TURITEC 2016 1 1 - udg. · PDF fileAndrés Aguayo Maldonado ... Email:...

XI Congreso internacional de Turismo y Tecnologías de la Información y la Comunicación

XI Information Communications Technology & Tourism international conference

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Autores:

Andrés Aguayo Maldonado

Alfonso Cerezo Medina

Antonio Guevara Plaza

Edita:

Facultad de Turismo – Universidad de Málaga

Campus de Teatinos s/n

29071 Málaga

Telf. 952 132735 – 952 133239

Email: [email protected]

I.S.B.N. 978-84-617-5596-7



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INTERNATIONAL TRACK

THE EFFECTS OF DIGITAL MEDIA ON DESTINATIONS LOYALTY. DMO´S MARKETING STRATEGIES ........................................................................................................................... 12

ACTUAL INTERNET USE BY MILLENNIALS: DOES IT ENHANCE TRIP SATISFACTION? .... 29

ADVANCING A REGIONAL DESTINATION CARD: THE USE OF THE ZEELANDPAS IN PRACTICE ................................................................................................................................ 39

DETERMINANTS OF REVENUE MANAGEMENT PERFORMANCE IN HOTELSIN MADRID, SPAIN ....................................................................................................................................... 53

ANALYSIS OF THE DESTINATION IMAGE AND THE WEB AND ICT COMPETITIVENESS OF TOURIST DESTINATIONS: THE CASE OF THE PROTECTED AREAS OF BASILICATA . 63

INTELIGENCIA TURÍSTICA

LOS DESTINOS TURÍSTICOS INTELIGENTES. UNA VISIÓN DESDE LA PERSPECTIVA DE LOS MILLENNIALS ............................................................................................................. 79

ESTRUCTURA CONCEPTUAL DE PLATAFORMA DE E-GOV PARA EL MINISTERIO DE TURISMO (MTur) DE BRASIL ................................................................................................... 94

TERRITORIOS Y CIUDADES INTELIGENTES: RETOS PARA CONVERTIRSE EN “SMART HERITAGE DESTINATION” .................................................................................................... 109

PARTICIPACIÓN DEL TURISTA EN LOS MEDIOS SOCIALES DE LOS SMART DESTINATIONS. ..................................................................................................................... 123

BIG DATA Y CREACIÓN DE CONOCIMIENTO EN LOS DESTINOS PATRIMONIO DE LA HUMANIDAD DE ESPAÑA. .................................................................................................... 136

LOS DESTINOS TURÍSTICOS INTELIGENTES. OPORTUNIDADES Y LÍMITES DESDE EL PUNTO DE VISTA DE LOS EXPERTOS. ............................................................................... 151

COMUNICACIÓN DIGITAL, e-WOM E IMAGEN

LA IMPLEMENTACIÓN DE LA SEGURIDAD Y VENTA CRUZADA EN LAS APLICACIONES AEROPORTUARIAS MEJORAN LA COMUNICACIÓN BOCA-OÍDO ONLINE ....................... 169

UN ANÁLISIS DE LA RELACIÓN ENTRE EL EWOM Y LOS RESULTADOS: EL CASO HOTELERO ............................................................................................................................ 199

FRECUENCIA DE USO, NIVEL DE HABILIDADES Y ESTRATEGIA DE GESTIÓN DE PLATAFORMAS DE VALORACIÓN: UN ANÁLISIS DE LA RELACIÓN CON LA INTENSIDAD Y EL CONTENIDO DEL EWOM ....................................................................... 218

LA COMUNICACIÓN INTERACTIVA COMO HERRAMIENTA DE PROMOCIÓN TURÍSTICA EN LA WEB: INDICADORES, PARÁMETROS Y CALIDAD COMUNICACIONAL ................... 231

META-ANÁLISIS MASIVO DE OPINIONES EN LÍNEA SOBRE VIAJES ................................ 243



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THE ROLE OF INFORMATION SOURCES ON THE IMAGE PERCEPTION OF NODES ...... 258

LAS NUEVAS TECNOLOGIAS DE LA INFORMACIÓN Y LOS DESTINOS TURÍSTICOS DE MONTAÑA: EL CASO DEL VALLE DE CAMPRODON ........................................................... 272

TIC Y COMPETITIVIDAD TURÍSTICA

CÓMO INVERTIR EN TIC PARA MAXIMIZAR EL RESULTADO HOTELERO ....................... 285

PROGRAMA DE BBPP EN EL USO DE LAS TIC; MEJORANDO LA COMPETITIVIDAD DE LAS PYMES TURÍSTICAS VACAS A TRAVÉS DE LAS COMPETENCIAS DIGITALES ........ 296

ESTUDIO DE LA INFLUENCIA DE LAS INFRAESTRUCTURAS DE TELECOMUNICACIONES EN LA COMPETITIVIDAD DE LOS DESTINOS TURÍSTICOS. APLICACIÓN AL CASO DE ESPAÑA. .................................................................................... 310

RUNTRIP: UNA PORPUESTA DE APLICACIÓN MÓVIL PARA EL SEGMENTO DEL TURISMO DEPORTIVO .......................................................................................................... 322

¿SE HA COLAPSADO EL RANKING DE TRIPADVISOR?: UN ANALISIS DE SU EVOLUCION TEMPORAL EN CIUDADES ESPAÑOLAS Y DOS PROPUESTAS DE MEJORA. ................................................................................................................................ 336

COMPORTAMIENTO DEL CONSUMIDOR Y COMERCIALIZACIÓN TURÍSTICA

COMPORTAMIENTO DE COMPRA EN LÍNEA DE LOS MAYORES FRENTE AL PRODUCTO TURÍSTICO ........................................................................................................ 353

ANÁLISIS DE LOS HÁBITOS DE LOS TURISTAS ANTE LA LLEGADA DEL CANAL MÓVIL 365

LA GEOLOCALIZACIÓN: OPORTUNIDADES DE MARKETING PARA EL HOTEL DEL FUTURO ................................................................................................................................. 375

EL TURISMO COLABORATIVO P2P. EL CASO COUCHSURFING.COM .............................. 389

CONVIVENCIA ENTRE EL NEGOCIO ONLINE Y OFFLINE EN LAS AGENCIAS DE VIAJES EN ESPAÑA A TRAVÉS DE LOS CAMBIOS PROPICIADOS POR LA APLICACIÓN DE LAS TIC Y LOS HÁBITOS DE CONSUMO. .................................................................................... 403

LA EVOLUCIÓN DE LA COMERCIALIZACIÓN TURÍSTICA: EL CASO DE EXPEDIA Y PRICELINE ............................................................................................................................. 418

MISCELÁNEA

DEPENDENCIA TECNOLOGICA, HIPERCONECTIVIDAD Y CIBERESPACIO EN EL TURISMO. RIESGOS, OPORTUNIDADES Y CIBERAMENAZAS .......................................... 436

LA APLICACIÓN DE LAS TIC A LOS MUSEOS DE TERRITORIALIDAD REDUCIDA. EL CAMINO DE SANTIAGO FRANCÉS. ...................................................................................... 452

REVISIÓN DE LAS PUBLICACIONES CIENTÍFICAS SOBRE TECNOLOGÍA MÓVIL Y TURISMO................................................................................................................................ 465



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EVOLUCIÓN DEL SOFTWARE Y HERRAMIENTAS DE REVENUE MANAGEMENT UTILIZADOS POR HOTELES ................................................................................................. 484

ESTADO DEL ARTE EN LA INVESTIGACIÓN SOBRE “SMART CITIES”: UN ESTUDIO BIBLIOMÉTRICO .................................................................................................................... 494



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Berta Ferrer Rosell1 ([email protected]) Estela Marine Roig1

Germa Coenders2

Universitat de Lleida Universitat de Girona

Abstract

The aim of this article is to test whether there is a relationship between overall trip satisfaction and the actual Internet use Millennials make when planning and booking accommodation and activities in overseas trips. This relationship has up to now been tested only with subjective variables referring to Internet as a medium itself (the so-called e-satisfaction), but not on actual Internet use. Subjective variables, including satisfaction, may share common variance and thus show spurious correlations. The present sample comes from official statistics micro data of young (between 15-25 years old) European leisure visitors to Spain in 2012 planning the trip by themselves and arriving by low cost airlines (n=1,717). To test the supposed relationship, we use structural equation models with ordinal variables, statistical power analysis, and trip characteristics are used as controls. We find no relationship between overall trip satisfaction and pre-trip Internet use by young travelers. Power analysis and confidence intervals show that the relationship is either null or extremely weak. The null results found contradict prior expectations on a relationship which the literature takes for granted but has never been put to test. Further research should consider better and more detailed measurements of both trip satisfaction and actual Internet use.

Keywords: Millennials; trip satisfaction; web trip planning; traveller Internet usage; statistical power; common method variance; null results

Resumen El objetivo de este trabajo es comprobar si existe una relación entre la satisfacción general del viaje y el uso de Internet que realiza la generación “Y” a la hora de planificar y reservar alojamiento y actividades en viajes al extranjero. Para ello se emplea una muestra que proviene de micro datos estadísticos oficiales de 1.717 jóvenes europeos de entre 15 y 25 años que visitaron España por motivos de ocio en 2012, habiendo llegado a través de compañías aéreas de bajo coste y planificado su viaje ellos mismos. Para comprobar la supuesta relación se utilizan modelos de ecuaciones estructurales con variables ordinales que señalan que la relación es nula o extremadamente nula, lo que contradice a la literatura académica previa que daba por sentada esta relación sin haber realizado experimentos que la confirmasen.

Palabras clave: Generación Y, satisfacción, uso de internet por viajeros, planificación del viaje vía web, poder estadístico, resultados nulos, método de varianza común



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Individuals born roughly between 1980 and 1994 has been referred to as generation Y, digital natives (Prensky, 2001), or Millennials (Howe & Strauss, 2000) because of their familiarity with ICT. They experience ICT instead of learning ICT; they are immersed in technology and use it in their every-day life. According to Eurobarometer (2015), young travellers (15-24 years old) are more likely to have organized a holiday through the Internet (69%). Beldona, Nusair and Demicco (2009) found differences on online travel purchase behaviour between Millennials and other generational cohorts (Generation X and Baby boomers). Differences in the use of information sources driven by age have also been reported in the tourism literature (Gretzel & Yoo, 2008). Wang and Law (2007) found that younger individuals are more likely to be ICT users, and Szopinski and Staniewski (2016) that respondents between the ages of 16 and 24 are the largest group that use e-tourism every day or almost every day. In spite of having great impact on tourism industry, limited attention has been devoted to understanding the behaviour of this market (Nusair, Parsa, & Cobanoglu, 2011).

On the other hand, research on tourist satisfaction in connection with the Internet has mostly dealt with e-satisfaction (satisfaction with the Internet as a medium itself), but not with overall trip satisfaction (Amaro & Duarte, 2013; Groth & Haslwanter, 2015). The relationship between the Internet and trip satisfaction has up to now been based only on subjective variables regarding the Internet, that is, perceived benefits of the Internet, but not on actual Internet use (e.g., Frías-Jamilena, Del Barrio-García, & López-Moreno, 2013). A stream of the literature has taken for granted that there is a positive relationship between actual pre-trip Internet use and trip satisfaction (Castañeda, Frías, & Rodríquez, 2007), but, to the best of our knowledge, this relationship has been never put to test.

This paper tests whether or not there is a relationship between the actual use young tourists do of the Internet when planning accommodation and activities in overseas trips and trip satisfaction. We use a particular case of structural equation model (SEM) for ordinal dependent variables, and as control variables we use trip characteristics, activities undertaken at destination and previous destination experience.

Unexpectedly we find no significant relationship between the use of Internet young travellers make and trip satisfaction. We use statistical power analysis to interpret the failure to reject the null statistical hypotheses and conclude that the relationship is either non-existent or extremely weak (less than 0.1 in standardized terms).

The literature on pre-trip Internet use generally supports the view that the Internet allows travelers to make better choices and to get more accurate and detailed information. In some cases, Internet booking additionally makes it possible to get lower prices. Several well-established theoretical connections between Internet use and trip satisfaction have been identified, which should all result in a positive correlation between Internet use and trip satisfaction. Millennial (or generation Y) tourists have been identified as the main Internet users when planning a trip and as those for whom the Internet plays the strongest role.



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Satisfaction and perceived quality depend on the extent to which perceptions on the spot meet expectations prior to the service delivery (Parasuraman, Zeithaml, & Berry, 1985). Accordingly, Internet use and, especially, access to user-generated content (UGC) should make expectations more realistic and thus increase trip satisfaction. In this respect, young travellers are more influenced by UGC as a travel information source (Ip, Lee, & Law, 2012). Yoo, Gretzel, and Zack (2011) and Grasser and Simun (2010) found that Millennials are both more likely to be opinion leaders and opinion seekers compared to other generational cohorts.

With the generalized use of social media for both daily lives and travel-related activities (Grasser & Simun, 2010; Öz, 2015) travelers increasingly rely on online UGC to reduce their uncertainty when making their travel choices (Marine-Roig & Anton Clavé, 2016; Memarzadeh, Blum, & Adams, 2015; Öz, 2015). Better choices which are closer to the travelers’ tastes should increase trip satisfaction.

The Internet theoretically reduces search costs and enables buyers to identify low-price sellers by means of search engines. Internet users themselves claim that the Internet allows them to get better prices (Öz, 2015), that is, better value for money. To this respect, Sabiote,

Frías, and Castañeda (2012) found e satisfaction to be related with perceived value. A second issue is that lower prices can contribute to lowering quality expectations (Rao, 2005), thus reducing the gap between expectations and perceptions (Parasuraman et al., 1985), and to increasing trip satisfaction. A final issue is related to negative cross-elasticities, or complementary goods (e.g., Dwyer, Forsyth, & Forsyth, 2010). When cross-elasticities between two trip components are negative, (Divisekera, 2010, p. 634), the reduction in price of one (e.g., accommodation) tends to increase consumption of the other (e.g., activities at destination). Assuming positive marginal utilities, higher consumption should increase satisfaction.

Regarding empirical research on the relationship between e-satisfaction and trip satisfaction, Lam, Tam, and Oh (2014) conclude that perceived functional benefits of Internet and perceived contribution of the Internet to reduce costs contribute to trip satisfaction. Castañeda et al. (2007) conclude that e-satisfaction contributes to trip satisfaction. Both studies use samples composed only by travelers who actually used the Internet. Along similar lines, customer's emotional response to the travel experience is reported to be affected by the consumer's evaluation of the Web value and the agreement with the information provided (Lakshmi & Ganesan, 2010). The above mentioned studies involve some form of perception or evaluation of Internet use. To the best of our knowledge, the contribution of actual use versus no actual use of the Internet to trip satisfaction is under researched.

The distinction between actual Internet use and Internet evaluation or e-satisfaction is important. Research on satisfaction has shown satisfaction and related subjective variables to share common variance (e.g. common method variance), which tends to increase their mutual correlation. For instance, Cho, Byun, and Shin (2014) found that the importance ratings of the seven sources of information they researched, were all positively related to destination satisfaction (although only the Internet, government and personal sources significantly so). If not accounted for, common variance creates endogeneity problems in models relating satisfaction variables to one another (Van Praag, Frijters, & Ferrer-i-



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Carbonell, 2003). Internet-related actual behavior provides a much stronger test of the impact of the Internet on trip satisfaction than Internet evaluation or e-satisfaction does.

We use secondary official statistics data by the Spanish Tourism Institute (ITE), an official agency of the Spanish Ministry of Tourism. The Tourist Expenditure Survey (EGATUR; see ITE, 2014) was conducted in 19 major Spanish airports in 2012 and interviewed tourists when waiting to board their trip back home. The sample used in this study is a subset which consists of young (less than 25 years-old) visitors, coming from European countries for leisure purposes. They arrived to Spain by low cost airlines, spent between 1 and 30 nights in a single destination in Spain and organized and paid the trip by themselves. We do not consider tourists for whom it does not make sense to use the Internet regarding accommodation (tourists who own a house or stay with friends or relatives). Our final sample size is n=1,717.

The EGATUR questionnaire includes four yes-no questions regarding the actual pre-trip use of the Internet regarding planning and booking accommodation and activities. Internet use for planning and booking the same service has been sometimes shown to converge into one single dimension (Coenders, Ferrer-Rosell, & Martínez-Garcia, 2016). We use tetrachoric correlations (a measure of association between binary variables) to measure convergence of the planning and booking variables. Tetrachoric correlations between the variables of planning and booking the same service are: 0.916 (between planning and booking accommodation) and 0.956 (between planning and booking activities). Drawing from these tetrachoric correlations, and following the recommendation by Coenders et al. (2016), we reduce the original four binary variables into two ordinal variables: overall use for accommodation, and overall use for activities) categorized as no use of the Internet, Internet use to look for information only and Internet use to reserve (Table 1).

Table 1 Internet use distribution (%)

Accommodation (web_acco) Activities (web_acti)

No Internet use 6.3 47.5 Internet use for information only 22.4 27.0 Internet use for booking 71.2 25.5

Trip satisfaction (satisf) is measured with a question asking respondents to rate the trip as a whole from 0 to 10 (the frequencies are as follows: from 0 to 5: 1.2%; 6: 1.6%; 7: 10.7%; 8: 38.8%, 9: 27.9%; 10: 19.8%).

As regards control variables, we include the trip characteristics considered by Coenders et al. (2016), and Del Chiappa and Zara (2015) with the addition of the variety of activities undertaken at destination, which may be especially relevant both to satisfaction (Zhang & Marcussen, 2007) and to Internet use for booking activities or gathering information about them.



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The list of trip characteristics we use as controls is given below, including variable names matching those in the statistical model, and descriptive statistics:

• Past loyalty and experience with Spain as a destination: has not been in Spain before (reference category: 43.7%), has been in Spain 4 times or less (loyal_1: 34.8%), has been in Spain 5 times or more (loyal_2: 21.5%).

• Variety of activities (act_var, mean: 1.85, SD: 1.12): number of different activities undertaken at destination among the following list: golf, skiing, yachting, other nautical sports, hunting, hiking, adventure sports, other sports, attending sports events, cultural sightseeing, cultural shows, other cultural activities, spa, theme parks, gambling, gastronomic/wine tourism, nightlife.

• Logarithm of daily expenditure at destination in Euro (l_d_exp, mean: 4.37, SD: 0.53):

• Logarithm of length of stay in days (l_stay, mean: 1.55, SD: 0.51):

• Traveling group: with family or partner only (reference category: 41.5 %), alone (alone: 18.5 %), with at least some friends/colleagues (friends: 40.0 %).

• Season: July to September (reference category: 41.2 %), January to June, October to December (low_s: 58.8 %).

• Time of advance booking: at least some service committed more than one month in advance (reference category: 65.6 %), everything booked less than 1 month in advance (last_min: 34.4 %).

• Trip motivation: other/mixed trip motivations (reference category, 45.4 %), seaside countryside leisure (seacount: 26.9 %), cultural trips to singular cities (urban: 27.7 %).

• Accommodation: 1 to 3-star hotels (reference category: 70.2 %), 4 and 5-star hotels (hot_45: 13.7 %), apartments/other accommodation (ap_oth: 16.1 %).

Figure 1 Path diagram of the proposed model. Covariances among exogenous variables are

omitted for the sake of simplicity



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The path diagram in Figure 1 shows the model we estimate, and does not include the effects of Internet use (web-acco and web_acti) on trip satisfaction (broken grey arrows in Figure 1). The only overidentification restrictions in this model are the absence of the two said effects.

Thus, the joint 2 test of the model is equivalent to testing the null hypothesis that none of the Internet use variables have any effect on trip satisfaction. The model contains all necessary information to test the effects one at a time, by means of the modification index tests,

distributed as a 2 with one degree of freedom (d.f.) under the null hypothesis that the effect is zero in the population. The expected estimate tells us the approximate standardized effect that would be obtained if the parameter would be added to the model. Confidence intervals for the expected estimate can be computed according to the procedure in Saris, Satorra, and Van der Veld (2009).

As regards the estimation method, in the particular case of having ordinal dependent variables a SEM is equivalent to a simultaneous Probit model (Muthén, 1984) and we use the WLSMV estimator in the M-Plus 7.3 program (Muthén & Muthén, 2012). All three dependent variables are treated as ordinal while all categorical explanatory variables are dummy coded.

The joint test of the model in Figure 1 does not make it possible to conclude that there is any

effect of Internet use on satisfaction (joint likelihood ratio test: 2 with 2 d.f.= 0.618; p-value =

0.734). The same can be said about the individual modification index 2 tests in Table 2.

Table 2 Individual modification index 2 tests, power, expected standardized estimates, and

95% confidence intervals (LCL-UCL)

2 1 d.f. p-value powera expected estimate LCL UCL

web_acco 0.420 0.517 0.900 0.020 -0.040 0.080

web_acti 0.538 0.463 0.937 0.021 -0.035 0.077

a To detect a true standardized effect equal to 0.1 with =5%

Failure to reject the null hypothesis is only interpretable when power is high. We therefore compute the test power with the method in Saris et al. (2009). The power shows that even a very small effect from actual Internet use on satisfaction (0.1 in standardized terms) would be detected by the tests with a very high probability (90% for web_acco and 94% for web_acti). In this case, the failure to reject the null hypothesis allows us to conclude that the effect of Internet use on trip satisfaction is either very small or non-existent. The lower and upper confidence limits (LCL and UCL) for the expected standardized parameter estimate tell basically the same message. The values of the standardized effects could be at most 0.080 and 0.077 with 95% confidence.



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Finally, the goodness of fit of the model without the effects is very good according to some commonly used fit indices in SEM (RMSEA: 0.000; 90 percent C.I. for the RMSEA: 0.000-0.034; CFI: 1.000; TLI: 1.038). Thus, we maintain the model without the effects of both Internet use variables. The standardized estimates of the model parameters are shown in Table 3. We only interpret significant effects which are larger than 0.1. Cultural and leisure trip motivation, last-minute booking, and travelling alone negatively affect Internet use for accommodation. As regards the use of the Internet for activities, it is negatively affected by being accommodated in a 4-5* hotel, by travelling during the low season, by the variety of activities undertaken, and by the previous visits at the destination. Daily expenditure at destination, positively affects both Internet use variables. Trip satisfaction does not show any substantial effect, as witnessed also by the low R-squared.

Table 3 Standardized model estimates and for each of the dependent variables

web_acco web_acti satisf

R-squared 0.210 0.177 0.027

Apartments/other accommod. (ap_oth) -0.071* -0.005 -0.023

4- and 5-star hotels (hot_45) -0.066* -0.142** -0.011

Cultural motivation (urban) -0.314** 0.157 -0.046

Sea/countryside leisure mot. (seacount) -0.184** 0.014 0.058*

<1 month advance booking (last_min) -0.138** -0.034 0.024

Low season (low_s) -0.031 -0.114** 0.043

Traveling with friends (friends) -0.024 0.029 0.031

Traveling alone (alone) -0.180** 0.050 0.022

Log-length of stay (l_stay) -0.035 -0.018 0.014

Log-daily at destin. expense (l_d_exp) 0.252** 0.171** 0.058*

Variety of activities (act_var) -0.085** -0.208** 0.079**

More than 5 previous visits (loyal_2) -0.046 -0.158** 0.072**

1-4 previous visits (loyal_1) -0.040 -0.204** 0.058*

** p-value<0.01; * p-value<0.05

This paper has surprisingly shown that there is no relationship, or it is extremely weak, between the use Millennials make of Internet for seeking information and booking, and overall trip satisfaction. The existence of this relationship has sometimes been taken for granted but never put to test. More precisely, it can also be the case that such tests have actually been carried out but have not been published due to the lower likelihood of research reporting null results being written up and published (Franco, Malhotra, & Simonovits, 2014). In fact, null results like the one found in this study are valuable only when statistical power is high (Franco et al. 2014). In addition, analyzing actual Internet use instead of e-satisfaction makes our results immune to common method variance or other sources of common variance often encountered among satisfaction variables.



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This article can trigger a debate on the role pre-trip Internet use can play on trip satisfaction. A first open question to debate is whether there is any role at all, or if the Internet is a mere marketing and management tool for destinations and the tourism industry. A second question is which Internet uses in particular (price search engines, destination websites, UGC, and so on) may be especially related to which domains of trip satisfaction (enjoyment, time use, accommodation, and so on). A third question is whether official statistics surveys such as EGATUR properly measure Internet use or trip satisfaction.

Some tentative explanations may be that more than using the Internet as an information source, what is important for trip satisfaction is the quality or usefulness of the specific information itself, regardless of whether it is encountered online or not. The positive or negative orientation of online information also plays a role in decision-making as people tend to make travel decisions based on positive UGC and not on negative reports, possibly distorting expectations (Memarzadeh et al. 2015). Moreover, increased quantities of information online can be both a blessing and a curse, as it can complicate decision-making, confuse the user or lead to dissatisfaction (O’Connor 2010).

Even though there are some advantages of using official statistics microdata (whole-country scope), the set of available variables cannot be controlled by the researcher. Further research can include a primary-data study, focused mainly on gathering richer data on satisfaction and Internet use (multi-item scales of satisfaction, detail on types of Web used and manners of use, notably UGC and search engines, and particular activities for which the Internet is used).

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