Does acceptance of a review on social networking

Does
Online Reviews Posted on Social Networking Sites Affect Consumer Attitude
towards products?

 

Abstract:

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The attitude of
reference groups towards a brand can possibly put a huge impact on the
decision-making process of consumers. Consisting of a massive user base and
virtual democracy, social media platforms have given greater power to the clan
to share experience and express word of mouth. Social networking sites are
becoming increasingly popular in Bangladesh, which not only allow people to
stay connected, but also avail feedbacks and reviews to make a smart purchase.
Although, only quantifiable rating points and simple words do not suffice when
it comes to revamp the attitude of consumers towards a brand. The acceptance of
a review on social networking sites are based on certain other factors which
the consumers examine, for instance, place, person, and authenticity of the
review. Therefore, this paper will assess the factors relating to use of social
media networking sites on consumer’s attitude towards products.

Keywords:
Online Review, Consumer Attitude.

 

Introduction:

The rise of Web 2.0
technologies has led to a wealth of social media websites; popular examples of
which are YouTube, Twitter and Facebook. These platforms offer many
opportunities for internet users to share and generate content about anything;
including brands. Social
media have thereby transformed online consumer behavior (Kaplan & Haenlein
2010)

According to Forrester
Research, 75% of Internet surfers use “Social Media” in the second quarter of
2008 by joining social networks, reading blogs, or contributing reviews to
shopping sites; this represents a significant rise from 56% in 2007. The growth
is not limited to teenagers, either; members of Generation X, now 35–44 years
old, increasingly populate the ranks of joiners, spectators, and critics. It is
therefore reasonable to say that Social Media represent a revolutionary new
trend that should be of interest to companies operating in online space—or any
space (Kaplan and Haenlein, 2010). Thus, social media have become a major
factor in in?uencing various aspects of consumer behavior including awareness,
information acquisition, opinions, attitudes, purchase behavior, and
post-purchase communication and evaluation (Mangold and Faulds, 2009)

In the world of social
networking, Word of Mouth (WOM) has taken a substantial turn and evolved to
electronic word-of-mouth. Electronic word-of-mouth (eWOM hereafter) refers to a
particular type of WOM which occurs in the online setting (Dwyer 2007) and can
be observed in many different online channels, such as discussion forums,
product reviews, and emails. Several researchers have observed the impact of
Internet-based eWOM on product success (Chevalier and Mayzlin 2006), virtual
consumer community (Hung and Li 2007), and explored how the eWOM process
influences consumers online behaviors (Arnaud De Bruyna, 2008)

 

While current research
have focused on the outcomes
of eWOM (e.g., sales), little is known about the drivers of eWOM or
factors influencing consumers’ WOM behavior in computer-mediated environments,
particularly from the perspective of social networking sites; an emerging
user-generated social medium. (Teng, Khong, Chong, & Lin, 2017)

However, the biggest
problem is, as reviews gain in more popularity, the problem of information
overload, spam  comments, influenced
reviews, paid reviews occur, which make it tougher for the consumers to trust
online reviews. As a consequence, the use of more signaling cues to help users
diagnose relevant reviews will help different brands utilize this marketing
tool more efficiently. 

Thus, this study
investigates how online reviews posted on social networking sites affect consumer
attitude towards products.

 

Literature
Review:

Review
Credibility: Message quality has been consistently
identified as a major standard in the persuasion and communication literature (Miller, 1996). The composition of
any online review is often used by consumers to determine the credibility of
the review. Composition can refer to the quality of the argument or the style
of writing.

Many studies of review
argument quality focused on message contents in the field of marketing. In the
case of a strong argument, if an argument is relevant, objective, and
verifiable it was found that they are perceived as credible (Petty RE, 1986). In the case of a weak argument, the
review content is reflected as emotional, subjective, and abstract (Teng et. al, 2017).

Author Na (2015),
classified online reviews into two categories, based on their presentation
formats: narrative reviews (reviews with stories) and non-narrative, structured
reviews (reviews with layout features such as headings, bullet points, and
numbered lists). Both narrative and structured review can persuade audiences
but in different ways: narratives lead to more persuasion through narrative
transportation, in which audiences are emotionally engaged in stories, whereas
well organized structures persuade people by helping them to process
information less purposefully. (Na, 2015).

Review
Quantity: Online review quantity is referred to the volume of
online reviews posted by reviewers to direct their opinions. It can also be
viewed as the amount of information consumers are exposed to. (Yang & Sarathy, 2016)

Prior research has
concluded that evaluative judgment of an object becomes more extreme as the
amount of information about that object increases. (Park D-H, 2008) Chevalier and
Mayzlin (2006) studied the impact of online reviews quantity on book sales (Chevalier JA, 2006) and Cui (Cui P, 2010) argued that online
review quantity had influenced sales of DVDs and other electronic products,
while Lin (2006) has seen that review quantity can be a good predictor of movie
sales. (Lin, 2006).

However, Park and Lee (2008) show that
the volume of information negatively affects consumers by overwhelming them.
The inability to process the large amount of information presented through
reviews may cause individuals, to be discouraged and feel less confident in
their decision making

Perceived Online Review Importance: Another very important factor
that influences consumers is their perception towards usefulness of online
reviews in general. Some consumers may simply
believe that looking for online reviews is important before making a purchase
decision, while others may find online reviews baseless. The construct perceived
usefulness is defined as “prospective users’ subjective probability that using
a specific application system will increase his or her job performance within
an organizational context” (Davis,
Bagozzi, & Warshaw, 1989, pg. 4).Authors Klaus & Changchit
(2017) has defined this construct as a “user’s subjective probability that
using the online review will be valuable for his or her online purchase
decision-making” (Pg. 3).Therefore, consumer’s perception towards the
usefulness of online review in general, is likely to have a positive influence
on their decision making process while making any purchase.

Consumer
Attitude: A basic definition of basic term, attitude is:
“a psychological tendency that is expressed by evaluating a particular
entity with some degree of favour or disfavour” (Eagly & Chaiken, 1993) Consumer attitude is
positive or negative/ favourable or unfavourable or indifferent towards a
product. (Anilkumar & Joseph, 2012). Attitude is defined
as a person’s overall evaluation of a concept. Two types of attitude can be
identified which are: attitudes toward objects, and attitudes toward behaviors.
(Al-Debei & Ashouri, 2015)

According to prior
studies, online reviews play an important role in forming and influencing
internet users’ attitudes, and behavioral intentions (Cheung, 2008; Jalilvand, 2012). Online opinions and
recommendations are perceived to be credible and trustworthy by consumers and
consumers are likely to trust the information provided by other shoppers like
themselves more than that provided by companies (Al-Debei & Ashouri, 2015)

Studies have also suggested
that online reviews of products and services can shape and form customers’
attitude toward products; ultimately leading to increased sales (Lin, 2006). It is generally
accepted that consumers’ attitude change is an effective measure for the
persuasiveness of online reviews (Cui P, 2010).
Thus, marketing practitioners consider attitude change of consumers to be the
most meaningful consequence of online review (Teng
et. al, 2017).

 

Conceptual
Framework:

The internet exposes
people to numerous reviews that can sometimes be overwhelming. In this
scenario, judging the credibility of the review becomes important for consumers.
Review Composition, Review Quantity and Reviewer Expertise are some of the
factors that consumers take into consideration to form their judgments about an
online review. 

Figure 1 illustrates
the conceptual framework, upon which the hypotheses of the study are based on.
The authors have proposed in this paper that Review Composition, Review
Quantity and Reviewer Expertise are associated with consumers’ attitude towards
purchase intention.

 

 

 

 

 

 

 

 

 

Figure
1: Conceptual Framework

 

Based on the framework,
we can hypnotize that:

H1:
Review quantity has a positive effect on consumer’s attitude towards using
online reviews

H2: Review Credibility
has a positive effect on consumer’s attitude towards using online reviews.

H3:
Perceived Online Review Importance has a positive effect on consumer’s attitude
towards using online reviews

 

Methodology

This study aims to find out whether
online reviews have an impact on the consumer’s purchase intention towards a product.
The Target Population were the social media users of different age,
gender, educational background and income level who are currently residing in
Dhaka. This research
is conclusive in nature as the major objective was to describe existing
phenomena and it is assumed that the researchers have much prior knowledge
about the problem situation.

 

A structured
and close-ended questionnaire was designed to assess the consumer’s attitude by
adopting existing scales. The questionnaires were self-Administered and had a 5 point
Likert Scale to determine the effect of independent variable on the dependable variables. The scale
rates the lowest level of agreement at 1= “Strongly Disagree”, 2= “Disagree”, 3= Neither Disagree or
Agree”, 4= “Agree” and 5=”Strongly Agree”.

 

This data collected from university
students (i.e. Undergraduate and Post Graduate) from various Public and Private
Universities in Dhaka to 300 respondents. These universities include
Independent University, Bangladesh (IUB), North South University (NSU), BRAC
University, East West University (EWU), American International University (AIUB),
Dhaka University (DU) and Jahangirnagar University (JnU). Out of 300
responses, 210 usable instruments were used for father analysis using SPSS (version 20.0); the rest were
omitted due to missing responses.

 

Data Analysis

Reliability test is conducted to measure the internal consistency. Reliability
coefficient of 0.7 is acceptable, more than 0.8 is good and more than 0.9 is considered excellent. (DR Cooper, 2006)

Table 1 shows all the alpha value from
the reliability test. We can observe
that all the variables have Alpha value in “good” range.

Table 1: Reliability analysis

Variable

No.
of  Items

  
Cronbach’s  ?

Review Credibility

4

0.814

Review Quantity

4

0.809

Perceived Online Review
Importance

4

          0.801

Consumer Attitude towards
Purchase Intention

4

          0.870

 

The table above shows the values of Cronbach’s Alpha (?). All four variables have
values more than 0.80, which indicates a high level of internal consistency for
our scales with this specific sample. All the items here are consisted of 4 items.

Regression analysis

The table below (Table
2) shows the values for unstandardized and
standardized coefficients which indicate how each independent variable is
affecting the dependent variable. R² and Adjusted R² indicate how much the
dependent variable is explained by all the independent variables together.

Table 2: Multiple Regression analysis
results

Model

Unstandardized
Coefficients

Standardized
Coefficients

t

Sig.

 

B

Std. Error

Beta

 

(Constant)

2.756

1.497

 

1.841

.067

ReviewCredibility

.340

.060

.317

5.656

.000

ReviewQuantity

.031

.048

.034

.649

.517

PORI

.448

.055

.457

8.170

.000

Other Values

     

0.416

Adjusted R²

0.408

Note:  **. Significant at 5 percent
level

 

Analysis of variance (ANNOVA) assesses the model’s overall
significance. Table 3 shows that
the model is significant as P value
is

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