Content uploaded by Sabau-Popa Claudia Diana
Author content
All content in this area was uploaded by Sabau-Popa Claudia Diana on Aug 11, 2022
Content may be subject to copyright.
Availableonlineatwww.researchinformation.co.uk
The Journal of Grey System
Volume 27 No.4, 2015
R
Re
es
se
ea
ar
rc
ch
h
I
In
nf
fo
or
rm
ma
at
ti
io
on
n
Consumers’ Decisions in Grey Online Social
Networks
Camelia Delcea1∗, Claudia Diana Sabău Popa2, Marcel Boloș2
1. Faculty of Economic Cybernetics, Informatics and Statistics, Bucharest
University of Economic Studies, Bucharest, Romania
2. Faculty of Economic Sciences, University of Oradea, Oradea, Romania
Abstract
The spectacular development of recent year’s technology has created new
opportunities for communication industry and beyond, through the new arises
possibilities of the different platforms’ users to actively participate on content
creation. The new Web 2.0 technologies have changed the communication channel
both between various users’ categories and between users and business community.
In time, such an approach has proven to be more efficiently than the traditional
communication methods used in marketing, having the power to influence the
consumers’ attitude and behaviour. Starting from this idea, the present paper tries
to shape the link between the consumers’ decision in the grey online social
networks and the discussions they are following or actively participating in these
online environments. For this, a grey incidence analysis is conducted and its results
are proving a strong positive connection among the considered variables. Knowing
that, companies can decide to act and invest on such a manner that will better
facilitate the increase of reputation and image on such discussion groups.
Keywords: Grey Systems Theory; Grey Knowledge; Grey Economic
Systems; Consumers’ Behaviour; Online Social Networks
1. Introduction
The recent researches in the field of online social networks have underlined
the fact that since the first online social network was born in 1977 (SixDegrees), a
whole new array of other social networks such as Facebook, LinkedIn, Google+,
Twitter, etc. have become popular platforms where people from all around the
world are gathering together and connect. [1]
It is estimated that nowadays more than a billion people are using the social
networks, while their number is predicted to increase in the following years.
In this context, a larger and larger number of organizations are using social
media for a better communication channel with their target audience. IBM
estimated that between 2015 and 2018 the number of companies that will make the
switch to this new communication path is going to triple.
Moreover, in 2010, 72% of the internet users were already members of at least
∗
Corresponding Author: Camelia Delcea, Department of Economic Informatics and Cybernetics,
University of Economic Studies, Dorobani Street, no. 15-17 ,Bucharest, Romania, 010572,
e-mail:camelia.delcea@yahoo.com
Online
Social
Networks
12
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
one online social network, while in 2013 65% of them were saying that they are
using the online reviews for product purchase decision-making. [2]
Recent studies are showing that based on both the analysis of the interactions
among online users and the analysis of the “friends’” evaluation on a product, there
can be determined the social influence on a user’s buying attitude and behaviour.
Also, based on the informal communication among users, certain opinions
regarding a company’s activity can be established with influence on the consumers’
long term perception on its image, with impact on its’ reputation.
2. Online Social Environments Research
The spectacular development of recent year’s technology has created new
opportunities for communication industry and beyond, through the new arisen
possibilities of the different platforms’ users to actively participate on content
creation. The new Web 2.0 technologies have changed the communication channel
both between various users’ categories and between users and business community.
In time, such an approach has proven to be more efficiently than the
traditional communication methods used in marketing, having the power to
influence the consumers’ attitude and behaviour. [3]
Berthon et al. believe that “the Web 2.0 technologies have succeed to produce
at least three major effects: a shift in the locus of activity from the desktop to the
Web, a shift in the locus of value production from the firm to the consumer and a
shift in the locus of power away from the firm to the consumer”. [4, 5]
Knowing the importance of the Web 2.0 technologies, the firms are rebuilding
the web pages trying to put a bigger accent on the aspect of communication with
the potential clients. The purpose is to obtain a higher interest from these
consumers on their company’s activity, products and services and also to build a
more powerful relationship with the online audience. A study made on a series of
companies [5] underlined the fact that the social media is used for achieving
different things within an organization, such as: promotion, brand consolidation,
information searching, building a closer relationship with the customers, etc. which
has determined an increase on the investments made on this direction.
Tikkanen et al. are analysing the factors which are facilitating the success of
the marketing advertising campaigns in the virtual world and they are suggesting
the fact that the social networks can be successfully used in connecting with clients,
putting a big puzzle piece on the increase of their learning rate, with effects on
obtaining a faster feedback form them. [6]
The same conclusions are drawn by Harris and Rae, while Curtis et al.
underline the advantages of using online social networks on the non-profit
organizations. [7, 8]
Heidemann et al. are illustrating the positive effect that the use of online social
networks has on the innovation process in companies: Fiat has freely received more
than 170000 prototypes for the Fiat500 model, while Lego had more than 1 billion
customer online evaluations for the new models proposed for sale. [1]
Moreover, Li and Bernoff shows how Chevrolet has used the social networks
in the marketing campaigns, adopting a “word-of-mouth” marketing, through
which the individuals were sharing with their friends the information they have on
this company. [9] As a result, thousands of users have become aware of the
Chevrolet’s products and services.
Bonchi et al. think that a company’s reputation can be monitored through
social networks [10], while Libai et al. are seeing in the social networks a way to
Online
Social
Networks
13
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
reduce products’ maintenance costs due to the fact that every user can receive a
feedback from another used in real time. [11]
Other studies regards: the users’ behaviour vs. the products promoted in online
environment [12], decisions making [2], creating advertising campaigns oriented
to the most “sensitive” consumers [14], information diffusion and modelling [15, 16, 17,
18], identification of key candidates for “viral” marketing campaigns [19, 20],
exploration of the organizational culture in dynamic social networks [21],
negotiating the crises in the social media environment [22].
As for the social media strategies impact on firm’s financial performance,
Schniederjans et al. are conducting an analysis based on the data gathered through
text mining and conclude that there is a positive correlation between the considered
elements. [23] Even more, the authors sustain that the social media succeeds to
intensify and consolidate the communication among suppliers, consumers,
shareholders and firms. Similar conclusions are drawn by Parveen et al. [5]
3. Grey Knowledge in Online Social Environments
Knowing that the main characteristic of the online social environments is the
fact that they are made by a multitude of people which are gathering together and
are interacting, no matter their background, religion, believes, culture, education,
etc., here, more than in other environments, individuals are not existing in isolation.
[24]The human component plays a tremendous role in the achieving, processing and
transmitting knowledge in such a network.
In the online social networks the human component becomes the central actor
and due to this fact, the knowledge that is passing through the network ties is a grey
one. [25]
The grey knowledge is that particular type of knowledge that creates a bridge
between the two most known types of knowledge: the tacit and the explicit one.
While the tacit knowledge is coming from the inner structure of each individual or
organization, being characterized by personal or organizational knowledge and the
explicit one (given by the organizational artifacts and collective knowledge) comes
from a clear and profound process of knowledge sharing and exploration, the grey
knowledge represents a dynamic component, that fuels the ties created in both
virtual and real networks in order to serve the knowledge society. Also, the grey
knowledge is strictly related to the feedback loops which are very frequent
encountered in the online social media, where everybody is expressing their own
opinions on a certain situation.
Even more, the grey knowledge is present in the online activities, in
argumentations and comments made on product sales websites, on blogs, in the
public or private messages made by the online social networks users, in the
Twitter’s tweets, etc.
As it has already been shown, individuals, with in this case are the main
component of the online social networks, are very distinct one of another and over
the time they tend to be unpredictable, have their own opinion regarding a specific
situation, have a personal way to respond to external stimuli, are unique, are
capable of innovation, all of these being the result of the free-will, self-awareness,
conscience, imagination. [26, 27] Moreover, recent studies have shown that also
people tend to compare themselves with other people, especially in the virtual
environments [28, 29], being influenced by others decisions, state of being,
preferences, etc. It has been proven that that there is a positive correlation between
the social comparison phenomenon and the appearance of a negative feeling about
Online
Social
Networks
14
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
oneself with effects on self-concept clarity, intolerance to uncertainty, anxiety and
even depression.
The now-a-days online social network reality proves that the grey knowledge
that is passing through the network’s ties can be very well explained through the
“go viral” property. [30] This property is shortly explaining the extremely high speed
of the information which is passing through the social networks and how it succeed
in a relatively short period of time to come across a large number of network’s
members.
Moreover, the grey knowledge lies between the implicit and explicit
knowledge due to its interpersonal sharing character as people are consciously
deciding what information to share in the online communities and to whom it will
be given. [31] Social platforms like Facebook, for example, allow people to share
this kind of information both directly and indirectly (see the “friends” and
“friends-of-friends’ share options) which clearly departs from the classical ways of
knowledge sharing. Even more, the grey knowledge is a function of social
contextual properties like relationship qualities and position within a network. [32, 33]
In this whole context, the consumers’ decision and perceptions become
influenced by the other online social networks members’ opinions, experiences and
thoughts about a certain company, brand or product. For this, the present paper tries
to shape the relationship between how a consumers is taking his consuming
decisions in a non-online social environment and how its perception about a
product is influenced by the other members he interacts with in online
environments. On this purpose, a grey incidence analysis is conducted, knowing
that the grey systems theory works better in this kind of environments, with a high
degree of uncertainty.
4. Grey Systems Theory – Incidence Analysis
The incidence analysis plays a central role in the grey systems theory, being
one of the most employed techniques when dealing with uncertain situations. Due
to this fact, over the time, there have been created a series of incidence degree,
among which it can be mentioned: the grey slope incidence degree [34], the grey
incidence degree based on Euclidian distance [35], the degree of grey B mode
incidence [36], the degree of grey C mode incidence [37], the degree of grey T mode
incidence [38], the absolute degree of grey incidence [39], the relative degree of grey
incidence [39], the synthetic degree of grey incidence [39], the local degree of grey
incidence [40], the Gini degree of grey incidence [41], grey distance incidence degree
[42], Deng’s degree of grey incidence [43] etc.
Among all these for the present analysis it has been chosen the Deng’s degree
of grey incidence due to its easiness of use and demonstrated practical applicability.
For this, it shall be considered the following:
A sequence of systems’ characteristics:
A number of relevant factors sequences for this system, given by:
where: ,t = the period of time and = the considered
variables and:
The calculated values of the incidence degree of a relevant factors sequence
on its main characteristics will be noted as:
Online
Social
Networks
15
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Deng’s degree of grey incidence [43] is calculated as follows:
where:
with:
This degree of incidence will be used in section 6 of this paper for establishing
the influence of the grey online social networks on consumers’ behaviour.
5. Online Social Networks Characteristics
Some of the online social network’s characteristics can be easily observed by
representing each one of these networks. Through representation, the communities
that are forming in an online environment are easily to be shaped and understand
and also some of the most influential leaders among a network can be identified.
By knowing the communities and their main leaders, the companies can target
their marketing campaigns in order to get to these leaders and influent their
opinions.
Also, the dynamics of the online social networks can be drawn by their
graphical representation and some can be used in order to measure different aspects
of these networks.
Figure 1 Examples of online networks
Online
Social
Networks
16
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Figure 1 is presenting some of the communities that can be formed among
different online social networks, these networks belonging to the persons that have
accepted to take part of our case study (presented in section 6). These networks
have been quantitatively and qualitatively analysed and depicted using the open
graph visualization and exploration platform Gephi 0.8.2.
Considering all the networks of the participants to the study some
measurements regarding centrality have been deployed, such as: closeness
centrality, decay centrality, normalized decay centrality, betweenness centrality,
Bonacich centrality, diffusion centrality along with some general measurement of
the networks: number of nodes, diameter, modularity, components, connected
components.
On average, considering all the 254 respondents that were using online social
networks, it has been observed using Gephi 0.8.2. that the network average
diameter is 8,156 (the longest shorter path between any two nodes), with a
modularity average degree of 0,488 (the recombination degree on the component
among the network – it has a quite high values, which can be translated into the
fact that the network’s components can be easily split in communities), a graph
density of 0,103 (showing that the components of each network have a connectivity
degree quite small), an average clustering coefficient of 0,287 (highlighting the
proportion between the number of existing connections among one’s friends and
the possible number of them) and an average path length of 2,651 (showing the
number of possible steps that a user has to take in order to pass from a network to
another). Having these elements that are characterising the network at a local level,
based on the observed characteristics, we shall proceed at studying the influence
these components have on one’s perception in online social networks.
6. Case Study on Consumers’ Decisions
The case study has been conducted on 258 respondents with ages going
between 18-41 years old. The main starting idea was the fact that people tend to
take into consideration the opinion of other online social media users [28, 29] and
therefore, it is possible that the contact they have in an online environment with
these people to influence their consuming behaviour.
6.1Questionnaire
In the start of this study it was interesting to see how many of them were using
the online social networks and, for this, a split question has been used, resulting
from here that 98.40% of them are members on the online social networks, 1.60%
declaring that they have never been a part of this kind of networks, reducing our
sample to 254 valid respondents.
Among these, 68,11% were female and 31,89% male, the most predominant
age category was the 27-34 category having 66.14%, followed by 18-26 with
24.41% and 35-41 with 9.45%. Also, 77.56% have declared that they own an
online social network account for more than three years.
Moreover, 90.94% have seen a commercial in the social media in the last year
or have seen and participated to a discussion related to a company’s product/
service.
The promoters of these discussion groups or campaigns have been pointed out
to be Facebook and YouTube, as it results from Figure 2.
Having these, the next step is to establish the influence that a discussion
conducted in an online social network has an incidence on the consumers’
behaviour. For this, the Deng’s degree of grey incidence presented above will be
Online
Social
Networks
17
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
calculated. Two constructions have been considered based on the literature (see [44,
45, 46, 47]), each of them being formed from a series of variables measured using a
5-point Likert scale: the consumers’ online activity (COA) and the consumers’
decision making process (CDMP).
Figure 2 Online discussion or advertising campaigns promoters
For the COA construction, the following variables have been evaluated
through a Likert scale:
Time spent on social media platforms (TS), measured on average in hours per
day: less than half an hour, half an hour – an hour, 1-2 hours, 2-3 hours, more than
3 hours;
Number of advertises watched on average during a week on social platforms
(NAW): none, 1-2, 2-4, 4-6, more than 6;
Number of discussions (ND) on which an user is actively participating or just
reading in online environment during a week: none, 1-2, 2-4, 4-6, more than 6;
• Discussions intensity (DI): Think on a product/service about
which you had a recent discussion with a friend/relative/
colleague/other person in an online environment. This
discussion stayed in your mind: a day, a couple of days, a week,
a couple of weeks, I forget it very quickly.
As for the CDMP construction, there have been considered three variables,
each of the being evaluated using a 5-point Likert scale (1-strongly disagree,
2-disagree, 3-undecided, 4-agree, 5-strongly agree):
• Decisions based on discussions in online environment (DD):
Regarding the products/services about which you had a recent
discussion with a friend/relative/colleague/other person in an
online environment, you can say that in the last period you have
bought them more often than usual;
• Decisions based on trust in online environment (DT): There are
some specific persons within my personal online network that I
trust in recommending products/services;
• Decisions based on product adoption (DPA): I take into
consideration buying a specific product/service if I see that
some of my friends are using it/discussing about it in online
social networks/following the company’s updates and websites.
Online
Social
Networks
18
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
6.2 Model Fit through a Confirmatory Factor Analysis
The confirmatory factor analysis was conducted using AMOS 22. The starting
construction is drawn in Figure 3 and has all the factor loadings above 0.700 which
can indicate a good start in the analysis.
Figure 3 Latent Construct (1)
Considering the model fit summary offered by AMOS, it can be seen that the
CMIN/DF is 3.632 (Table 1) which is under the limit of 5.000, but it can be
improved, the GFI is above the 0.900 limit, while the AGFI is below the 0.900 limit
(Table 2), showing that the proposed model can be improved.
Tab le 1 CMIN (AMOS 22 Output)
Model NPAR CMIN DF P CMIN/DF
Default model 15 47.220 13 .000 3.632
Saturated model 28 .000 0
Independence model 7 440.103 21 .000 20.957
Tab le 2 RMR and GFI (AMOS 22 Output)
Model RMR GFI AGFI PGFI
Default model .099 .952 .897 .442
Saturated model .000 1.000
Independence model .332 .631 .508 .474
Also, the three incremental indices NFI, RFI and IFI are at the edge of the
threshold value of 0.9 (Table 3), PCLOSE is below the 0.050 limit, while the
RMSEA is above the 0.100 limit (Table 4) showing that there is a degree to which
the lack of fit is due to misspecification of the model tested versus being due to
sampling error.
Online
Social
Networks
19
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Tab le 3 Baseline Comparison (AMOS 22 Output)
Model NFI
Delta1
RFI
rho1
IFI
Delta2
TLI
rho2 CFI
Default model .893 .827 .920 .868 .918
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Tab le 4 RMSEA (AMOS 22 Output)
Model RMSEA LO 90 HI 90 PCLOSE
Default model .102 .072 .134 .003
Independence model .281 .258 .304 .000
Therefore, the model can be improved using the standardized residual
covariance matrix offered by AMOS 22 output, by considering the values above
0.400 within this matrix and eliminating step-by-step the variables exceeding this
value. As the ND variables has a standardized residual covariance of 3.409 with
DPA, 1.465 with DT, 0.625 with DD and 0.545 with DI (Table 5), the ND variable
has been eliminated from the construction (Figure 4).
Tab le 5 Standardized Residual Covariance (AMOS 22 Output)
DT TS NAW ND DI DPA DD
DT .000
TS 2.579 .000
NAW 2.189 .054 .000
ND 1.465 .393 -.331 .000
DI .210 -.584 .170 .545 .000
DPA -.373 -.126 -.205 3.409 -1.981 .000
DD -.039 -1.296 -1.230 .625 -2.505 .264 .000
Tab le 6 CMIN (AMOS 22 Output)
Model NPAR CMIN DF P CMIN/DF
Default model 13 19.767 8 .011 2.471
Saturated model 21 .000 0
Independence model 6 337.721 15 .000 22.515
For the new construction, it can be seen that the goodness of fit are getting
better than in the first case: CMIN/DF has decreased at 2.471 (Table 6), GFI and
AGI are both above 0.900 (Table 7), two of the incremental indices (NFI and IFI)
are above 0.900 (Table 8), CFI is above 0.900 (Table 8), PCLOSE is 0.134 above
the threshold limit of 0.050 and RMSEA is below 0.100 (Table 9). As there can still
be made some improvements, the DT variable is eliminated from the construction
as it gets high values on the standardized residual covariance matrix, obtaining the
new construct (Figure 5).
Tab le 7 RMR and GFI (AMOS 22 Output)
Model RMR GFI AGFI PGFI
Default model .080 .975 .935 .371
Saturated model .000 1.000
Independence model .293 .687 .562 .491
Online
Social
Networks
20
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Figure 4 Latent Construct (2)
Tab le 8 Baseline Comparison (AMOS 22 Output)
Model NFI
Delta1
RFI
rho1
IFI
Delta2
TLI
rho2 CFI
Default model .941 .890 .964 .932 .964
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Figure 5 Latent Construct (3)
Online
Social
Networks
21
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Tab le 9 RMSEA (AMOS 22 Output)
Model RMSEA LO 90 HI 90 PCLOSE
Default model .076 .034 .119 .134
Independence model .292 .265 .319 .000
This third construct has a better CMIN/DF than the two previous constructions
(Table 10), both GFI and AGI exceeding 0.900 and approaching 1 (Table 11), all
the three incremental indexes NFI, RFI, IFI exceeding 0.900 (Table 12) and
PCLOSE exceeding 0.050 (Table 13).
Tab le 10 CMIN (AMOS 22 Output)
Model NPAR CMIN DF P CMIN/DF
Default model 11 6.404 4 .171 1.601
Saturated model 15 .000 0
Independence model 5 297.158 10 .000 29.716
Tab le 11 RMR and GFI (AMOS 22 Output)
Model RMR GFI AGFI PGFI
Default model .044 .990 .963 .264
Saturated model .000 1.000
Independence model .306 .691 .536 .460
Tab le 12 Baseline Comparison (AMOS 22 Output)
Model
NFI
Delta1
RFI
rho1
IFI
Delta2
TLI
rho2
CFI
Default model .978 .946 .992 .979 .992
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Tab le 13 RMSEA (AMOS 22 Output)
Model RMSEA LO 90 HI 90 PCLOSE
Default model .049 .000 .116 .430
Independence model .337 .305 .370 .000
Moreover, the RMSEA is below the 0.1 threshold (Table 13) and its 90 percent
confidence interval is between LO90 of 0.000 and HI90 of 0.116. As the upper
bound is close to 0.080, it also indicates a good model fit.
Along with the indices related to the goodness of fit, the validity and
reliability of the model can also be tested.
As AMOS 22 is not evaluating the validity and reliability, the indicators that
are needed, namely the average variance extracted (AVE) and construct reliability
(CR), can be determined based on the following two equations [48]:
Online
Social
Networks
22
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
n
AVE
n
i
i
∑
=
=1
2
λ
where ∑
=
n
i
i
1
2
λ
is the sum squared factor loadings determined through AMOS
22.
∑∑
∑
==
=
+
=n
i
n
i
ii
n
i
i
CR
11
2
1
2
)()(
)(
δλ
λ
with ∑
=
n
i
i
1
δ
sum of error standardized variance.
The calculated values of AVE are: 0.882 for CDMP and 0.584 for COA, both
of them exceeding the threshold value of 0.500, showing a good convergence,
while the values of CR are: 0.862 for CDMP and 0.869 for COA, also both greater
than 0.700, also indicating a good feasibility.
Therefore, it can be concluded that the overall construct validity and reliability
is good and that the considered measures are consistently representing the reality.
6.3 Grey Incidence Analysis
The computed values of Deng’s degree for the implied variables is presented
in Table 14.
Tab le 14 Deng’s degree of grey incidence
Deng’s degree DI NAW TS
DD .763 .541 .722
DPA .817 .689 .705
The highest degree of incidence is found among DPA and DI variables, 0.817,
showing a strong relationship between the online discussions’ intensity and the
decisions consumers are taking regarding the product adoption. (Figure 6)
Moreover, relatively high values are also registered between the intensity of
the discussions that are taking place in the online social networks regarding the
products/ services of a company and the acquisition decisions of these users,
Deng’s degree being in this case 0.763. Also, acceptable values can be found even
between the time spent on online social networks and the decisions consumers are
taking regarding the product adoption or acquisition, shaping one more time the
linkage between the considered variables. On the other hand, poor Deng’s degree
values have been obtained among the number of advertisings watched in online
environment and the discussions and decisions made based on those advertisings.
This can be due to some of the advertisings’ characteristics such as: inappropriate
message, wrong target public and so on.
Online
Social
Networks
23
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
Figure 6 Deng’s degree of grey incidence
Still, the obtained values for the Deng’s degree of incidence proves once more
the strength of the linkage between the customers’ online social network activity
and their buying decisions, which opens new research areas such as: what kind of
discussion made on social media are best received by the consumers, what is the
impact of the message received through these discussions on the quantity bought,
which are the elements that should be more emphasised in a discussion, what
aspects related to the product/ service should be discussed, how is the overall
image of a product influenced by an online social network discussion.
7. Concluding Remarks
The widespread adoption of social networks has significantly contributed to
the individualisation of marketing where the providers of goods and services are
increasingly communicating with individual consumers and users, gaining
feedback on a one-to-one basis and providing bespoke solutions for clients. Even
more, in the online social networks a whole array of discussions are made
regarding the characteristics of a product, the position of a company regarding a
certain issue, the type of advertising one company adopts. Here, the feedback loops
are one of the constant characteristics of everything is happening in an online
environment. The grey knowledge flow which is passing through the billions of ties
has the power to influence consumers’ opinions and preferences. The simplest way
to do this is by the word-of-mouth: passing the information from one user to
another. Once with this process, a whole array of personal believes, observations,
life events and feedback is also expressed, having the power to change one’s
attitude and way of thinking.
Along with the marketing campaigns conducted in an online social network,
the discussions conducted here and the time spent on have a great impact on a
consumers’ decision. This impact was analysed in the present paper by using some
of the techniques offered by the grey systems theory, namely the degree of grey
Online
Social
Networks
24
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
incidence.
Through the analysis, it has been observed that the online discussions’
intensity is strictly related to the buyers’ decision related to a certain product
adoption and that there is a positive relation among the time spent on social
networks and the decisions taken. Therefore, the results can be useful in the context
of impression management, as the companies’ could try to focus their advertises
more on the social networks’ key users.
Acknowledgement
This paper was co-financed from the European Social Fund,
through the Sectorial Operational Program Human Resources
Development 2007-2013, project number POSDRU/159/1.5/S/138907
“Excellence in scientific interdisciplinary research, doctoral and
postdoctoral, in the economic, social and medical fields -EXCELIS”,
coordinator The Bucharest University of Economic Studies. Also, the authors are
acknowledging the support of Leverhulme Trust International Network research
project "IN-2014-020".
References
[1] Heidemann, J., Klier, M., Probst, F.. Online social networks: A survey of a global phenomenon.
Computer Networks, 2012, 56: 3866-3878.
[2] Xu, Y., Zhang, C., Xue, L..Measuring product susceptibility in online product review social network.
Decioin Support Systems, Elsevier, 2013.
[3] Thackeray, R., Neiger, B.L., Hanson, C.L., McKenzie, J.F.. Enhancing promotional strategies within
social marketing programs: use of Web 2.0 social media, Health Promotion. Practice,2008, 9(4):
338–343.
[4] Berthon, P.R., Pitt, L.F., Plangger, K. , Shapiro, D.. Marketing meets Web 2.0, socialmedia, and
creative consumers: implications for international marketing strategy, Business Horizons, 2012, 55(3):
261-71.
[5] Parveen, F., Jaafar, N.I., Ainin, S.. Social media usage and organizational performance: Reflections
of Malaysian social media managers. Telematics and Informatics, 2014, 31,
http://dx.doi.org/10.1016/j.tele.2014.03.001, 2014.
[6] Tikkanen, H., Hietanen, J., Henttonen, T., Rokka, J.. Exploring virtual worlds: success factors in
virtual world marketing, Management Decision, 2009, 47(8): 1357-1381.
[7] Harris, L., Rae, A.. Social networks: the future of marketing for small business.Journal of Business
Strategy, 2009, 30(5): 24 – 31.
[8] Curtis, E., De Vries, J., Sheerin, F.. Developing leadership in nursing: exploring core factors. British
Journal of Nursing, 2011, 20: 306 -309.
[9] Li., C., Bernoff, J.. Attaining social maturity: the challenges your company will face on the path to
empowerment through social technology.Harvard Business Press Chapters: 1-28, 2011.
[10] Bonchi, F., Castillo, C., Gionis, A.. Social Network Analysis and Mining for Business Applications.
ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-38.
[11] Libai, B., Bolton, R., Bugel, M., de Ruyer, K., Gotz, O., Risselda, H., Stephen, A.. Customer to
customer interraction: broadening the scope and word of mouth research. Journal of Service Research,
2010, 13(3): 267-282.
[12] Kang, R., Brown, S., Kiesler, S.. Why do people seek anonymity on the internet? Informing Policy
and Design, Proceedings of CHI’13 Conference: 1-10, 2013.
[13] Ho, C.I., Lin, M.H., Chen, H.M.. Web users’ behavioural patterns of tourism information search:
From online to offine. Tourism Management, 2012, 33(6): 1468-1482.
[14] Bass, F.M., Jain, D., Krishnan, T.V.. Modelling the marketing-mix influence in new-product
diffusion, in Mahajan, V., Muller, E., Wind, Y., New-product diffusion models, Kluwe Academis,
Boston: 99-122, 2000.
[15] Del Bianco, V., Chinosi, M., Lavazz, L., Morasca, S., Taibi, D.. How European software industry
perceives OSS trustworthiness amd what are the specific criteria to establish trust in OSS.
www.qualipso.eu/node/45, 2008.
[16] Turlska, M., West, B.J.. Cristical social networks. Physica A: Statistical Mechanic and its
applications, 2014, 395: 466-475.
[17] Ma, N., Liu, Y.. SuperedgeRank algorithm and its application in identifying opinion leader of
online public supernetwork. Expert Systems with Applications, 2014, 41(4): 1357-1368.
Online
Social
Networks
25
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
[18] Yang, J., Yao, C., Ma, W. Chen, G.. A study of the spreading for viral marketing based on a
complex network model. Physica A: Statistical Mechanic and its applications, 2010, 389(4): 859-870.
[19] Goyal, A., Bonchi, F., Lakshmanan, L.. Discovering leaders from community actions, The
proceedings of the 17th ACM conference on information and knowledge management, 2008.
[20] Carley, K.M., Diesner, J., Reminga, J., Tsvetovat, M.. Toward an interoperable dynamic network
analysis toolkit. Decision Support Systems, 2007, 43: 1324-1347.
[21] Qui, J., Lin. Z..A framework for exploring organizational structure in dynamic social networks.
Decision Support Systems, 2011, 51(4): 760-771.
[22] Pang, A., Hassan, N.B.A., Chong, A.C.Y.. Negotiating crisis in the social media environment:
Evolution of crises online, gaining credibility offline. Corporate Communications: An
International Journal, 2014, 19(1): 96 – 118.
[23] Schniederjans, D., Cao, E., Schniederjans, M.. Enhancing financial performance with social media:
An impression management perspective. Decision Support Systems, 2013, 55(4): 911-918.
[24] Cotfas, L.A.. A finite-dimensional quantum model for the stock market. Physica A: Statistical
Mechanics and its Applications, 2013, 392(2): 371-380.
[25] Delcea, C.. Not Black. Not even White. Definitively Grey Economic Systems. The Journal of Grey
System, 2014, 26(1): 11-25.
[26] Andrew, A.. Why the world is grey. Keynote speech, The 3th International Conference IEEE
GSIS,Nanjing, China, 2011.
[27] Forrest, J.. A Systemic Perspective on Cognition and Mathematics. CRC Press, 2013.
[28] Delcea, C., Cotfas L, Paun, R. Grey Social Networks – a Facebook Case Study. Proceedings of the
6th International Conference on Computational Collective Intelligence, Technologies and Applications,
Seoul, Korea, 2014.
[29] Lee, S.Y.. How do people compare themselves with others on social network sites?: The case of
Facebook. Computers in Human Behavior, 2014, 32: 253-260.
[30] Kawamoto, T., Hatano, N.. Viral spreading of daily information in the online social networks.
Physica A, 2014, 45: 34-41.
[31] Sohn, D.. Coping with information in social media: The effects of network structure and knowledge
on perception of information value. Computers in Human Behavior, 2014, 32: 145-151.
[32] Sohn, D.. Disentagling the effects of social network density on electronic word-of-mouth intention.
Journal of Computer Mediated Communication, 2009, 14(2): 352-367.
[33] Chu, S.C, Choi, S.M.. Electronic word-of-mouth in social networkin sites: A cross-cultural study of
the United States and China. Journal of Global Marketing, 2011,4(3): 263-281.
[34] Dang, Y.G., Liu, S.F.. Improvement degree of grey slope incidence. Engineering Science, 2004,
23-26.
[35] Zhao, L.Y,. Wei, S.Y., Mei, Z.X.. Grey Euclid Relation Grade. Journal of Guan Xi University,
1998, 10-13.
[36] Wang, Q.Y.. The Grey Relational Analysis of B-Model. Journal of Huazhong University of Science
and Technology, 1999, 77-82.
[37] Wang, Q.Y., Zhao, X.H.. The Relational Analysis of C-Model. Journal of Huazhong University of
Science and Technology, 1999, 75-77.
[38] Tang, W.X.. The concept and the computation method of T’s correlation degree. Application of
Statistics and Management, 1995, 14: 34-37.
[39] Liu, S.F., Lin, Y..Grey Systems – Theory and Applications, Understanding Complex Systems
Series. Springer-Verlag Berlin Heidelberg, 2010.
[40] Xiao, X.P.. Theoretical study and reviews on the computation method of grey interconnect degree.
Systems Engineering Theory and Practice,1997, 76-81.
[41] Liu, R., Cui, J.F., Wang, Z.X.. Gini degree of grey incidence and its application in central Henan
urban agglomeration economic development. Proceedings of 2009 IEEE International Conference on
Grey Systems and Intelligent Services, 2009.
[42] Lian, Z.W., Dand, Y.G., Wang, Z.W., Song, R.X.. Grey Distance Incidence Degree and Its
Properties, Proceedings of 2009 IEEE International Conference on Grey Systems and Intelligent
Services, 2009.
[43] Deng, J.L. Theory basis. Huanzhong University of Science and Technology Publishing House,
2002.
[44] Wu, Y.L., Tao, Y.H., Li, C.P., Wang, S.Y., Chiu, C.Y.. User-switching behavior in social network
sites: A model perspective with drill-down analyses. Computers in Human Behavior, 2014, 33: 92-103.
[45] Kang, Y.S., Min, J., Kim, J., Lee, H.. Roles of alternative and self-oriented perspectives in the
context of the continued use of social network sites. International Journal of Information Management,
2013, 33: 496-511.
[46] Brooks, B., Hogan, B., Ellison, N., Lampe, C., Vitak, J.. Assessing structural correlates to social
capital in Facebook ego networks.Social Networks, 2014, 38: 1-15.
[47] Chiu, C.M., Cheng, H.L., Huang, H.Y., Chen, C.F.. Exploring individuals’ subjective well-being
Online
Social
Networks
26
Camelia Delcea et al/ The Journal of Grey System 2015 (27)
and loyalty towards social network sites from the perspective of network externalities: the Facebook
case. International Journal of Information Management, 2013, 33: 539-552.
[48] Spanos, Y.E, Lioukas, S.. An examination into the causal logic of rent generation: contrasting
Porter’s competitive strategy framework and the resource-based perspective. Strategic Management
Journal, 2001, 22: 907-934.
Online
Social
Networks
27
