Satisfaction and Continuance
Intention of online information
search
Case example of Finnish travel search
Master’s Thesis
Huyen Vu
28 July 2017
Information and Service
Economy
Approved in the Department of Information and Service Economy
__ / __ / 20__ and awarded the grade
_______________________________________________________
Aalto University, P.O. BOX 11000, 00076 AALTO
www.aalto.fi
Abstract of master’s thesis
Author Huyen Vu
Title of thesis Satisfaction and Continuance Intention of online information search
Degree Master of Science in Economics and Business Administration
Degree programme Information and Service Economy
Thesis advisor(s) Advisors
Year of approval 2016
Number of pages 60
Language English
Abstract
Georgia Regular, 11pt/14pt. Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Sed posuere
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lacinia eu tincidunt et eleifend nec lacus. Donec ultricies nisl ut felis, suspendisse potenti. Lorem
ipsum ligula ut hendrerit mollis, ipsum erat vehicula risus, eu suscipit sem libero nec erat. Aliquam
erat volutpat. Sed congue augue vitae neque. Nulla consectetuer porttitor pede. Fusce purus morbi
tortor magna condimentum vel, placerat id blandit sit amet tortor.
Sisennetty kappale, sisennys 3mm. Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet,
lorem velit accumsan velit vel mattis libero nisl et sem. Proin interdum maecenas massa turpis
sagittis in, non lobortis vitae massa. Quisque purus lectus, posuere eget imperdiet nec id arcu.
Vestibulum elit pede dictum eu, viverra non tincidunt eu ligula.
Mauris sed libero. Suspndisse facilisis nulla in lacinia laoreet, lorem velit accumsan velit vel mattis
libero nisl et sem. Proin interdum maecenas massa turpis sagittis in, interdum non lobortis vitae
massa. Quisque purus lectus, posuere eget imperdiet nec sodales id arcu. Vestibulum elit pede
dictum eu, viverra non tincidunt eu ligula.
Sisennetty kappale, sisennys 3mm. Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet,
lorem velit accumsan velit vel mattis libero nisl et sem. Proin interdum maecenas massa turpis
sagittis in, interdum non lobortis vitae massa. Quisque purus lectus, posuere eget imperdiet nec id
arcu. Vestibulum elit pede dictum eu, viverra non tincidunt eu ligula.
Sisennetty kappale, sisennys 2mm. Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet,
lorem velit accumsan velit vel mattis libero nisl et sem. Proin interdum maecenas massa turpis
sagittis in, non lobortis vitae massa. Quisque purus lectus, posuere eget imperdiet nec id arcu.
Vestibulum elit pede dictum eu, viverra non tincidunt eu ligula.
Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet, lorem velit accumsan velit vel
mattis libero nisl et sem. Proin interdum maecenas massa turpis sagittis in, interdum non lobortis
vitae massa. Quisque purus lectus, posuere eget imperdiet nec sodales id arcu. Vestibulum elit pede
dictum eu, viverra non tincidunt eu ligula.
Sisennetty kappale, sisennys 3mm. Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet,
lorem velit accumsan velit vel mattis libero nisl et sem. Proin interdum maecenas massa turpis
sagittis in, interdum non lobortis vitae massa. Quisque purus lectus, posuere eget imperdiet nec id
arcu. Vestibulum elit pede dictum eu, viverra non tincidunt eu ligula. Sisennetty kappale, sisennys
3mm. Mauris sed libero. Suspendisse facilisis nulla in lacinia laoreet, lorem velit accumsan velit vel
mattis libero nisl et sem. Proin interdum maece.
Keywords information search, online search, purchase satisfaction, continuance intention of IS
use, uncertainty, SEM
i
Acknowledgements
Upon the completion of this thesis, I would like to thank my supervisor Timo Saarinen for his
constant support, especially for his guidance in forming my research topic. Secondly, my
gratitude goes to James Gaskin, professor of Information Systems in the Marriott School of
Management (Utah, USA), who has produced an excellent and comprehensive Youtube tutorial
series on Structural Equation Modelling. I strongly recommend this series, which is both useful
and entertaining, to students using this method for their research projects.
ii
Table of Contents
Acknowledgements ........................................................................................................................ ii
Abbreviations ................................................................................................................................... 6
1
2
3
4
Introduction ............................................................................................................................. 7
1.1
Research objectives and research questions .................................................................... 8
1.2
Structure of the study ................................................................................................................ 9
Literature review.................................................................................................................... 9
2.1
The role of information search in interactive consumer behavior ........................... 9
2.2
Pre-purchase Uncertainty and its consequences...........................................................11
2.3
Information search strategy and its effects .....................................................................14
2.4
Consumer Perceived satisfaction and its antecedents ................................................15
2.5
Experience with Internet usage ...........................................................................................18
2.6
Case example of Finnish travel information search .....................................................19
Hypothesis development ................................................................................................... 20
3.1
Research constructs .................................................................................................................20
3.2
Control variables .......................................................................................................................21
Data and Method ................................................................................................................... 23
4.1
Data collection method ...........................................................................................................23
4.2
The profile of the respondents .............................................................................................24
4.3
Variable measures ....................................................................................................................26
4.4
Analysis Method .........................................................................................................................27
4.5
Data analysis ...............................................................................................................................28
5
Findings.................................................................................................................................... 41
6
Conclusion ............................................................................................................................... 43
7
Limitation and suggestions for future research ........................................................ 45
Appendix: Travel service survey questionnaire ................................................................ 46
References ....................................................................................................................................... 48
iii
List of Figures
Figure 1 The decision making phases with linkages to the four uncertainty constructs
(Lauraeus-Niinivaara, 2011) ............................................................................................ 12
Figure 2 Information system continuance model (Bhattacherjee, 2001) ................................. 17
Figure 3 Proposed conceptual model ...................................................................................... 23
Figure 4 Results of SEM analysis ............................................................................................ 43
iv
List of Tables
Table 1 Demographics of the sample....................................................................................... 25
Table 2 Research items ............................................................................................................ 26
Table 3: Control variables ........................................................................................................ 26
Table 4 Variables with high kurtosis ....................................................................................... 30
Table 5 Rotated Component Matrix (EFA result) ................................................................... 30
Table 6 KMO and Bartlett’s Test result ................................................................................... 31
Table 7 Communalities ............................................................................................................ 31
Table 8 Constructs’ reliability ................................................................................................. 32
Table 9 Model fit statistics of measurement model ................................................................. 32
Table 10 Validity and Reliability statistics .............................................................................. 33
Table 11 Inter-factor correlations ............................................................................................ 33
Table 12 Effect of Common Latent Factor .............................................................................. 34
Table 13 Configural invariance test – measurement model goodness-of-fit ........................... 35
Table 14 Multi-group moderation test result .......................................................................... 36
Table 15 Model fit statistics of structural model .................................................................... 38
Table 16 Regression weights of structural model ................................................................... 39
Table 17 Moderating effect of Internet usage experience ....................................................... 40
Table 18 Mediation effect testing ............................................................................................ 40
Table 19 Result of SEM .......................................................................................................... 41
v
Abbreviations
Abbreviations
EDT Expectation disconfirmation theory
EDM Expectation disconfirmation model
IS Information System
CLF Common Latent Factor
TAM Technology Acceptance Model
SEM Structural Equation Modeling
6
Introduction
1 Introduction
Information search is an essential activity that consumers involve in prior to purchasing
a product or a service. The amount of time spent searching, search behaviors, motivations
behind search are among the topics that have been of great interest for researchers in the past
decades (Brucks, 1985; Schmidt & Spreng, 1996; Fodness & Murray, 1999). Information
search was being investigated in the field of consumer behavior, economics, marketing (Beatty
and Smith 1987; Klein and Ford 2003; Moorthy et al. 1997; Punj and Staelin 1983). Nowadays
general consumers benefit from the increasing Internet penetration and global market
expansions, which give them better access than ever to online sources of information, to
educate themselves about the market, products and services before making a purchase. Ipsos
Market Research reported that approximately 61 % of global Internet users research products
online, and 48% used the Internet in the past three months to purchase products or services
online (Ipos, 2012).
Understand external information search behavior is crucial for researchers, market
players as well as policy makers. External information search is the acquisition of information
from the marketplace, and distinguished from internal search, which is the cognitive process
of information retrieval from memory (Brucks 1985; Engel et al. 1995). The study of external
information search will facilitate the functioning of an effective and viable market economy,
in which purchase decisions are made based on consumers’ knowledge of the price, quality
and other attributes of products and services (Srinivasan 1990). As past studies have suggested,
intention to search leads to intention to purchase (Shim et al., 2001), thus it is important to
understand consumers’ decision making behavior, their information needs, in order to facilitate
effective search, allow consumers to become well-informed and more confident with their
purchase decisions.
There are several angles of external information search that are of interest to be examined
in this thesis. Firstly, information search is suggested to have a direct link with purchase
satisfaction. Fodness and Murray’s study (1999) had concluded that pre-purchase information
search may result in customer’s purchase satisfaction. As consumer satisfaction is related to
desirable goals in customer relation management, such as repurchase intention and brand
loyalty (Niinivaara, 2015), this relationship between intention of information search and
purchase satisfaction will benefit further analysis. Secondly, past studies proposed that
information search was motivated by pre-purchase uncertainty. The overloading of information
7
Introduction
available on products and services, consumers’ own ability to process information and perform
at different stages of decision making are among the critical factors leading to consumers’
uncertainty prior to purchase. It is beneficial to study how pre-purchase uncertainty affects the
quality of information search and purchase satisfaction, in order to find mitigations to its
effects. Last but not least, information search was linked to search types or search behaviors.
Search types have been suggested to have a significant effect on purchase satisfaction
(Niinivaara, 2015). A better understand of the relationship among the elements of search types,
information search and purchase satisfaction will benefit the design of a more user-oriented
system, which tailors to the needs and expectations of consumers with different search
behaviors and cognitive ability.
This study is an attempt to bridge the links of studies among the components of online
information search, search type, pre-purchase uncertainty and purchase satisfaction. Also the
study takes into account Internet usage experience when examining the relationships of
interest, to test the potential effect of Internet usage experience in distinguishing different
online consumer groups.
1.1 Research objectives and research questions
Consumers carry out information search before they purchase. They do so because they
feel uncertain in the decision making process. The levels of uncertainty they have, and the
information search strategy they use, with the moderating effect of their own experience with
using the Internet, may affect their perceived satisfaction of the purchase, and their intention
to continue using online information search for future travel. The research objective of this
thesis is to study the effect of pre-purchase uncertainty and search types on purchase
satisfaction and intention to continue using online information search. The effects being
examined take into account the moderating effect of consumers’ Internet usage experience.
Research questions:
a.
What are the effects of pre-purchase uncertainty and type of online search on
purchase satisfaction and intention to continue using the Internet for information search?
b.
How Internet usage experience moderates these effects?
8
Literature review
1.2 Structure of the study
Part 2- Literature review will be the discussion of the studies and findings on the topics
of information search, pre-purchase uncertainty, search type, purchase satisfaction, and the
links among them. Next in part 3, the case example of travel information search in Finland will
be described. Part 4 presents the data analysis, which will be followed by discussion of the
findings in part 5. Conclusion and limitation with suggestions for future research are drawn in
part 6 and part 7, respectively.
2 Literature review
2.1 The role of information search in interactive consumer behavior
This chapter reviews the earlier studies of consumer search behavior. Studies of
consumer information search can be dated back as early as 1923, when marketing researcher
started investigating this area (Copeland 1923). Two major theoretical streams of consumer
information search literature are the economics approach (Stigler 1961; Urban, Hulland, and
Weinberg 1993) and the psychological/motivational approach (Bettman and Park 1980).
Taking the psychological/motivational approach, Bettman and Park (1980)’s theory
states that individual ability to search and motivation to search are strong determinants of
information search, and that search behavior will not take place if one of these two determinants
is missing. This notion is aligned with Petty and Cacioppo (1986)’s Elaboration Likelihood
model (ELM), which suggests that both motivation and ability are required for a person to
acquire information and perform cognitive processing.
In the economics approach, one of the seminal studies that contribute largely to studies
of information search behavior is Stigler’s Economics of information theory (1961). According
to Stigler, consumers stop the information search process when perceived marginal costs start
exceeding the perceived benefits. Search costs consist of time, access to search platform,
monetary costs. Benefits include the extent and time length of search, nature of search sources.
Stigler’s model indicates that motivation to search is positively affected by search benefits, and
negatively affected by search costs. This is consistent with some studies which found that as
consumers detect more perceived risks from the potential purchase, the more effort they put
into pre-purchase information search (Dowling, 1986; Mitchell & Boustani, 1994).
9
Literature review
Information theory by Stigler (1961) was applied in a number of studies, to use the cost
and benefit framework in examining consumer’s information search behavior (Klein 1988).
Many factors influence search cost and benefits. One of the important factors is nature of the
product. Darby and Kami (1973) categorize goods that consumers seek information about into
three quality groups: search, experience and credence. Search goods is defined as goods that
consumers can readily evaluate the quality prior to purchase, with books as an example.
Experience goods are the goods that can be evaluated after purchase, when the goods/service
is being used or experienced, like a travel tour package. Credence goods “cannot be evaluated
in normal use; instead the assessment of their value requires additional costly information”
(Darby and Kami, 1973), for example, automobile repair or dietary supplements. With this
categorization of nature of goods, Nelson (1970)’s findings showed that consumers conduct
fewer, less extensive search for experience goods than for search goods. Klein (1998) also
argue that online information search is especially useful for search goods because of the low
perceived costs of dispersing objective data (by the sellers) and evaluating the data (by
consumers). Confirming this point, Liang and Huang (1998) report that search goods have
lower perceived acquisition cost on the Internet than experience goods.
Yeh et al. (2010) examine the determinants of consumer information search behavior on
the Internet. Results showed that product involvement strongly affect consumer's information
search behavior. The most sought-after types of information include store reputation, product
functions and payment methods.
Involvement is traditionally defined in the behavioral
literature as “the importance a consumer assign to the product category”. Antil (1984)’s
proposed another, more articulated definition of product involvement as “level of perceived
personal importance and/or interest evoked by a stimulus within a specific situation.”
Moon (2004) proposed an exploratory model to examine the factors that motivates
consumers to adopt information search on the Internet instead of traditional sources of
information search. This work adopted the contingent consumer decision making model by
Bettman and Park. (1980), and categorized the factors motivating consumers to search online
into three categories: person, problem and context. Specifically, the contingent model states
that there are three key variables that influence consumers’ decision making: the characteristics
of the decision maker, the decision problem, and the social context where the problem takes
place. According to the contingent consumer decision making model and based on review of
earlier consumer behavior theories, Moon (2004) summed up the influencing factors of
consumer online information search into three groups: consumer characteristics, information
10
Literature review
search objectives and website characteristics. Consumer characteristics factor group includes
“knowledge level, internet usage skill, challenging mindset towards the Internet, lifestyle, and
demographic characteristics”. Information search objectives factors include continuous search
and pre-purchase search. Website characteristics factors include “information quantity, design,
access and transmission speed, user friendliness of search structure, and update frequency”
(Moon, 2004).
Klein (1998) argues that online information search is a key part of the interactive media
process and critical predictors of consumer’s online purchase. To support this argument, Klein
developed the Interaction Model of pre-purchase consumer information search, which
emphasized the principles of information economics by Stigler (1961), stating that consumers
search to compare the relative costs and benefits of an additional search. According to Klein,
Consumers perceive online information search as effective due to low perceived costs of
dispersing and evaluating data.
Also to study the determinants of online information search, Kulviwat et al. (2004)
proposes a conceptual framework, which was developed by reviewing the traditional constructs
from information search studies and new variables related to the Internet from Management
information systems literature. This integrated conceptual model theorizes that perceived
benefits and perceived costs are the two major antecedents of online search, and this
relationship is mediated by motivation to search. Other antecedents of online search, also
mediated by motivation to search, include ability to search, buying strategies, situational factor
and personal factor. One of the studies that later brought this model and its hypotheses to
empirical testing is Jepsen (2007), who confirmed the effect of lower costs and benefits on
consumer online information search. Using structural equation modeling to analyze responses
from 233 Danish Internet user, Jepsen’s findings supported that low search costs and benefits
are important determinants of online pre-purchase information search. Results also confirmed
that the amount of Internet usage is an even stronger determinant of online information search
compared to perceived costs and benefits.
2.2 Pre-purchase Uncertainty and its consequences
Consumers experience the feeling of uncertainty when making purchase decisions.
Hence the statement by Stigler (1961) in his seminal paper on economics of information:
“Uncertainty is the driving force behind consumer search”. Uncertainty has been studied
extensively in consumer behavior research, being one of most often cited reasons for
11
Literature review
consumers not purchasing from Internet shops, as they feel a lack of trust and discouraged from
making online purchases (Cheung & Lee, 2006). Uncertainty is defined as the lack of
individual’s control about the consequences of certain course of action or what will happen in
the future (Niinivaara et al., 2015). Some other researchers also suggest that the definition of
uncertainty is closely related to perceived risk (Ross, 1975). Urbary et al. (1989) defines
uncertainty as the amount of information that the buyers acquire before purchase. The more
information they have, the more confident they are with the purchase decisions and the less
uncertain they feel. To deconstruct uncertainty in purchase decision making, many studies have
been conducted based on Herbert Simon’s model - a well-known model of decision making.
Herbert Simon’s model is a structure of consumers’ decision making process with four phases:
Intelligence, Design, Choice and Implementation. According to Lauraeus-Niinivaara et al.
(2015), in each phase of this decision making process, consumers have distinguished level of
uncertainty. The types of uncertainty that consumers deal with in each phase has been
investigated in a number of studies.
Figure 1 The decision making phases with linkages to the four uncertainty constructs (Lauraeus-Niinivaara,
2011)
Intelligence phase is at the first step of the decision making process, when customers
gather information about the general market, product or service of interest. Knowledge
uncertainty exist in this phase. According to Lauraeus-Niinivaara et al. (2015), knowledge
uncertainty is the uncertainty customers have as they lack the knowledge about the available
12
Literature review
alternatives considering a particular decision making problem, or how they can find the
information to support their decision making.
The second phase - Design phase - involves with consumer considering all the
consequences that come as a result of each strategy or alternative of the specific decision
problem, and assessing this set of consequences. Evaluation uncertainty exists in this phase:
uncertainty of how to use the information they have to design/create the criteria to evaluate the
brands or alternatives.
The third phase - Choice phase – involves with Choice uncertainty. In this phase
consumers are supposed to choose a specific alternative which best suit them. Urbany et al.
(1989) gave the definition of Choice uncertainty as uncertainty regarding which alternative to
choose, for example, what to buy or where to buy.
The last phase of Hebert’s decision making process - Implementation phase- is involved
with Implementation uncertainty. Implementation uncertainty is defined by LauraeusNiinivaara et al. (2015) as the “uncertainty about fulfillment of purchase”. It includes
uncertainty of facing difficulties in purchase, whether the product/service is in stock at the time
of purchasing, of the fulfilment and delivery, and the like. Customers usually have high level
of uncertainty in this phase, especially when purchase will be made on the Internet due to the
fact that the product orservice to be bought is either intangible (for example: travel package),
or a physical product that consumers can not touch to try (for example: shopping for shoes
online) (Torkzadeh 2002).
Urbany et al. (1989) reports that consumers who have high level of knowledge
uncertainty will have less intention of search or extent of search activities due to their higher
search cost. They have limited capability to process search information, and thus find it difficult
to conduct the information search prior to purchase. Lanzetta’s study (1963) has empirically
tested and concluded that choice uncertainty intensifies search activity. He argued that when
the alternatives in the choice set are very similar, with less significant differences, consumer’
choice uncertainty is greater, and will result in more search. On the contrary, Stigler (1961)’s
Economics of Information theory and cost-benefit model argues that the more similar in the
purchase options, the less active consumers will search information prior to purchase, because
of the lower expected benefit from additional search.
Lauraeus-Niinivaara et al. (2007) conducted an empirical study in 2004, which was an
interview with 56 grade students from 12 to 15 years old in Finland. It is assumed in the study
13
Literature review
that people of this age group have not yet established fixed ways of searching information on
the Internet, i.e., having no or low experience with Internet usage or online search. The study
asks students to perform a simple product search and comparison assignments. The findings
suggested that knowledge uncertainty has a significant effect on shopping time; choice
uncertainty is strongly related to number of alternatives and the number of attributes taken into
consideration in the decision making process.
Lauraeus-Niinivaara et al. (2015) argued that pre-purchase uncertainty drives consumers
to do more extensive search, which may lead to perceived satisfaction of purchase. And their
empirical findings gave support to this hypothesis: pre-purchase uncertainty strongly
influences purchase satisfaction. Specifically, Evaluation uncertainty, Choice uncertainty and
Implementation uncertainty are strongly related to perceived satisfaction of purchase.
Knowledge uncertainty however does not influence satisfaction. This was interpreted as a high
possibility that the existing electronic markets have provided consumers with adequate amount
of information regarding product and service offerings. Consumers can find sufficient product
information with easy access from the Internet. The difficulty in decision making lies in
processing the abundant sources of information. Lauraeus-Niinivaara et al. (2015) suggested
there is increasing need for tools to help consumers in evaluating and choosing (Design and
Choice phases), such as recommendation aids or intelligent agents.
Some studies have investigated the consequences of pre-purchase uncertainty. LauraeusNiinivaara et al. (2015) found evidence of uncertainty relating to perceived satisfaction of
online purchase. Lauraeus-Niinivaara et al. (2008) suggested that uncertainty strongly
influences search behavior, but not have significant effect on search pattern employed.
2.3 Information search strategy and its effects
Information search strategies or search types affect quality of search. Consumers with
different levels of cognitive processing ability tend to have different strategies to search for
information they need prior to a purchase. Experienced consumers utilize to a large extent their
long-term memory in the decision making, while inexperienced consumers are more likely to
need external source of information. There are three most common information search
strategies: simultaneous search, sequential search and iterative search.
Simultaneous search is often found in experienced consumers. According to Choi et al.
(1997), simultaneous search comprises of “a single information retrieval phase”, during which
the consumers compare different alternatives at once, and thereafter the purchase decision will
14
Literature review
be made. This strategy is characterized by low search costs and high efficiency (LauraeusNiinivaara et al., 2008), but requires that the consumers are capable of accessing and comparing
the available products. Sequential search often consists of “multiple consecutive information
retrieval and decision phases”, which may imply higher search costs, but it requires less of
consumers’ cognitive processing ability. The third type of search is iterative search. LauraeusNiinivaara et al. (2015) described iterative search as the strategy that comprises of “back-andforth movement” in searching, learning and comparing different alternatives. It is an ideal
combination of cognitive effort and efficiency, a hybrid of sequential search and simultaneous
search.
From Lauraeus-Niinivaara et al. (2015), iterative search is the only one among three
search strategies that empirically connected with perceived satisfaction of purchase. It is
interpreted that iterative search most likely, among three types of search, to make consumers
satisfied with the purchase. Thanks to the nature of iterative search as “search with recall”,
consumers have the opportunity to educate themselves and make sure that they find the best
alternative possible. According to Lauraeus-Niinivaraa et al. (2015), the iterative search
strategy consists of both sequential steps and simultaneous comparing. The sequential steps
facilitate the constructing of the problem and so consider helpful in educating consumers of
their preferences. This gives iterative search an advantage over the demanding practice of
purely simultaneous search.
According to Bloemer (1995), information search prior to purchase plays an important
role as a learning process, during which the consumers educated themselves about the product
or service of interest and refine their preferences.
2.4 Consumer Perceived satisfaction and its antecedents
Purchase satisfaction is defined as an “ex post evaluation” by consumers after their firsthand experience with the service or product, which may induce positive feelings, indifference
or negative feelings, depending on the gap between expectations and perceived performance
of the purchased object (Anderson 1973). Westbrook and Oliver (1981) described purchase
satisfaction as the overall assessment of the purchase experience and outcome, which specifies
a particular transaction. This definition distinguished satisfaction from attitude, which concerns
with the general liking of an entity, for example, a product or a brand. Also, Oliver (1981)
observed that customer satisfaction is transient and consumption-specific, which is opposite to
the enduring nature of attitude. Anderson and Srinivasan (2003) defined satisfaction in e15
Literature review
commerce as customers’ contentment with the purchase experience from a particular online
shopping site. According to Lauraeus-Niinivaara et al. (2015), all the different definitions of
consumer satisfaction seem to intersect at that satisfaction is a response, either emotionally or
cognitively, to a “particular focus determined at a particular time”.
Many researchers have attempted to examine customer satisfaction antecedents and
consequences (Oliver, 1980, 1997, 1999; Yi, 1990, McKinney et al., 2002). For example,
satisfaction has been traditionally suggested to be a key determinants of customer loyalty, and
online purchase satisfaction is suggested to drive e-loyalty (Anderson and Srinivasan, 2003;
Balabanis et al., 2006). It is commonly seen in these studies the adoption of the expectation –
disconfirmation theory (EDT). Originated from marketing to study consumers’ satisfaction
with products and services (Oliver 1980), the theory later on is used to analyze user satisfaction
with information technology systems (Bhattacherjee 2001). EDT stated that users have prepurchase expectation and post-usage perceptions about the quality of a product or service. A
positive disconfirmation is achieved as performance exceeding expectation, and negative
disconfirmation takes place when performance is worse than expectation. In the essence,
greater the positive disconfirmation, greater satisfaction is achieved for the user and hence
leads to repurchase intention (Yi 1990; He et al. 2008; Wu & Huang 2015).
Bhattacherjee (2001) extended EDT by integrating it with perceived usefulness, to study
users’ satisfaction of IS usage and continuance intention to use, and called it expectationdisconfirmation model (EDM). With empirical evidence, EDM suggests that, similar to
consumer’s repurchase decision in ECT, IS users’ satisfaction are strongly determined by
confirmation of expectation from prior IS use and perceived usefulness. Also, IS users’
satisfaction is one of the key factors leading to continuance intention to use IS.
16
Literature review
Figure 2 Information system continuance model (Bhattacherjee, 2001)
EDM has been widely used to analyze e-commerce consumers’ behaviors. Wu and
Huang (2015) applied EDM to study online shoppers’ complaint behavior, with results
indicating that confirmation of expectations, customer satisfaction together with perceived
usefulness are positively related to each other. Satisfaction is one of the key determinants of
intentions to complain. McKinney et al. (2002) use expectation-disconfirmation paradigm to
examine online shoppers’ satisfaction during the information-searching phase. Empirical study
of information quality and system quality indicates that prior-use expectation, perceived
performance, disconfirmation regarding Information quality and System quality are valid and
strong determinants.
Another plausible approach to study consumer satisfaction is to examine an attributelevel conceptualization. There are a great number of attribute-level antecedents leading to
satisfaction as suggested in earlier studies. According to Lauraeus-Niinivaara et al. (2015),
online consumers’ perceived satisfaction is empirically related to information search strategy
and pre-purchase uncertainty. Ballantine (2005) investigate how the level of interactivity and
the amount of information available on the Internet related to the purchase affect consumer
satisfaction. The research was conducted by using a simulated online shopping environment,
where respondents who access the website will perform a purchasing assignment. Result shows
that the level of interactivity and the amount of information strongly affect consumer
satisfaction. Nusair & Kandampully (2008) performed content analyses on popular travel
websites to investigate the travel sites’ quality dimensions that affect customers’ satisfaction.
It suggests that the dimensions of consideration include navigability, playfulness, information
quality, trust, personalization and responsiveness. Devaraj et al. (2002) investigate and
17
Literature review
empirically test the antecedents of consumer satisfaction in online shopping using three
theoretical frameworks (Technology Acceptance Model, Transaction Cost Analysis and
Service Quality model). Results indicated that ease of use and usefulness are the key
antecedents of system users’ satisfaction.
2.4.1 Relationship between Uncertainty and satisfaction
Lauraeus-Niinivaara et al. (2015)’s findings gave support to the hypothesis that prepurchase uncertainty is a strong determinant of consumers’ perceived satisfaction. In this
empirical study, pre-purchase uncertainty was deconstructed into four components
corresponding to Simon Heberts’ four phases of decision making process (as introduced in part
2.2): knowledge uncertainty, evaluation uncertainty, choice uncertainty and implementation
uncertainty. Empirical study resulted in that knowledge uncertainty does not influence
purchase satisfaction, while evaluation uncertainty, choice uncertainty and implementation
uncertainty strongly influenced purchased satisfaction.
2.4.2 Relationship between search strategy with satisfaction
Lauraeus-Niinivaara et al. (2015) reported the study result of the relationship between
search patterns and satisfaction. It is suggested that Iterative search has strong effect on
purchase satisfaction. Consequence search and Simultaneous search have insignificant
influence.
2.5 Experience with Internet usage
Internet experience is among the most cited determinants of online user behavior as it
plays a significant part in explaining online customers’ attitudes and behaviors in e-commerce
(Blake & Neuendorf, 2003; Ondrusek, 2004). Internet experience has been examined in a
number of studies for its moderating effect, for example, on effectiveness of websites (Hysyeen
& Pedersen, 2004), on the relationship between satisfaction, switching costs and e-loyalty
(Chang & Chen, 2008). It is defined as a general experience in using a browser to connect to
World Wide Web pages. As consumers have more time and frequency in visiting online
resources for information search and various other value-added services, they generally are
more capable and so more confident in conducting online information search and online
shopping. It was observed that Internet experience has a positive effect on users’ attitude
towards websites and on users’ ability to utilize information systems (Gordon and Anand,
2000; Cheney et al., 1986; Khalifa and Liu, 2007).
18
Literature review
Frambach et al. (2007)’s empirical findings of data from 300 mortgage consumers
reported that Internet experience has significant moderating effect on consumer’s channel
usage intention. Specifically, consumers with good Internet experience are substantially more
driven to use online channel in pre- and post- purchase stages.
Khan and Locatis (1998) conducted experiments to assess the ability to search of Internet
novices and experts, with findings indicate that experts in general do a better job at prioritizing
search tasks. Hsieh‐Yee (1993) suggested that the higher search performance by people with
more Internet experience can be explained by the fact that these Internet users have better
understanding of the relations among the task stimulus elements, and are more capable of
differentiating useful and non-useful information.
Mimoun et al. (2014) conducted an empirical study with a 2x2x2 factorial design on 292
participants in an online experiment. Results indicate that Internet experience positively affects
e-consumer productivity as it enhances efficiency and reduces the time required to perform
tasks in the online environment.
2.6 Case example of Finnish travel information search
Information search is an essential part of planning to purchase services prior to travelling,
for example: information about flight tickets, destinations, accommodations, etc. Purchase
transactions and information acquisition used to take place only in physical stores, but there
was a dramatic shift to online transactions and information search through the Internet, which
has become the dominant mode in tourism industry nowadays. As online travel information
search entails all elements of interactive information search, pre-purchase uncertainty, types of
search, purchase satisfaction and continuance intention, in this thesis, the empirical part is
determined to study the case of online travel information search, with geographical area of
interest is in Finland, as the author currently resides in this country.
As e-commerce has demonstrated its advantages compared to physical brick-and-mortar
businesses, for example due to the former’s logistic convenience and 24/7 availability, it is
important to understand how tourists acquire and process knowledge through the Internet to
support their decision making. This understanding will help tourism marketers and system
designers to develop and improve the online channels so as to improve their communications
with consumers and increase buyers’ satisfaction and loyalty. Schmidt and Spreng (1996) also
19
Hypothesis development
suggested that it is during information acquisition phase prior-to-purchase that marketing
messages can be most effectively delivered and influence consumers’ buying decisions.
Conceptual and empirical studies of how tourists acquire and process information have
been of focal interest in tourism marketing (Vogt and Fesenmaier 1998; Fodness and Murray
1999; Gursoy and McCleary 2004). For example, Vogt and Fesenmaier (1998) examined the
costs accompanying any given search strategy, suggesting that there are three main costs
incurred in each information acquisition process: time spent, financial costs and effort required.
Fordness and Murray (1999) reported that for most tourists involving in the decision making
process, the search is external information search to a greater extent compared to internal
search. In external search, time spent is the greatest cost compared to other cost components
(Gursoy and McCleary 2004).
3 Hypothesis development
3.1 Research constructs
Uncertainty
Lanzetta (1968) hypothesized that higher level of uncertainty encourages consumers to
conduct more information search. Lauraeus-Niinivaara et al. (2015) also agreed with this
statement and suggested further that extensive search may result in greater purchase
satisfaction. According to Korhonen et al. (2011), purchase-related uncertainties is a strong
determinants of satisfaction.
H1: Pre-purchase uncertainty is related to purchase satisfaction.
Type of search
Lauraeus-Niinivaara et al. (2015) argues that Iterative search is the most likely, compared
to Consequential search and Simultaneous search, to make consumers satisfied with the
purchase. With iterative search, consumers can move back- and forth from different purchase
alternatives, learn and compare product information. They have the opportunity to educate
themselves and make sure that they find the best alternative possible.
H2: Iterative search is positively related to purchase satisfaction.
Purchase satisfaction
20
Hypothesis development
According to Information system continuance model (Bhattacherjee 2001), user
satisfaction with prior use has a relatively strong effect on the intention to continue information
system use. Thus, it is hypothesized that satisfaction with online purchase will probably lead
to intention to continue using the Internet for information search. (H3)
Moreover, from Online Pre-purchase Intention model (Shim et al., 2001): Empirical
findings supported that past online purchase experience is the most influential/powerful
predictor of intention to search for information online. This finding is also supported by
Bentler and Speckart (1979, 1981), Sutton and Hallett (1989), who suggested that past behavior
is a predictor of future behavior. Thus, the satisfaction perceived in the past online purchase
may also has a strong influence on the intention to search for information online in the future.
H3: Online purchase satisfaction is positively associated with intention to continue online
information search.
Internet usage experience
Nysveen and Pedersen (2004) suggest that Internet experience should be included as a
potential moderating variable in studies focusing on the effectiveness of websites. Frambach
et al. (2007)’s empirical findings of data from 300 mortgage consumers reported that Internet
experience has significant moderating effect on consumer’s channel usage intention.
Specifically, consumers with good Internet experience are substantially more driven to use
online channel in pre- and post- purchase stages. The present study is to test the moderating
effect that Internet usage experience may have on the relationships between purchase
satisfaction and continuance intention. Specifically the hypothesis is as below:
H4: More Internet usage experience amplifies the effect of purchase satisfaction on
continuance intention of online information search.
3.2 Control variables
Control variables are potentially confounding variables that may influence the results and
lead to invalid causal inferences, but they do not belong to the key theory of interest (Atinc et
al., 2012) To mitigate the risk these variables may have on the study results, the analysis must
take into account the effects of these variables, so that their effects can be recognized and dealt
with accordingly. The selection of control variables are usually based on demographic factors.
In this study, the control variables include Education, Age and Gender.
Gender
21
Hypothesis development
Gender differences in consumer behavior and IS usage behavior have been studied
extensively. Venkatesh and Morris (2000) examined the dissimilarities through Technology
Acceptance Model framework, with results indicating that males have more tendency to be
positively influenced by perceived usefulness of technology. On the other hand, females are
more likely to appreciate or prioritize perceived ease of use. Kim et al. (2007) studied the
gender differences in online travel information search. Survey result from 1334 US respondents
and one-way ANOVA analysis concluded that that there are significant differences between
males and females in terms of attitude towards information channels and preferences for
Website attributes. Specifically, compared to their male counterparts, females are more likely
to appreciate a combination of both online and traditional offline information channels when
considering travel options. Besides, females appreciate a greater variety of website
functionalities and contents, and they tend to visit more travel sites and spend more time
researching. It would be interesting to see whether gender, as a potentially relevant control
variable, can cause any differences in perceived satisfaction and continuance intention to use
online information search.
Age
The relevance of age as a factor determining technology acceptance and usage behavior
has been confirmed in earlier studies. Robert and Manolis (2000) observed that age is an
important demographic variable, as consumers of different age groups have different
preference and attitude towards goods and services consumption. According to Venkatesh et
al. (2003), for younger people, performance of a technology has significant influence on their
intention to use the technology, meanwhile for older people, ease of use has more weight in
determining their intention to use the technology.
Education
Educational level has been studied in a number of studies as a control variable which has
certain effect on the consumer behaviors. Kambele et al. (2015) conducted a questionnaire of
700 US and 352 Chinese respondents, result from chi-square test analysis indicated that
educational level has a significant effect on travel behaviors. Specifically, people with college
experience take more trips than others. Claudia (2012) examined the effects of demographic
control variables, including educational level, on consumers’ intention to purchase through the
Internet. Analysis using SEM resulted in that when controlled for educational level, meaning
22
Data and Method
regardless of consumers’ educational level, their intention to use online shopping is predicted
by their attitude towards online shopping and its perceived usefulness.
3.2.1 Conceptual model
The model in figure 3 presents the relationships that will be analyzed in this study.
Figure 3 Proposed conceptual model
4 Data and Method
4.1 Data collection method
The dataset used in this thesis is provided by Department of Information and Service
Management, Aalto University School of Business. The dataset was collected in 2006 as part
of the research work by Lauraeus-Niinivaara for her doctoral dissertations “Uncertainty in
Consumer Online Search and Purchase Decision Making” (2015). The focus of the dissertation
was to study Uncertainty in decision making phases and search determinants related to
uncertainty and purchase satisfaction. This thesis is an attempt to use this same dataset to
examine consumers’ perceived satisfaction and continuance intention to use online information
search, which is an unexplored angle in Lauraeus-Niinivaara’s research (2015).
The dataset was conducted in 2006 in the form of a mail-survey, which took place in
May and June 2006 in Finland. The target list of potential respondents to send the mail-survey
to was ordered from Statistics Finland, and developed by taking a random sample 2000 Finnish
23
Data and Method
residents in the age range of 18-65 years old. To increase the response rate, some tactics used
include offering a €500 gift certificate to be a lottery prize among all participants, reminder
mail-survey to those who failed to respond the to the first survey request. The final number of
respondents achieved was 639 out of 2000, which gives a 32% response rate. (LauraeusNiinivaara, 2015, p.46)
4.2 The profile of the respondents
To make sure the target sample for data collection represents the Finnish population,
Lauraeus-Niinivaara (2015) assessed the demographic variables of the sample and compare it
with most current census figures for the Finnish population, which was Statistics Finland 2000
and Statistics Finland 2004. There are five key demographic variables that are examined.
Gender: Respondents have gender ratio of 54,9% males and 39,6% females. Finnish
population in 2000 has the gender ratio of 48,8% males and 51,2% females. It is interpreted
that as men deem to be use Internet more than women, this difference in the two gender ratios
is acceptable.
Age: Survey respondents are aged from 18 to 80 years old in the year 2006. The age
distribution in the sample corresponds well to the Finnish population.
Education: Education level has certain effects on response rates. Higher education group
has higher response rate compared to lower education group. There were also slight differences
in sample and Finnish population regarding education level distribution, for example:
polytechnic or university education groups have higher percentage in the sample (25,51% and
15,02%, respectively) than in Finnish population (12,6% and 10,3%). It could be explained
that more educated people may have more experience with Internet usage, thus more willing
to answer to this questionnaire.
Income: Sample group is in average have higher income than the Finnish population.
Location of residence: The distribution of residence location in the sample group and in
Finnish population are not very well corresponding. For example, urban or semi-urban
municipality group takes 6,1% in the sample and 16,5% in Finnish population. It could be
explained that in the urban or semi-urban municipality people find it less convenient to respond
to the mail-survey (to submit or send the responses back to the researcher).
24
Data and Method
Table 1 Demographics of the sample
Demographic variable
Gender
Data %
Population* %
Male
Female
Missing
Total
54,9
39,6
5,5
100
48,8
51,2
Comprehensive school education
Upper secondary school education
Vocational and professional education
Polytechnic education
University education
Total
19,87
7,82
24,88
25,51
15,02
100
41,5
22,9
12,7
12,6
10,3
Under 3,000 €
3,000 - 4,999 €
5000 - 9,999 €
10,000 - 13,999 €
14,000 - 19,999 €
20,000 - 24,999 €
25,000 - 29,999 €
30,000 - 39,999 €
40,000 - 49,999 €
50,000 - 59,999 €
60,000 - 79,999 €
Over 80000 €
Missing
Total
4,85
3,44
2,82
5,16
7,04
10,8
7,67
11,27
10,95
7,67
7,67
6,73
13,93
100
7,8
3,34
17,23
12,39
14,23
12,47
9,77
10,43
4,53
2,01
1,66
1,47
The Metropolitan area
Town, > 45,000 inhabitants
Town, < 45,000 inhabitants
Urban or semi-urban municipality
Rural Municipality
Others
Missing
Total
20,34
19,25
25,04
6,1
19,87
1,25
8,14
100
18,3
21
21,1
16,5
23,1
Education
Yearly income
Community size
*Population statistics from Statistics Finland 2000 and 2004
To conclude, there are certain differences in the demographic variables between the
sample and the Finnish population. However, most of the differences are not extreme and can
be well-explained by the characteristics of the Finnish population. It is thus assumed by
25
Data and Method
Lauraeus-Niinivaara that the data represents the Finnish population to an acceptable extent,
further analysis can be conducted using this dataset. (Lauraeus-Niinivaara 2015, p.48)
4.3 Variable measures
Table below presents the variables chosen from the original dataset to be used in this
analysis, which focused on pre-purchase uncertainty, search type, purchase satisfaction and
continuance intention of online information search.
Table 2 Research items
Construct
Pre-purchase
Uncertainty
(Uncertainty)
Search type
(Stype)
Purchase
Satisfaction
(Satisfaction)
Continuance
Intention of
online search
(Intention)
PU1
PU2
PU3
PU4
IteSearch
ConSearch*
SimSeach*
SAT1
SAT2
SAT3
INT1
INT2
INT3
Items
I felt uncertain about knowning the travel services offered
I felt uncertain about my purchase criteria
I felt uncertain about which alternative to choose
I felt uncertain that I would be able to buy the alternative I had chosen
I iterated the purchase process when searching for and comparing alternatives
I searched for and evaluated each travel, one at a time, before turning to the next alternative
I used search search agent or comparison shopping tool for searching alternative travels at the same time
I am satisfied with the trip
I am satisfied with the price of the trip
I could not find any better trip with the time I spent searching
I aim to search travel information in internet
Next time, I would search travel information in internet
I believe to be more interested to search travel information in internet
The variables in the table above were evaluated on a Likert scale with value ranging from
1 to 7. Value 1 means “Totally disagree” and value 7 means “Totally agree”.
In addition, there are three control variables (Age, Gender and Education) and
moderating variable of Internet usage experience, which are included in the analysis.
Table 3: Control variables
Control variable
Age
Gender
Education
Abbreviation
AGE
GENDER
EDU
Age is continuous variable, measured in years.
Gender is binary variable with two values 0 and 1. Value 0
means Female and value 1 means Male.
Education is categorical variable, with value ranging from 1 from 5.
1 = Primary school/Junior High School
2 = High School/Matriculation examination
3 = Vocational college
4 = College graduate
5 = University graduate
26
Data and Method
YearInt measures the number of years that a respondent has been using the Internet; it is a
continuous variable.
4.4 Analysis Method
The method elected to use to analyze the dataset is two-step Structural Equation
Modeling (SEM). SEM is widely used by IS researchers to quantitatively evaluate relationships
among multiple variables. SEM belongs to the second generation data analysis techniques
(Petter et al., 2007), which has a dramatic advances compared to the first generation techniques
(linear regression, ANOVA and MANOVA). The first generation techniques are based on
correlations to find relationships among different phenomena of interest. The second
generation techniques (eg: Lisrel, Mplus, PLS) are based on covariance analysis, which
provides more accurate results of the research model, and allows researchers to perform factor
analysis and hypothesis testing at the same time (Gefen et al., 2000).
SEM is considered a confirmatory method, which must be employed following a solid
theoretical arguments to build the relationship model to be tested. Data is collected to assess
the goodness of fit between the hypothetical model and the empirical evidence (Hair et al.
2010). SEM model is comprised of two part: Measurement Model and Structural Model.
Measurement Model is used to establish constructs of the overall model by assigning
measurement variables for each construct. Structural Model in SEM is used to evaluate the
characteristics of relationship among reflective research constructs, and does not accept
formative constructs. (Petter et al., 2007). To distinguish reflective and formative constructs, it
is noted that reflective construct have direction of causality from construct to measure and
correlated measures. In the opposite, formative constructs have direction of causality from
measure to construct, and uncorrelated measures. (Gaskin, 2012; Jarvis et al. 2003)
There are two key approaches of SEM: Covariance-based (eg: Amos, LISREL, MPlus)
and Partial Least Square (PLS) (eg: PLS-Graph). According to Gefen et al. (2000), one of the
key difference between the two approaches lies in their statistical assumptions. PLS is suitable
for formative model, with a small sample size and/or variables that are not normally distributed.
Covariance-based SEM is suitable for reflective model with large sample size and normally
distributed variables. Using formative measurement models is more logical and easier in PLS
SEM rather than covariance-based SEM.
SPSS Amos, which belongs to the covariance-based SEM approach, is the software
offered for students at Aalto University, and it is suitable for the dataset of interest (reflective
27
Data and Method
model, large sample size, normally distributed variables). Thus it is chosen to be the analysis
method for this thesis.
4.5 Data analysis
4.5.1 Data Screening
Missing data
The original dataset has 639 cases and 40% missing values. According to Hair et al.
(2010, p.48), cases with 50% or more missing data are intractable and should be discarded.
After removing these poor quality cases, the dataset has 383 cases and missing values of 8%.
Among 38 variables chosen from the questionnaire for this analysis, three variables of type of
search have highest percentage of missing values (13.2% for each variable). Gender has the
lowest percentage of missing data (0%).
The separate-variance t tests indicate that AGE is the only variable whose pattern of
missing values influences the missing patterns of other quantitative (scale) variables. For
example: older people are more likely to have missing values in their responses for the question
of grading “your skill of using Internet”. Age mean of the group that answer to this question is
45 years old, while the Age mean of the group that does not answer this question is 62 years
old. This can be explained that people of older age group may be less familiar with or less
extensively use the Internet, so that they are less willing to report their self-assessment of
Internet usage skills. For other variables, the differences in Age group mean also are
significant.
Cross-tabulation tables of category variables (Gender, Education) also indicate that there
are certain differences in mean of missing values across categories. These result from crosstabulation and separate-variance t tests suggest that the data may not be missing completely at
random. To confirm this, the data was put to perform the Little's MCAR Test. The null
hypothesis for Little's MCAR test is that the data are missing completely at random (MCAR).
Data are MCAR when the pattern of missing values does not depend on the data values.
Because the significance value is less than 0.05 (p < 0.05), it is concluded that the data is not
missing completely at random. The dataset is thus imputed using Multiple Imputation method
in SPSS.
28
Data and Method
Unengaged responses
All 383 cases have standard deviation of 0.7 or more. Low standard deviation value (less
than 0.5) indicates that the respondent answer to all the questions similarly to undesirable
extent, for example, choose all answers as 5. Thus, the dataset does not contain unengaged
responses.
Outliers
All variables, except for Age and YearInt (number of years that the respondent has been
using the Internet), were on ordinal scales with 7 or fewer intervals, thus there are no extreme
values or outliers for these variables. For Age variable, a box plot was examined, and no outlier
was found. All observations range from age 18 to 80, and are centered on the mean of 44. For
YearInt, boxplot shows several outliers but none of these outliers are necessarily invalid. For
example, the most extreme outlier identified by the boxplot is the case ID 231, Age = 50 and
YeartInt = 25, which is not unrealistic. Thus, no outliers need to be removed from the dataset.
For further analysis of multi group effects, respondents are divided into two groups based
on their Internet usage experience: YearIntLow and YearIntHigh. YearIntLow includes the
respondents who have less than 8 years of using Internet (YearInt has median of 8 years).
YearIntHigh includes corresponds to respondents who has more than 8 years of using Internet.
Normality
To check normality, data was examined for variables’ kurtosis and skewness. Skewness
is used for continuous variables and Kurtosis is used for ordinal variables like Likert-scales
variables. Kurtosis and Skewness statistics both indicate whether the shape of the data
distribution is well corresponding to the Gaussian distribution. A Kurtosis or Skewness value
of 0 represents a Gaussian distribution or normal distribution. Negative value indicates a flatter
distribution, and vice versa (SPSS Statistics Version 23 Manual guide). The values for
Skewness and Kurtosis between -2 and +2 are considered acceptable in order to prove normal
univariate distribution (George & Mallery, 2010).
Variables that have high Kurtosis value are presented in Table 4 below (the rest all have
Kurtosis value with absolute value less than 2). These are the variables with distribution of
values very small, centered around the median, which mean that people answer very similarly
to each of these questions in the questionnaire.
29
Data and Method
Table 4 Variables with high kurtosis
N
Valid
Missing
Kutosis
PU2
PU3
PU4
SAT1
SAT2
383
383
383
383
383
0
0
0
0
0
2.493
2.296
2.18
3.604
4.37
The variables in the Table 4 have kurtosis value greater than 2 and thus suggest possible
problematic kurtosis (Finney & DiStefano 2006). Nevertheless, according to Hair et al. (2010,
p.605), all normal distribution estimation methods still work even if the normality is
moderately violated. Hence, the issue was simply noted and the analysis was carried on.
4.5.2 Exploratory Factor Analysis
In order to build measurement model, firstly Exploratory Factor Analysis (EFA) was
performed using Principle Component with Varimax rotation. As EFA cannot be used for
categorical variable, search type variable (IteSearch) is not yet included in this step. The
analysis of EFA includes variables of Uncertainty, Satisfaction and Intention. Result of EFA
was discussed below considering factor loadings, correlation adequacy, criteria of reliability
and validity.
Table 5 Rotated Component Matrix (EFA result)
Rotated Component Matrix
a
Component
1
2
The extracted three-factor model had a total
3
PU2
.854
variance explained of 69% and all extracted factors
PU3
.840
PU4
having eigenvalues above 1. According to Hair et al.
.728
PU1
.663
(2010, p.662), with a minimum sample size of 300,
INT2
.944
INT1
.927
INT3
.840
measurement model can have seven or fewer
constructs, lower communalities (below 0.45)
SAT2
.800
and/or multiple under-identified (fewer than three
SAT1
.795
indicators) constructs.
SAT3
.760
Extraction Method: Principal Component
Analysis.
a. Rotation converged in 4 iterations.
Adequacy
The Kaiser-Meyer-Olkin Measure of Sampling Adequacy refers to a statistic that
specifies the extent to which variance of the variables can be explained by underlying factors.
High value of KMO test (close to 1.0) suggests that there may be underlying factors, thus a
30
Data and Method
factor analysis should be conducted to find out these factors. KMO test value of less than 0.5
means that there is no underlying factor to be examined. Bartlett’s test of Sphericity examines
the possibility that variables are unrelated and thus structure detection is not useful.
Significance level of the test with small value (less than 0.05) suggests that factor analysis is
useful and should be conducted. (SPSS Statistics Version 23 Manual guide). Table below
presents result of KMO and Bartlett’s Test.
Table 6 KMO and Bartlett’s Test result
KMO value of 0.709 and
Bartlett’s
test
significance
level less than 0.05 indicate
that there may be underlying
factors, and that variables are not unrelated. Thus, a factor analysis will be useful
Table 7 Communalities
The communalities for each variable was well above 0.3. This
indicates that the variables chosen for the analysis were
adequately correlated for a factor analysis.
Reliability
The Cronbach’s alphas for the extracted factors are shown below, along with their labels
and specification. All Alphas were above 0.7, which is the recommended threshold for the
fulfillment of construct reliability. Satisfaction has Cronbach’s Alpha of 0.699, very close to
the threshold and is considered acceptable in this case. The factors are all reflective because
their measurement items (indicators) are highly correlated and largely interchangeable (Jarvis
et al. 2003).
31
Data and Method
Table 8 Constructs’ reliability
Factor Label
Uncertainty
Satisfaction
Intention
Nr of items
4
3
3
Cronbach's Alpha
0.771
0.699
0.893
Specification
Reflective
Reflective
Reflective
Validity
The factors indicate sufficient convergent validity: factor loadings (see table 5 - Rotated
Component Matrix) were all above the threshold requirement of 0.300 for a sample size of over
300 observations (Hair et al., 2010). The discriminant validity of the factors is also satisfied,
as the correlation matrix shows that all correlations are below 0.700, and that there is no cross
loading among the factors extracted.
4.5.3 Measurement model
Model Fit
The measurement model was built in SPSS Amos, and based on the modification indices,
the measurement model was improved by adding covariance between the error terms e2 of PU2
and e1 of PU1. The table below present the satisfied goodness of fit for the measurement model.
Table 9 Model fit statistics of measurement model
Metric
Observed value
Recommended
cmin/df
1.093
Between 1 and 3
CFI
0.998
> 0.950
RMSEA
0.009
< 0.060
PCLOSE
1
> 0.050
SRMR
0.0313
< 0.090
Validity and Reliability
There are a few measures that are useful for establishing validity and reliability:
Composite Reliability (CR), Average Variance Extracted (AVE), Maximum Shared Variance
(MSV), and Average Shared Variance (ASV).
32
Data and Method
Convergent validity refers to the extent to which “indicators of a specific construct
converge”, i.e., share a high proportion of variance in common (Hair et al., p. 689). To evaluate
convergent validity, AVE was calculated. An AVE of above 0.5 is recommended to satisfy the
convergent validity requirement. Two factors Uncertainty and Satisfaction has AVE scores of
just close to 0.5. According to Gaskin (2012), convergent validity issues suggest that the
variables chosen in the analysis do not correlate well with each other within their parent factor;
i.e., the latent factor is not well explained by its observed variables. However, because the
reliability scores of these two factors are 0.781 and 0.722, respectively, which are greater than
0.700, this case is tolerable. This can be interpreted about the measurement model as, even
though not particularly strong internally, it is a reliable and comprises of distinct constructs
within the model.
Table 10 Validity and Reliability statistics
CR
AVE
MSV
ASV
Intention Uncertainty Satisfaction
Intention
0.900
0.752
0.016
0.010
0.867
Uncertainty
0.781
0.493
0.189
0.097
-0.073
0.702
Satisfaction
0.722
0.473
0.189
0.102
0.123
-0.436
0.688
Next, discriminant validity was examined. It refers to the extent to which a construct is
distinct from other constructs (Hair et al., 2010, p. 689). Discriminant validity was evaluated
by comparing the square root of AVE (bolded values on the diagonal in table 10) to all interfactor correlations (table 11). All factors indicated sufficient discriminant validity as the
diagonal values are greater than the inter-construct correlations.
Table 11 Inter-factor correlations
Correlation
Estimate
intention
<--> uncertainty -0.073
satisfaction <--> uncertainty -0.436
intention
<--> satisfaction 0.123
e1
<-->
e2
0.397
Also, MSV and ASV values are smaller than the corresponding AVE for each construct. It is
thus concluded that the measurement model fulfills discriminant validity requirements.
33
Data and Method
To evaluate measurement model’s reliability, composite reliability (CR) for each
construct was computed and presented in table 10. As CR values are greater than 0.7 for all
constructs, it indicates that the measurement model has good reliability.
Common Method Bias
Common Method Bias refers to a bias in the dataset, which leads to the undesirable
situation that a majority of the variance in the variables can be explained by a single factor.
Common Method Bias is caused by something external to the measures and it influenced the
respondents’ answers. One typical cause of Common Method Bias is collecting data with one
single method, for example, online survey or interview (Gaskin, 2012).
Because the mail questionnaire was used as the sole instrument to gather data for both
independent variables and dependent variables in this study, there is certain risk that a method
bias had affected the results of the measurement model. To examine this issue, a common
method bias test was performed. The test in use was the “unmeasured latent factor” method,
suggested by Podsakoff et al. (2003) for studies that do not directly measure a common factor.
Standardized regression weights before and after adding the Common Latent Factor (CLF)
were computed and compared. Results show that the regression weights of several paths
coming from construct Uncertainty to indicators PU3 and PU4 are dramatically affected by the
CLF, with differences more than 0.200 as shown in table 12.
This suggests that Uncertainty
Table 12 Effect of Common Latent Factor
Path
Standardized Regression Weight
Without CLF
With CLF
may be affected by common method
bias. Thus, it is advised by Gaskin
PU3 <---uncertainty
0.925
0.551
(2012) to retain the CLF when
PU4 <---uncertainty
0.62
0.026
moving into analyzing the structural
model.
Invariance test
Before building and analyzing the structural model, measurement invariance among
groups is examined in order to ensure that measurement models conducted under different
conditions (i.e., with different sample groups) will result in “equivalent representations of the
same construct” (Hair et al., 2010, p. 759). Measurement invariance includes configural
invariance and metric invariance, which should be tested to ensure that the dataset is similar
enough across groups to be analyzed jointly.
34
Data and Method
Measurement model invariance tests are conducted with grouping variable: YearInt
(which categorizes respondents into two groups based on the number of years a respondent has
been using the Internet). Two groups YearIntLow (people with less than 8 years of Internet
usage; 8 is the median of number of years of Internet usage for the sample dataset) and
YearIntHigh (people with more than 8 years of Internet usage)
Configural invariance
Configural invariance refers to the event where the same factor structure exists in all of
the groups (Hair et al., 2010, p.760). Model fit of measurement model with two groups set up
is presented in table below.
Table 13 Configural invariance test – measurement model goodness-of-fit
Metric
Observed value
Recommended
cmin/df
1.058
Between 1 and 3
CFI
0.998
> 0.950
RMSEA
0.012
< 0.060
PCLOSE
1.000
> 0.050
SRMR
0.0389
< 0.090
The measurement model achieves adequate fit when both groups are tested together and
freely (i.e., without any cross-group path constraints). Thus, configural invariance is fulfilled,
which means that in both groups YearIntLow and YearIntHigh have the same basic factor
structure.
Metric invariance
Metric invariance evaluates the extent to which factor loading estimates are equivalent
across groups. When metric invariance is fulfilled, it means that the respondents answer to the
questionnaire with the rating scales similarly across groups, thus the differences between
values can be compared directly (Hair et al. 2010, p.690). To test for metric invariance, a multi
group moderation test was conducted using critical ratios for differences in SPSS Amos.
35
Data and Method
Table 14 Multi-group moderation test result
YearIntHigh
YearIntLow
Estimate P-value z-score
Estimate P-value
Path
1.307
0.000
1.519
0.000
1.278
INT1 <--- Intention
0.982
0.000
1.545
0.000
1.354
INT2 <--- Intention
1.352
0.000
1.300
0.000
0.917
SAT1 <--- Satisfaction
1.062
0.000
1.066
0.000
0.807
SAT3 <--- Satisfaction
-0.301
0.000
0.633
0.000
0.682
PU1 <--- Uncertainty
0.928
0.000
1.384
0.000
1.206
PU3 <--- Uncertainty
1.455
0.000
1.151
0.000
0.894
PU4 <--- Uncertainty
Note: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10
All z-score of the paths examined in the measurement model are not significant, meaning that
the measurement model has satisfied metric invariance. The measurement model passed both
of the invariance tests, thus it is concluded that the dataset across groups are similar enough
to be analyze jointly. Thus the data is suitable to create composite variables for path analysis
of structural model.
36
Data and Method
The hypotheses to be examined with the structural model are restated below, with
additional hypotheses regarding the mediation effects.
Hypotheses
Main effects
H1: Pre-purchase uncertainty is related to purchase satisfaction.
H2: Iterative search is positively related to purchase satisfaction.
H3: Purchase satisfaction is positively related to Continuance intention of using
information search.
Multi-group effect
H4: Internet usage experience moderates the positive effect of purchase satisfaction on
continuance intention of using online information search such that the effect is stronger for
higher Internet usage experience.
Mediation
H5a: Purchase satisfaction mediates the negative relationship between Uncertainty and
Continuance Intention of using online information search.
H5b: Purchase satisfaction mediates the positive relationship between Iterative search
and Continuance Intention of using online information search.
4.5.4 Structural Model
Create Composites from factor scores
Composite variables were created using factor scores in SPSS Amos with CLF being
added to the model. The measurement model was transformed into a structural model, which
is the research model of this study. First the SEM model is examined for model fit, and next
will be used to test the proposed hypotheses.
Multivariate assumptions
Linearity
To test linearity, a curve estimation regression was performed for all direct effects in the
model (linearity test between two particular constructs in the model). The results show that all
37
Data and Method
direct effects in the model are sufficiently linear to be tested using covariance-based SEM
algorithms.
Multicollinearity
Multicollinearity refers to the extent to which two or more variables have linear
dependence. High level of multicollinearity is undesirable as it will be difficult to interpret the
effect of each variable on the inter-relationships in the analysis. (Hair et al., 2010, p.633)
To test multicollinearity, Variance Inflation Factor (VIF) was examined. It measures how
much the variance of regression coefficients is inflated by multicollinearity issues. VIF value
between 0 and 10 indicate acceptable correlation between independent measures. The VIF
value between IteSearch and Uncertainty is 1, thus the current model has no multicollinearity.
Model fit of structural model
The fitted structural model indicates adequate fit. The fit statistics are within the
recommended range, as presented in table 15.
Table 15 Model fit statistics of structural model
Metric
Observed value
Recommended
cmin/df
1.335
Between 1 and 3
CFI
0.990
> 0.950
RMSEA
0.030
< 0.060
PCLOSE
0.646
> 0.050
SRMR
0.019
< 0.090
38
Data and Method
Table 16 Regression weights of structural model
Path
Satisfaction <--- Uncertainty
Satisfaction <--- IteSearch
Intention <--- Satisfaction
Satisfaction <--- GENDER
Satisfaction <--- AGE
Satisfaction <--- EDU
Intention <--- GENDER
Intention <--- AGE
Intention <--- EDU
Estimate p-value
-0.505
***
0.152
*
0.272
**
-0.043
n.s.
-0.003
n.s.
0.004
n.s.
0.088
n.s.
-0.038
***
0.29
***
*** p < .001; ** p < .01; * p < .05
According to the regression weights results of the SEM model, all hypotheses are
statistically significant. Specifically, the results support the effect of Satisfaction on
Continuance Intention (coefficient = 0.272 and p < .01, supporting H3. Satisfaction is driven
by Uncertainty with path coefficient = -0.505 and p < 0.001. Thus, H1 is supported. According
to Chin (1998), path coefficients should be at least 0.2 for the SEM model to be meaningful.
Thus, the path (Satisfaction  Iterative Search) is not sufficiently strong. H2 is not supported.
Together, the model account for 19.7% of the observed variance in Continuance intention
of using online information search. The model can explain 15.6% of the observed variance in
Purchased satisfaction.
Control variables
Also known as moderated mediators or conditional indirect effect, control variables are
potentially confounding variables that should be accounted for, but they do not belong to the
focus of the study. In this study, the control variables include Education, Age and Gender.
From Regression table results above (table 16), it is concluded that: Among the control
variables examined, Age and Education have significant effects on Continuance Intention (p <
0.001). Education has stronger and positive effect, coefficient = 0.29. Age has negative and
much weaker effect, coefficient = -0.038.
This means that: people with more education are more likely to continue using online
information search, as compared to people with less education. Also, older people have lower
intention to continue using online information search. Gender does not have any significant
effect on any of the dependent variables.
39
Data and Method
Multi-group effects
Variable YearInt (number of years have been using Internet) is a continuous variable
with median value of 8 (years). YearInt is tested to see if Internet usage experience has any
moderating effect on the relationship between Uncertainty and Intention.
Table 17 Moderating effect of Internet usage experience
YearIntLow
YearIntHigh
Estimate
P
Estimate
P
satisfaction <--- uncertainty
-0.534
0.000
-0.459
0.000
satisfaction <--- IteSearch
0.222
0.031
0.077
0.484
intention <--- satisfaction
0.326
0.022
0.197
0.113
Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10
z-score
0.577
-0.968
-0.679
As the z-score is not significant for all path examined, it indicates that there is no
significant difference between two groups. Multi-group effect analysis concludes that the
negative effect of Uncertainty onto Intention, mediated by Satisfaction, is not
moderated/affected by YearInt (number of years using Internet). H4 is not supported.
Mediation
To examine the effect of Satisfaction as a mediator on the relationship between by
Uncertainty and Intention, a mediation test with Baron and Kenny approach (Zhao et al., 2010)
was conducted. The test first examined the effect without mediator (Satisfaction), and later
examine with mediator. Results are shown in table 18 below.
Table 18 Mediation effect testing
Relationship
Direct without Mediator Direct with Mediator
Uncertainty (Satisfaction) Intention
-0.383 (p = 0.004)
-0.282 (p = 0.047)
IteSearch (Satisfaction) Intention
-0.043 (Not sig.)
-0.073 (Not sig.)
From the table above, it can be seen that when mediator is present or not, the effect is
significant (p-value < 0.05), it indicates that this path has a partial mediation effect. In the other
words, both direct and indirect effects from the independent variable (Uncertainty) to
dependent variable (Intention) are significant. Together with results from regression weights
40
Findings
table (table 16), it can be concluded that Satisfaction negatively and partially mediates the
negative relationship between Uncertainty and Intention. H5a is supported.
The second path examined: effects of Iterative Search as mediated by Satisfaction onto
Intention: are not significant as p-values of both effects are less than threshold value 0.05. H5b
is not supported.
5 Findings
The purpose of this study is to examine the effects of pre-purchase uncertainty and
information search strategy on purchase satisfaction and continuance intention to use online
information search. Also Internet usage experience was examined to see whether it influences
the aforementioned main effects. To achieve this, the study develops hypotheses based on prior
literature and test the conceptual model on the dataset of Finnish residents searching travel
information. Analysis summary is presented in table 19 and key findings are as follows.
Table 19 Result of SEM
H1: Pre-purchase uncertainty is related to purchase satisfaction.
H2: Iterative search is positively related to purchase satisfaction.
H3: Purchase satisfaction is positvely related to Continuance intention of using online
information search.
H4: Internet usage experience moderates the positive effect of purchase satisfaction on
continuance intention of using online information search such that the effect is stronger
for higher Internet usage experience.
H5a: Purchase satisfaction mediates the negative relationship between Uncertainty and
Continuance Intention.
H5b: Purchase satisfaction mediates the positive relationship between Iterative search
and Continuance Intention.
Supported
Not supported
Supported
Not supported
Supported
Not supported
Firstly, in the hypothesized model, Uncertainty and Search type together account for
19.7% of the observed variance in Continuance intention of using online information search.
The model can explain 15.6% of the observed variance in Purchased satisfaction. Compared to
previous studies such as the IS continuance model by Bhattacherjee (2001), which studied the
antecedents of perceived satisfaction and continuance intention of information system (in
which 43% of variance of continuance intention of IS use was explained by the independent
constructs), the proposed model in this thesis does not have very strong explanatory power.
This could be a result of the scope of the study being too narrow, focusing on only two
exogenous constructs Uncertainty and Search type to predict Purchase satisfaction and
41
Findings
Continuance intention to use online information search. Despite the limited explanatory power
of the overall model, some of the key hypotheses examined are sufficiently supported. Purchase
satisfaction is strongly influenced by pre-purchase uncertainty. In turn, purchase satisfaction is
a strong predictor of Continuance intention to use online information search. The effect of
Iterative search as a search strategy on purchase satisfaction is not significant.
Secondly, there is no significant difference between two groups divided by Internet usage
experience. Multi-group effect analysis concludes that the negative effect of Uncertainty onto
Intention, mediated by Satisfaction, is not moderated by Internet usage experience. However,
this finding could be resulted from the dividing of respondents into two groups based on the
number of years that they have been using Internet. This way of grouping may not give
meaningful result to analyze group differences, for example, a person who has fewer years of
using Internet but use it extensively, may have a better understanding of Internet usage and
search skill than a person who has been an Internet user for many years but use it sparsely.
Thirdly, certain demographic variables have significant effects on information search
behavior. Specifically, people with more education are more likely to continue using online
information search, as compared to people with less education. Older people have lower
intention to continue using online information search. Gender does not have any significant
effect on any of the dependent variables.
Fourthly, Satisfaction negatively and partially mediates the negative relationship
between Uncertainty and Intention. This can be interpreted as even in the case of having prepurchase uncertainty, people may still have continuance intention to use online information
search in the future if the previous purchase was considered satisfactory. The effects of Iterative
Search as mediated by Satisfaction onto Intention is not significant.
To sum up, the key findings of the study include that pre-purchase uncertainty is strongly
related to perceived satisfaction, which in turn is a strong predictor of continuance intention to
use online information search. The result is also summarized in figure 6 below.
42
Conclusion
Figure 4 Results of SEM analysis
6 Conclusion
The present study offers several contributions to the knowledge stream and practice of
consumer information search. The initial intent to carry out the study is to examine prepurchase uncertainty, search type and their effects on consumers’ perceived satisfaction and
continuance intention to use online information search. Findings show that uncertainty has
negative effect on perceived satisfaction and on continuance intention to use online information
search. Purchase satisfaction is a strong predictor of continuance intention to use online
information search.
Moreover, in circumstances when consumers have pre-purchase uncertainty but have
satisfactory purchase, they will be more likely to look forward to conducting information
search through the Internet in the future. Even though the study cannot come to conclusion
about the effects of Internet usage experience on consumers’ information search behavior, the
results suggest that Age and Education, as control variables, have significant effects.
Specifically, older people are less likely to use online information search, people with lower
level of education are less likely to use online information search.
43
Conclusion
From the time of collecting this dataset in 2006 until now in year 2016, there has been
dramatic shift in how people search for information about products and services, especially for
people looking for information to plan their travels, thanks to technology advancement that
makes personal computer and Internet easier to access than ever before. It is no doubt that the
Internet has taken over as the dominant channel for information search. Study by D'ambra and
Wilson (2004) is one of the empirical studies that confirm the effectiveness of Internet as an
information source, with the power to reduce uncertainty and minimize costs of information
seeking.
Even though the context of study has changed, the knowledge reviewed and findings
resulted from the present study still have certain relevant contributions. Generally the findings
help marketer and system designers to enhance their understanding of consumer information
search behavior, the relationship among the elements of pre-purchase uncertainty, perceived
satisfaction and continuance intention to use online information search. With proper
segmentation based on demographic factors like age and educational level, there should be
tailored communication strategy for separate consumers groups. With the findings show that
more senior people and people with fewer years of education are less likely to rely solely on
the Internet as the information source, it is suggested that marketers design their promotional
mix which include both online and offline traditional information channels. To improve the
online information search and online shopping experience for consumers, websites should be
developed with integration of product comparison tools and recommendation agents. As a key
finding of the present study, perceived satisfaction of an online purchase is a strong predictor
of continuance intention to use online information search. Shim et al. (2001)’s study also
confirmed that intention to use online information search strongly influences consumers’
intention to shop through the Internet. Thus, online purchase satisfaction and satisfied online
information search are complementary in converting a consumer into a loyal customer of ecommerce. As e-commerce benefits companies in terms of reducing logistics costs and
extending coverage to prospective markets, investments in improving quality of online
information presence will increase the dominance of e-commerce, and consequently result in
greater economic gains.
44
Limitation and suggestions for future research
7 Limitation and suggestions for future research
The present study has a number of limitations. Firstly, as recognized earlier, data
collection was conducted in 2006, and since then the context of the study has changed
significantly. Specifically, usage of the Internet as the primary information resource and
shopping channel have become much more common in Finland in present day compared to in
2006. Secondly, the study was set out to have a narrow focus, which resulted in a conceptual
model with limited explanatory power. Thirdly, the formation of the variable Internet usage
experience is not fruitful. Merely counting on the number of years that respondents have been
using Internet is not a reliable method to measure their skills or ability to conduct information
search through the Internet. This limitation has resulted in that the study do not have a
meaningful interpretation of the effect of Internet usage experience on consumers’ information
search behavior.
Future research in the same knowledge stream will benefit from re-conducting the
analysis using a more current dataset, especially when the limitations recognized above are
sufficiently resolved. Besides, the present study of information search behavior was conducted
in the context of travel service information search in Finland. Further research of online
information search behavior in another industries and in different geographical regions will
bring different perspectives to this knowledge stream.
45
Appendix: Travel service survey questionnaire
Appendix: Travel service survey questionnaire
1.
Your gender
[ ] Female
[ ] Male
2. Year of birth ______________________
3. Education (Please tick only the highest level attained)
[ ] Primary school/ Junior High School
[ ] High School / Matriculation examination
[ ] Vocational college
[ ] College graduate
[ ] University graduate
4. a) I have used internet since year of_________.
b) I use Internet _________ hours weekly, _________ days every week,
of which the e-mail usage is _________ hours weekly.
5. a) How would you grade your skills of using the Internet (using the school grading 410)?_______
b) How would you grade your skills of searching the information on the Internet (410)?_______
c) How would you grade your skills of searching and comparing travel services (410)?_______
6. What did you think, when you began to search your latest trip?
uncertain
certain 
I had uncertainty of my own knowledge about the
alternatives
I had Uncertainty about my decision criteria to
conduct my choice
I was uncertain of which product to choose
I was uncertain of being able to purchase the product
I have chosen already in my mind
7. Which of following alternatives describes best your search behavior?
[ ] Sequential search (I searched and evaluated each of trip as a whole entity before going on
to the next alternative)
[ ] Simultaneous search (I searched trips with search agent, I compared several alternatives in
the same time)
[ ] Iterative search (I searched and evaluate each trip before moving to next alternative and I
returned back to earlier alternative.)
8. Are you satisfied with your latest trip?
46
Totally disagree

Totally
agree

I am satisfied with the trip
I am satisfied with the price of the trip
I could not find any better trip, fast seeking more
9. What is your opinion about the following statements about seeking travel information?
Totally
disagree

I aim to search travel information in internet
Next time, I would search travel information in
internet
I believe to be more interested to search travel
information in internet
47
Totally agree

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