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Journal of Travel Research
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DOI: 10.1177/0047287515583357
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Empirical Research Articles
Introduction
Research into productivity is relevant because it is an indica-
tor that gives us an insight into an economy’s long-term
growth potential. Economic theory and available empirical
evidence (Jorgenson, Ho, and Stiroh 2005) have shown that
the level and growth of productivity derive from two basic
sources: capital intensification/dependence (capital endow-
ment per worker or hour worked) and the efficiency with
which production factors are combined (total factor produc-
tivity, TFP). Because of the existence of diminishing returns,
the mere accumulation of factors (extensive growth models)
ends up weakening the sources of long-term economic
expansion. Only when the accumulation of factors is accom-
panied by TFP improvements can the growth model become
intensive and therefore sustainable in the long term (Timmer
et al. 2010).
In the transition toward the global knowledge economy,
empirical evidence has shown that information and commu-
nication technologies (ICT) are emerging as new explana-
tory sources of productivity (Dolfsma and Soete 2006;
Torrent-Sellens and Vilaseca 2008; Torrent-Sellens 2015).
The reason for this is twofold: first, their direct contribution
to increased productivity and economic growth (Jorgenson
and Vu 2007; Jorgenson, Ho, and Stiroh 2008), and second,
their indirect contribution resulting from the generation of
co-innovation, especially through human capital improve-
ment and organizational change, that enhances an economy’s
TFP (Pilat 2006). In this context, one of the reasons explain-
ing the current severity of the economic crisis in Spain is the
country’s lack of adaptation to the knowledge economy. The
Spanish economy’s sources of productivity are not the best
583357JTRXXX10.1177/0047287515583357Journal of Travel ResearchDíaz-Chao et al.
research-article2015
1Applied Economics Department, Rey Juan Carlos University (URJC),
Madrid, Spain
2Internet Interdisciplinary Institute (IN3), Open University of Catalonia
(UOC), Barcelona, Spain
3Economic and Business Studies, Open University of Catalonia (UOC),
Barcelona, Spain
4Business School and Internet Interdisciplinary Institute (IN3), Open
University of Catalonia (UOC), Barcelona, Spain
Corresponding Author:
Joan Torrent-Sellens, Business School and Internet Interdisciplinary
Institute (IN3), Open University of Catalonia (UOC), MediaTIC Building,
Roc Boronat Street, 117, 6th floor, Barcelona, 08018, Spain.
Email: jtorrent@uoc.edu
Information and Communication
Technologies, Innovation, and Firm
Productivity in Small and
Medium-Sized Travel Agencies: New
Evidence from Spain
Ángel Díaz-Chao1,2, Oriol Miralbell-Izard3,
and Joan Torrent-Sellens4
Abstract
This article analyzes new co-innovative sources of firm labor productivity. Using survey data for 120 small and medium-sized
travel agencies based in Catalonia (a region in the northeast of Spain) and partial least square–structural equation modeling
(PLS-SEM) estimation techniques, three main findings emerged from the study. First, labor productivity is directly explained
by those agencies’ capacity to exploit their assets, to use local networks, and to make international transactions. Second,
the analysis of indirect effects on labor productivity suggests a circular causality, which is determined by the influence of the
workers’ local network use on a firm’s export capacity. In this circular relationship, a firm’s capacity to generate market-
leading product innovations and the stock of human capital and training play a decisive role. Third, co-innovation practices
exert a negative effect, which may be related to difficulties in terms of securing productivity improvements in the short term.
Keywords
Information and communication technologies (ICT), innovation, firm productivity, small and medium enterprises (SMEs),
travel agencies, partial least square–structural equation modeling (PLS-SEM), Spain
2 Journal of Travel Research
suited to competition in global knowledge-based markets
(Mas and Stehrer 2012).
From the perspective of analyzing the impact of ICT on
firm productivity, empirical evidence has highlighted two
complementary trends. First, that returns on ICT investment
and use are relatively much higher than those on investment
in other physical components, and second, that ICT invest-
ment and use often go hand in hand with other endeavors,
generally human capital improvement and organizational
structure change (Bresnahan, Brynjolfsson, and Hitt 2002;
Arvanitis 2005). Indeed, the transformative impact of ICT
investment and use on firm productivity becomes more evi-
dent through co-innovation processes (Black and Lynch
2001, 2004; Brynjolfsson and Hitt 2003; Torrent-Sellens and
Ficapal 2010).
Empirical evidence of new co-innovative sources of pro-
ductivity has been obtained mainly from samples of large
firms, while research into small and medium enterprises
(SMEs) in general, and into small and medium-sized travel
agencies in particular, is rather scarce (Wymenga et al. 2012;
Hadad et al. 2012; Torrent-Sellens and Díaz-Chao 2014).
Therefore, the aim of the study is to analyze new co-innova-
tive sources of firm productivity in small and medium-sized
travel agencies and, consequently, to expand the available
evidence. To that end, we have used microdata gathered in
2010 from a sample of 120 small and medium-sized travel
agencies in Catalonia (a region in the northeast of Spain) in
order to propose and contrast, through partial least square–
structural equation modeling (PLS-SEM) estimation tech-
niques, a model of causal relationships that includes direct
and indirect determinants of firm productivity. This method
allows for the analysis of relationships not only between the
various factors considered explanatory of productivity, but
also between such factors (Nunkoo, Ramkissoon, and Gursoy
2013). Thus, the analysis completes the structural form
explaining the productivity of small and medium-sized travel
agencies.
This article is organized as follows. The next section pres-
ents a review of the literature on relationships between ICT,
innovation, and firm productivity, especially in small and
medium-sized travel agencies. The third section describes
the data and research design. The fourth section reports the
model and the research hypothesis. The fifth section describes
the empirical findings, and the sixth section provides conclu-
sions, discussion, and policy implications based on those
findings.
ICT, Innovation, and Productivity
in Small and Medium-Sized Travel
Agencies: Literature Review
ICT investment and use do not give rise to generalized pro-
ductivity improvements until firms and their workers have
achieved the required technological, educational/training,
organizational, business, labor and cultural competencies. In
other words, the role of ICT as a general-purpose technology
needs organizational and business process changes to fully
exploit its growth opportunities (Ceccobelli, Gitto, and
Mancuso 2012). New evidence has demonstrated the exis-
tence of these co-innovative sources of productivity among
broad samples of firms, first in the United States and then in
the rest of the world (for a review of the empirical literature
see Matteucci et al. 2005; Draca, Sadun, and Van Reenen
2007; Jiménez-Rodríguez 2012; Cardona, Kretschmer, and
Strobel 2013).
Despite this abundant evidence, generally obtained from
large firms, there is relatively little available evidence on co-
innovative sources of productivity in SMEs in general (Hall,
Lotti, and Mairesse 2009; Torrent-Sellens and Díaz-Chao
2014), and in small and medium-sized travel agencies in par-
ticular (Barros and Alves 2004; Blake, Sinclair, and Soria
2006; Fuentes and Alvarez 2012; Hadad et al. 2012).
Indeed, although there is considerable evidence in the lit-
erature of the impact that ICT (Buhalis 1998; Buhalis and
Law 2008) and innovation (Novelli, Schmitz, and Spencer
2006; Hjalager 2010; Camisón and Montfort-Mir 2012;
Williams 2014) have on tourism firms, few studies have
addressed specific productivity-related problems in small
and medium-sized travel agencies (Thomas, Shaw, and Page
2011; Spencer, Buhalis, and Moital 2012). Thus, a validated
model of sources of productivity in small and medium-sized
travel agencies would be a very practical and useful instru-
ment for evaluating firm efficiency, a task that is not without
its difficulties in the context of SMEs, whatever their type
(Audretsch 2002, 2006; Hall, Lotti, and Mairesse 2009).
Regarding research into ICT, innovation, and firm pro-
ductivity in travel agencies, the available evidence suggests
that physical capital together with human capital and innova-
tion explain the level of productivity in travel agencies
(Blake, Sinclair, and Soria 2006). Other determinants of effi-
ciency in travel agencies are the implementation of e-book-
ing systems, the level of wages, and the age of the firm
(Sellers-Rubio and Mas-Ruiz 2009).
In turn, performance in the intermediation industry is
explained by technical progress. Hence, investment in orga-
nizational and managerial factors in combination with a good
balance between inputs and outputs helps firms to achieve
positive technical efficiency change. To perform better,
travel agencies must adopt new technologies and upgrade
managerial skills (Barros, Botti, and Peypoch 2009).
The action of improving productivity in travel agencies
usually focuses on capital accumulation and innovation in a
push effect that integrates an upward shift to a higher technol-
ogy change (Barros and Matias 2006; Assaf, Barros, and
Machado 2011). In travel agencies, the technology efficiency
score is actually related to the dissemination of best techno-
logical practices in the business, where assets, human capital,
training, management, and organization play an important
role (Barros and Dieke 2007). Thus, local networks become
critical to improving technological exchange, since efficiency
Díaz-Chao et al. 3
and productivity increase through knowledge transfer
between firms (Assaf, Barros, and Dieke 2011).
Although this initial evidence points to some relation-
ships of complementarity in explaining the productivity of
travel agencies, the intention of this study is to go one step
further. In small and medium-sized travel agencies, the rela-
tionships between ICT, innovation, and productivity are not
necessarily direct, as they may also be generated indirectly,
that is, through the impact on a firm’s other productivity-
related results, such as its export capacity or its assets. In this
respect, and based on the idea that ICT and co-innovation are
levers of change, the aim of this study is to design and test,
using PLS-SEM, a more comprehensive model explaining
the direct and indirect sources of productivity in small and
medium-sized travel agencies (Nunkoo, Ramkissoon, and
Gursoy 2013).
This approach is quite consistent with the further progress
made by recent research on innovation and productivity in
tourism. The first reason is that innovation in tourism is not
an isolated event. Much innovative power in tourism does
not originate from tourism itself. Tourism innovation is
strongly interrelated with other economic and social fields
(Hjalager 2015). The second reason is that the tourism inno-
vation analysis should be made with a specific approach, tak-
ing into account their different typologies. Usually,
innovation term has been used to signify any change under-
taken by an organization, business, or individual, without
regard to its extent, context, or value contribution to tourism.
Terminology suggesting that innovation is either an incre-
mental or radical change to existing conditions has simply
been transferred from manufacturing to tourism. However,
since radical innovation is rare in the tourism sector, a
broader framework based on its characteristics is required to
clearly delineate distinctive innovation approaches (Brooker
and Joppe 2014). Finally, more recent research has also indi-
cated the existence of specific pathways in tourism produc-
tivity. Empirical evidence have addressed what drives
productivity improvements and the role of changes in physi-
cal capital, innovation, and the competitive environment, and
others studies have focused on drivers that include informa-
tion and communication technology, size of firm, competi-
tion versus cooperation, and clustering. However, most
research in this area focuses on the critical role played by
human capital and has shown that tourism productivity is
more likely to come from innovations that result in enhanced
product and service labor quality than from cost-cutting
(Joppe and Lee 2014).
Data and Research Design
The study used survey data for a sample of 120 small and
medium-sized travel agencies (firms with 50 or fewer
employees) operating in Catalonia. The sampling universe
comprised 1,790 firms with an overall margin of error of
±6.7% in the case of maximum indetermination, p = q = 50,
for a confidence level of 95.5%. Catalonia is a region in the
northeast of Spain where SMEs account for the bulk of eco-
nomic activity. Generally, small and medium-sized travel
agencies make medium-intensity use of ICT and have high
levels of worker and manager education and training, a good
record in innovation activities, and important competitive-
ness problems as a result of the severity of the economic cri-
sis (Torrent-Sellens 2011). Table 1 shows some of the main
statistics describing the value process in the sample of small
and medium-sized travel agencies in Catalonia.
A preliminary version of the questionnaire was drafted
following the literature review (Joppe and Lee 2014), at the
same time, drawing on the research team’s experience in
similar studies (Torrent-Sellens and Vilaseca 2008; Torrent-
Sellens and Ficapal 2010; Torrent-Sellens and Díaz-Chao
2014). This version was used for the study pilot test. The
questionnaire validation process was undertaken in the third
week of March 2010 on a sample of 20 respondents. A num-
ber of issues were thrown up by the pilot test. The first was
the length of the questionnaire and the time taken to com-
plete it. Most of the respondents felt that the initial question-
naire was very long and difficult to answer. To solve this
problem, customized response paths were incorporated into
it according to the size of the firm being surveyed. The sec-
ond was associated with difficulties understanding the tech-
nical and technological terms, especially those connected
with ICT applications and business (e.g., ERP, CRM, B2B,
B2C). To solve this problem, simpler definitions of these
terms were incorporated into the final version of question-
naire. The third was the lack of response options to certain
questions. The number of options was increased based on
the respondents’ suggestions. The fourth and final issue was
the complexity of response options to questions about newly
created firms’ economic and financial information. The
options were simplified based on the respondents’ com-
ments. The findings obtained from the pilot test therefore
suggested that the research team should make a number of
changes to the original questionnaire in order to simplify the
technical questions and facilitate the response process.
Despite these issues, the pilot test respondents considered
the study to be very positive and showed themselves to be
very receptive, collaborative, and interested in the final
results of it.
The final version of the questionnaire used in the survey
contained 42 questions, against which a scoring value had to
be assigned. It was answered by business owners or directors
with an overall view of the activities of their firms, in tele-
phone interviews using computer-assisted telephone inter-
viewing (CATI), lasting for half an hour each. By gathering
data on the value chain, the aim of the study was to analyze
new sources of productivity in small and medium-sized
travel agencies in Catalonia. A study presentation letter was
written to inform potential respondents about the confidenti-
ality of any data provided and the academic aim of the
research. The business owners and directors voluntarily
4 Journal of Travel Research
answered the questionnaire and did not receive any payment
in cash or kind. While the questionnaire was being imple-
mented, an expert was on hand at all times (on the phone and
by e-mail) to resolve any queries that the respondents had.
The respondent firms were selected by means of probability
sampling applied to the small and medium-sized travel agen-
cies contained in the official database of Spain’s Mercantile
Register. The response rate was 17% (one respondent firm
for every six small and medium-sized travel agencies con-
tacted). The fieldwork was carried out between April and
May 2010. The research was conducted by researchers from
the interdisciplinary research group on ICT, i2TIC (http://
i2TIC.net), and was funded by the Information Society
Observatory Foundation (FOBSIC), belonging to the
Government of Catalonia.
Hypotheses and Model
In order to identify the presence of relationships of comple-
mentarity (co-innovation) in the explanation of the level of
productivity in Catalan small and medium-sized travel agen-
cies, PLS-SEM was used to estimate the model and test the
proposed hypotheses. In recent years, researchers have
become increasingly interested in using PLS-SEM because
of its capacity to model latent constructs under conditions of
non-normality and small-to-medium sample sizes (Díaz-
Casero, Hernandez, and Roldán 2011). For these reasons,
PLS-SEM has now gained acceptance in the management
and economics field (Hair, Ringle, and Sarstedt 2011;
Nunkoo, Ramkissoon, and Gursoy 2013).The use of this
technique involves two stages or approaches. The first
requires the evaluation of the measurement model, allowing
the relationships between the observable variables and the
theoretical concepts to be specified. The second assesses the
structural model and evaluates the consistency of the rela-
tionship proposed with the theory utilized (Henseler, Ringle,
and Sinkovics 2009).
The general analysis model in this study establishes eight
hypotheses to be tested. The dependent variable is labor pro-
ductivity (LABPROD) in Catalan small and medium-sized
travel agencies, approximated by the logarithm of turnover
divided by the number of full-time equivalent workers. The
numerator of this ratio was obtained from direct data on firm
turnover. The denominator was constructed by taking into
account the full-time and part-time jobs in a firm and express-
ing the number of workers as full-time equivalents.
The direct explanatory factors of labor productivity in the
sample of firms are considered to be the logarithm of assets
per full-time equivalent worker (ASSETS), the logarithm of
the percentage of workers using local networks for their job
(LOCNET), and a firm’s capacity to export to international
markets (EXPORTS), that is, the logarithm of sales to the
European Union and to the rest of the world. Thus, three of
the hypotheses identified in international empirical evidence
Table 1. Descriptive Statistics of Small and Medium-Sized Travel Agencies in Catalonia.
Valid Percentage Valid Percentage
Firm’s year of creation: Firm innovation:
- Before 1980 2.5 - Innovation in the last 2 years 74.2
- From 1981 to 1990 15.8 - Product innovation in the last 2 years 67.5
- From 1991 to 2000 25.0 - Process innovation in the last 2 years 45.8
- 2001 or later 56.7 - Organizational innovation in the last 2 years 35.0
- Innovation success 76.0
Firm size: Internet use in value chain:
- 1–9 employees 94.2 - No Internet connection 0.0
- 10–19 employees 4.4 - Internet, without own website 14.2
- 20–50 employees 1.4 - Internet, with own website 51.7
- Internet, with own web and e-commerce
practices
34.2
Business owner: Firm turnover:
- Female 56.7 - 2009/2008 (%) −10.3
- Age: 35–44 years 39.7 - 2010/2009 (%) 5.0
- Age: 45–54 years 30.6 - E-commerce B2B (% purchases) 36.8
- University education 75.0 - E-commerce B2C (% sales) 26.7
- ICT skills, formal training 15.6
Worker training: Destination of sales (customer territory origin):
- Untrained or primary education 1.1 - Catalonia 69.3
- Secondary education 27.5 - Spain 10.2
- University education 71.4 - European Union 12.7
- Extended education paid by the firm 12.6 - Rest of the world 7.8
Source: Own elaboration.
Díaz-Chao et al. 5
would be valid in small and medium-sized travel agencies in
Catalonia: first, the higher the assets per worker, the higher
the productivity (hypothesis 1); second, the higher the local
network use, the higher the productivity (hypothesis 2); and
third, the higher the export intensity, measured as a firm’s
capacity to export goods and services, the higher the produc-
tivity (hypothesis 3). Hypothesis 1 is related to a firm’s
capacity to increase turnover per worker by being bigger and
probably better financed. Hypothesis 2 is related to a firm’s
capacity to increase productivity through ICT use, especially
through local network use. Hypothesis 3 is related to a firm’s
capacity to increase turnover per worker through economies
of learning, scale, reach, and scope, which can be achieved
by growing export intensity.
After establishing the hypotheses related to direct factors
of productivity, the analysis model also established a set of
hypotheses related to indirect factors and their interrelation-
ships. Specifically, an indirect causal relationship was estab-
lished between a firm’s export capacity and local network
use. Hypothesis 4 argues that a firm’s export capacity is
explained by local network use. Similarly, hypothesis 5
argues that a small and medium-sized travel agency’s export
capacity also depends on its capacity to generate market-
leading product innovations (LEADINNOV). The
LEADINNOV latent variable was estimated from two origi-
nal variables. First, the INNOV variable shows a firm’s inno-
vatory dynamics and takes two values: 0, when a firm has not
implemented any innovation in the last two years; and 1,
when a firm has implemented some type of innovation in the
last two years. Second, the LIDNEWPR variable shows a
firm’s leadership capacity in launching new products or ser-
vices on the market. This variable takes two values: 0, when
a firm has not implemented any product innovation that leads
the market; and 1, when a firm has implemented some prod-
uct innovation that leads the market. Thus, this latent vari-
able reflects firm owners’ or directors’ perceptions of a firm’s
capacity to innovate and, more specifically, to generate mar-
ket-leading product or service innovations.
Hypothesis 6 establishes a causal relationship between
innovation leadership and human capital and training in a
firm. Its capacity to generate market-leading product innova-
tions explains its greater stock of human capital. The human
capital and training (HCT) latent variable was estimated
from two variables. First, the human capital (HC) variable is
formed by the workers’ level of completed studies (primary,
secondary, and university education). And second, the train-
ing (TRAIN) variable is formed by the actual percentage of
workers on training programs. Thus, the human capital and
training construct reflects a firm’s educational stock and
training of its workers. Hypothesis 7 argues that local net-
work use by a firm’s workers is explained by its human capi-
tal and training stock. The higher the level of human capital
and training, the more workers use a firm’s local networks.
Finally, hypothesis 8 establishes a reverse causality
between a firm’s co-innovation and assets. A greater
presence of co-innovation in a firm explains fewer assets and
a smaller size. In other words, small and medium-sized travel
agencies with fewer assets tend to encourage more use of co-
innovation, although this leads to less productivity in the
short term. By contrast, larger travel agencies with more
assets tend to make less use of co-innovation and, as a result,
obtain higher levels of productivity. In short, this hypothesis
highlights the fact that more use of co-innovation in small
and medium-sized travel agencies only results in labor pro-
ductivity gains in the medium to long term because of the
costs associated with learning and implementation. The
COINNOVATION latent variable was estimated from two
variables. The work team (WORKTEAM) variable indicates
the presence or absence of work teams in a firm (0 = absent;
1 = present). The Internet use (INTERUSE) indicator indi-
cates the intensity of Internet use in a firm’s value process. It
takes four values: 1 = very low use, where a firm is not con-
nected to the Internet; 2 = low use, where a firm is connected
to the Internet but does not have its own website; 3 = normal
use, where a firm is connected to the Internet and has its own
website; and 4 = advanced use, where a firm is connected to
the Internet and has its own website and e-commerce prac-
tices. The co-innovation construct reflects the establishment
of relationships of complementarity between a variable that
considers new forms of work organization (work teams) and
an ICT variable (Internet use). Figure 1 shows the model and
the hypotheses postulated in this study.
PLS-SEM Estimation Results
The PLS-SEM algorithm was used to estimate the model and
the proposed hypotheses. This technique was chosen over
others for several reasons (Henseler and Chin 2010). First, it
is a highly evolved validated prediction modeling technique
in which data multinormality can be relatively relaxed
(Henseler, Ringle, and Sinkovics 2009). Second, it allows
causal relationships between latent dimensions and measure-
ment variables to be determined. Third, in addition to esti-
mating causal relationships between latent variables,
PLS-SEM allows formative latent variables to be calculated
by setting the weights of each explanatory (and observable)
variable. Estimating models of this type naturally requires
appropriate measurements of goodness of fit and robustness
of analysis to validate the proposed model.
We shall therefore present the validation of the proposed
structural and measurement models before proceeding to
report on the results. Regarding the assessment of the mea-
surement model, several tests were conducted while taking
account of the fact that the latent variables included in the
model were formative constructs. Thus, on the one hand, the
analysis of content validity allowed us to check whether the
indicators had captured the full scope of the model
(Diamantopoulos and Winklhofer 2001). The analysis con-
firmed that the indicators had been appropriately selected
(Straub, Boudreau, and Gefen 2004).
6 Journal of Travel Research
On the other hand, the analysis of construct reliability
allowed us to check the internal consistency of the measure-
ment model. The validity and reliability of each indicator and
construct was assessed and no multicollinearity problems
were found (Table 2). The variance inflation factor was lower
than the threshold value of 3.3 (Diamantopoulos and Siguaw
2006). All of the indicators were significant (p < 0.10), as
detailed below in the section on the results of estimation
using PLS-SEM.
Two further validation measurements were made: con-
struct validity and discriminant validity. For construct valid-
ity, the correlations between the constructs were lower than
0.5. Pairwise, they were therefore sufficiently different. For
discriminant validity, Table 3 shows the cross-loadings
obtained from the correlations between each item and each
latent variable. The coefficient between each indicator and
respective latent was high in all cases.
After analyzing the measurement model validation crite-
ria, we dealt with the structural model validation criteria
(Table 4). There were two criteria: model validity and pre-
dictive power. For model validity, the estimation was per-
formed using 200 bootstrap resamples. Thus, the R2 result
obtained for the labor productivity (LABPROD) variable
was 0.285. While the result was relatively moderate, it did
not undermine the validity of the estimations as a whole,
because the causal effects obtained were in accordance with
expectations. Moreover, all the variables showed the
expected signs, and t statistics were sufficiently high.
Indeed, the model’s predictive power was robust because
the coefficient obtained for the Q2-statistic communality
values were greater than zero for the variables analyzed.
The Q2-statistic cross-validated redundancy values were
also greater than zero, thus supporting the predictive rele-
vance of the whole model. The Q2-statistic was evaluated
Table 2. Latent Variable Correlations.
(1) (2) (3) (4) (5) (6) (7)
COINNNOVATION 1
LOCNET 0.0229 1
EXPORTS 0.0432 0.2039 1
ASSETS −0.2585 0.1455 0.1316 1
LEADINNOV −0.0789 0.2175 0.2493 0.0014 1
HCT 0.0075 0.8385 0.1239 0.0898 0.2156 1
LABPROD 0.0171 0.3263 0.3193 0.4032 0.1896 0.2992 1
Note: (1) COINNOVATION; (2) Local Network (LOCNET); (3) EXPORTS; (4) ASSETS; (5) Leader innovation (LEADINNOV); (6) Human capital and
training (HCT); (7) Labor productivity (LABPROD).
Source: Own elaboration.
Labor Productivity
Local network
Assets
Leader
innovation
Human Capital
& Training
Co-innovation
H1(+)
H2(+)
H8(-)
H3(+)
H4 (+)
H5(+)
H7(+)
H6(+)
Exports
Figure 1. Modeling the interaction between sources of labor productivity in small and medium-sized travel agencies.
Source: Own elaboration.
Díaz-Chao et al. 7
by applying the blindfolding procedure with an omission
distance of 7. The proposed threshold value was Q2 > 0; a
higher Q2 value would mean a higher predictive relevance
of the model.
After analyzing the validation of the estimated model,
we addressed the factors explaining labor productivity in
Catalan small and medium-sized travel agencies. First, we
analyzed the variables that made up the formative con-
structs and, second, the relationships between the con-
structs themselves. With respect to the constructs (Table 5),
four of them were formed by a single variable and were
therefore assigned an estimated weight of 1. The results of
latent construct estimation consisting of two variables are
also shown. All coefficients except for the TRAIN variable
are significant (p < 0.10).
The results of latent variable estimation (path analysis)
explaining labor productivity in Catalan small and
medium-sized travel agencies are presented in Table 6 and
Figure 2. First, it is important to point out that all the coef-
ficients obtained were significant at a maximum 90% con-
fidence level (p < 0.10), and that their values were
consistent with the hypotheses postulated. Second, it
should be noted that the main direct determinants of labor
productivity are a firm’s assets (hypothesis 1: β = 0.340),
the workers’ local network use (hypothesis 2: β = 0.231),
and a firm’s capacity to export to international markets
(hypothesis 3: β = 0.228). In this respect, the results sug-
gest that labor productivity in small and medium-sized
travel agencies in Catalonia is directly explained by their
capacity to exploit their assets and to make international
transactions. Exploiting assets and attracting customers
from international markets are complemented by a par-
ticular use of ICT: the worker’s local network use in a
firm.
After establishing that export intensity and local net-
works had direct effects on labor productivity in small and
medium-sized travel agencies, a set of indirect effects was
validated. First, it should be noted that a small and
medium-sized travel agency’s export capacity was
explained by the workers’ local network use (hypothesis
4: β = 0.157) and by a firm’s capacity to generate market-
leading product innovations (hypothesis 5: β = 0.215).
Second, the workers’ local network use had a dual basis. A
firm’s capacity to generate market-leading product inno-
vations explains its stock of human capital and training
(hypothesis 6: β = 0.216), which in turn determine local
network use (hypothesis 7: β = 0.839). In summary, the
analysis of the indirect effects of labor productivity in
Table 3. Cross-loadings.
(1) (2) (3) (4) (5) (6) (7)
INNOV −0.082 0.178 0.108 −0.016 0.728 0.243 0.002
ASSETS −0.259 0.146 0.132 1.000 0.001 0.090 0.403
EXPORTS 0.043 0.204 1.000 0.132 0.249 0.124 0.319
LABPROD 0.017 0.326 0.319 0.403 0.190 0.299 1.000
LIDNEWPR −0.042 0.157 0.264 0.016 0.801 0.098 0.271
WORKTEAM 0.613 −0.074 −0.104 −0.158 −0.060 0.023 0.033
INTERUSE 0.681 0.098 0.150 −0.176 −0.043 −0.012 −0.009
LOCNET 0.023 1.000 0.204 0.146 0.218 0.839 0.326
HC −0.002 0.837 0.126 0.112 0.207 0.996 0.304
TRAIN 0.101 0.152 −0.003 −0.219 0.127 0.206 −0.006
Note: (1) COINNOVATION; (2) Local Network (LOCNET); (3) EXPORTS; (4) ASSETS; (5) Leader innovation (LEADINNOV); (6) Human capital and
training (HCT); (7) Labor productivity (LABPROD).
Source: Own elaboration.
Table 4. Quality Model Measurements Overview.
R-Squared Communality Redundancy
COINNOVATION 0.000 0.420 0.000
Local Network (LOCNET) 0.703 1.000 0.703
EXPORTS 0.086 1.000 0.039
ASSETS 0.067 1.000 0.067
Leader innovation (LEADINNOV) 0.000 0.586 0.000
Human capital and training (HCT) 0.046 0.517 0.026
Labor productivity (LABPROD) 0.285 1.000 0.097
Source: Own elaboration.
8 Journal of Travel Research
small and medium-sized travel agencies suggests a circu-
lar causality, which is determined by the influence of the
workers’ local network use on a firm’s export capacity. In
this circular relationship, a small and medium-sized travel
agency’s capacity to generate market-leading product
innovations and its stock of human capital and training
play a decisive role.
Finally, the analysis of indirect effects confirmed the
inverse causal relationship between a firm’s co-innovation
and assets (hypothesis 8: β = −0.259). This negative relation-
ship confirms that in small and medium-sized travel agen-
cies, co-innovation, as represented by Internet use and work
teams, adversely affects asset exploitation. In this respect,
the positive impact on labor productivity would only be
reflected in the medium to long term, once the costs associ-
ated with learning and implementing co-innovation practices
had been amortized.
As a result, the total effects on labor productivity in small
and medium-sized travel agencies in Catalonia (Table 7) is
explained by assets (β = 0.340), the workers’ local network
use (β = 0.266), a firm’s capacity to export to international
markets (β = 0.228), the stock of human capital and training
(β = 0.223), a firm’s capacity to generate market-leading
product innovations (β = 0.097) and co-innovation practices
(β = −0.088). These results suggest that labor productivity in
Catalan small and medium-sized travel agencies are due to a
wide range of determinants that include elements of perfor-
mance, such as their capacity to exploit their assets and to
export, and elements that improve a firm’s internal value
process, such as local network use, the capacity to generate
market-leading product innovations or the stock of human
capital and training. Only co-innovation, secured by Internet
use and the presence of work teams, exerts an overall nega-
tive effect on labor productivity. This negative effect could
Table 5. Outer Weights (Mean, Standard Deviation, and t Values).
Original Sample (O) Sample Mean (M)
Standard Deviation
(STDEV)
Standard Error
(STERR)
t Statistics
(|O/STERR|)
Leader innovation (LEADINNOV)
INNOV 0.608 0.568 0.348 0.348 1.744
LIDNEWPR 0.696 0.608 0.317 0.317 2.198
Assets
ASSETS 1.000 1.000 0.000 0.000 0.000
Exports
EXPORTS 1.000 1.000 0.000 0.000 0.000
Labor productivity
LABPROD 1.000 1.000 0.000 0.000 0.000
COINNOVATION
WORKTEAM 0.742 0.635 0.316 0.316 2.348
INTERUSE 0.801 0.764 0.248 0.248 3.229
Local network
LOCNET 1.000 1.000 0.000 0.000 0.000
Human capital and training
HC 0.985 0.983 0.018 0.018 53.686
TRAIN 0.092 0.094 0.071 0.071 1.290
Source: Own elaboration.
Table 6. Path Coefficients (Mean, Standard Deviation, and t Values).
Original Sample (O) Sample Mean (M)
Standard Deviation
(STDEV)
Standard Error
(STERR)
t-Statistics
(|O/STERR|)
COINNOVATION ASSETS −0.259 −0.279 0.085 0.085 3.053
LOCNET EXPORTS 0.157 0.159 0.091 0.091 1.730
LOCNET LABPROD 0.231 0.224 0.116 0.116 1.995
EXPORTS LABPROD 0.228 0.221 0.102 0.102 2.240
ASSETS LABPROD 0.340 0.321 0.072 0.072 4.724
LEADINNOV EXPORTS 0.215 0.218 0.122 0.122 1.766
LEADINNOV HCT 0.216 0.250 0.131 0.131 1.652
HCT LOCNET 0.839 0.834 0.064 0.064 13.078
Source: Own elaboration.
Díaz-Chao et al. 9
be related to the difficulties that co-innovation practices have
in terms of securing productivity improvements in the short
term.
Conclusion, Discussion, and Policy
Implications
Using 2010 survey data for a sample of 120 small and
medium-sized travel agencies based in Catalonia (a region in
the northeast of Spain), this article analyzed the new sources
of firm labor productivity based on the establishment of rela-
tionships of complementarity (co-innovation) between ICT
investment and use, new forms of work organization and
labor relations, and human capital and training.
The study’s focus on ICT and innovation had a dual basis.
On the one hand, and as noted in the extensive literature, a
firm’s ICT use can become a lever of change for introducing
new value processes and new sources of productivity. In this
Labor Productivity
Local network
Assets
Leader
innovation
Human Capital
& Training
Co-innovation
0.340
0.231
-0.259
(+)
0.228
0.157
0.215
0.839
0.216
Exports
Figure 2. Sources of labor productivity in small and medium-sized travel agencies (path analysis).
Source: Own elaboration.
Table 7. Total Effects (Mean, Standard Deviation, and t Values).
Original Sample (O) Sample Mean (M)
Standard Deviation
(STDEV)
Standard Error
(STERR)
t Statistics
(|O/STERR|)
Total effects on labor productivity
ASSETS 0.340 0.321 0.072 0.072 4.724
LOCNET 0.266 0.262 0.096 0.096 2.769
EXPORTS 0.228 0.221 0.102 0.102 2.240
HCT 0.223 0.219 0.082 0.082 2.721
LEADINNOV 0.097 0.107 0.040 0.040 2.458
COINNOVATION −0.088 −0.089 0.033 0.033 2.669
Other effects
COINNOVATION ASSETS −0.259 −0.279 0.085 0.085 3.053
LOCNET EXPORTS 0.157 0.159 0.091 0.091 1.730
LEADINNOV LOCNET 0.181 0.210 0.111 0.111 1.632
LEADINNOV EXPORTS 0.244 0.251 0.111 0.111 2.204
LEADINNOV HCT 0.216 0.250 0.131 0.131 1.652
HCT LOCNET 0.839 0.834 0.064 0.064 13.078
HCT EXPORTS 0.132 0.132 0.075 0.075 1.766
Source: Own elaboration.
10 Journal of Travel Research
respect, the purpose of the study was to test whether, in the
field of small and medium-sized travel agencies, ICT use
(measured by local network use) and the generation of co-
innovation (such as the creation of new products and ser-
vices, improving human capital, or relationships of
complementarity between Internet use and work teams)
explain firm productivity. On the other hand, and as high-
lighted by the latest research, relationships between ICT,
innovation, and productivity in SMEs may also be generated
indirectly, that is, through the impact on a firm’s other pro-
ductivity-related results, such as its export capacity or its
assets.
In this respect, and based on the idea that ICT and co-
innovation are a lever, the aim of this study was to design and
test a model explaining the direct and indirect sources
of productivity in small and medium-sized travel agen-
cies. For that purpose, a PLS-SEM model was developed and
tested. PLS-SEM allows for the analysis of relationships not
only between the various factors considered explanatory of
productivity, but also between such factors. Thus, in the case
in hand, the analysis completed the structural form explain-
ing the productivity of small and medium-sized travel agen-
cies (94.2% had fewer than 10 workers), producing mostly
for internal markets (79.5% of turnover in the Catalan and
Spanish markets).
The results obtained confirmed the suitability of the pro-
posed model. In explaining the productivity of small and
medium-sized travel agencies, ICT and innovation simulta-
neously exerted direct and indirect effects. Regarding the
path analysis of direct effects, the results suggested that labor
productivity in small and medium-sized travel agencies in
Catalonia was directly explained by their capacity to exploit
their assets and to make international transactions. Exploiting
assets and attracting customers from international markets
were complemented by a particular use of ICT: the worker’s
local network use in a firm. The path analysis of indirect
effects on labor productivity suggested a circular causality,
which was determined by the influence of the workers’ local
network use on a firm’s export capacity. In this circular rela-
tionship, a small and medium-sized travel agency’s capacity
to generate market-leading product innovations and its stock
of human capital and training played a decisive role.
Regarding the analysis of total effects, the results suggested
that labor productivity in Catalan small and medium-sized
travel agencies was due to a wide range of determinants that
included elements of performance, such as their capacity to
exploit their assets and to export, and elements that improved
a firm’s internal value process, such as local network use, the
capacity to generate market-leading product innovations, or
the stock of human capital and training. Only co-innovation,
captured by Internet use and the presence of work teams,
exerted an overall negative effect on labor productivity. This
negative effect could be related to the difficulties that co-
innovation practices have in terms of securing productivity
improvements in the short term. In summary, these results
add to the available evidence, going beyond the traditional
analysis of the relationships between ICT, innovation, and
productivity and providing a more comprehensive and realis-
tic view of the productivity path of small and medium-sized
travel agencies.
Furthermore, the results obtained suggest that new direc-
tions in public policy are required to improve productivity in
small and medium-sized travel agencies. First, it is important
to emphasize the need to coordinate efforts in the joint pro-
motion of ICT use, organizational change, and training among
employers and employees. For example, partial public poli-
cies to promote ICT use, without considering other determi-
nants of co-innovative productivity, may not produce the
desired effects. Second, it is important to point out the link
between productivity and internationalization. In SMEs pro-
ducing mostly for internal markets, promoting the interna-
tionalization of their products and services is something that
public policy should address because international competi-
tion introduces efficiency enhancement mechanisms. And
third, the study results also suggest the need to consider the
costs associated with learning and implementing co-innova-
tion practices. To overcome the negative effects on asset
exploitation and labor productivity that co-innovation prac-
tices have in the short term, public policy should promote sus-
tainable policies to promote change in firms over time.
The study presented in this article has several limitations.
Besides the variables and restrictions imposed on the analy-
sis, perhaps the most significant is the unavailability of a
time series. However, the availability of survey data for a
sample of small and medium-sized travel agencies has pro-
vided an excellent opportunity to analyze the determinants of
their growth potential. In this respect, and bearing in mind
the economic importance of small and medium-sized travel
agencies to the regional economy, the availability of data for
(1) other territories or business groups, and their possible
comparisons; (2) a time series; (3) better indicators; and (4)
new criteria for grouping firms would suggest that new
approaches could be taken. Such major lines of improvement
give this study a preliminary character and suggest that fur-
ther research needs to be conducted on this issue.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
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Author’s Biographies
Angel Díaz-Chao is a professor of Statistics and Econometrics at
Rey Juan Carlos University (Madrid, Spain) and a researcher at the
Internet Interdisciplinary Institute in Open University of Catalonia
(Barcelona, Spain). His research interests are mainly within the
fields of information and communication technologies, productiv-
ity and competitiveness, knowledge work and the network firm, and
structural equation modeling, subjects on which he has published 5
books and 10 articles in indexed journals.
Oriol Miralbell-Izard is a professor of Tourism at Open University
of Catalonia (Barcelona, Spain). Her research interests are mainly
within the fields of information and communication technologies
and tourism, destination management, and social networks and
knowledge. He was CIO of the General Direction for Tourism of
the Government of Catalonia, is the president of the Spanish
Chapter of the International Federation for IT and Travel & Tourism
(IFITT), and a member of GRATET, a research group on Tourism
and Geography at Rovira i Virgili University.
Joan Torrent-Sellens is a professor of Economics and Business at
Open University of Catalonia and a director at the ICTs interdisciplin-
ary research group (i2TIC) in the Internet Interdisciplinary Institute
(IN3). His research interests are mainly within the fields of informa-
tion and communication technologies, productivity and growth, the
knowledge economy, knowledge work and the network firm, and
eHealth, subjects on which he has published 20 books and 30 articles
in indexed journals. He was the director of the Open University of
Catalonia Business School and the Economics and Business Studies.
