Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
What Drives Readers to Follow Recommendations from
Bloggers?
Hernan E. Riquelme* and Muna H. Saeid*
Recent reports indicate the number of bloggers has increased considerably throughout the
world and more and more people are using blogger’s recommendations. However, little is
known about what drives blog readers to follow recommendations from bloggers. A survey
of 264 bloggers in Kuwait yields the following results: Blog contributors are more prone to
follow bloggers’ recommendations than just blog readers. Self-reported perceived
usefulness of the recommendation, the attitude towards blog recommendations and the
past and current blog behaviours namely the number of previous recommendations
undertaken and the number of blogs visited per week respectively predict the intention to
follow a recommendation.
Introduction
More and more internet users are moving from simply browsing the internet to creation of
content through weblogs or just blogs (Hsu & Lin, 2008). Blogs have increasingly
become a crucial online communication channel for sharing recommendations, ideas,
and experience of products and services among the public, and bloggers (the
contributors) are influencing the decisions of blog readers and consumers in general
(Baker & Moore, 2008).
A recent survey on how consumers read reviews and are affected by them
(BrightLocal.com, 2013) shows there is a positive trend in user's adoption of someone
else’s recommendation prior to their purchasing and that consumers trust and appreciate
online recommendations. The survey notes that 72% of worldwide consumers rely and
trust online recommendations compared to 69% in 2010, indicating the increase in
importance of the usefulness of recommendations. This increase in reliance on bloggers
is interesting considering that many bloggers, due to their reputation and diffusion power,
may provide biased recommendations; many of the most popular bloggers are said to
participate in Google AdSense implying they may monetize their comments and advice
(Ko, 2011).
This research aims to understand the factors that drive blog readers’ intention to follow a
blogger's recommendation. The main research questions guiding this study are: What
influences blog readers to follow bloggers’ recommendations? Are blog readers more
prone to follow bloggers’ recommendation than blog contributors? If so, how do they
differ? Does past behaviour (e.g. followed recommendations) predict future behaviour?
This empirical study is important in view that companies are trying to influence
consumers by using bloggers, therefore learning more about what persuades consumers
______________________________________________________________________
Prof. Hernan E. Riquelme* and Ms. Muna H. Saeid*, Both authors are affiliated to Kuwait Maastricht
Business School (KMBS).
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
in a blog environment will help companies to enlist individuals with the appropriate
characteristics. Also, many bloggers, because of their popularity, are enlisted by
companies thus compromising their objectivity. However the general public may not be
aware of their personal connections with firms, in some other cases, A-listed bloggers
participate in Google AdSense providing ads on their blogs. Finally, investigating
bloggers’ recommendations is important since their recommendations may have
unintended repercussions, especially in some areas such as health, finance, make-ups,
etc.
Current research on blogging has explored several areas namely factors that influence
blogging intention and continue to blog (Miura & Yamashita, 2007); behavioural
differences between female and male bloggers (Pedersen & Macafee, 2007) on filter,
personal journals and notebooks; drivers that move readers to visit blogs; blog stickiness
(H. P. Lu & Lee, 2010); blog privacy concerns; blog credibility (Burleon & Lowrey, 2011;
Mertzger, Flanagin, & Medders, 2010; Sweetser, Porter, Chung, & Kim, 2008) and
characteristics of A-type blogger (Ko, 2011; Trammell & Keshelashvili, 2005).
However not much research has been done from the perspective of the blog reader, in
particular, what persuades them to follow recommendations? This study builds on
previous literature on web site and product reviews credibility and also on concepts from
technology acceptance and sociological frameworks.
This study examines the relative effects of blogger characteristics to induce trust in the
recommendation and its subsequent effect on creating a positive attitude towards
recommendations from bloggers. From the technology acceptance model, we borrow the
concept of usefulness to explain why blog readers intend to follow recommendations. We
add to our predictors (attitude and usefulness) previous blogging behaviors such as
whether the individual has followed bloggers’ recommendations in the past, the number
of time they spent blogging and the number of blogs visited.
Although there are existing studies, specifically regarding the impact of reviews and
recommendation provided on websites, there is not much referring to weblogs in
particular. Thus, our study contributes to the emerging literature on blog as influencers
and blog readers as followers of recommendations from bloggers.
The research objectives of our study can be summarized as follows: (1) To understand
what drives intention to follow bloggers’ recommendations in Kuwait. (2) To investigate to
what extent blog viewers trust blogger's recommendation in Kuwait. (3) To further our
understanding of the relationships between perceiving usefulness, attitude and trust of
recommendations on the intention of blog viewers to follow a recommendation. Finally,
(4) To build and test a model of intention to follow a blogger’s recommendation.
The paper is structured as follows: First, we provide a review of the literature with the
aim to build a theoretical model and justifies each of the hypotheses in the model.
Following this section we describe the methodology to test the model. Subsequently, we
describe and discuss the findings and their implications.
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
Literature Review
Development of the theoretical model
Early studies (during the off line era) have noted that consumer purchasing intentions
and behaviors are greatly affected by other people’s recommendations even more than
what they are affected by marketers or firms (Herr, Kardes, & Kim, 1991). In a study of
online peer and editorial recommendations, Smith, Menon and Sivakumar (2005) explain
that consumers seek and accept recommendations to manage the amount of information
available during the online process and in doing so consumers search for
recommendations to reduce the risk of asymmetric information. Senecal and Nantel
(2004) on a study of online recommendations found that users who rely on
recommendations tend to take decision faster than those who do not seek
recommendations. Perhaps, this is the case because consumers are basing their
decision on heuristics or rules of thumb. Recently, Metzger, Fannigin and Medders
(2010) have suggested that due to overload of information, consumers try to minimize
their cognitive efforts and time through the use of cognitive heuristics, for example
frequently their participants looked at the number of testimonials or reviews and relied
more heavily on the negative than positive reviews (p 420).
Previous studies on peer reviews and recommendations on the internet refer to advice
received from the system (website) in the form of ratings. Although this context is
different from weblogs because of the interactivity that blogs allowed compared to static
ratings, the findings from this literature is informative. In one of these studies, Gershoff,
Mukherjee and Mukhopadhyay (2003) found that people consider agreement on extreme
opinions when assessing the usefulness of agent advice acceptance. Positive extreme
agreement was more influential than negative extreme agreement when the agent
provided positively valenced advice. And participants indicated they would have provided
the same rating when they perceived similarity between the agent and themselves. In
relation to WOM (face-to-face) communication, negative information is found to have a
greater impact on a recommendation therefore it is more diagnostic or informative (Herr,
et al., 1991). This negativity bias is said to be moderated by the nature of the product
thus, utilitarian products are more affected by the negativity bias than hedonic products
(Sen & Lerman, 2007).
Usefulness
From the literature on technology acceptance, we find that people are more willing to
adopt a technology when they find it useful because of a users’ beliefs that the system
(the Web or any other technology) will help them in becoming more productive or
efficient (Agarwal & Karahanna, 2000). Usefulness is defined by Davis (1989, p. 320) as
"the level that people accept as true that using a specific system would improve his or
her job performance". Perceived usefulness is also argued to have a direct influence on
behavioral intention, without having the same type of influence on attitude. For example,
employees may have a positive intention towards using a new system because they
think it is useful such as mobile banking (Riquelme & Rios, 2010), however, they do not
necessarily have a positive attitude towards it, Thus, perceived usefulness has been
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
used as a predictor of attitude (Hsu & Lin, 2008; Taylor & Todd, 1995) in the technology
acceptance context and a direct antecedent of intention (Agarwal & Karahanna, 2000;
Venkatesh, Morris, Davis, & Davis, 2003) when predicting behavioral intention on the
Web. Usefulness, in studies of online reviews, has been found to interact with the nature
of the product namely utilitarian or hedonic. Usefulness is more appreciated in the case
of the former. Negative online ratings are considered by consumers more useful for
utilitarian products than for hedonic ones ((Sen & Lerman, 2007).
More recently, research on online purchasing behaviour and acceptance of
recommendation from reviews have also found that perceived usefulness influences the
intention and attitude to follow travel advice (Casalo, Flavian, & Guinaliu, 2011). From
the previous review we hypothesized that:
H1a: The more the Perceived usefulness of recommendations, the more positive the
Attitude towards the recommendation.
H1b: The more the perceived usefulness of the recommendation, the greater the
intention to follow the recommendation.
Trust
Trust is one of the most significant for attracting and retaining consumers online
((Hoffman, Novak, & Peralta, 1999; Urban, Amyx, & Lorenzon, 2009) because
individuals do not have access to the same social cues to detect risk and uncertainty as
in the physical world (Cheshire, 2011). Trust has been defined as "the expectation of
good will in others" (Glanville & Paxton, 2007) because, in trusting, people behave like
certain situations will not occur to them (Lewis & Weigert, 1985). The origin of trust has
been explained by using two theoretical perspectives namely the psychological
propensity model that views trust as a personality trait and the social learning model that
assumes trust is learned throughout the experiences in different contexts (Glanville &
Paxton, 2007).
Many studies have confirmed that trust is an important determinant of online shopping
attitude toward products/services, shopping intention, and loyalty because when
customers trust a web site or a blog, they feel safe about their transaction (Rios &
Riquelme, 2008). Trust is particularly relevant in the context of health websites that
provide advice for example of personalized nutrition (Nordstrom et al., 2013). In the
context of blogs, trust influences attitude and intention to shop online (Hsu, Lin, &
Chiang, 2012), and affective and cognitive trust influence perceived reviews credibility
(Xu, 2014). The importance of trust is so important that Li and Chen (2009) incorporated
trust model algorithms to enhance trustworthiness, reliability and robustness of the
collaborative-filtering based-recommendation system. In our model we posit that:
H2: Trust in the recommendations positively correlates with Attitude toward
recommendations.
Attitude
Perhaps one of the most active concepts in research is the concept of Attitude. Attitude
has been defined as "an individual mental process which determines both the actual and
potential response of each person in the social world” (Allport, 1985). Attitude is said to
consist of three dimensions namely an evaluative dimension, a potency and activity
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
dimensions. An evaluation component of attitude refers to valence or whether an
individual has a positive or negative view of an object. Potency gives an indication of
how powerful the object or topic is for the individual (cruel –kind) (also a measure of
affective attitude) and activity is concerned with whether the object is seen as passive or
active (McLeod, 2009). More than three decades, attitude has been incorporated in
perhaps one of the most cited theories namely Theory of Reasoned Action and its
extension, the Theory of Planned Behavior by Ajzen and Fishbein in the 80s (Ajzen,
1988, 1991) and 90s respectively. Hundreds of articles published using TRA and TPB
theories have solidified the predictive validity of the theory of planned behaviour and
confirmed the importance of attitude in predicting intentions. The attitude predictive
ability of intention has been supported across a disparate range of domains, including
preference for a soft drink (J. Smith et al., 2008) intention to shop online (Rios &
Riquelme, 2008; Yoon, 2002), intention to become an entrepreneur (Robinson,
Stimpson, Huefner, & Hunt, 1991), and in particular to blogger recommendations (Hsu,
et al., 2012). Hence, we expect that:
H3: Attitude toward recommendations is positively correlated to Intention to follow the
blogger's recommendations.
Blogger Characteristics
Early studies that investigated credibility or trustworthiness in the internet suggested that
the identification and characteristics of the author of the information make a web site and
its information more credible (Benoy, 1982). A study that investigated peer and editorial
recommendations found that the greater the expertise of a peer recommender the
greater the perceived trust of the recommender (D. Smith, et al., 2005). A longitudinal
study by Silence et al (2007) found that people that went online to get health advice
based their trust on the perception of the source namely, knowledgeable, professional,
objective (independent from sponsors), and the advice provided the reasons behind it. In
the context of Questions & Answers websites researchers have confirmed the
importance of the recommenders’ characteristics to signal credibility such as author’s
experience, familiarity with the topic, a professional in the field (Oh, Yoon, & Kim, 2013;
Rowley & Johnson, 2013) and whether the author includes an email to contact him/her
back (Hargittai, Fullerton, Menchen-Trevino, & Yates, 2010). The number of individuals
that seem to endorse a review also played an important role to signal credibility in
studies on electronic word of mouth. Participants in a quasi-experiment trusted reviews
more when they saw a large number of people who trusted the review than a small
number and, negative reviews were considered more credible when they were endorse
by a large number of members rather than a small group (Xu, 2014). The presence of a
picture of the author did not contribute to e-word of mouth credibility however it
influenced indirectly affective trust.
From the previous literature we hypothesize that the perception of the recommender as
being uncompromised (unpaid) by a source and also perceived as having experienced
the product/ service s/he suggest are interpreted as cues to infer trust in the
recommendation.
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
H4b: Blogger characteristic-Perceived as Unpaid is positively correlated to Trust in the
recommendations.
H4c: Blogger characteristic-Perceived Personal Experience with the product/service
is positively correlated to Trust in the recommendations.
In addition to the hypotheses above, we are using demographic characteristics as control
variables and blogging past behaviors such as the number of times they had followed
recommendations, the number of blogs visited, the purpose of blogging (reading only or
contributing), time spent blogging, and number of years using weblogs.
Gender, occasionally has been found to moderate the motivation and intention to post
information on blogs (H-P Lu & Hsiao, 2009), male and female differ in a number of
respects regarding blogs (Pedersen & Macafee, 2007), for example male are more
absorbed by using blogs, female bloggers prefer to know who has visited their blogs
(Hsi-Peng Lu, Lin, Hsiao, & Cheng, 2010) and women are linguistically different from
males when writing blogs (Nowson & Oberlander, 2006).
The literature suggests that when an individual performs an activity recurrently, the future
behaviour or intention may be determined by his/her past behaviour, especially for
frequently performed behaviors (Bamberg, Ajzen, & Schmidt, 2003). This has been
corroborated in the prediction of the purchase of a soft drink (J. Smith, et al., 2008). In
our context, we have considered the number of times a person has followed previous
recommendations from bloggers, the number of blogs visited per week.
Figure 1 depicts the schematic research model hypothesized in the previous section and
the following section describes the methodology used to test the model.
FIGURE 1
RESEARCH MODEL
Useful
Trust
Intention follow
recommendation
Attitude
Unpaid
Product
experience
Blogs
visited
Previous
recommen
Methodology:
Sample: The unit of analysis was individuals who read or write blogs. The sample is
comprised of a convenience self-selected sample. There were 25 reflective items and
according to the rule of thumb of 10 questionnaires per reflective item (Hair Jr.,
Anderson, Tatham, & Black, 1998), the sample size seems to be just right.
Data Collection Instrument: Hard and soft copies of a questionnaire were distributed in
addition to a link to the survey on line which was sent to potential respondents using
social media such as Blogs. The electronic version of the survey was done using Google
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
Drive. 282 questionnaires were received and 18 were discarded because they indicated
they had not read or written blogs however they continued with the survey. Most of the
questionnaires came from respondents that received the online link. A handful number of
hard copies were collected. Perhaps this gives evidence of the relevance of the sample
since most bloggers are expected to be using the internet. The sample size used in the
analysis is 264 respondents. The questionnaire was distributed in Arabic and English
languages.
The constructs used in the model such as Perceived usefulness of the
recommendation, Trust in the recommendation, Attitude toward blog’s recommendations,
Intention to follow a blogger’s recommendation were measured using items borrowed
from previous research studies but contextualized to blogs, please see Appendix.
Respondents were requested to indicate their degree of agreement or disagreement with
the statements on either of a five-point intervals where 1 = totally disagree and 5 = totally
agree with the statement. The dependent variable, intention to follow a blogger’s
recommendation, contained four items.
Respondents were also asked to provide demographic information e.g. gender, age,
education, nationality and some behavioral information regarding to the number of times
they had followed recommendations in the past, the number of blog visited, the purpose
of blogging (just reading or contributing), time spent blogging and years using weblogs.
RESULTS
Description of the sample 60 % of the sample is male, one third of the sample is
between the range of 30-39 years old, 29% between 24-29 years old, and 28% between
18-23 years of age. The smallest proportion (10%) of respondents was 40 years old or
older. Half (53% to be precise) of the respondents had a bachelor degree (140 cases)
and another 19% had post graduate studies. The majority reported to be of Kuwaiti
nationality (74%). The sample seems to be reasonably represented in terms of education
and age distribution when it comes to describe the population of Kuwaitis; a young and
educated population, however, the proportion of male seems to be higher than the actual
proportion in the population. However, it may well represent the blogging population that
appear to be more male dominated, young and educated.
Results from the behavioral questions A large proportion (83%) of the respondents
indicated they had more than four years using the weblogs and a very small proportion
(6%) reported using blogs between one and two years only. Almost 60% reported to visit
up to nine blogs per week, 22% indicated they visit between 10 and 30 blogs per week,
and the rest reported visiting more than 30 blogs. Slightly above half (54%) of the
respondents reported to spend up to one our blogging, 38% spend more than one hour
and less than three hours, and the rest of the sample spend more than three hours
blogging. About half (49%) of the bloggers write comments and the rest report only
reading blogs. 22% has never followed blogger’s recommendations, about ten per cent
indicate they have followed a blogger’s recommendation only once, 44% has followed
bloggers recommendation between two and five times and the rest one quarter of the
sample has followed six or more recommendations. The blog behaviors reflect the fact
that the sample comes mainly from weblogs where the survey was distributed. The blog
topics more frequently viewed are as follows: Technology-related (28%), fashion (14%),
food-dietician (10%), health (7%), motors (7%) and others (34%). No missing values
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
were observed in the used questionnaires. All of these blog-related behaviors are
categorical variables and were used to predict intention to follow bloggers’
recommendations.
Cross tabulations between blog use (blog reader or blog contributor) and past behavior
namely followed bloggers’ recommendations before, indicates that blog readers have
followed fewer times recommendations in the past than blog contributors, and there is
also a larger percentage among blog readers than have not ever followed a
recommendation compared to blog contributors (Pearson Chi square=9.71, df=3, sig (2tail) = 0.02). Female, in our sample, are slightly more contributors than blog readers (chi
square = 2.5 df =1 2-tail sign 0.07). Time spent blogging per day between blog readers
and contributors also differ, blog readers spend about one hour per day, in the majority
of the cases, while contributors spend between up to three hours in the majority of the
cases (contingency coefficient .277 sig = 0.000). In relation to number of blogs visited
per week, as expected, blog contributors visit more blogs than blog readers. The majority
of blog readers visit up to 10 blogs per week whereas blog contributors, in the majority of
the cases, visit beyond twenty blogs per week (contingency coefficient = .290, sig. =
0,000).
Table 1 provides basic descriptive statistics of the latent variables. In general, the
variables display a tolerable discrepancy from normality i.e. skewness and kurtosis
between -1 and +1 (Hair Jr., et al., 1998), except for the categorical variable (EXPER)
years of experience with weblogs (which was expected to be skewed as the majority
came from links to blogs). The variable Number of Blogs Visited is also skewed towards
the lower range of 10 blogs per week. Given the descriptive statistics, the variables seem
appropriate to use parametric tools and, in particular, PLS which is said to be robust
when applied to highly skewed data (Hair et al 2012).
From the mean values in Table 1 we can conclude that respondents have positive
perceptions regarding the usefulness of bloggers’ recommendations, they trust the
recommendations, and perceive bloggers as trustworthy and honest, they are also
perceived as recommending something they have experienced themselves, however
respondents are divided in terms of the perception of bloggers being independent from
the influence of sponsors. Overall, respondents have a positive attitude towards
bloggers’ recommendations and intend to follow bloggers’ recommendations.
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
TABLE 1 DESCRIPTIVE STATISTICS
Note: INTEXPER = Experience in using weblogs; BLOGVISIT =Number of blogs visited; TIMESPENT = Time spent blogging;
Descriptive Statistics
N
INTEXPER
Minimum
Maximum
Mean
Statistic
Statistic
Statistic
Statistic
Std.
Error
Std.
Deviation
Skewness
Statistic
Statistic
Std. Error Statistic
Std. Error
Kurtosis
264
1
5
3.77
.040
.645
-2.562
.150
6.724
.299
BLOGVISIT 264
1
4
1.65
.057
.927
1.276
.150
.523
.299
TIME
SPENT
264
1
3
1.53
.039
.634
.787
.150
-.399
.299
FOLLOWBL 264
OG
1
4
2.72
.065
1.063
-.511
.150
-.962
.299
MeanPU
264
2.00
5.00
3.8627
.03370 .54750
-.415
.150
.546
.299
MeanTR
264
1.00
5.00
3.4939
.04450 .72309
-.367
.150
.234
.299
MeanAT
264
2.00
5.00
3.8977
.03618 .58792
-.326
.150
-.088
.299
MeanINFR
264
1.25
5.00
3.5483
.04359 .70831
-.363
.150
.271
.299
MeanPEX
264
1.33
5.00
3.4912
.04375 .71078
-.097
.150
-.203
.299
MeanUNP
264
1.00
5.00
2.9482
.05071 .82387
.049
.150
-.281
.299
Valid
(listwise)
N 264
FOLLOWBLOG = Number times followed bloggers’ recommendations; MeanPU = Perceived Usefulness of recommendations,
MeanTR = Trust, MeanAT= Attitude, MeanINFR = Intention to follow bloggers' recommendations, MeanPEX = mean blogger’s
Personal experience with product, MeanUNP = Perceived as Unpaid
Table 2 provides a summary of the correlations, composite reliability and Average
Variance Extracted of the variables entered in the model and run in SmartPLS version
2.0 M3 and PLS algorithm as follows: Data metric: path weighting scheme; 5,000
iterations; abort criterion 1.0E-5 and initial weights = 1.0. In this Table we can check the
validity and further appropriateness of the data on several aspects described below.
Discriminant validity has been tested following Fornell and Larcker’s rule: The AVE of
each latent construct should be higher than the construct’s highest squared correlation
with any other latent construct. The data seem to justify the existence of discriminant
validity, thus the constructs in the model are distinct from each other. Convergent validity
is measured and satisfied by observing AVE greater than 0.50.
Internal consistency reliability is indicated preferably (instead of Cronbach alpha) by
observing composite reliabilities equal or above 0.70 (Hair et al 2012). From the
observed correlations we note Attitude towards the recommendations is positively
correlated with Intention to follow recommendations as hypothesized in the model. Other
relationships between the constructs are also satisfied by the results from the correlation,
for example perceived Usefulness is correlated with Attitude and Trust in the
recommendation.
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
TABLE 2 CORRELATIONS, COMPOSITE RELIABILITY, AND AVE
Prod
Experience
Intention
0.60
0.70
& 0.87
Intention
0.70
0.74
0.72
& 0.91
Trust
0.66
0.76
0.86
0.77
& 0.91
Unpaid
0.46
0.56
0.55
0.59
0.64
& 0.84
Usefulness
Follow
Recom
0.68
0.70
0.77
0.75
0.49
0.58
& 0.85
0.42
0.37
0.47
0.42
0.21
0.47
#
#
0.36
0.43
0.35
0.35
#
Attitude
Attitude
Prod
Experience
Trust
Unpaid
Usefulness
Follow
Recom
N
blogVisit
0.56
N blogVisit
0.35
0.22
0.42
next to diagonal). # = indicates categorical variable.
All
correlation
s
are
significant
at
the
0.000 level
(2-tailed).
N = 264.
AVE
=
Diagonal
values
(bold); &
=
Composite
Reliability
Indicator reliability is measured by obtaining standardized loadings greater or equal to
0.70. Table 3 shows outer loadings generated by bootstrapping settings established for
264 observed cases, 5,000 samples and individual changes. Except for three indicators
with loadings slightly below the benchmark, all others comply with the decision rule. The
indicators below 0.70 were retained in the final running of the model despite this fact
since we consider this study exploratory which allows for a loading below 0.70 (Hair et al
2012). Categorical variables do not have any reflective measures therefore loadings are
fixed to the value of 1.
TABLE 3 OUTER LOADINGS
Original
Sample (O)
Sample Mean
(M)
Standard
Deviation
(STDEV)
Standard Error
(STERR)
T
Statistics
(|O/STERR|)
AT1 <- Attitude
0.76
0.76
0.05
0.05
15.52
AT2 <- Attitude
0.81
0.81
0.03
0.03
23.43
AT3 <- Attitude
0.67
0.67
0.07
0.07
9.73
FOLL_RECOMM <- fol recom
1.00
1.00
0.00
0.00
0.00
INFR1 <- Intention
0.84
0.84
0.03
0.03
29.11
INFR2 <- Intention
0.89
0.89
0.01
0.01
60.45
INFR3 <- Intention
0.78
0.78
0.04
0.04
21.60
INFR4 <- Intention
0.87
0.87
0.02
0.02
51.73
PEX1 <- ProdExperience
0.89
0.89
0.01
0.01
60.20
PEX2 <- ProdExperience
0.78
0.78
0.04
0.04
20.70
PEX3 <- ProdExperience
0.84
0.84
0.02
0.02
46.98
PU1 <- Usefulness
0.65
0.65
0.08
0.08
8.64
PU2 <- Usefulness
0.75
0.75
0.04
0.04
19.62
PU3 <- Usefulness
0.80
0.80
0.03
0.03
27.91
PU4 <- Usefulness
0.84
0.84
0.02
0.02
37.43
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
TR1 <- Trust
0.88
0.87
0.02
0.02
51.98
TR2 <- Trust
0.89
0.89
0.02
0.02
50.09
TR3 <- Trust
0.87
0.87
0.02
0.02
51.32
TR4 <- Trust
0.88
0.88
0.02
0.02
48.55
TR5 <- Trust
0.83
0.83
0.03
0.03
26.45
UNP1 <- Unpaid
0.86
0.86
0.02
0.02
40.75
UNP2 <- Unpaid
0.68
0.68
0.05
0.05
12.60
UNP3 <- Unpaid
0.84
0.84
0.03
0.03
30.21
1
1
0
0
0
VISITED_BLOGS <- visited blog
Structural model evaluation
We first examined the effects of the control variables on the intention to follow bloggers’
recommendations. The variables entered were gender, education, age, nationality, and
blog behavioral measures namely number of recommendations followed in the past,
years of experience using weblogs, number of blogs visited per week, use of blogs (e.g.
write comments, read only), and number of hours spent reading, writing blogs. Of all
these variables only two were statistically significant namely the number of times
respondents have followed recommendations in the past (B =0.384; t = 6.60 sig. =0.000)
and the number of blogs visited per week (B =0.310; t = 4.94 sign. = 0.000). These two
predictors of intention to follow a blogger’s recommendations plus the constant
accounted for relatively weak R square = 0.29. Consequently, only these two control
variables, in addition to the main constructs Attitude and perception of Usefulness were
used in the prediction of intention to follow recommendation.
When the variable Attitude towards bloggers’ recommendations and perceived
Usefulness were used as predictors, R square (= 0.67) of Intention to follow a
recommendation increase significantly, in the statistical sense; R square change =0.38;
F change= 314.7; df =2, 259; sig. = 0.000). The total R square (0.67) predicted can be
considered as relatively strong. VIF values ranged from 1.14 to 2.44 and tolerance range
of values was between 0.40 and 0.87 thus, VIF and tolerance values are all within
acceptable regions, i.e. VIF < 5 and tolerance > 0.20.
Table 4 shows the path results from using SmartPLS. From this table we conclude that
all hypothesized relationships in the model are supported by the data. Perceived
Usefulness contributes the highest direct effect on intention to follow a recommendation
and Attitude is the second most important construct. Past behaviour such as Followed
recommendations before and current behaviour namely number of blogs visited
contributed marginally to predict Intention to follow bloggers’ recommendations.
From the outer model we observe that both Trust and Usefulness predict the attitude
towards bloggers’ recommendations and each has a relative similar contribution in the
prediction. Finally, the construct Trust is predicted to a large extent by the perception of
the blogger’s experience with the product/service they recommend, and to a much less
extent by the perception of independence of the bloggers’ recommendations (perceived
as unpaid).
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
TABLE 4
PATH COEFFICIENTS
Original
Sample (O)
Sample Mean
(M)
Standard
Deviation
(STDEV)
Standard Error
(STERR)
T
Statistics
(|O/STERR|)
0.29
0.29
0.06
0.06
5.15
0.63
0.63
0.04
0.04
14.02
0.35
0.35
0.07
0.07
4.75
Unpaid -> Trust
0.24
0.24
0.06
0.06
4.29
Usefulness -> Attitude
0.42
0.42
0.07
0.07
5.92
Usefulness -> Intention
0.50
0.50
0.06
0.06
8.96
fol recom -> Intention
0.07
0.07
0.04
0.04
1.73
visited blog -> Intention
0.12
0.12
0.03
0.03
3.36
Attitude -> Intention
Prod Experience -> Trust
Trust -> Attitude
An examination of total effects in Table 5 shows that Trust and perceived Usefulness of
the recommendation have indirect significant effects on intention to follow a
recommendation the latter has larger effect than Trust. We, therefore, can infer that
Attitude is a partial mediator between Trust and intention to follow a blogger’s
recommendation and perceived Usefulness and intention to follow a blogger’s
recommendation.
TABLE 5
TOTAL EFFECTS
Original
Sample (O)
Sample Mean
(M)
Standard
Deviation
(STDEV)
Standard Error
(STERR)
T
Statistics
(|O/STERR|)
Attitude -> Intention
0.29
0.29
0.06
0.06
5.15
ProdExperience -> Attitude
0.22
0.22
0.05
0.05
4.39
ProdExperience -> Intention
0.06
0.06
0.02
0.02
2.74
ProdExperience -> Trust
0.63
0.63
0.04
0.04
14.02
0.35
0.35
0.07
0.07
4.75
0.10
0.10
0.04
0.04
2.85
0.08
0.08
0.03
0.03
3.15
Unpaid -> Intention
0.02
0.03
0.01
0.01
2.30
Unpaid -> Trust
0.24
0.24
0.06
0.06
4.29
Usefulness -> Attitude
Trust -> Attitude
Trust -> Intention
Unpaid -> Attitude
0.42
0.42
0.07
0.07
5.92
Usefulness -> Intention
0.63
0.63
0.05
0.05
13.65
fol recom -> Intention
0.07
0.06
0.04
0.04
1.60
view blog -> Intention
0.12
0.12
0.03
0.03
3.35
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
DISCUSSSION
In this study, we investigate what predicts blog readers’ intention to follow bloggers’
recommendations, and whether his/her past and current behaviors contribute above the
cognitive and attitudinal variables.
Blogger recommendations pose particular challenges for consumers and the public
interested in obtaining information on the internet because bloggers provide advice or
recommendations that the public cannot verify nor has an indication of the authenticity of
the blog contributor. However, many people try to reduce their cognitive load and reduce
their uncertainty before making a decision by reading posts from bloggers. The sample
of respondents in our study seems to trust bloggers and demonstrate intentions to use
bloggers’ recommendations in the future. All together, the results support the conclusion
that perceived usefulness of the recommendation and a positive attitude towards the
recommendation determine the intention to follow an advice from a blogger.
Although our model did not posit a direct link between Trust and Intention to follow a
recommendation, the data also support such a connection. Trust not only influences the
attitude a person will have towards the recommendation but also the intention to follow
the advice. Gender did not have an influence on intention to follow a blogger’s
recommendation, thus both are equally prone to follow recommendations from bloggers.
Our sample has a slightly more numbers of female contributors which is more in line with
findings from Taiwan (Ko, 2011) than the Western world.
The trust a person places on a peer recommender is significantly affected by the
recommender’s characteristics (D. Smith, et al., 2005), our results also reflect this. Blog
readers and contributors take the cues associated with an uncompromised (unpaid)
blogger and his or her personal experience with the product to attribute trust to the
recommendation. This finding seems to be in line with previous studies that find
consumers are more trustworthy of a recommendation than an advertisement or a
recommendation by a website (Senecal & Nantel, 2004), presumably because bloggers
are free to discuss product features more openly (D. Smith, et al., 2005) and allow the
interaction with the blog reader.
We find that when respondents only read blogs they have less intention to follow advice
from a blogger than when the blogger is a contributor (writer). We believe this can be the
result of the influence of past behavior, that is, blog readers have followed fewer
recommendations from bloggers in the past and also the less positive attitude towards
the recommendations.
The Attitude construct has been widely used to predict intention, and in our case, this
confirms its ability to predict. A positive attitude towards blogs is created when
consumers perceive the bloggers’ recommendations as useful and they trust the
recommendation.
Implications of the findings
Our study has important implications by understanding what makes people follow
bloggers’ recommendations. Based on the findings, perceived usefulness of the
recommendation is critical to influence followers, hence bloggers need to provide
recommendations that are practical and easy to implement.
Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
The second most important recommendation is that bloggers need to develop a positive
attitude towards the recommendations and this is done mainly by instilling
trustworthiness and a sense of usefulness of the recommendations.
Third, bloggers must be perceived as truthful in their recommendations. To develop this
trustworthiness, bloggers must be perceived as having experience with the product or
service they recommend. To a lesser extent, bloggers need to be perceived as
uncompromised or objective in their recommendations, that is, they need to be perceived
as unpaid for their recommendations.
Limitations
Our study, despite its contribution, has some limitations. First, the sample is drawn
mainly from individuals that have visited blogs since the links were provided in the
weblogs. It is possible that readers of blogs coming from other sources than weblogs
may exhibit different characteristics. Our sample is non-probabilistic and represents
people who just lurk on the weblogs (readers of blogs only) and another half of the
sample that are active bloggers (blog writers). Secondly, although we have, to a large
extent, used previous measures and adapted them to our context, there is still a need to
distil concepts and dimensions of trust, trustworthiness and credibility. These concepts
may have escaped the empirical attention they deserve. The literature on web site
credibility suggests the product an individual looks for interacts with the characteristic of
the source. For example, people who are interested in hedonic products do not take into
consideration the level of expertise of the source of the recommendation whereas in the
case of utilitarian product, the peer expertise is important. Studies may consider product
differences in the recommendations of bloggers. Our study did not consider any
particular product in the recommendation and future studies may prove fruitful given the
evidence above in the online literature.
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Proceedings of 26th International Business Research Conference
7 - 8 April 2014, Imperial College, London, UK, ISBN: 978-1-922069-46-7
APPENDIX
Perceived usefulness of recommendations (PU)
(PU 1) Bloggers’ recommendations improve my decision making process
(PU2) Bloggers’ recommendations enhance shopping effectiveness
(PU3) Bloggers’ recommendations increase my productivity when deciding on one
product or service over another
(PU4) Bloggers' recommendations are useful
Trust (TR)
(TR1) I believe bloggers’ recommendations to be true
(TR2) I trust information from bloggers
(TR3) I believe that Bloggers are trustworthy
(TR4) I trust the product/services recommendations of the bloggers
(TR5) I think that the recommendations of bloggers are credible
Attitude (AT)
(AT1) Bloggers' recommendations are good
(AT2) Bloggers' recommendations are bad
(AT3) Bloggers' recommendations should be banned
Intention to follow the bloggers' recommendations(INFR)
(INFR1) I will frequently shop as bloggers recommend
(INFR2) I intend to follow bloggers recommendations in the future
(INFR3) I plan to consider the recommendations of bloggers when buying products
/services
(INFR4) I intend to continue using the bloggers for recommendation
Blogger characteristics- Reputation (BCR)
(BCR1) Bloggers have a reputation for being honest
(BCR2) Bloggers have a good reputation
(BCR3) I do not doubt the honesty of Bloggers
Blogger characteristics- Personal experience of blogger (PEX)
(PEX1) I believe that bloggers have experienced the products/services they talk about
(PEX2) I believe that bloggers really use the products/services they recommend
(PEX3) I believe bloggers have a deep experience of the products/services they
recommend
Blogger characteristics- Perceived as Unpaid Blogger (UNP)
(UNP1) Bloggers are not paid by sponsors to recommend
(UNP2) Bloggers are independent when they recommend (not linked to sponsors)
(UNP3) Bloggers do not receive benefits from sponsors to recommend