance, firm
innovativeness, age and geographic location have a significant impact on Twitter adoption
by SMEs (Wamba and Carter, 2013). Other studies indicate that factors such as compatibility (Wang et al., 2010), trust (Chai et al., 2011), cost effectiveness (Chong and Chan,
2012), and interactivity (Lee and Kozar, 2012) affect social media adoption
6. In this paper we focus only on the primary official Twitter account for each firm.
7. Each tweet has the text of the tweet, and such metadata as date, language, retweets, likes, and
the number of followers (a static number at the time of data collection, which is 21
December 2021).
8. Note that, in the other years, the correlations remain mainly constant in absolute value, and
the sign of the correlations do not change. Tables related to correlation matrices for the rest
of the period are available upon request.
9. As a robustness check, we repeat the econometric analysis by employing as dependent
variables ROA, ROE, and ROCE obtained using net income. See section 2.2.1 for the
definition of profitability and Table 1 for the description of variables.
10. The use of a Fixed effect (FE) estimator is precluded to avoid the exclusion from estimation
results of the main regressor (Twitter adoption), that is binary variable. Also, accounting for
each individual firm’s fixed effect implies technical challenges and time consumption. As a
result, the Hausman test – useful to discriminate between FE and RE estimators – cannot be
applied.
11. For a comprehensive explanation of this approach, see Lewbel (2012).
12. For the sake of clarity, the Stata software – used to carry out the analysis – offers two
different Breusch-Pagan tests. In more detail, the command “estat hettest” tests heteroskedasticity after OLS regression and is not supported by panel estimation methods. Instead,
the evidence of a random effect in a panel dataset can be tested after running the Random
Effect estimator thought the command “xttest0”, which check for panel-wise effect.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The work was supported by the Regione Calabria [PAC CALABRIA 2014-2020 - Asse Prioritario
12, Azione B) 10.5.12 CUP: H28D19000040006].
ORCID
Filippo Domma
http://orcid.org/0000-0002-1489-1065
http://orcid.org/0000-0002-8645-6847
Lucia Errico
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Wong, C.B. 2012. “Facebook Usage by Small and Medium-Sized Enterprise: The Role of DomainSpecific Innovativeness.” Global Journal of Computer Science and Technology 12 (4): 52–59.
Xiong, F., J. Nelson, and K. Bodle. 2018. “The Adoption of New Technology by Listed Companies:
The Case of Twitter.” Technology Analysis & Strategic Management 30 (7): 852–865.
Yazdanfar, D. 2013. “Profitability Determinants Among Micro Firms: Evidence from Swedish
Data.” International Journal of Managerial Finance 9 (2): 151–160.
Zhang, M., L. Guo, M. Hu, and W. Liu. 2017. “Influence of Customer Engagement with Company
Social Networks on Stickiness: Mediating Effect of Customer Value Creation.” International
Journal of Information Management 37 (3): 229–240.
Zolkepli, I.A., and Y. Kamarulzaman. 2015. “Social Media Adoption: The Role of Media Needs and
Innovation Characteristics.” Computers in Human Behavior 43: 189–209.
1
0.2446
−0.0388
0.0428
0.0464
−0.0808
−0.064
−0.0445
−0.0863
Age
1
0.0096
0.0179
0.052
−0.0421
−0.0559
−0.0129
−0.0417
Working
capital
1
0.0219
−0.0595
0.0982
0.0744
0.0494
0.1037
Firm
Growth
1
0.0934
0.0515
−0.0189
0.0231
0.0535
Leverage
1
−0.0183
−0.0577
0.0362
0.0265
Debt
Sustainability
1
0.6809
0.5589
0.8462
GDP
per capita
1
0.3501
0.3789
Financial
Development
1
0.5626
Education
1
Social
Capital
For the description o the variables see Table 1. Table A1 reports the correlation matrix o variables used in the analysis or 2018. In the other years, the correlations remain substantially constant
in absolute value and the sign o the correlations does not change. The latter are available upon request.
Twitter adoption
Age
Working capital
Firm Growth
Leverage
Debt Sustainability
GDP per capita
Financial Development
Education
Social Capital
Twitter
adoption
1
−0.0314
0.0254
0.0827
0.0336
0.015
0.0585
0.0376
0.0072
0.0435
Table A1. Correlation matrix (year 2018).
Appendix
350
F. DOMMA AND L. ERRICO
R-squared overall model
R-squared within model
R-squared between model
F statistic
Observations
Wald chi2 test
2018
2017
2016
Debt Sustainability
Leverage
Firm Growth
Working capital
Age
Twitter adoption
0.083
0.053
0.089
ROA
(1)
−0.0219**
(0.010)
0.0018***
(0.000)
0.0837***
(0.010)
0.0488***
(0.004)
−0.0927***
(0.010)
−0.6462***
(0.159)
0.0455**
(0.019)
0.0413**
(0.019)
0.0473**
(0.019)
5575
351.30
0.000
0.071
0.091
0.062
ROE
(2)
−0.0832*
(0.044)
0.0087***
(0.002)
0.3770***
(0.053)
0.2337***
(0.022)
−0.9481***
(0.055)
−2.7596***
(0.842)
0.2496***
(0.083)
0.1307
(0.081)
0.2353***
(0.083)
5429
492.62
0.000
0.069
0.071
0.070
ROCE
(3)
−0.0672**
(0.027)
0.0026**
(0.001)
0.2593***
(0.030)
0.1514***
(0.012)
−0.3878***
(0.031)
0.1299
(0.474)
0.1216**
(0.051)
0.0830*
(0.049)
0.1348***
(0.051)
5554
420.33
0.000
RANDOM EFFECT ESTIMATOR
29.90
0.000
ROA
(4)
−0.0235***
(0.005)
0.0021***
(0.000)
0.1488***
(0.010)
0.0361***
(0.005)
−0.0619***
(0.009)
−0.8877***
(0.168)
0.0491***
(0.008)
0.0491***
(0.008)
0.0479***
(0.008)
5575
25.05
0.000
ROE
(5)
−0.0590***
(0.023)
0.0101***
(0.001)
0.5988***
(0.048)
0.1718***
(0.027)
−0.5593***
(0.048)
−3.1533***
(0.827)
0.2571***
(0.041)
0.1732***
(0.041)
0.2603***
(0.042)
5429
26.41
0.000
ROCE
(6)
−0.0566***
(0.013)
0.0048***
(0.001)
0.4158***
(0.028)
0.1154***
(0.016)
−0.2006***
(0.028)
−0.0161
(0.489)
0.1337***
(0.024)
0.1161***
(0.024)
0.1516***
(0.025)
5554
POOLED OLS ESTIMATOR
Table A2. Results or innovative SMEs proftability. Robustness check: changing dependent variable.
17.92
0.000
5575
ROA
(7)
−0.2712**
(0.124)
0.0020***
(0.001)
0.1506***
(0.021)
0.0551***
(0.012)
−0.0826***
(0.026)
−0.7094**
(0.343)
18.59
0.000
5429
ROE
(8)
−0.8209*
(0.450)
0.0098***
(0.002)
0.6102***
(0.085)
0.2297***
(0.048)
−0.6331***
(0.104)
−2.6480*
(1.529)
LEWBEL IV ESTIMATOR
(Continued)
20.83
0.000
5554
ROCE
(9)
−0.4496
(0.278)
0.0049***
(0.001)
0.4230***
(0.056)
0.1463***
(0.026)
−0.2234***
(0.057)
0.2693
(0.901)
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
351
2477.43
0.000
ROA
(1)
1470.43
0.000
ROE
(2)
ROCE
(3)
2501.35
0.000
RANDOM EFFECT ESTIMATOR
280.11
0.000
ROA
(4)
0.097
2206.19
0.000
ROE
(5)
0.085
POOLED OLS ESTIMATOR
1685.89
0.000
ROCE
(6)
0.087
14.04
0.447
ROA
(7)
0.413
19.45
0.149
ROE
(8)
0.125
LEWBEL IV ESTIMATOR
22.76
0.064
ROCE
(9)
0.081
***p < 0.01; **p < 0.05; *p < 0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using net income. Sectorial dummies always
included but not reported.
Breusch-Pagan/
Cook-Weisberg test
Hansen test
R-squared
Breusch-Pagan LM test
Table A2. (Continued).
352
F. DOMMA AND L. ERRICO
Education
Financial Development
GDP per capita squared
GDP per capita
2018
2017
2016
Debt Sustainability
Leverage
Firm Growth
Working capital
Age
Twitter adoption
ROA
(1)
−0.0214**
(0.010)
0.0020***
(0.000)
0.0824***
(0.010)
0.0493***
(0.004)
−0.0915***
(0.010)
−0.6746***
(0.159)
0.0502***
(0.018)
0.0479***
(0.018)
0.0555***
(0.018)
0.1449
(0.088)
−0.1091***
(0.038)
−0.0008
(0.001)
0.0005
(0.001)
ROE
(2)
−0.0796*
(0.043)
0.0096***
(0.002)
0.3689***
(0.053)
0.2379***
(0.022)
−0.9481***
(0.055)
−2.8758***
(0.841)
0.2643***
(0.082)
0.1522*
(0.080)
0.2652***
(0.082)
0.3560
(0.419)
−0.3378*
(0.180)
−0.0026
(0.005)
0.0007
(0.004)
ROCE
(3)
−0.0654**
(0.026)
0.0038***
(0.001)
0.2529***
(0.030)
0.1536***
(0.012)
−0.3860***
(0.031)
0.0437
(0.473)
0.1361***
(0.049)
0.1036**
(0.048)
0.1614***
(0.049)
0.5660**
(0.247)
−0.3759***
(0.106)
−0.0002
(0.003)
−0.0021
(0.003)
RANDOM EFFECT ESTIMATOR
ROA
(4)
−0.0217***
(0.005)
0.0020***
(0.000)
0.1473***
(0.010)
0.0412***
(0.005)
−0.0563***
(0.009)
−0.8997***
(0.166)
0.0520***
(0.008)
0.0510***
(0.008)
0.0527***
(0.008)
0.0050
(0.052)
−0.0491**
(0.022)
−0.0005
(0.001)
0.0025***
(0.001)
ROE
(5)
−0.0525**
(0.022)
0.0096***
(0.001)
0.5949***
(0.048)
0.1933***
(0.027)
−0.5487***
(0.048)
−3.2724***
(0.821)
0.2681***
(0.041)
0.1807***
(0.040)
0.2804***
(0.042)
−0.0655
(0.258)
−0.1743
(0.110)
−0.0020
(0.003)
0.0082***
(0.003)
ROCE
(6)
−0.0522***
(0.013)
0.0044***
(0.001)
0.4085***
(0.028)
0.1308***
(0.016)
−0.1893***
(0.028)
−0.0487
(0.482)
0.1431***
(0.024)
0.1223***
(0.024)
0.1669***
(0.024)
0.1312
(0.152)
−0.1971***
(0.065)
0.0001
(0.002)
0.0046***
(0.002)
POOLED OLS ESTIMATOR
0.0765
(0.130)
−0.0744
(0.055)
−0.0001
(0.001)
0.0013
(0.001)
ROA
(7)
−0.2553**
(0.117)
0.0019***
(0.001)
0.1480***
(0.020)
0.0585***
(0.011)
−0.0757***
(0.025)
−0.7459**
(0.327)
Table A3. Results or innovative SMEs proftability. Robustness check: changing dependent variable and adding context actors.
0.1044
(0.436)
−0.2410
(0.195)
−0.0007
(0.006)
0.0054
(0.005)
ROE
(8)
−0.7145*
(0.422)
0.0096***
(0.002)
0.6034***
(0.082)
0.2426***
(0.044)
−0.6104***
(0.100)
−2.8503*
(1.456)
LEWBEL IV ESTIMATOR
(Continued)
0.2092
(0.255)
−0.2274**
(0.113)
0.0008
(0.003)
0.0030
(0.003)
ROCE
(9)
−0.3698
(0.258)
0.0046***
(0.001)
0.4149***
(0.054)
0.1561***
(0.024)
−0.2045***
(0.054)
0.1840
(0.850)
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
353
0.088
0.092
0.093
1372.13
0.000
2366.36
0.000
ROE
(2)
0.1745*
(0.094)
5422
533.07
0.000
0.111
0.054
0.147
ROA
(1)
0.0208
(0.018)
5568
418.43
0.000
2331.56
0.000
0.098
0.074
0.121
ROCE
(3)
0.0942*
(0.053)
5547
490.75
0.000
RANDOM EFFECT ESTIMATOR
406.42
0.000
0.125
31.78
0.000
ROA
(4)
0.0132
(0.017)
5568
2403.10
0.000
0.103
24.71
0.000
ROE
(5)
0.1640*
(0.084)
5422
POOLED OLS ESTIMATOR
2009.23
0.000
0.115
28.69
0.000
ROCE
(6)
0.0592
(0.050)
5547
13.53
0.810
0.330
12.31
0.000
ROA
(7)
0.0276
(0.039)
5568
19.97
0.396
0.058
12.65
0.000
ROE
(8)
0.2202*
(0.132)
5422
LEWBEL IV ESTIMATOR
20.85
0.345
0.042
14.00
0.000
ROCE
(9)
0.0879
(0.073)
5547
***p < 0.01; **p < 0.05; ***p < 0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using net income.35Sectorial dummies always
included but not reported.
Breusch-Pagan/
Cook-Weisberg test
Hansen test
F statistic
P-value or model test
R-squared overall model
R-squared within model
R-squared between model
R-squared
Breusch-Pagan LM test
Observations
Wald chi2 test
Social Capital
Table A3. (Continued).
354
F. DOMMA AND L. ERRICO
(0.000)
Working capital
(0.010)
Firm Growth
(0.004)
Leverage
(0.010)
Debt Sustainability
Rocent 1(0.012)
Roent 1-
Multiple SM adoption
(0.011)
Roant 1-
LinkedIn adoption
(1)
ROA
Twitter adoption
(0.011)
Twitter adoption beore 2011
(0.021)
Twitter adoption ater 2011
(0.010)
Facebook adoption
−0.0870
(0.093)
−0.0839*
(0.046)
−0.0975*
(0.056)
−0.0620**
(0.028)
(3)
ROCE
0.0032
(0.011)
−0.0003
(0.012)
(4)
ROA
−0.0236**
0.0277
(0.049)
−0.0169
(0.052)
(5)
ROE
−0.0911*
(0.049)
−0.0117
(0.030)
−0.0175
(0.032)
(6)
ROCE
−0.0600**
(0.030)
−0.0203*
(7)
ROA
−0.0815*
(0.046)
(8)
ROE
−0.0712**
(0.028)
(9)
ROCE
0.5461***
(0.011)
(10)
ROA
−0.0122***
(0.004)
0.3294***
(0.013)
(11)
ROE
−0.0358
(0.025)
0.4095***
(12)
ROCE
−0.0294**
(0.015)
(Continued)
0.0018*** 0.0086***
0.0026**
0.0017*** 0.0084***
0.0024**
0.0017*** 0.0085***
0.0025**
0.0011*** 0.0075*** 0.0028***
(0.002)
(0.001)
(0.000)
(0.002)
(0.001)
(0.000)
(0.002)
(0.001)
(0.000)
(0.001)
(0.001)
0.0838*** 0.3782*** 0.2594*** 0.0827*** 0.3743*** 0.2592*** 0.0839*** 0.3799*** 0.2604*** 0.0756*** 0.4102*** 0.2479***
(0.053)
(0.030)
(0.010)
(0.053)
(0.030)
(0.010)
(0.053)
(0.030)
(0.008)
(0.046)
(0.026)
0.0488*** 0.2336*** 0.1514*** 0.0486*** 0.2333*** 0.1512*** 0.0487*** 0.2331*** 0.1512*** 0.0670*** 0.2356*** 0.1690***
(0.022)
(0.012)
(0.004)
(0.022)
(0.012)
(0.004)
(0.022)
(0.012)
(0.004)
(0.023)
(0.013)
−0.0928*** −0.9472*** −0.3881*** −0.0922*** −0.9456*** −0.3913*** −0.0923*** −0.9446*** −0.3865*** −0.0248*** −0.5691*** −0.2337***
(0.055)
(0.031)
(0.010)
(0.056)
(0.031)
(0.010)
(0.055)
(0.031)
(0.008)
(0.046)
(0.027)
−0.6456*** −2.7508***
0.1312
−0.6480*** −2.7439***
0.1180
−0.6484*** −2.7624***
0.1226
−0.4301*** −2.0874***
0.1872
(0.159)
(0.842)
(0.474)
(0.160)
(0.847)
(0.477)
(0.159)
(0.842)
(0.474)
(0.138)
(0.765)
(0.433)
−0.0193*
−0.0370*
(2)
ROE
Table A4. Results or innovative SMEs proftability. Robustness check: changing dependent variable and changing model specifcation.
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
355
0.084
0.053
0.089
2472.35
5575
351.93
0.0414**
(0.019)
0.0469**
0.0457**
(2)
ROE
0.2497***
(0.083)
0.1323
(0.081)
0.2352***
(0.083)
5429
491.93
0.000
0.070
0.091
0.061
1470.19
0.000
(3)
ROCE
0.1220**
(0.051)
0.0832*
(0.049)
0.1340***
(0.051)
5554
420.66
0.000
0.069
0.071
0.070
2500.72
0.000
(4)
ROA
0.0443**
(0.019)
0.0416**
(0.019)
0.0466**
(0.019)
5537
343.17
0.000
0.082
0.053
0.087
2441.27
0.000
(5)
ROE
0.2482***
(0.083)
0.1319
(0.081)
0.2359***
(0.083)
5392
480.84
0.000
0.070
0.089
0.061
1450.51
0.000
(6)
ROCE
0.1184**
(0.051)
0.0811
(0.050)
0.1347***
(0.051)
5516
417.36
0.000
0.068
0.071
0.070
2481.13
0.000
(7)
ROA
0.0448**
(0.019)
0.0394**
(0.018)
0.0467**
(0.019)
5575
350.06
0.000
0.082
0.053
0.088
2481.27
0.000
(8)
ROE
0.2468***
(0.083)
0.1248
(0.081)
0.2330***
(0.082)
5429
491.23
0.000
0.070
0.091
0.061
1468.48
0.000
(9)
ROCE
0.1191**
(0.051)
0.0770
(0.049)
0.1332***
(0.050)
5554
420.25
0.000
0.069
0.071
0.070
2495.41
0.000
(10)
ROA
0.0208***
(0.008)
0.0203***
(0.008)
0.0195**
(0.008)
5520
3138.26
0.000
0.431
0.059
0.814
18.12
0.000
(11)
ROE
0.1731***
(0.047)
0.1064**
(0.046)
0.1580***
(0.047)
5303
1215.68
0.000
0.263
0.041
0.574
56.49
0.000
(12)
ROCE
0.0734***
(0.027)
0.0576**
(0.027)
0.0822***
(0.027)
5175
1632.14
0.000
0.357
0.046
0.685
44.35
0.000
***p < 0.01; **p<0.05; *p<0.1. The standard errors are reported in parentheses. The dependent variables are ROA, ROE and ROCE calculated using net income. Sectorial dummies always included
but not reported.
2018
(0.019)
Observations
Wald chi2 test
0.000
R-squared overall model
R-squared within model
R-squared between model
Breusch-Pagan LM test
0.000
(1)
ROA
2016
(0.019)
2017
Table A4. (Continued).
356
F. DOMMA AND L. ERRICO