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1. Worldwide Hospitality and Tourism Themes
Emerald Article: e-Learning as a tool to improve quality and productivity
in hotels
Manuela Sarmento
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To cite this document: Manuela Sarmento, (2010),"e-Learning as a tool to improve quality and productivity in hotels", Worldwide
Hospitality and Tourism Themes, Vol. 2 Iss: 4 pp. 398 - 409
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WHATT
2,4 e-Learning as a tool to improve
quality and productivity in hotels
Manuela Sarmento
398 Research Centre on Tourism, Innovation and Service,
´
University Lusıada, Lisbon, Portugal
Abstract
Purpose – The purpose of this paper is to analyze the contribution of e-learning in the improvement
of quality and productivity in hotels.
Design/methodology/approach – The methodology is based on an inquiry answered by 34 hotels
that are using e-learning. For this purpose, a survey on five, four and three star hotels, located
throughout Portugal, was conducted between January and March 2009.
Findings – The research reveals that hotels consider that e-learning increases productivity and
production volume. On the other hand, e-learning contributes significantly to employees’ motivation.
The paper also concludes that managers’ opinions about e-learning strategies are dependent on the
hotel category and head-office nationality.
Originality/value – e-Learning is based on information and communication technology and
supports the educational process. Owing to the important results achieved, e-learning is continuously
gaining relevance in hotels, and in educational institutions. As such, analysing the contribution of
e-learning for quality improvement in hotels brings originality to the research whilst adding value to
the body of knowledge in the industry.
Keywords E-learning, Hotel and catering industry, Portugal, Quality
Paper type Research paper
Introduction
In a knowledge society, people must be able to update their knowledge in order to cope
with the rate of change. e-Learning is a powerful tool that can help to facilitate the
objectives of training and education (Abbey, 2000; Hartley, 2001). It is an application of
computer science based on information technology and the internet that allows the
individual to control aspects of the content, the learning process, and the application of
learning (Lee et al., 2000; Machado, 2000). These factors in conjunction with the relatively
low delivery costs can help to optimize investment in training.
The objectives of e-learning are in essence to achieve:
.
productivity enhancement;
.
quality improvement; and
.
cost reduction (Horton, 2000; Rosenberg, 2000).
The scope of this research paper is to analyse the contribution of e-learning to the
improvement of quality and productivity in hotels.
Worldwide Hospitality and Tourism
Themes e-Learning concept
Vol. 2 No. 4, 2010
pp. 398-409 The idea of using computers as a learning tool is not new, and e-learning is one of
q Emerald Group Publishing Limited several concepts (others include: flexible learning, distance learning, telelearning and
1755-4217
DOI 10.1108/17554211011074056 computer supported learning) that link similar learning methodologies (Conole, 2004).

3. As an established and widely adopted approach to distributed learning, the wider e-Learning
socio-cultural factors are still being explored (Martin and Webb, 2001). to improve
There are numerous definitions for e-learning: Falch (2004) suggests that e-learning
represents “the use of new multimedia technologies and the internet to improve the quality quality
of learning by facilitating access to resources and services as well as remote exchanges
and collaboration”. At its simplest level, e-learning is little more than the use of electronic
tools and technologies to assist us in our teaching and learning. The term has evolved in 399
recent years, along with e-commerce and e-business, linked to the extremely rapid growth
and uptake of internet useage. The creation of virtual communities may in time replace or
provide alternatives to the traditional bricks-and-mortar classroom (Martin and Webb,
2001). The wider concept of e-learning also includes innovation elements in relation to
other kind of technologies used in education, and it presents a value added contribution to
learning. (Nagi, 2006).
The analytical dimensions of e-learning
Advances in information technology and new developments in methodologies provide
opportunities to create e-learning environments that are:
.
well designed;
. learner centered;
.
interactive;
.
affordable;
.
efficient; and
.
accessible.
On the other hand, an e-learning project requires the participation of a multidisciplinary
team, due to his multiple dimensions. But what are the analytical dimensions of this
phenomenon? The answers are not always easy to identify, due to the relative immaturity
of e-learning.
Khan (2001) suggests that there are eight dimensions to an e-learning framework:
(1) institutional;
(2) pedagogical;
(3) technological;
(4) interface design;
(5) evaluation;
(6) management;
(7) resource support; and
(8) ethical.
In Khan’s e-learning framework, each one of the eight dimensions has a special role and
depends on the others. The final result is the addition of these eight dimension.
Zualkernan (2006) offers a constructivist view of e-learning, based on a framework
that has five dimensions and each dimension have two variables. The final result is the
addition of these five dimensions:

4. WHATT (1) Learner characteristics. Cognitive and constraints learning styles plus goals and
2,4 motivations.
(2) Physical environment. Available information plus successful action.
(3) Structural characteristics. Available information plus cognitive and constraints
learning styles.
400 (4) Semantic characteristics. Successful action plus goals and motivation.
(5) Task environment. Adaptation between physical environment and learner.
The research
The research presented in this paper sought to analyse and evaluate the relationship
between e-learning versus quality and productivity in hotels. The methodology was
based on an inquiry with responses from 34 hotels that were using e-learning. For this
purpose, we surveyed hotels with five, four and three stars, located throughout
Portugal, between January and March 2009 (Tourism of Portugal, 2008).
The following sections outline the data analysis methods, the survey implementation,
the sample characterization, and the cause-effect relationship between the ten strategic
factors and the hotel categories. The research is also intended to identify groups of hotels
that are sharing comparable strategies for e-learning, quality management and
productivity.
Methodology and analysis
The project was based on a survey with 40 questions. We used the Likert scale with
five levels, 1 ¼ nothing, 2 ¼ little, 3 ¼ moderate, 4 ¼ much and 5 ¼ strong, in order to
measure the strategies pursued by hotels, concerning e-learning. The market research
commences with survey validation done by a panel with ten hotel managers.
To analyze the survey, we built the database and we used the statistical software
package SPSS 16.0. The statistical methods applied were as follows:
.
Descriptive analysis: to gauge the frequency and percentage of the hotels
characterization, as well as the mean value, standard deviation, maximum and
minimum values of the ten strategic factors under investigation.
.
Bivariate analysis, namely the x 2-test: to evaluate whether the survey responses
given by hotel managers about the ten strategic factors are dependent or
independent of hotel category, location and head-office nationality.
.
Cluster analysis: to determine homogenous groups, whereby each element of a
group is more similar to the other elements of this group than to the elements of
any other group.
.
One-way analysis of variance: to check whether there are significant differences
within the groups identified via cluster analysis and to characterise each group.
Survey implementation and identification of the sample
A total of 250 survey questionnaires were sent by post to hotels that were using
e-learning in Portugal. We received 41 survey replies, but only 34 were valid, since seven
were rejected due to several missing values.
The hotel’s characterization – category, location in Portugal and head-office
nationality – is shown in Figures 1-3.

5. Hotel category e-Learning
Five stars, eight;
24%
to improve
Three stars, 14; quality
41%
401
Figure 1.
Four stars, 12; Hotel category
35%
Hotel location
Madeira, seven; Azores, 0; 0%
21% North, five; 15%
Centre, four;
12%
Algarve, eight; Lisbon and
23% Figure 2.
Alentejo, two; Tagus Valley,
Hotel location
six% eight; 23%
Head office nationality
Other, 15;
44% Figure 3.
Portuguese, Hotel head-
19; 56% office nationality
The sample has 41 per cent of three star hotels and the minimum percentage is 24 per cent
of five star hotels.
The main regions that answered the survey were Algarve and the Lisbon region
with 23 per cent of the total answers, representing 46 per cent of the sample and
Madeira Islands represents 21 per cent.
Head-office nationality is mainly Portuguese (56 per cent). Other nationalities
represent 44 per cent of the sample. The mean value, standard deviation, maximum
and minimum of the ten strategic factors under investigation are presented in Table I.
These factors were obtained through the application of principal components analysis.

6. WHATT The highest mean value (xm) was obtained in Factor 10 “e-learning decreases training
2,4 costs” (xm ¼ 4.29) and the lowest mean value in Factor 9 “e-learning decreases the
absenteeism” (xm ¼ 2.53). The answer to the Factor 1 “e-learning increases productivity”
is the most consensual (s ¼ 0.52) and to the Factor 8 “e-learning decreases employees
turnover” is less consensual (s ¼ 1.11).
Figure 4 shows the mean values of the factors in decreasing order.
402 The global mean value for the ten factors is xm ¼ 3.59, and there are six factors,
which have mean values superior to it. However, if we consider the scale mean value,
there are seven factors, which mean values are above 3. Thus, the importance of
e-learning in hotels is indubitable.
The percentage for each scale level and per strategic factor is shown in Table II.
The highest percentage value was 47.1 per cent obtained in level 4 of Factor 3
“E-learning increases the performance quality level”. The lowest percentage of 0 per cent
occurred in Factor 4 “e-learning increases the performance quality level” in level 1.
Relationship between hotels identification and survey responses
To determine whether a company strategy is dependent or independent of the
identification variables (category, location and head-office nationality), the x 2-test
was used.
Factors Mean value SD Minimum Maximum
1 e-Learning increases productivity 4.15 0.52 1 5
2 e-Learning increases production volume 4.09 0.72 1 5
3 e-Learning increases the performance quality level 4.06 0.72 2 5
4 e-Learning increases the employees motivation 3.71 0.86 1 5
5 e-Learning increases the employees satisfaction 3.15 0.86 1 5
Table I. 6 e-Learning increases the employees salaries 2.85 0.86 1 5
Mean, standard 7 e-Learning decreases the time for the task execution 4.15 0.85 1 5
deviation, minimum 8 e-Learning decreases employees turnover 2.94 1.11 1 5
and maximum values 9 e-Learning decreases the absenteeism 2.53 0.91 1 5
of the factors 10 e-Learning decreases training costs 4.29 0.86 1 5
Mean values of e-learning factors
10 e-learning decreases training costs 4.29
7 e-learning decreases the time for the task execution 4.15
1 e-learning increases productivity 4.15
2 e-learning increases production volume 4.09
3 e-learning increases the performance quality level 4.06
4 e-learning increases the employees’ motivation 3.71
MV Mean value 3.59
5 e-learning increases the employees’ satisfaction 3.15
8 E-learning decreases employees’ turnover 2.94
Figure 4. 6 e-learning increases the employees’ salaries 2.85
Mean values of 9 e-learning decreases the absenteeism 2.53
e-learning factors
1 2 3 4 5

7. e-Learning
Nothing 1 Little 2 Moderate 3 Much 4 Strong 5
Factors (%) (%) (%) (%) (%) to improve
1 e-Learning increases productivity 0.0 5.9 17.6 32.4 44.1
quality
2 e-Learning increases production
volume 0.0 2.9 23.5 35.3 38.2
3 e-Learning increases the performance 403
quality level 2.9 5.9 11.8 41.2 38.2
4 e-Learning increases the employees
motivation 0.0 14.7 20.6 44.1 20.6
5 e-Learning increases the employees
satisfaction 8.8 17.6 35.3 26.5 11.8
6 e-Learning increases the employees
salaries 14.7 20.6 38.2 17.6 8.8
7 e-Learning decreases the time for the
task execution 0.0 5.9 14.7 38.2 41.2
8 e-Learning decreases employees
turnover 14.7 26.5 26.5 14.7 17.6 Table II.
9 e-Learning decreases the absenteeism 14.7 38.2 32.4 8.8 5.9 Frequency percentage
10 e-Learning decreases training costs 0.0 5.9 14.7 23.5 55.9 per strategic factor
The x 2-test compares the observed and expected frequencies of two variables
of the sample. The H0 checks whether it is possible to accept the hypothesis of
independence between these variables within the population. The H0 is tested against
the alternative Ha. To test the independence of variables, the T statistic equation (1)
is used:
X ðFoi 2 Fei Þ2
n
T¼ ð1Þ
i¼1
Fei
Where Fei the expected frequency is verified for category (1) of each variable; Foi is the
observed frequency for category (1) of each variable; (Foi 2 Fei) is the difference
between the observed and the expected frequency for the crosstab (1).
The comparison between Pearson and a significances, allows accepting or rejecting
the null hypothesis. If Pearson significance is less than 5 per cent there are no reasons
to accept the H0.
Table III shows the results and conclusions of the x 2-test applied to the factors
and hotels category, location and head-office nationality.
Managers’ opinions are independent from the hotel category, location and head-office
nationality in four factors, namely: in Factor 4 “e-learning increases the employees
motivation”, in Factor 7 “e-learning decreases the time for the task execution”, in Factor 9
“e-learning decreases the absenteeism“ and in Factor 10 “e-learning decreases
training costs“.
However, the opinion given to Factor 8 “e-learning decreases employees turnover”
is dependent on hotel category, location and head-office nationality.
Table IV reveals that the answers to questions are independent on hotels category
(60 per cent), location (90 per cent) and head-office nationality (50 per cent).

8. 2,4
404
Table III.
identification
WHATT
factors and hotels
Relationship between
Category Location Head office
Pearson Pearson Pearson
Factors significance Conclusion significance Conclusion significance Conclusion
1 e-Learning increases productivity 0.145 Independent 0.245 Independent 0.001 Dependent
2 e-Learning increases production volume 0.221 Independent 0.362 Independent 0.000 Dependent
3 e-Learning increases the performance quality level 0.000 Dependent 0.131 Independent 0.000 Dependent
4 e-Learning increases the employees’ motivation 0.088 Independent 0.301 Independent 0.099 Independent
5 e-Learning increases the employees’ satisfaction 0.002 Dependent 0.143 Independent 0.602 Independent
6 e-Learning increases the employees’ salaries 0.000 Dependent 0.721 Independent 0.000 Dependent
7 e-Learning decreases the time for the task execution 0.074 Independent 0.123 Independent 0.084 Independent
8 e-Learning decreases employees’ turnover 0.000 Dependent 0.274 Dependent 0.327 Dependent
9 e-Learning decreases the absenteeism 0.287 Independent 0.089 Independent 0.431 Independent
10 e-Learning decreases training costs 0.274 Independent 0.384 Independent 0.211 Independent

9. Determination of groups e-Learning
Cluster analysis was used in order to identify groups of hotels sharing the same to improve
opinions about e-learning. On this basis, hotels within any one group are implementing
similar strategies, distinct from those used by hotels belonging to other groups. quality
The cluster analysis used, attempts to identify groups of hotels based on ten
strategic factors, using a specific algorithm. The division into four groups is the
appropriate solution, using the Ward method and squared Euclidean distance. This 405
solution can be validated using one-way analysis of variance and confirmed through
the discriminant analysis.
This analysis demonstrates that 100 per cent of the assembled hotels are correctly
classified in the four groups. Each strategic group is denominated according to the
relevant strategic factor and has the following number of hotels:
.
Group 1: “e-Learning increases the employees’ motivation” – seven hotels.
.
Group 2: “e-Learning decreases the time for the task execution” – 15 hotels.
.
Group 3: “e-Learning increases productivity” – eight hotels.
.
Group 4: “e-Learning decreases training costs” – four hotels.
The one-way analysis of variance tests the hypothesis of equal means amongst the
groups. If the mean values of the groups are equal, then the groups are not different in
respect to the ten strategic factors. All the preconditions required and steps concerning
this analysis, including the Levene and F-test were accomplished, whereby we can
conclude that there are four different groups. The mean values of the factors for each
group are displayed in Table V:
.
Factors 1: “e-learning increases productivity” and 2 “e-learning increases
production volume” have the maximum mean value at group 3 and the minimum
at group 1.
.
Factors 3: “e-learning increases the performance quality level” and 4 “e-learning
increases the employees motivation” have the maximum mean value at group 1
and the minimum at group 2.
. Factors 5: “e-learning increases the employees’ satisfaction” and 6 “e-learning
increases the employees salaries” have the maximum mean value at group 4 and
the minimum at groups 2 and 1, respectively.
.
Factor 7: “e-learning decreases the time for the task execution“ has the maximum
mean value at group 2 and the minimum at group 4.
. Factor 8: “e-learning decreases employees turnover” has the maximum mean
value at group 1 and the minimum at group 3.
.
Factor 9: “e-learning decreases the absenteeism” has the maximum mean value at
group 2 and the minimum at group 4.
Hotels identification Dependent factor (%) Independent factor (%)
Table IV.
Category 40 60 Percentage
Location 10 90 of dependent/
Head-office nationality 50 50 independent responses

10. WHATT Group 1 Group 2 Group 3 Group 4 Total
2,4 seven 15 eight four 34
hotels hotels hotels hotels hotels
Factors 20% 44% 24% 12% 100%
1 e-Learning increases productivity 3.30 4.40 4.80 4.10 4.15
2 e-Learning increases production volume 3.50 3.60 4.74 4.50 4.09
406 3 e-Learning increases the performance
quality level 4.22 3.84 4.18 4.00 4.06
4 e-Learning increases the employees’
motivation 4.34 3.00 3.70 3.80 3.71
5 e-Learning increases the employees’
satisfaction 2.50 2.20 3.40 4.50 3.15
6 e-Learning increases the employees’
salaries 1.96 3.63 1.60 4.22 2.85
7 e-Learning decreases the time for the
task execution 4.14 4.50 4.20 3.74 4.15
8 e-Learning decreases employees’
turnover 3.33 3.10 2.16 3.16 2.94
Table V. 9 e-Learning decreases the absenteeism 2.60 2.95 2.46 2.12 2.53
Mean values of factors 10 e-Learning decreases training costs 4.47 3.70 3.99 5.00 4.29
for each group Mean value 3.44 3.49 3.52 3.91 3.59
.
Factor 10: “e-learning decreases training costs” has the maximum mean value at
group 4 and the minimum at group 2.
The maximum mean value of all groups is presented by group 5 (xm ¼ 5.00) and the
minimum by group 2 (xm ¼ 3.70).
Table VI shows how each group is compound in percentage as far as the hotel
category, location and head-office nationality are concerned.
Characterization of the strategic groups
As shown in the previous section, the hotels in the sample can be aggregated into four
strategic groups. Each group has distinct approach in relation to e-learning versus
´
quality and productivity. The groups’ characterization was based on Scheffe test,
F-test and mean values of the ten factors.
Group 1: “e-learning increases the employees’ motivation”
This group of hotels represents 20 per cent of the sample. It includes hotels pertaining
to three and four stars, whereby 29 per cent of the hotels are located in the centre of
Portugal and in Algarve. About 71 per cent of the hotels are Portuguese.
e-Learning conclusions. This group has a mean value of xm ¼ 3.44, denoting that
hotels moderately consider that e-learning is a critical factor for the increasing of
productivity regarding the ten strategic factors.
The hotels pertaining to this group consider that e-learning strongly decreases
training costs (xm ¼ 4.47), increases very much the employees motivation (xm ¼ 4.34)
and also increases the performance quality level (xm ¼ 4.22). These hotels assume that
e-learning increases the employees salaries in a low level (xm ¼ 1.96).

11. Group 1 Group 2 Group 3 Group 4 Total 34
e-Learning
seven hotels 15 hotels eight hotels four hotels hotels to improve
20% 44% 24% 12% 100% quality
Hotels characteristics xm ¼ 3.44 xm ¼ 3.49 xm ¼ 3.52 xm ¼ 3.91 xm ¼ 3.59
Category
Five stars (%) 7 38 100 24
Four stars (%) 14 40 63 35
407
Three stars (%) 86 53 41
Location
North of Portugal (%) 14 13 25 15
Centre of Portugal (%) 29 13 12
Lisbon and Tagus Valley (%) 14 20 25 50 23
Alentejo (%) 13 6
Algarve (%) 29 20 25 25 23
Madeira Islands (%) 14 20 25 25 21
Azores Islands (%)
Head office
Portuguese (%) 71 47 63 50 54 Table VI.
Nationality Characteristics
Other country (%) 29 53 38 50 46 of hotels per group
Group 2: “e-learning decreases the time for the task execution”
This group of hotels is the largest of the sample representing 44 per cent. Among the
four groups, this has the highest percentage of three star hotels 53 per cent, which are
located all over the country, being 53 per cent foreign hotels.
e-Learning conclusions. This group has a mean value of xm ¼ 3.49, expressing a
moderate concern about the ten strategic factors. The hotels belonging to this group
strongly consider that e-learning decreases the time for the task execution (xm ¼ 4.50)
and increases productivity (xm ¼ 4.40). However, e-learning increases the employees
satisfaction in a low level (xm ¼ 2.20).
Group 3: “e-learning increases productivity”
This group of hotels represents 24 per cent of the total sample. Of all groups, this has the
highest percentage of four star hotels 63 per cent, located in north of Portugal (25 per cent),
Lisbon (25 per cent), Algarve (25 per cent) and Madeira (25 per cent).The hotels are mainly
Portuguese 63 per cent.
e-Learning conclusions. Group 3 has a mean value of xm ¼ 3.52 which means
that hotels demonstrate a moderate opinion about the ten factors under investigation.
The hotels pertaining to this group highly consider that e-learning increases productivity
(xm ¼ 4.80) and production volume (xm ¼ 4.74). However, these hotels hardly believe that
e-learning increases the employees salaries (xm ¼ 1.60).
Group 4: “e-learning decreases training costs”
This group of hotels is the smallest representing 12 per cent of the sample. About 100 per
cent are five star hotels, mainly located in Lisbon region. The hotels are half Portuguese
and half foreign.
e-Learning conclusions. This group has the highest overall mean value of xm ¼ 3.91
expressing a profile with a profound interest in the ten strategic factors. They strongly

12. WHATT think that e-learning decreases training costs (xm ¼ 5.0), and increases employees
2,4 satisfaction (xm ¼ 4.50). The minimum mean value (xm ¼ 2.12), is expressed in
e-learning decreases the absenteeism.
In conclusion, the data analysis reveals that most hotels consider that investing in
e-learning training, will increase productivity and performance quality levels. In general,
terms, hoteliers belief that if they can implement e-learning effectively, it will improve
408 quality and productivity levels and consequently enhance profitability and customer
satisfaction (employees and clients).
Summary and conclusions
The main purpose of the research presented in this paper was to analyse the relationship
between e-learning, quality and productivity. The hotels selected for the sample were
those who used e-learning as a method of education and training for their employees.
Strategic profiles of three, four and five star hotels were studied on the basis of ten
strategic factors and by considering their location and head-office nationality.
This research was based on a survey carried out between January and March 2009.
In 250 surveys sent directly to the general managers, 34 valid answers were received
and afterwards processed using the statistical software package SPSS 16.0.
The research reveals that hotels consider e-learning to be a key determinant in the effort
to improve productivity and quality, since the mean values of the factors “e-learning
increases productivity” and “e-learning increases production volume” are xm ¼ 4.15
and 4.09, respectively.
e-Learning accounts for a significant increase in the production volume of the hotels
in the survey (73.5 per cent), the productivity level (76.5 per cent) and the performance
quality level (79.4 per cent) of the hotels. Additionally e-learning greatly decreases the
task execution time in 79.4 per cent of the hotels.
e-Learning also contributes to motivation levels (xm ¼ 3.71). The influence of
e-learning is less evident in terms of reducing absenteeism (xm ¼ 2.53) and turnover
(xm ¼ 2.94). Overall, the hotels in the sample consider that e-learning greatly enhances the
competitiveness regarding the ten strategic factors (xm ¼ 3.59). Further, the research
reveals four organised groups which have independent strategic profiles and behaviour in
terms of their approaches to evaluating the impact of e-learning on quality and
productivity. Their mean values are superior to moderate: group 1 xm ¼ 3.44, group 2
xm ¼ 3.49, group 3 xm ¼ 3.52, and group 4 xm ¼ 3.91.
The managers’ e-learning strategies are dependent on the hotels’ category in 40 per
cent and on head-office nationality in 50 per cent.
In synthesis, the competitiveness of the hotels is related to its intellectual capital.
e-Learning consolidates and transforms knowledge into competitive advantage,
especially in terms of increasing productivity and performance levels. It is clear that
e-learning participants who are better informed and attempting to stay in touch with
change are key to driving sustainable development in ever evolving markets.
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Further reading
Newbold, P. (1995), Statistics for Business and Economics, 4th ed., Prentice-Hall, Englewood
Cliffs, NJ.
´
Sarmento, M. (1997), “Behavior of quality groups facing key variables”, Tecnica, Vol. 2, pp. 17-27.
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Corresponding author
Manuela Sarmento can be contacted at: manuela.sarmento@lis.ulusiada.pt
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